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tests/__init__.py create mode 100644 tests/test_anchor_generator.py create mode 100644 tests/test_box2box_transform.py create mode 100644 tests/test_boxes.py create mode 100644 tests/test_checkpoint.py create mode 100644 tests/test_config.py create mode 100644 tests/test_data_transform.py create mode 100644 tests/test_fast_rcnn.py create mode 100644 tests/test_mask_ops.py create mode 100644 tests/test_nms_rotated.py create mode 100644 tests/test_roi_align.py create mode 100644 tests/test_roi_align_rotated.py create mode 100644 tests/test_roi_heads.py create mode 100644 tests/test_roi_pooler.py create mode 100644 tests/test_rotated_boxes.py create mode 100644 tests/test_rpn.py create mode 100644 tests/test_visualizer.py create mode 100755 tools/benchmark.py create mode 100755 tools/train_net.py create mode 100755 tools/visualize_coco_results.py create mode 100755 tools/visualize_data.py diff --git a/.clang-format b/.clang-format new file mode 100644 index 0000000000..a757d4fff0 --- /dev/null +++ b/.clang-format @@ -0,0 +1,85 @@ +AccessModifierOffset: -1 +AlignAfterOpenBracket: AlwaysBreak +AlignConsecutiveAssignments: false +AlignConsecutiveDeclarations: false +AlignEscapedNewlinesLeft: true +AlignOperands: false +AlignTrailingComments: false +AllowAllParametersOfDeclarationOnNextLine: false +AllowShortBlocksOnASingleLine: false +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: Empty +AllowShortIfStatementsOnASingleLine: false +AllowShortLoopsOnASingleLine: false +AlwaysBreakAfterReturnType: None +AlwaysBreakBeforeMultilineStrings: true +AlwaysBreakTemplateDeclarations: true +BinPackArguments: false +BinPackParameters: false +BraceWrapping: + AfterClass: false + AfterControlStatement: false + AfterEnum: false + AfterFunction: false + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: false + AfterUnion: false + BeforeCatch: false + BeforeElse: false + IndentBraces: false +BreakBeforeBinaryOperators: None +BreakBeforeBraces: Attach +BreakBeforeTernaryOperators: true +BreakConstructorInitializersBeforeComma: false +BreakAfterJavaFieldAnnotations: false +BreakStringLiterals: false +ColumnLimit: 80 +CommentPragmas: '^ IWYU pragma:' +ConstructorInitializerAllOnOneLineOrOnePerLine: true +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +Cpp11BracedListStyle: true +DerivePointerAlignment: false +DisableFormat: false +ForEachMacros: [ FOR_EACH, FOR_EACH_ENUMERATE, FOR_EACH_KV, FOR_EACH_R, FOR_EACH_RANGE, ] +IncludeCategories: + - Regex: '^<.*\.h(pp)?>' + Priority: 1 + - Regex: '^<.*' + Priority: 2 + - Regex: '.*' + Priority: 3 +IndentCaseLabels: true +IndentWidth: 2 +IndentWrappedFunctionNames: false +KeepEmptyLinesAtTheStartOfBlocks: false +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBlockIndentWidth: 2 +ObjCSpaceAfterProperty: false +ObjCSpaceBeforeProtocolList: false +PenaltyBreakBeforeFirstCallParameter: 1 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 200 +PointerAlignment: Left +ReflowComments: true +SortIncludes: true +SpaceAfterCStyleCast: false +SpaceBeforeAssignmentOperators: true +SpaceBeforeParens: ControlStatements +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 1 +SpacesInAngles: false +SpacesInContainerLiterals: true +SpacesInCStyleCastParentheses: false +SpacesInParentheses: false +SpacesInSquareBrackets: false +Standard: Cpp11 +TabWidth: 8 +UseTab: Never diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000000..74ff132c14 --- /dev/null +++ b/.flake8 @@ -0,0 +1,9 @@ +# This is an example .flake8 config, used when developing *Black* itself. +# Keep in sync with setup.cfg which is used for source packages. + +[flake8] +ignore = W503, E203, E221, C901 +max-line-length = 100 +max-complexity = 18 +select = B,C,E,F,W,T4,B9 +exclude = build,__init__.py diff --git a/.github/Detectron2-Logo-Horz.svg b/.github/Detectron2-Logo-Horz.svg new file mode 100644 index 0000000000..eb2d643ddd --- /dev/null +++ b/.github/Detectron2-Logo-Horz.svg @@ -0,0 +1 @@ +Detectron2-Logo-Horz \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/feature-request.md b/.github/ISSUE_TEMPLATE/feature-request.md new file mode 100644 index 0000000000..5bd0aab250 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature-request.md @@ -0,0 +1,16 @@ +--- +name: "\U0001F680Feature Request" +about: Submit a proposal/request for a new detectron2 feature + +--- + +## 🚀 Feature + + +## Motivation + + + +## Pitch + + diff --git a/.github/ISSUE_TEMPLATE/questions-help-support.md b/.github/ISSUE_TEMPLATE/questions-help-support.md new file mode 100644 index 0000000000..67aa105124 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/questions-help-support.md @@ -0,0 +1,14 @@ +--- +name: "❓Questions/Help/Support" +about: Do you need support? + +--- + +## ❓ Questions and Help + +General questions about detectron2. + +NOTE: + +If you met an unexpected error when using detectron2, +please use the "Unexpected Problems / Bugs" issue category instead. diff --git a/.github/ISSUE_TEMPLATE/unexpected-problems-bugs.md b/.github/ISSUE_TEMPLATE/unexpected-problems-bugs.md new file mode 100644 index 0000000000..133a94270b --- /dev/null +++ b/.github/ISSUE_TEMPLATE/unexpected-problems-bugs.md @@ -0,0 +1,29 @@ +--- +name: "Unexpected Problems / Bugs" +about: Report unexpected problems or bugs in detectron2 + +--- + +If you do not know the root cause of the problem / bug, and wish someone to help you, please +include: + + + +## To Reproduce + +1. what changes you made / what code you wrote +2. what command you run +3. what you observed (full logs are preferred) + +## Expected behavior + +If there are no obvious error in "what you observed" provided above, +please tell us the expected behavior. + +If you expect the model to work better, only in one of the two conditions we will help with it: +(1) You're unable to reproduce the results documented in detectron2 model zoo. +(2) It indicates a detectron2 bug. + +## Environment + +Please paste the output of `python -m detectron2.utils.collect_env`. diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 0000000000..34d04b5011 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,6 @@ +Thanks for your contribution! + +If you're sending a large PR (e.g., >50 lines), +please open an issue first about the feature / bug, and indicate how you want to contribute. + +Before submitting a PR, please run `dev/linter.sh` to lint the code. diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000..a82738aded --- /dev/null +++ b/.gitignore @@ -0,0 +1,34 @@ +# output dir +output +instant_test_output + +# compilation and distribution +__pycache__ +_ext +*.pyc +*.so +detectron2.egg-info/ +build/ +dist/ + +# pytorch/python/numpy formats +*.pth +*.pkl +*.npy + +# ipython/jupyter notebooks +*.ipynb +**/.ipynb_checkpoints/ + +# Editor temporaries +*.swn +*.swo +*.swp +*~ + +# Pycharm editor settings +.idea + +# project dirs +/datasets +/models diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000..0f7ad8bfc1 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,5 @@ +# Code of Conduct + +Facebook has adopted a Code of Conduct that we expect project participants to adhere to. +Please read the [full text](https://code.fb.com/codeofconduct/) +so that you can understand what actions will and will not be tolerated. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000..23ba08d62c --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,31 @@ +# Contributing to detectron2 +We want to make contributing to this project as easy and transparent as +possible. + +## Pull Requests +We actively welcome your pull requests. + +1. Fork the repo and create your branch from \muaster`. +2. If you've added code that should be tested, add tests. +3. If you've changed APIs, update the documentation. +4. Ensure the test suite passes. +5. Make sure your code lints. +6. If you haven't already, complete the Contributor License Agreement ("CLA"). + +## Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Facebook's open source projects. + +Complete your CLA here: + +## Issues +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. + +Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe +disclosure of security bugs. In those cases, please go through the process +outlined on that page and do not file a public issue. + +## License +By contributing to detectron2, you agree that your contributions will be licensed +under the LICENSE file in the root directory of this source tree. diff --git a/GETTING_STARTED.md b/GETTING_STARTED.md new file mode 100644 index 0000000000..4a264f68e9 --- /dev/null +++ b/GETTING_STARTED.md @@ -0,0 +1,62 @@ + +## Getting Started with Detectron2 + +This document provides a brief intro of the usage of builtin command-line tools in detectron2. + +For a tutorial that involves actual coding with the API, +see our [Colab Notebook](TODO) which covers how to run inference with an +existing model, and how to train a builtin model on a custom dataset. + +For more advanced tutorials, refer to our [documentation](https://detectron2.readthedocs.io/tutorials/extend.html). + + +### Inference with Pre-trained Models + +1. Pick a model and its config file from + [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md), + for example, `mask_rcnn_R_50_FPN_3x.yaml`. +2. Run the demo with +``` +python demo/demo.py --config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \ + --input input1.jpg input2.jpg \ + --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl +``` +It will run the inference and show visualizations in an OpenCV window. + +To run on webcam, replace `--input files` with `--webcam`. +To run on a video, replace `--input files` with `--video-input video.mp4`. +To save outputs to a directory (for images) or a file (for webcam or video), use `--output`. + + +### Train a Standard Model + +We provide a script in "tools/train_net.py", that is made to train +all the configs provided in detectron2. +You may want to use it as a reference to write your own training script for a new research. + +To train a model with "train_net.py", do: +``` +python tools/train_net.py --num-gpus 8 \ + --config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml +``` + +The configs are made for 8-GPU training. To train on 1 GPU, use: +``` +python tools/train_net.py \ + --config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \ + SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025 +``` + +(Note that we applied the [linear learning rate scaling rule](https://arxiv.org/abs/1706.02677) +when changing the batch size.) + +For more options, see `python tools/train_net.py -h`. + +### Use Detectron2 in Your Code + +See our [Colab Notebook](TODO) to how to use detectron2 APIs to: +1. run inference with an existing model +2. train a builtin model on a custom dataset + +See [detectron2/projects](https://github.com/facebookresearch/detectron2/tree/master/projects) +for more ways to build your project on detectron2. diff --git a/INSTALL.md b/INSTALL.md new file mode 100644 index 0000000000..7233ecbef8 --- /dev/null +++ b/INSTALL.md @@ -0,0 +1,53 @@ +## Installation + +Our [Colab Notebook](TODO) also has step-by-step instructions that install detectron2. + +### Requirements: +- Python >= 3.6 +- PyTorch 1.3 +- [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation. + You can install them together at [pytorch.org](http://pytorch.org) to make sure of this. +- OpenCV, needed by demo and visualization +- [fvcore](https://github.com/facebookresearch/fvcore/): `pip install 'git+https://github.com/facebookresearch/fvcore'` +- pycocotools: `pip install cython; pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'` +- GCC >= 4.9 + + +### Build detectron2 + +After having the above dependencies, run: +``` +git clone git@github.com:facebookresearch/detectron2.git +cd detectron2 +python setup.py build develop + +# or if you are on macOS +# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develop + +# or, as an alternative to `setup.py`, do +# pip install . +``` + +### Common Installation Issues + ++ Undefined torch/aten symbols, or segmentation fault immediately when running the library. + This may mean one of the two: + + * detectron2 or torchvision is not compiled with the version of PyTorch you're running. + + If you use a pre-built torchvision, uninstall torchvision & pytorch, and reinstall them + following [pytorch.org](http://pytorch.org). + If you manually build detectron2 or torchvision, remove the files you built (`build/`, `**/*.so`) + and rebuild them. + + * detectron2 or torchvision is not compiled using gcc >= 4.9. + + You'll see a warning message during compilation in this case. Please remove the files you build, + and rebuild them. + Technically, you need the identical compiler that's used to build pytorch to guarantee + compatibility. But in practice, gcc >= 4.9 should work OK. + ++ Undefined cuda symbols. The version of NVCC you use to build detectron2 or torchvision does + not match the version of cuda you are running with. + This happens sometimes when using anaconda. + diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000..5a90478a33 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ +Apache License +Version 2.0, January 2004 +http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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We also recommend that a +file or class name and description of purpose be included on the +same "printed page" as the copyright notice for easier +identification within third-party archives. + +Copyright 2019, Facebook, Inc + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. diff --git a/MODEL_ZOO.md b/MODEL_ZOO.md new file mode 100644 index 0000000000..8c821147e8 --- /dev/null +++ b/MODEL_ZOO.md @@ -0,0 +1,807 @@ +# Detectron2 Model Zoo and Baselines + +## Introduction + +This file documents a large collection of baselines trained +with detectron2 in Sep-Oct, 2019. +The corresponding configurations for all models can be found under the `configs/` directory. +Unless otherwise noted, the following settings are used for all runs: + +#### Common Settings +* All models were trained on [Big Basin](https://engineering.fb.com/data-center-engineering/introducing-big-basin-our-next-generation-ai-hardware/) + servers with 8 NVIDIA V100 GPUs, with data-parallel sync SGD and a total minibatch size of 16 images. +* All models were trained with CUDA 9.2, cuDNN 7.4.2 or 7.6.3 (the difference on performance is found to be negligible). +* The default settings are not directly comparable with Detectron. + For example, our default training data augmentation uses scale jittering in addition to horizontal flipping. + For configs that are closer to Detectron's settings, see + [Detectron1-Comparisons](configs/Detectron1-Comparisons/). +* No test-time augmentation is used for inference. +* Inference time is measured with batch size 1. It contains the time taken to postprocess results for evaluation. Therefore it does not accurately + reflect time-to-results. +* The *model id* column is provided for ease of reference. +* To check downloaded file integrity: any model on this page contains its md5 prefix in its file name. +* All COCO models were trained on `train2017` and evaluated on `val2017`. +* For Faster/Mask R-CNN, we provide baselines based on __3 different backbone combinations__: + * __FPN__: Use a ResNet+FPN backbone with standard conv and FC heads for mask and box prediction, + respectively. It obtains the best + speed/accuracy tradeoff, but the other two are still useful for research. + * __C4__: Use a ResNet conv4 backbone with conv5 head. The original baseline in the Faster R-CNN paper. + * __DC5__ (Dilated-C5): Use a ResNet conv5 backbone with dilations in conv5, and standard conv and FC heads + for mask and box prediction, respectively. + This is used by the Deformable ConvNet paper. +* Most models are trained with the 3x schedule (~37 COCO epochs). + Although 1x models are heavily under-trained, we provide some ResNet-50 models with the 1x (~12 COCO epochs) + training schedule for comparison when doing quick research iteration. + +#### ImageNet Pretrained Models + +We provide backbone models pretrained on ImageNet-1k dataset. +These models are __different__ from those provided in Detectron: we do not fuse BatchNorm into an affine layer. +* [R-50.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl): converted copy of MSRA's original ResNet-50 model +* [R-101.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-101.pkl): converted copy of MSRA's original ResNet-101 model +* [X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl): ResNeXt-101-32x8d model trained with Caffe2 at FB + +Pretrained models in Detectron's format can still be used. For example: +* [X-152-32x8d-IN5k.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl): + ResNeXt-152-32x8d model trained on ImageNet-5k with Caffe2 at FB (see ResNeXt paper for details on ImageNet-5k). +* [R-50-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47261647/R-50-GN.pkl): + ResNet-50 with Group Normalization. +* [R-101-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47592356/R-101-GN.pkl): + ResNet-101 with Group Normalization. + +#### License + +All models available for download through this document are licensed under the +[Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/). + +### COCO Object Detection Baselines + +#### Faster R-CNN: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		model iddownload
R50-C41x0.5930.1104.835.7137257644model | metrics
R50-DC51x0.3800.0895.037.3137847829model | metrics
R50-FPN1x0.2100.0603.037.9137257794model | metrics
R50-C43x0.5890.1084.838.4137849393model | metrics
R50-DC53x0.3780.0955.039.0137849425model | metrics
R50-FPN3x0.2090.0583.040.2137849458model | metrics
R101-C43x0.6560.1375.941.1138204752model | metrics
R101-DC53x0.4520.1036.140.6138204841model | metrics
R101-FPN3x0.2860.0714.142.0137851257model | metrics
X101-FPN3x0.6380.1396.743.0139173657model | metrics
+ +#### RetinaNet: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		model iddownload
R501x0.2000.0823.936.5137593951model | metrics
R503x0.2010.0813.937.9137849486model | metrics
R1013x0.2800.0875.139.9138363263model | metrics
+ +#### RPN & Fast R-CNN: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		prop.
AR		model iddownload
RPN R50-C41x0.1300.0561.551.6137258005model | metrics
RPN R50-FPN1x0.1860.0532.758.0137258492model | metrics
Fast R-CNN R50-FPN1x0.1400.0562.637.8137635226model | metrics
+ +### COCO Instance Segmentation Baselines with Mask R-CNN + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		model iddownload
R50-C41x0.6210.1405.236.832.2137259246model | metrics
R50-DC51x0.4710.1266.538.334.2137260150model | metrics
R50-FPN1x0.2610.0873.438.635.2137260431model | metrics
R50-C43x0.6220.1375.239.834.4137849525model | metrics
R50-DC53x0.4700.1116.540.035.9137849551model | metrics
R50-FPN3x0.2610.0793.441.037.2137849600model | metrics
R101-C43x0.6910.1636.342.636.7138363239model | metrics
R101-DC53x0.5450.1297.641.937.3138363294model | metrics
R101-FPN3x0.3400.0924.642.938.6138205316model | metrics
X101-FPN3x0.6900.1557.244.339.5139653917model | metrics
+ +### COCO Person Keypoint Detection Baselines with Keypoint R-CNN + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		kp.
AP		model iddownload
R50-FPN1x0.3150.1025.053.664.0137261548model | metrics
R50-FPN3x0.3160.0955.055.465.5137849621model | metrics
R101-FPN3x0.3900.1066.156.466.1138363331model | metrics
X101-FPN3x0.7380.1688.757.366.0139686956model | metrics
+ +### COCO Panoptic Segmentation Baselines with Panoptic FPN + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		PQmodel iddownload
R50-FPN1x0.3040.1294.837.634.739.4139514544model | metrics
R50-FPN3x0.3020.1274.840.036.541.5139514569model | metrics
R101-FPN3x0.3920.1376.042.438.543.0139514519model | metrics
+ + +### LVIS Instance Segmentation Baselines with Mask R-CNN + +Mask R-CNN baselines on the [LVIS dataset](https://lvisdataset.org), v0.5. +These baselines are described in Table 3(c) of the [LVIS paper](https://arxiv.org/abs/1908.03195). + +NOTE: the 1x schedule here has the same amount of __iterations__ as the COCO baselines. +They are roughly 24 epochs of LVISv0.5 data. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		model iddownload
R50-FPN1x0.3190.3696.624.024.4134714017model | metrics
R101-FPN1x0.3950.3857.625.826.1134807205model | metrics
X101-FPN1x1.3300.46110.027.327.9135397361model | metrics
+ + +### Cityscapes & Pascal VOC Baselines + +Simple baselines for +* Mask R-CNN on Cityscapes instance segmentation (trained on fine annotations only) +* Faster R-CNN on PASCAL VOC object detection (trained on VOC 2007 train+val + VOC 2012 train+val, tested on VOC 2007 using 11-point interpolated AP) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Nametrain
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		box
AP50		mask
AP		model iddownload
R50-FPN, Cityscapes0.2400.3974.436.5142423278model | metrics
R50-C4, VOC0.5370.0964.851.980.3142202221model | metrics
+ + +### Other Settings + +Ablations for Deformable Conv and Cascade R-CNN: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		model iddownload
Baseline R50-FPN1x0.2610.0873.438.635.2137260431model | metrics
Deformable Conv1x0.3420.0843.541.537.5138602867model | metrics
Cascade R-CNN1x0.3170.0904.042.136.4138602847model | metrics
+ + +Ablations for GroupNorm: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		model iddownload
Baseline R50-FPN3x0.2610.0793.441.037.2137849600model | metrics
GroupNorm3x0.3560.1027.342.638.6138602888model | metrics
GroupNorm (scratch)3x0.4000.1069.839.936.6138602908model | metrics
+ +A few very large models trained for a long time, for demo purposes. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Nameinference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		PQmodel iddownload
Panoptic FPN R1010.17211.447.441.346.1139797668model | metrics
Mask R-CNN X1520.27815.149.343.218131413model | metrics
above + test-time aug.51.445.5
diff --git a/README.md b/README.md new file mode 100644 index 0000000000..d95e65d240 --- /dev/null +++ b/README.md @@ -0,0 +1,46 @@ + + +Detectron2 is Facebook AI Research's next generation software system +that implements state-of-the-art object detection algorithms. +It is a ground-up rewrite of the previous version, +[Detectron](https://github.com/facebookresearch/Detectron/), +and it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/). +It is written in Python and powered by the [PyTorch](https://pytorch.org) deep +learning framework. + +
+ +
+ + +## Installation + +See [INSTALL.md](INSTALL.md). + +## Quick Start + +See [GETTING_STARTED.md](GETTING_STARTED.md), or our [Colab Notebook](TODO). + + +## Model Zoo and Baselines + +We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md). + + +## License + +Detectron2 is released under the [Apache 2.0 license](LICENSE). + +## Citing Detectron + +If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry. + +``` +@misc{wu2019detectron2, + author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and + Wan-Yen Lo and Ross Girshick}, + title = {Detectron2}, + howpublished = {\url{https://github.com/facebookresearch/detectron2}}, + year = {2019} +} +``` diff --git a/configs/Base-RCNN-C4.yaml b/configs/Base-RCNN-C4.yaml new file mode 100644 index 0000000000..f44bc6a265 --- /dev/null +++ b/configs/Base-RCNN-C4.yaml @@ -0,0 +1,18 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + RPN: + PRE_NMS_TOPK_TEST: 6000 + POST_NMS_TOPK_TEST: 1000 + ROI_HEADS: + NAME: "Res5ROIHeads" +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + diff --git a/configs/Base-RCNN-DilatedC5.yaml b/configs/Base-RCNN-DilatedC5.yaml new file mode 100644 index 0000000000..458a7785e0 --- /dev/null +++ b/configs/Base-RCNN-DilatedC5.yaml @@ -0,0 +1,30 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + RESNETS: + OUT_FEATURES: ["res5"] + RES5_DILATION: 2 + RPN: + IN_FEATURES: ["res5"] + PRE_NMS_TOPK_TEST: 6000 + POST_NMS_TOPK_TEST: 1000 + ROI_HEADS: + NAME: "StandardROIHeads" + IN_FEATURES: ["res5"] + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_FC: 2 + POOLER_RESOLUTION: 7 + ROI_MASK_HEAD: + NAME: "MaskRCNNConvUpsampleHead" + NUM_CONV: 4 + POOLER_RESOLUTION: 14 +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) diff --git a/configs/Base-RCNN-FPN.yaml b/configs/Base-RCNN-FPN.yaml new file mode 100644 index 0000000000..f61493dab0 --- /dev/null +++ b/configs/Base-RCNN-FPN.yaml @@ -0,0 +1,41 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + BACKBONE: + NAME: "build_resnet_fpn_backbone" + RESNETS: + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + FPN: + IN_FEATURES: ["res2", "res3", "res4", "res5"] + ANCHOR_GENERATOR: + SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map + ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps) + RPN: + IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"] + PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level + PRE_NMS_TOPK_TEST: 1000 # Per FPN level + # Detectron1 uses 2000 proposals per-batch, + # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue) + # which is approximately 1000 proposals per-image since the default batch size for FPN is 2. + POST_NMS_TOPK_TRAIN: 1000 + POST_NMS_TOPK_TEST: 1000 + ROI_HEADS: + NAME: "StandardROIHeads" + IN_FEATURES: ["p2", "p3", "p4", "p5"] + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_FC: 2 + POOLER_RESOLUTION: 7 + ROI_MASK_HEAD: + NAME: "MaskRCNNConvUpsampleHead" + NUM_CONV: 4 + POOLER_RESOLUTION: 14 +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) diff --git a/configs/Base-RetinaNet.yaml b/configs/Base-RetinaNet.yaml new file mode 100644 index 0000000000..3f47c64c3d --- /dev/null +++ b/configs/Base-RetinaNet.yaml @@ -0,0 +1,23 @@ +MODEL: + META_ARCHITECTURE: "RetinaNet" + BACKBONE: + NAME: "build_retinanet_resnet_fpn_backbone" + RESNETS: + OUT_FEATURES: ["res3", "res4", "res5"] + ANCHOR_GENERATOR: + SIZES: !!python/object/apply:eval ["[[x, x * 2**(1.0/3), x * 2**(2.0/3) ] for x in [32, 64, 128, 256, 512 ]]"] + FPN: + IN_FEATURES: ["res3", "res4", "res5"] + RETINANET: + IOU_THRESHOLDS: [0.4, 0.5] + IOU_LABELS: [0, -1, 1] +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.01 # Note that RetinaNet uses a different default learning rate + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) diff --git a/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml b/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000..773ac10e87 --- /dev/null +++ b/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,17 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + LOAD_PROPOSALS: True + RESNETS: + DEPTH: 50 + PROPOSAL_GENERATOR: + NAME: "PrecomputedProposals" +DATASETS: + TRAIN: ("coco_2017_train",) + PROPOSAL_FILES_TRAIN: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_train_box_proposals_21bc3a.pkl", ) + TEST: ("coco_2017_val",) + PROPOSAL_FILES_TEST: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", ) +DATALOADER: + # proposals are part of the dataset_dicts, and take a lot of RAM + NUM_WORKERS: 2 diff --git a/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml b/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml new file mode 100644 index 0000000000..db142cd671 --- /dev/null +++ b/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: False + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml b/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml new file mode 100644 index 0000000000..bceb6b3436 --- /dev/null +++ b/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: False + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml b/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml new file mode 100644 index 0000000000..57a098f53e --- /dev/null +++ b/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: False + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml b/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml new file mode 100644 index 0000000000..f96130105c --- /dev/null +++ b/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 diff --git a/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml b/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml new file mode 100644 index 0000000000..bc51bce390 --- /dev/null +++ b/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml b/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml new file mode 100644 index 0000000000..0fe96f57fe --- /dev/null +++ b/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 diff --git a/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml b/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml new file mode 100644 index 0000000000..33fadeb87d --- /dev/null +++ b/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml b/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000..3262019a12 --- /dev/null +++ b/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 diff --git a/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml b/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml new file mode 100644 index 0000000000..41395182bf --- /dev/null +++ b/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml b/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml new file mode 100644 index 0000000000..9c9b5ab771 --- /dev/null +++ b/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml @@ -0,0 +1,13 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + MASK_ON: False + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml b/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml new file mode 100644 index 0000000000..4abb1b9a54 --- /dev/null +++ b/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "../Base-RetinaNet.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml b/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml new file mode 100644 index 0000000000..4a24ce3a9a --- /dev/null +++ b/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml @@ -0,0 +1,5 @@ +_BASE_: "../Base-RetinaNet.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 diff --git a/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml b/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml new file mode 100644 index 0000000000..3b5412d4a7 --- /dev/null +++ b/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "../Base-RetinaNet.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Detection/rpn_R_50_C4_1x.yaml b/configs/COCO-Detection/rpn_R_50_C4_1x.yaml new file mode 100644 index 0000000000..e04821156b --- /dev/null +++ b/configs/COCO-Detection/rpn_R_50_C4_1x.yaml @@ -0,0 +1,10 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + META_ARCHITECTURE: "ProposalNetwork" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + RPN: + PRE_NMS_TOPK_TEST: 12000 + POST_NMS_TOPK_TEST: 2000 diff --git a/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml b/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000..dc9c95203b --- /dev/null +++ b/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "ProposalNetwork" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + RPN: + POST_NMS_TOPK_TEST: 2000 diff --git a/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml b/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml new file mode 100644 index 0000000000..1a94cc45a0 --- /dev/null +++ b/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: True + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml b/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml new file mode 100644 index 0000000000..67b70cf4be --- /dev/null +++ b/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: True + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml b/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml new file mode 100644 index 0000000000..1935a302d2 --- /dev/null +++ b/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: True + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml new file mode 100644 index 0000000000..a9aeb4eac3 --- /dev/null +++ b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 diff --git a/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml new file mode 100644 index 0000000000..38ed867d89 --- /dev/null +++ b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml new file mode 100644 index 0000000000..b13eefab2a --- /dev/null +++ b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 diff --git a/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml new file mode 100644 index 0000000000..d401016358 --- /dev/null +++ b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-DilatedC5.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000..d50fb866ca --- /dev/null +++ b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 diff --git a/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml new file mode 100644 index 0000000000..be7d06b8e0 --- /dev/null +++ b/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml b/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml new file mode 100644 index 0000000000..d14c63f743 --- /dev/null +++ b/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml @@ -0,0 +1,13 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + MASK_ON: True + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml b/configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml new file mode 100644 index 0000000000..4e03944a42 --- /dev/null +++ b/configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml @@ -0,0 +1,15 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + KEYPOINT_ON: True + ROI_HEADS: + NUM_CLASSES: 1 + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 0.5 # Keypoint AP degrades (though box AP improves) when using plain L1 loss + RPN: + # Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2. + # 1000 proposals per-image is found to hurt box AP. + # Therefore we increase it to 1500 per-image. + POST_NMS_TOPK_TRAIN: 1500 +DATASETS: + TRAIN: ("keypoints_coco_2017_train",) + TEST: ("keypoints_coco_2017_val",) diff --git a/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml b/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml new file mode 100644 index 0000000000..9309535c57 --- /dev/null +++ b/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "Base-Keypoint-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml b/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000..7bf85cf745 --- /dev/null +++ b/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,5 @@ +_BASE_: "Base-Keypoint-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 diff --git a/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml b/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml new file mode 100644 index 0000000000..a07f243f65 --- /dev/null +++ b/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "Base-Keypoint-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml b/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml new file mode 100644 index 0000000000..d4bfa20a98 --- /dev/null +++ b/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml @@ -0,0 +1,12 @@ +_BASE_: "Base-Keypoint-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml b/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml new file mode 100644 index 0000000000..755c12018c --- /dev/null +++ b/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "PanopticFPN" + MASK_ON: True + SEM_SEG_HEAD: + LOSS_WEIGHT: 0.5 +DATASETS: + TRAIN: ("coco_2017_train_panoptic_separated",) + TEST: ("coco_2017_val_panoptic_separated",) diff --git a/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml b/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml new file mode 100644 index 0000000000..0e01f6fb31 --- /dev/null +++ b/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "Base-Panoptic-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml b/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml new file mode 100644 index 0000000000..6afa2c1cc9 --- /dev/null +++ b/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml @@ -0,0 +1,5 @@ +_BASE_: "Base-Panoptic-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 diff --git a/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml b/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml new file mode 100644 index 0000000000..b956b3f673 --- /dev/null +++ b/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml @@ -0,0 +1,8 @@ +_BASE_: "Base-Panoptic-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml b/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml new file mode 100644 index 0000000000..d6e7014526 --- /dev/null +++ b/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml @@ -0,0 +1,25 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + # WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + # For better, more stable performance initialize from COCO + WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl" + MASK_ON: True + ROI_HEADS: + NUM_CLASSES: 8 +# This is the setting used in Mask R-CNN paper, Appendix A +INPUT: + MIN_SIZE_TRAIN: (800, 832, 864, 896, 928, 960, 992, 1024) + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 1024 + MAX_SIZE_TRAIN: 2048 + MAX_SIZE_TEST: 2048 +DATASETS: + TRAIN: ("cityscapes_fine_instance_seg_train",) + TEST: ("cityscapes_fine_instance_seg_val",) +SOLVER: + BASE_LR: 0.01 + STEPS: (18000,) + MAX_ITER: 24000 + IMS_PER_BATCH: 8 +TEST: + EVAL_PERIOD: 8000 diff --git a/configs/Detectron1-Comparisons/README.md b/configs/Detectron1-Comparisons/README.md new file mode 100644 index 0000000000..18d13e9574 --- /dev/null +++ b/configs/Detectron1-Comparisons/README.md @@ -0,0 +1,77 @@ + +Detectron2's default settings and a few implementation details are different from Detectron. + +The differences in implementation details are shared in +[Compatibility with Other Libraries](../../docs/notes/compatibility.md). + +The differences in default config includes: +* Use scale augmentation during training. +* Use L1 loss instead of smooth L1 loss. +* Use `POOLER_SAMPLING_RATIO=0` instead of 2. +* Use `ROIAlignV2`. + +In this directory, we provide a few configs that mimic Detectron's behavior as close as possible. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Namelr
sched		train
time
(s/iter)		inference
time
(s/im)		train
mem
(GB)		box
AP		mask
AP		kp.
AP		model iddownload
Faster R-CNN1x0.2190.0583.136.9137781054model | metrics
Keypoint R-CNN1x0.3130.1035.053.164.2137781195model | metrics
Mask R-CNN1x0.2730.0843.437.834.9137781281model | metrics
+ +## Comparisons: + +* Faster R-CNN: Detectron's AP is 36.7, similar to ours. +* Keypoint R-CNN: Detectron's AP is box 53.6, keypoint 64.2. Fixing a Detectron's + [bug](https://github.com/facebookresearch/Detectron/issues/459) lead to a drop in box AP, and can be + compensated back by some parameter tuning. +* Mask R-CNN: Detectron's AP is box 37.7, mask 33.9. We're 1 AP better in mask AP, due to more correct implementation. diff --git a/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml b/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml new file mode 100644 index 0000000000..6ce77f137f --- /dev/null +++ b/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml @@ -0,0 +1,17 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + # Detectron1 uses smooth L1 loss with some magic beta values. + # The defaults are changed to L1 loss in Detectron2. + RPN: + SMOOTH_L1_BETA: 0.1111 + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 1.0 + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" +INPUT: + # no scale augmentation + MIN_SIZE_TRAIN: (800, ) diff --git a/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml b/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000..aacf868ba5 --- /dev/null +++ b/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,27 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + KEYPOINT_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + NUM_CLASSES: 1 + ROI_KEYPOINT_HEAD: + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" + # Detectron1 uses smooth L1 loss with some magic beta values. + # The defaults are changed to L1 loss in Detectron2. + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 1.0 + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" + RPN: + SMOOTH_L1_BETA: 0.1111 + # Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2 + # 1000 proposals per-image is found to hurt box AP. + # Therefore we increase it to 1500 per-image. + POST_NMS_TOPK_TRAIN: 1500 +DATASETS: + TRAIN: ("keypoints_coco_2017_train",) + TEST: ("keypoints_coco_2017_val",) diff --git a/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml b/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml new file mode 100644 index 0000000000..4ea86a8d8e --- /dev/null +++ b/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml @@ -0,0 +1,20 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + # Detectron1 uses smooth L1 loss with some magic beta values. + # The defaults are changed to L1 loss in Detectron2. + RPN: + SMOOTH_L1_BETA: 0.1111 + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 1.0 + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" + ROI_MASK_HEAD: + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" +INPUT: + # no scale augmentation + MIN_SIZE_TRAIN: (800, ) diff --git a/configs/LVIS-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml b/configs/LVIS-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml new file mode 100644 index 0000000000..f0c3a1bbc0 --- /dev/null +++ b/configs/LVIS-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml @@ -0,0 +1,19 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: True + RESNETS: + DEPTH: 101 + ROI_HEADS: + NUM_CLASSES: 1230 + SCORE_THRESH_TEST: 0.0001 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +DATASETS: + TRAIN: ("lvis_v0.5_train",) + TEST: ("lvis_v0.5_val",) +TEST: + DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300 +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 diff --git a/configs/LVIS-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml b/configs/LVIS-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000..64b4caa4ef --- /dev/null +++ b/configs/LVIS-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,19 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + NUM_CLASSES: 1230 + SCORE_THRESH_TEST: 0.0001 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +DATASETS: + TRAIN: ("lvis_v0.5_train",) + TEST: ("lvis_v0.5_val",) +TEST: + DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300 +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 diff --git a/configs/LVIS-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml b/configs/LVIS-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml new file mode 100644 index 0000000000..c8b822c6c0 --- /dev/null +++ b/configs/LVIS-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml @@ -0,0 +1,23 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + MASK_ON: True + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 + ROI_HEADS: + NUM_CLASSES: 1230 + SCORE_THRESH_TEST: 0.0001 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +DATASETS: + TRAIN: ("lvis_v0.5_train",) + TEST: ("lvis_v0.5_val",) +TEST: + DETECTIONS_PER_IMAGE: 300 # LVIS allows up to 300 +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 diff --git a/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml b/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml new file mode 100644 index 0000000000..abb33b6189 --- /dev/null +++ b/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml @@ -0,0 +1,12 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + NAME: CascadeROIHeads + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + RPN: + POST_NMS_TOPK_TRAIN: 2000 diff --git a/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml b/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml new file mode 100644 index 0000000000..fc117f6b5e --- /dev/null +++ b/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml @@ -0,0 +1,36 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + MASK_ON: True + WEIGHTS: "catalog://ImageNetPretrained/FAIR/X-152-32x8d-IN5k" + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 152 + DEFORM_ON_PER_STAGE: [False, True, True, True] + ROI_HEADS: + NAME: "CascadeROIHeads" + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_CONV: 4 + NUM_FC: 1 + NORM: "GN" + CLS_AGNOSTIC_BBOX_REG: True + ROI_MASK_HEAD: + NUM_CONV: 8 + NORM: "GN" + RPN: + POST_NMS_TOPK_TRAIN: 2000 +SOLVER: + IMS_PER_BATCH: 128 + STEPS: (35000, 45000) + MAX_ITER: 50000 + BASE_LR: 0.16 +INPUT: + MIN_SIZE_TRAIN: (640, 864) + MIN_SIZE_TRAIN_SAMPLING: "range" + MAX_SIZE_TRAIN: 1440 + CROP: + ENABLED: True +TEST: + EVAL_PERIOD: 2500 diff --git a/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml b/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml new file mode 100644 index 0000000000..04ff988d07 --- /dev/null +++ b/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml @@ -0,0 +1,8 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3,Res4,Res5 + DEFORM_MODULATED: False diff --git a/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml b/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml new file mode 100644 index 0000000000..74d274e5a5 --- /dev/null +++ b/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml @@ -0,0 +1,21 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "catalog://ImageNetPretrained/FAIR/R-50-GN" + MASK_ON: True + RESNETS: + DEPTH: 50 + NORM: "GN" + STRIDE_IN_1X1: False + FPN: + NORM: "GN" + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_CONV: 4 + NUM_FC: 1 + NORM: "GN" + ROI_MASK_HEAD: + NORM: "GN" +SOLVER: + # 3x schedule + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml b/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml new file mode 100644 index 0000000000..34016cea3c --- /dev/null +++ b/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml @@ -0,0 +1,26 @@ +# A large PanopticFPN for demo purposes. +# Use GN on backbone to support semantic seg. +# Use Cascade + Deform Conv to improve localization. +_BASE_: "../COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml" +MODEL: + WEIGHTS: "catalog://ImageNetPretrained/FAIR/R-101-GN" + RESNETS: + DEPTH: 101 + NORM: "GN" + DEFORM_ON_PER_STAGE: [False, True, True, True] + STRIDE_IN_1X1: False + FPN: + NORM: "GN" + ROI_HEADS: + NAME: CascadeROIHeads + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_MASK_HEAD: + NORM: "GN" + RPN: + POST_NMS_TOPK_TRAIN: 2000 +SOLVER: + STEPS: (105000, 125000) + MAX_ITER: 135000 + IMS_PER_BATCH: 32 + BASE_LR: 0.04 diff --git a/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml b/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml new file mode 100644 index 0000000000..c963606c44 --- /dev/null +++ b/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml @@ -0,0 +1,11 @@ +_BASE_: "mask_rcnn_R_50_FPN_3x_gn.yaml" +# INPUT: + # It makes sense to divide by STD when training from scratch + # But it seems to make no difference on the results and C2's models did't do this. + # So we keep things consistent with C2. + # PIXEL_STD: [57.375, 57.12, 58.395] +MODEL: + WEIGHTS: "" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 diff --git a/configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml b/configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml new file mode 100644 index 0000000000..ea2a6baaeb --- /dev/null +++ b/configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml @@ -0,0 +1,18 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + ROI_HEADS: + NUM_CLASSES: 20 +INPUT: + MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800) + MIN_SIZE_TEST: 800 +DATASETS: + TRAIN: ('voc_2007_trainval', 'voc_2012_trainval') + TEST: ('voc_2007_test',) +SOLVER: + STEPS: (12000, 16000) + MAX_ITER: 18000 # 17.4 epochs + WARMUP_ITERS: 100 diff --git a/configs/dcn/faster_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml b/configs/dcn/faster_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml new file mode 100644 index 0000000000..2fda6bb5c5 --- /dev/null +++ b/configs/dcn/faster_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml @@ -0,0 +1,8 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3, Res4,Res5 + DEFORM_MODULATED: False diff --git a/configs/dcn/faster_rcnn_R_50_FPN_1x_dconv_c5.yaml b/configs/dcn/faster_rcnn_R_50_FPN_1x_dconv_c5.yaml new file mode 100644 index 0000000000..ba104554cf --- /dev/null +++ b/configs/dcn/faster_rcnn_R_50_FPN_1x_dconv_c5.yaml @@ -0,0 +1,8 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + DEFORM_ON_PER_STAGE: [False, False, False, True] # on Res5 + DEFORM_MODULATED: False diff --git a/configs/dcn/faster_rcnn_R_50_FPN_1x_mdconv_c3-c5.yaml b/configs/dcn/faster_rcnn_R_50_FPN_1x_mdconv_c3-c5.yaml new file mode 100644 index 0000000000..4922df01be --- /dev/null +++ b/configs/dcn/faster_rcnn_R_50_FPN_1x_mdconv_c3-c5.yaml @@ -0,0 +1,8 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3, Res4,Res5 + DEFORM_MODULATED: True diff --git a/configs/dcn/faster_rcnn_R_50_FPN_1x_mdconv_c5.yaml b/configs/dcn/faster_rcnn_R_50_FPN_1x_mdconv_c5.yaml new file mode 100644 index 0000000000..258d8e2990 --- /dev/null +++ b/configs/dcn/faster_rcnn_R_50_FPN_1x_mdconv_c5.yaml @@ -0,0 +1,9 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 + DEFORM_ON_PER_STAGE: [False, False, False, True] # on Res5 + DEFORM_MODULATED: True + diff --git a/configs/dcn/mask_rcnn_R_50_FPN_1x_mdconv_c3-c5.yaml b/configs/dcn/mask_rcnn_R_50_FPN_1x_mdconv_c3-c5.yaml new file mode 100644 index 0000000000..87fa9013c8 --- /dev/null +++ b/configs/dcn/mask_rcnn_R_50_FPN_1x_mdconv_c3-c5.yaml @@ -0,0 +1,8 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + DEFORM_ON_PER_STAGE: [False, True, True, True] # on Res3, Res4,Res5 + DEFORM_MODULATED: True diff --git a/configs/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml b/configs/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml new file mode 100644 index 0000000000..685589cc03 --- /dev/null +++ b/configs/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml @@ -0,0 +1,10 @@ +_BASE_: "Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_MASK_HEAD: + CLS_AGNOSTIC_MASK: True diff --git a/configs/quick_schedules/README.md b/configs/quick_schedules/README.md new file mode 100644 index 0000000000..569cb5bec2 --- /dev/null +++ b/configs/quick_schedules/README.md @@ -0,0 +1,51 @@ +These are quick configs for performance or accuracy regression tracking purposes. + +## Perf testing configs: + +### Inference + +Reference devgpu configuration: + + - 48 core Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz + - 2x M40 (12GB) + - buck build @mode/dev-nosan + +configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml +``` +# Before https://github.com/fairinternal/detectron2/pull/84 +Total inference time: 0:00:30.808294 (0.6161658811569214 s / img per device, on 2 devices) +# After https://github.com/fairinternal/detectron2/pull/84 +Total inference time: 0:00:36.952044 (0.7390408849716187 s / img per device, on 2 devices) +``` + +configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml +``` +# Before https://github.com/fairinternal/detectron2/pull/84 +Total inference time: 0:00:21.773355 (0.435467095375061 s / img per device, on 2 devices) +# After https://github.com/fairinternal/detectron2/pull/84 +Total inference time: 0:00:28.766723 (0.5753344583511353 s / img per device, on 2 devices) +``` + +### Training + +TODO + +They are equivalent to the standard C4 / FPN models, only with extremely short schedules. + +Metrics to look at: + +``` +INFO: Total training time: 0:3:20.276231 +... +INFO: Total inference time: 0:01:20.276231 +``` + + +## Accuracy testing configs: + +They are simplified versions of standard models, trained and tested on the same +minival dataset, with short schedules. + +The schedule is designed to provide a stable enough mAP within minimal amount of training time. + +Metrics to look at: mAPs. diff --git a/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml b/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000..a2f37e5e2c --- /dev/null +++ b/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/model_final_e5f7ce.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 45.70, 0.02]] diff --git a/configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml b/configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000..52fc0ec03c --- /dev/null +++ b/configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml @@ -0,0 +1,15 @@ +_BASE_: "../COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" +DATASETS: + TRAIN: ("coco_2017_val_100",) + PROPOSAL_FILES_TRAIN: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", ) + TEST: ("coco_2017_val_100",) + PROPOSAL_FILES_TEST: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", ) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml b/configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000..14cf2aa82a --- /dev/null +++ b/configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl" +DATASETS: + TEST: ("keypoints_coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 52.47, 0.02], ["keypoints", "AP", 67.36, 0.02]] diff --git a/configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml b/configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000..dc09034bdd --- /dev/null +++ b/configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml @@ -0,0 +1,14 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + KEYPOINT_ON: True +DATASETS: + TRAIN: ("keypoints_coco_2017_val_100",) + TEST: ("keypoints_coco_2017_val_100",) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/configs/quick_schedules/keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml b/configs/quick_schedules/keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml new file mode 100644 index 0000000000..4b92392f1c --- /dev/null +++ b/configs/quick_schedules/keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml @@ -0,0 +1,30 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + KEYPOINT_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 256 + NUM_CLASSES: 1 + ROI_KEYPOINT_HEAD: + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 2 + NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: False + LOSS_WEIGHT: 4.0 + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 1.0 # Keypoint AP degrades when using plain L1 loss + RPN: + SMOOTH_L1_BETA: 0.2 # Keypoint AP degrades when using plain L1 loss +DATASETS: + TRAIN: ("keypoints_coco_2017_val",) + TEST: ("keypoints_coco_2017_val",) +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +SOLVER: + WARMUP_FACTOR: 0.33333333 + WARMUP_ITERS: 100 + STEPS: (5500, 5800) + MAX_ITER: 6000 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 55.35, 1.0], ["keypoints", "AP", 76.91, 1.0]] diff --git a/configs/quick_schedules/keypoint_rcnn_R_50_FPN_training_acc_test.yaml b/configs/quick_schedules/keypoint_rcnn_R_50_FPN_training_acc_test.yaml new file mode 100644 index 0000000000..9bd962878f --- /dev/null +++ b/configs/quick_schedules/keypoint_rcnn_R_50_FPN_training_acc_test.yaml @@ -0,0 +1,28 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + KEYPOINT_ON: True + RESNETS: + DEPTH: 50 + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 256 + NUM_CLASSES: 1 + ROI_KEYPOINT_HEAD: + POOLER_RESOLUTION: 14 + POOLER_SAMPLING_RATIO: 2 + ROI_BOX_HEAD: + SMOOTH_L1_BETA: 1.0 # Keypoint AP degrades when using plain L1 loss + RPN: + SMOOTH_L1_BETA: 0.2 # Keypoint AP degrades when using plain L1 loss +DATASETS: + TRAIN: ("keypoints_coco_2017_val",) + TEST: ("keypoints_coco_2017_val",) +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +SOLVER: + WARMUP_FACTOR: 0.33333333 + WARMUP_ITERS: 100 + STEPS: (5500, 5800) + MAX_ITER: 6000 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 53.5, 1.0], ["keypoints", "AP", 72.4, 1.0]] diff --git a/configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml b/configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml new file mode 100644 index 0000000000..b2d5b7ff87 --- /dev/null +++ b/configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 47.37, 0.02], ["segm", "AP", 40.99, 0.02]] diff --git a/configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml b/configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml new file mode 100644 index 0000000000..6c4f1214ef --- /dev/null +++ b/configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml @@ -0,0 +1,14 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True +DATASETS: + TRAIN: ("coco_2017_val_100",) + TEST: ("coco_2017_val_100",) +SOLVER: + BASE_LR: 0.001 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/configs/quick_schedules/mask_rcnn_R_50_C4_training_acc_test.yaml b/configs/quick_schedules/mask_rcnn_R_50_C4_training_acc_test.yaml new file mode 100644 index 0000000000..f68dd8f96c --- /dev/null +++ b/configs/quick_schedules/mask_rcnn_R_50_C4_training_acc_test.yaml @@ -0,0 +1,22 @@ +_BASE_: "../Base-RCNN-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 256 + MASK_ON: True +DATASETS: + TRAIN: ("coco_2017_val",) + TEST: ("coco_2017_val",) +INPUT: + MIN_SIZE_TRAIN: (600,) + MAX_SIZE_TRAIN: 1000 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1000 +SOLVER: + IMS_PER_BATCH: 8 # base uses 16 + WARMUP_FACTOR: 0.33333 + WARMUP_ITERS: 100 + STEPS: (11000, 11600) + MAX_ITER: 12000 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 41.88, 0.7], ["segm", "AP", 33.79, 0.5]] diff --git a/configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml b/configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml new file mode 100644 index 0000000000..e3ce6cf922 --- /dev/null +++ b/configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/model_final_84107b.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 47.44, 0.02], ["segm", "AP", 42.94, 0.02]] diff --git a/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml b/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000..a41dc53b41 --- /dev/null +++ b/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,11 @@ +_BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 47.34, 0.02], ["segm", "AP", 42.67, 0.02]] + # expected results do not use test-time augmentation. TTA results are not verified. + AUG: + ENABLED: True + MIN_SIZES: (400, 500) # to save some time diff --git a/configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml b/configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000..6dbfcde0bf --- /dev/null +++ b/configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml @@ -0,0 +1,14 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True +DATASETS: + TRAIN: ("coco_2017_val_100",) + TEST: ("coco_2017_val_100",) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/configs/quick_schedules/mask_rcnn_R_50_FPN_training_acc_test.yaml b/configs/quick_schedules/mask_rcnn_R_50_FPN_training_acc_test.yaml new file mode 100644 index 0000000000..4992104a53 --- /dev/null +++ b/configs/quick_schedules/mask_rcnn_R_50_FPN_training_acc_test.yaml @@ -0,0 +1,21 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + ROI_HEADS: + BATCH_SIZE_PER_IMAGE: 256 + MASK_ON: True +DATASETS: + TRAIN: ("coco_2017_val",) + TEST: ("coco_2017_val",) +INPUT: + MIN_SIZE_TRAIN: (600,) + MAX_SIZE_TRAIN: 1000 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1000 +SOLVER: + WARMUP_FACTOR: 0.3333333 + WARMUP_ITERS: 100 + STEPS: (5500, 5800) + MAX_ITER: 6000 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 42.8, 0.8], ["segm", "AP", 35.7, 0.8]] diff --git a/configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml b/configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml new file mode 100644 index 0000000000..70874e3a92 --- /dev/null +++ b/configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/model_final_c10459.pkl" +DATASETS: + TEST: ("coco_2017_val_100_panoptic_separated",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 46.47, 0.02], ["segm", "AP", 43.39, 0.02], ["sem_seg", "mIoU", 42.55, 0.02], ["panoptic_seg", "PQ", 38.99, 0.02]] diff --git a/configs/quick_schedules/panoptic_fpn_R_50_instant_test.yaml b/configs/quick_schedules/panoptic_fpn_R_50_instant_test.yaml new file mode 100644 index 0000000000..26f3b140c6 --- /dev/null +++ b/configs/quick_schedules/panoptic_fpn_R_50_instant_test.yaml @@ -0,0 +1,19 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "PanopticFPN" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + SEM_SEG_HEAD: + LOSS_WEIGHT: 0.5 +DATASETS: + TRAIN: ("coco_2017_val_100_panoptic_separated",) + TEST: ("coco_2017_val_100_panoptic_separated",) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/configs/quick_schedules/panoptic_fpn_R_50_training_acc_test.yaml b/configs/quick_schedules/panoptic_fpn_R_50_training_acc_test.yaml new file mode 100644 index 0000000000..8aeba40f82 --- /dev/null +++ b/configs/quick_schedules/panoptic_fpn_R_50_training_acc_test.yaml @@ -0,0 +1,20 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "PanopticFPN" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: True + RESNETS: + DEPTH: 50 + SEM_SEG_HEAD: + LOSS_WEIGHT: 0.5 +DATASETS: + TRAIN: ("coco_2017_val_panoptic_separated",) + TEST: ("coco_2017_val_panoptic_separated",) +SOLVER: + BASE_LR: 0.01 + WARMUP_FACTOR: 0.001 + WARMUP_ITERS: 500 + STEPS: (5500,) + MAX_ITER: 7000 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 46.80, 1.1], ["segm", "AP", 38.93, 0.7], ["sem_seg", "mIoU", 63.99, 0.9], ["panoptic_seg", "PQ", 48.23, 0.8]] diff --git a/configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml b/configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000..36b998833b --- /dev/null +++ b/configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-Detection/retinanet_R_50_FPN_3x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-Detection/retinanet_R_50_FPN_3x/137849486/model_final_4cafe0.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 44.36, 0.02]] diff --git a/configs/quick_schedules/retinanet_R_50_FPN_instant_test.yaml b/configs/quick_schedules/retinanet_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000..8d95c1f614 --- /dev/null +++ b/configs/quick_schedules/retinanet_R_50_FPN_instant_test.yaml @@ -0,0 +1,13 @@ +_BASE_: "../COCO-Detection/retinanet_R_50_FPN_1x.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" +DATASETS: + TRAIN: ("coco_2017_val_100",) + TEST: ("coco_2017_val_100",) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/configs/quick_schedules/rpn_R_50_FPN_inference_acc_test.yaml b/configs/quick_schedules/rpn_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000..c7c3f908a9 --- /dev/null +++ b/configs/quick_schedules/rpn_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,7 @@ +_BASE_: "../COCO-Detection/rpn_R_50_FPN_1x.yaml" +MODEL: + WEIGHTS: "detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/model_final_02ce48.pkl" +DATASETS: + TEST: ("coco_2017_val_100",) +TEST: + EXPECTED_RESULTS: [["box_proposals", "AR@1000", 58.16, 0.02]] diff --git a/configs/quick_schedules/rpn_R_50_FPN_instant_test.yaml b/configs/quick_schedules/rpn_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000..402d432477 --- /dev/null +++ b/configs/quick_schedules/rpn_R_50_FPN_instant_test.yaml @@ -0,0 +1,13 @@ +_BASE_: "../COCO-Detection/rpn_R_50_FPN_1x.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" +DATASETS: + TRAIN: ("coco_2017_val_100",) + TEST: ("coco_2017_val_100",) +SOLVER: + STEPS: (30,) + MAX_ITER: 40 + BASE_LR: 0.005 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/configs/quick_schedules/semantic_R_50_FPN_inference_acc_test.yaml b/configs/quick_schedules/semantic_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000..bca74987d5 --- /dev/null +++ b/configs/quick_schedules/semantic_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,10 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "SemanticSegmentor" + WEIGHTS: "detectron2://semantic_R_50_FPN_1x/111802073/model_final_c18079783c55a94968edc28b7101c5f0.pkl" + RESNETS: + DEPTH: 50 +DATASETS: + TEST: ("coco_2017_val_100_panoptic_stuffonly",) +TEST: + EXPECTED_RESULTS: [["sem_seg", "mIoU", 39.53, 0.02], ["sem_seg", "mACC", 51.50, 0.02]] diff --git a/configs/quick_schedules/semantic_R_50_FPN_instant_test.yaml b/configs/quick_schedules/semantic_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000..14ab606f21 --- /dev/null +++ b/configs/quick_schedules/semantic_R_50_FPN_instant_test.yaml @@ -0,0 +1,18 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "SemanticSegmentor" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +DATASETS: + TRAIN: ("coco_2017_val_100_panoptic_stuffonly",) + TEST: ("coco_2017_val_100_panoptic_stuffonly",) +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +SOLVER: + BASE_LR: 0.005 + STEPS: (30,) + MAX_ITER: 40 + IMS_PER_BATCH: 4 +DATALOADER: + NUM_WORKERS: 2 diff --git a/configs/quick_schedules/semantic_R_50_FPN_training_acc_test.yaml b/configs/quick_schedules/semantic_R_50_FPN_training_acc_test.yaml new file mode 100644 index 0000000000..1f78d77588 --- /dev/null +++ b/configs/quick_schedules/semantic_R_50_FPN_training_acc_test.yaml @@ -0,0 +1,20 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "SemanticSegmentor" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +DATASETS: + TRAIN: ("coco_2017_val_panoptic_stuffonly",) + TEST: ("coco_2017_val_panoptic_stuffonly",) +SOLVER: + BASE_LR: 0.01 + WARMUP_FACTOR: 0.001 + WARMUP_ITERS: 300 + STEPS: (5500,) + MAX_ITER: 7000 +TEST: + EXPECTED_RESULTS: [["sem_seg", "mIoU", 76.51, 1.0], ["sem_seg", "mACC", 83.25, 1.0]] +INPUT: + # no scale augmentation + MIN_SIZE_TRAIN: (800, ) diff --git a/configs/semantic_R_101_FPN_1x.yaml b/configs/semantic_R_101_FPN_1x.yaml new file mode 100644 index 0000000000..ba8eb98119 --- /dev/null +++ b/configs/semantic_R_101_FPN_1x.yaml @@ -0,0 +1,11 @@ +_BASE_: "Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "SemanticSegmentor" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + RESNETS: + DEPTH: 101 +DATASETS: + TRAIN: ("coco_2017_train_panoptic_stuffonly",) + TEST: ("coco_2017_val_panoptic_stuffonly",) +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) diff --git a/configs/semantic_R_50_FPN_1x.yaml b/configs/semantic_R_50_FPN_1x.yaml new file mode 100644 index 0000000000..fcc3ba2d74 --- /dev/null +++ b/configs/semantic_R_50_FPN_1x.yaml @@ -0,0 +1,11 @@ +_BASE_: "Base-RCNN-FPN.yaml" +MODEL: + META_ARCHITECTURE: "SemanticSegmentor" + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +DATASETS: + TRAIN: ("coco_2017_train_panoptic_stuffonly",) + TEST: ("coco_2017_val_panoptic_stuffonly",) +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) diff --git a/datasets/README.md b/datasets/README.md new file mode 100644 index 0000000000..cde1919164 --- /dev/null +++ b/datasets/README.md @@ -0,0 +1,80 @@ + +For a few datasets that detectron2 natively supports, +the datasets are assumed to exist in a directory called +"datasets/", under the directory where you launch the program. +with the following directory structure: + +## Expected dataset structure for COCO instance/keypoint detection: + +``` +coco/ + annotations/ + instances_{train,val}2017.json + person_keypoints_{train,val}2017.json + {train,val}2017/ + # image files that are mentioned in the corresponding json +``` + +You can use the 2014 version of the dataset as well. + +Some of the builtin tests (`run_*_tests.sh`) uses a tiny version of the COCO dataset, +which you can download with `./prepare_for_tests.sh`. + +## Expected dataset structure for PanopticFPN: + +``` +coco/ + annotations/ + panoptic_{train,val}2017.json + panoptic_{train,val}2017/ + # png annotations +``` + +Install panopticapi by: +``` +pip install git+https://github.com/cocodataset/panopticapi.git +``` +Then, run `./prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations. + +## Expected dataset structure for LVIS instance detection/segmentation: +``` +coco/ + {train,val,test}2017/ +lvis/ + lvis_v0.5_{train,val}.json + lvis_v0.5_image_info_test.json +``` + +Install lvis-api by: +``` +pip install git+https://github.com/lvis-dataset/lvis-api.git +``` + +## Expected dataset structure for cityscapes: +``` +cityscapes/ + gtFine/ + train/ + aachen/ + color.png, instanceIds.png, labelIds.png, polygons.json + labelTrainIds.png (created by cityscapesscripts/preparation/createTrainIdLabelImgs.py) + ... + val/ + test/ + leftImg8bit/ + train/ + val/ + test/ +``` +Install cityscapes scripts by: +``` +pip install git+https://github.com/mcordts/cityscapesScripts.git +``` + +## Expected dataset structure for Pascal VOC: +``` +VOC20{07,12}/ + Annotations/ + ImageSets/ + JPEGImages/ +``` diff --git a/datasets/prepare_for_tests.sh b/datasets/prepare_for_tests.sh new file mode 100755 index 0000000000..d59b5643c9 --- /dev/null +++ b/datasets/prepare_for_tests.sh @@ -0,0 +1,22 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +# Download some files needed for running tests. + +cd "${0%/*}" + +BASE=https://dl.fbaipublicfiles.com/detectron2 +mkdir -p coco/annotations + +for anno in instances_val2017_100 \ + person_keypoints_val2017_100 \ + instances_minival2014_100 \ + person_keypoints_minival2014_100; do + + dest=coco/annotations/$anno.json + [[ -s $dest ]] && { + echo "$dest exists. Skipping ..." + } || { + wget $BASE/annotations/coco/$anno.json -O $dest + } +done diff --git a/datasets/prepare_panoptic_fpn.py b/datasets/prepare_panoptic_fpn.py new file mode 100644 index 0000000000..88007b3ba2 --- /dev/null +++ b/datasets/prepare_panoptic_fpn.py @@ -0,0 +1,116 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import time +import functools +import json +import multiprocessing as mp +import numpy as np +import os +from PIL import Image + +from detectron2.data.datasets.builtin import COCO_CATEGORIES +from fvcore.common.download import download + +from panopticapi.utils import rgb2id + + +def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map): + panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32) + panoptic = rgb2id(panoptic) + output = np.zeros_like(panoptic, dtype=np.uint8) + 255 + for seg in segments: + cat_id = seg["category_id"] + new_cat_id = id_map[cat_id] + output[panoptic == seg["id"]] = new_cat_id + Image.fromarray(output).save(output_semantic) + + +def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories): + """ + Create semantic segmentation annotations from panoptic segmentation + annotations, to be used by PanopticFPN. + + It maps all thing categories to class 0, and maps all unlabeled pixels to class 255. + It maps all stuff categories to contiguous ids starting from 1. + + Args: + panoptic_json (str): path to the panoptic json file, in COCO's format. + panoptic_root (str): a directory with panoptic annotation files, in COCO's format. + sem_seg_root (str): a directory to output semantic annotation files + categories (list[dict]): category metadata. Each dict needs to have: + "id": corresponds to the "category_id" in the json annotations + "isthing": 0 or 1 + """ + os.makedirs(sem_seg_root, exist_ok=True) + + stuff_ids = [k["id"] for k in categories if k["isthing"] == 0] + thing_ids = [k["id"] for k in categories if k["isthing"] == 1] + id_map = {} # map from category id to id in the output semantic annotation + assert len(stuff_ids) <= 254 + for i, stuff_id in enumerate(stuff_ids): + id_map[stuff_id] = i + 1 + for thing_id in thing_ids: + id_map[thing_id] = 0 + id_map[0] = 255 + + with open(panoptic_json) as f: + obj = json.load(f) + + pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4)) + + def iter_annotations(): + for anno in obj["annotations"]: + file_name = anno["file_name"] + segments = anno["segments_info"] + input = os.path.join(panoptic_root, file_name) + output = os.path.join(sem_seg_root, file_name) + yield input, output, segments + + print("Start writing to {} ...".format(sem_seg_root)) + start = time.time() + pool.starmap( + functools.partial(_process_panoptic_to_semantic, id_map=id_map), + iter_annotations(), + chunksize=100, + ) + print("Finished. time: {:.2f}s".format(time.time() - start)) + + +if __name__ == "__main__": + dataset_dir = os.path.join(os.path.dirname(__file__), "coco") + for s in ["val2017", "train2017"]: + separate_coco_semantic_from_panoptic( + os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)), + os.path.join(dataset_dir, "panoptic_{}".format(s)), + os.path.join(dataset_dir, "panoptic_stuff_{}".format(s)), + COCO_CATEGORIES, + ) + + # Prepare val2017_100 for quick testing: + + dest_dir = os.path.join(dataset_dir, "annotations/") + URL_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/" + download(URL_PREFIX + "annotations/coco/panoptic_val2017_100.json", dest_dir) + with open(os.path.join(dest_dir, "panoptic_val2017_100.json")) as f: + obj = json.load(f) + + def link_val100(dir_full, dir_100): + print("Creating " + dir_100 + " ...") + os.makedirs(dir_100, exist_ok=True) + for img in obj["images"]: + basename = os.path.splitext(img["file_name"])[0] + src = os.path.join(dir_full, basename + ".png") + dst = os.path.join(dir_100, basename + ".png") + src = os.path.relpath(src, start=dir_100) + os.symlink(src, dst) + + link_val100( + os.path.join(dataset_dir, "panoptic_val2017"), + os.path.join(dataset_dir, "panoptic_val2017_100"), + ) + + link_val100( + os.path.join(dataset_dir, "panoptic_stuff_val2017"), + os.path.join(dataset_dir, "panoptic_stuff_val2017_100"), + ) diff --git a/demo/README.md b/demo/README.md new file mode 100644 index 0000000000..359affedce --- /dev/null +++ b/demo/README.md @@ -0,0 +1,16 @@ +## Webcam and Jupyter notebook demo + +This folder contains a simple webcam demo that illustrates how you can use `detectron2` for inference. + +You can start it by running it from this folder, using one of the following commands: +```bash +# by default, it runs on the GPU +# for best results, use min-image-size 800 +python webcam.py --min-image-size 800 +# can also run it on the CPU +python webcam.py --min-image-size 300 MODEL.DEVICE cpu +# or change the model that you want to use +python webcam.py --config-file ../configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml --min-image-size 300 MODEL.DEVICE cpu +# in order to see the probability heatmaps, pass --show-mask-heatmaps +python webcam.py --min-image-size 300 --show-mask-heatmaps MODEL.DEVICE cpu +``` diff --git a/demo/demo.py b/demo/demo.py new file mode 100644 index 0000000000..26f4c487c7 --- /dev/null +++ b/demo/demo.py @@ -0,0 +1,142 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import argparse +import glob +import multiprocessing as mp +import os +import time +import cv2 +import tqdm + +from detectron2.config import get_cfg +from detectron2.data.detection_utils import read_image +from detectron2.utils.logger import setup_logger + +from predictor import VisualizationDemo + + +def setup_cfg(args): + # load config from file and command-line arguments + cfg = get_cfg() + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + # Set score_threshold for builtin models + cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold + cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold + cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold + cfg.freeze() + return cfg + + +def get_parser(): + parser = argparse.ArgumentParser(description="Detectron2 Demo") + parser.add_argument( + "--config-file", + default="configs/quick_schedules/e2e_mask_rcnn_R_50_FPN_inference_acc_test.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") + parser.add_argument("--video-input", help="Path to video file.") + parser.add_argument("--input", nargs="+", help="A list of space separated input images") + parser.add_argument( + "--output", + help="A file or directory to save output visualizations. " + "If not given, will show output in an OpenCV window.", + ) + + parser.add_argument( + "--confidence-threshold", + type=float, + default=0.5, + help="Minimum score for instance predictions to be shown", + ) + parser.add_argument( + "--opts", + help="Modify model config options using the command-line", + default=None, + nargs=argparse.REMAINDER, + ) + return parser + + +if __name__ == "__main__": + mp.set_start_method("spawn", force=True) + args = get_parser().parse_args() + logger = setup_logger() + logger.info("Arguments: " + str(args)) + + cfg = setup_cfg(args) + + demo = VisualizationDemo(cfg) + + if args.input: + if len(args.input) == 1: + args.input = glob.glob(os.path.expanduser(args.input[0])) + for path in tqdm.tqdm(args.input, disable=not args.output): + # use PIL, to be consistent with evaluation + img = read_image(path, format="BGR") + start_time = time.time() + predictions, visualized_output = demo.run_on_image(img) + logger.info( + "{}: detected {} instances in {:.2f}s".format( + path, len(predictions["instances"]), time.time() - start_time + ) + ) + + if args.output: + if os.path.isdir(args.output): + assert os.path.isdir(args.output), args.output + out_filename = os.path.join(args.output, os.path.basename(path)) + else: + assert len(args.input) == 1, "Please specify a directory with args.output" + out_filename = args.output + visualized_output.save(out_filename) + else: + cv2.imshow("COCO detections", visualized_output.get_image()[:, :, ::-1]) + if cv2.waitKey(0) == 27: + break # esc to quit + elif args.webcam: + assert args.input is None, "Cannot have both --input and --webcam!" + cam = cv2.VideoCapture(0) + for vis in tqdm.tqdm(demo.run_on_video(cam)): + cv2.imshow("COCO detections", vis) + if cv2.waitKey(1) == 27: + break # esc to quit + cv2.destroyAllWindows() + elif args.video_input: + video = cv2.VideoCapture(args.video_input) + width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) + frames_per_second = video.get(cv2.CAP_PROP_FPS) + num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) + basename = os.path.basename(args.video_input) + + if args.output: + if os.path.isdir(args.output): + output_fname = os.path.join(args.output, basename) + output_fname = os.path.splitext(output_fname)[0] + ".mkv" + else: + output_fname = args.output + assert not os.path.isfile(output_fname), output_fname + output_file = cv2.VideoWriter( + filename=output_fname, + # some installation of opencv may not support x264 (due to its license), + # you can try other format (e.g. MPEG) + fourcc=cv2.VideoWriter_fourcc(*"x264"), + fps=float(frames_per_second), + frameSize=(width, height), + isColor=True, + ) + assert os.path.isfile(args.video_input) + for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames): + if args.output: + output_file.write(vis_frame) + else: + cv2.imshow(basename, vis_frame) + if cv2.waitKey(1) == 27: + break # esc to quit + video.release() + if args.output: + output_file.release() + else: + cv2.destroyAllWindows() diff --git a/demo/predictor.py b/demo/predictor.py new file mode 100644 index 0000000000..e470128616 --- /dev/null +++ b/demo/predictor.py @@ -0,0 +1,218 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import atexit +import bisect +import multiprocessing as mp +from collections import deque +import cv2 +import torch + +from detectron2.data import MetadataCatalog +from detectron2.engine.defaults import DefaultPredictor +from detectron2.utils.video_visualizer import VideoVisualizer +from detectron2.utils.visualizer import ColorMode, Visualizer + + +class VisualizationDemo(object): + def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False): + """ + Args: + cfg (CfgNode): + instance_mode (ColorMode): + parallel (bool): whether to run the model in different processes from visualization. + Useful since the visualization logic can be slow. + """ + self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0]) + self.cpu_device = torch.device("cpu") + self.instance_mode = instance_mode + + self.parallel = parallel + if parallel: + num_gpu = torch.cuda.device_count() + self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu) + else: + self.predictor = DefaultPredictor(cfg) + + def run_on_image(self, image): + """ + Args: + image (np.ndarray): an image of shape (H, W, C) (in BGR order). + This is the format used by OpenCV. + + Returns: + predictions (dict): the output of the model. + vis_output (VisImage): the visualized image output. + """ + vis_output = None + predictions = self.predictor(image) + # Convert image from OpenCV BGR format to Matplotlib RGB format. + image = image[:, :, ::-1] + visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode) + if "panoptic_seg" in predictions: + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_output = visualizer.draw_panoptic_seg_predictions( + panoptic_seg.to(self.cpu_device), segments_info + ) + else: + if "sem_seg" in predictions: + vis_output = visualizer.draw_sem_seg( + predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) + ) + if "instances" in predictions: + instances = predictions["instances"].to(self.cpu_device) + vis_output = visualizer.draw_instance_predictions(predictions=instances) + + return predictions, vis_output + + def _frame_from_video(self, video): + while video.isOpened(): + success, frame = video.read() + if success: + yield frame + else: + break + + def run_on_video(self, video): + """ + Visualizes predictions on frames of the input video. + + Args: + video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be + either a webcam or a video file. + + Yields: + ndarray: BGR visualizations of each video frame. + """ + video_visualizer = VideoVisualizer(self.metadata, self.instance_mode) + + def process_predictions(frame, predictions): + frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) + if "panoptic_seg" in predictions: + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_frame = video_visualizer.draw_panoptic_seg_predictions( + frame, panoptic_seg.to(self.cpu_device), segments_info + ) + elif "instances" in predictions: + predictions = predictions["instances"].to(self.cpu_device) + vis_frame = video_visualizer.draw_instance_predictions(frame, predictions) + elif "sem_seg" in predictions: + vis_frame = video_visualizer.draw_sem_seg( + frame, predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) + ) + + # Converts Matplotlib RGB format to OpenCV BGR format + vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR) + return vis_frame + + frame_gen = self._frame_from_video(video) + if self.parallel: + buffer_size = self.predictor.default_buffer_size + + frame_data = deque() + + for cnt, frame in enumerate(frame_gen): + frame_data.append(frame) + self.predictor.put(frame) + + if cnt >= buffer_size: + frame = frame_data.popleft() + predictions = self.predictor.get() + yield process_predictions(frame, predictions) + + while len(frame_data): + frame = frame_data.popleft() + predictions = self.predictor.get() + yield process_predictions(frame, predictions) + else: + for frame in frame_gen: + yield process_predictions(frame, self.predictor(frame)) + + +class AsyncPredictor: + """ + A predictor that runs the model asynchronously, possibly on >1 GPUs. + Because rendering the visualization takes considerably amount of time, + this helps improve thoughput when rendering videos. + """ + + class _StopToken: + pass + + class _PredictWorker(mp.Process): + def __init__(self, cfg, task_queue, result_queue): + self.cfg = cfg + self.task_queue = task_queue + self.result_queue = result_queue + super().__init__() + + def run(self): + predictor = DefaultPredictor(self.cfg) + + while True: + task = self.task_queue.get() + if isinstance(task, AsyncPredictor._StopToken): + break + idx, data = task + result = predictor(data) + self.result_queue.put((idx, result)) + + def __init__(self, cfg, num_gpus: int = 1): + """ + Args: + cfg (CfgNode): + num_gpus (int): if 0, will run on CPU + """ + num_workers = max(num_gpus, 1) + self.task_queue = mp.Queue(maxsize=num_workers * 3) + self.result_queue = mp.Queue(maxsize=num_workers * 3) + self.procs = [] + for gpuid in range(max(num_gpus, 1)): + cfg = cfg.clone() + cfg.defrost() + cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu" + self.procs.append( + AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue) + ) + + self.put_idx = 0 + self.get_idx = 0 + self.result_rank = [] + self.result_data = [] + + for p in self.procs: + p.start() + atexit.register(self.shutdown) + + def put(self, image): + self.put_idx += 1 + self.task_queue.put((self.put_idx, image)) + + def get(self): + self.get_idx += 1 # the index needed for this request + if len(self.result_rank) and self.result_rank[0] == self.get_idx: + res = self.result_data[0] + del self.result_data[0], self.result_rank[0] + return res + + while True: + # make sure the results are returned in the correct order + idx, res = self.result_queue.get() + if idx == self.get_idx: + return res + insert = bisect.bisect(self.result_rank, idx) + self.result_rank.insert(insert, idx) + self.result_data.insert(insert, res) + + def __len__(self): + return self.put_idx - self.get_idx + + def __call__(self, image): + self.put(image) + return self.get() + + def shutdown(self): + for _ in self.procs: + self.task_queue.put(AsyncPredictor._StopToken()) + + @property + def default_buffer_size(self): + return len(self.procs) * 5 diff --git a/detectron2/__init__.py b/detectron2/__init__.py new file mode 100644 index 0000000000..d1f75b3ba5 --- /dev/null +++ b/detectron2/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + +from .utils.env import setup_environment + +setup_environment() + + +__version__ = "0.1" diff --git a/detectron2/checkpoint/__init__.py b/detectron2/checkpoint/__init__.py new file mode 100644 index 0000000000..a6022946c6 --- /dev/null +++ b/detectron2/checkpoint/__init__.py @@ -0,0 +1,10 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# File: + + +from . import model_zoo as _UNUSED # register the handler +from .detection_checkpoint import DetectionCheckpointer +from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer + +__all__ = ["Checkpointer", "PeriodicCheckpointer", "DetectionCheckpointer"] diff --git a/detectron2/checkpoint/c2_model_loading.py b/detectron2/checkpoint/c2_model_loading.py new file mode 100644 index 0000000000..10703358b8 --- /dev/null +++ b/detectron2/checkpoint/c2_model_loading.py @@ -0,0 +1,313 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy +import logging +import re +import torch +from fvcore.common.checkpoint import ( + get_missing_parameters_message, + get_unexpected_parameters_message, +) + + +def convert_basic_c2_names(original_keys): + """ + Apply some basic name conversion to names in C2 weights. + It only deals with typical backbone models. + + Args: + original_keys (list[str]): + Returns: + list[str]: The same number of strings matching those in original_keys. + """ + layer_keys = copy.deepcopy(original_keys) + layer_keys = [ + {"pred_b": "linear_b", "pred_w": "linear_w"}.get(k, k) for k in layer_keys + ] # some hard-coded mappings + + layer_keys = [k.replace("_", ".") for k in layer_keys] + layer_keys = [re.sub("\\.b$", ".bias", k) for k in layer_keys] + layer_keys = [re.sub("\\.w$", ".weight", k) for k in layer_keys] + # Uniform both bn and gn names to "norm" + layer_keys = [re.sub("bn\\.s$", "norm.weight", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.bias$", "norm.bias", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.rm", "norm.running_mean", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.running.mean$", "norm.running_mean", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.riv$", "norm.running_var", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.running.var$", "norm.running_var", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.gamma$", "norm.weight", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.beta$", "norm.bias", k) for k in layer_keys] + layer_keys = [re.sub("gn\\.s$", "norm.weight", k) for k in layer_keys] + layer_keys = [re.sub("gn\\.bias$", "norm.bias", k) for k in layer_keys] + + # stem + layer_keys = [re.sub("^res\\.conv1\\.norm\\.", "conv1.norm.", k) for k in layer_keys] + # to avoid mis-matching with "conv1" in other components (e.g. detection head) + layer_keys = [re.sub("^conv1\\.", "stem.conv1.", k) for k in layer_keys] + + # layer1-4 is used by torchvision, however we follow the C2 naming strategy (res2-5) + # layer_keys = [re.sub("^res2.", "layer1.", k) for k in layer_keys] + # layer_keys = [re.sub("^res3.", "layer2.", k) for k in layer_keys] + # layer_keys = [re.sub("^res4.", "layer3.", k) for k in layer_keys] + # layer_keys = [re.sub("^res5.", "layer4.", k) for k in layer_keys] + + # blocks + layer_keys = [k.replace(".branch1.", ".shortcut.") for k in layer_keys] + layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys] + layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys] + layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys] + + # DensePose substitutions + layer_keys = [re.sub("^body.conv.fcn", "body_conv_fcn", k) for k in layer_keys] + layer_keys = [k.replace("AnnIndex.lowres", "ann_index_lowres") for k in layer_keys] + layer_keys = [k.replace("Index.UV.lowres", "index_uv_lowres") for k in layer_keys] + layer_keys = [k.replace("U.lowres", "u_lowres") for k in layer_keys] + layer_keys = [k.replace("V.lowres", "v_lowres") for k in layer_keys] + return layer_keys + + +def convert_c2_detectron_names(weights): + """ + Map Caffe2 Detectron weight names to Detectron2 names. + + Args: + weights (dict): name -> tensor + + Returns: + dict: detectron2 names -> tensor + dict: detectron2 names -> C2 names + """ + logger = logging.getLogger(__name__) + logger.info("Remapping C2 weights ......") + original_keys = sorted(weights.keys()) + layer_keys = copy.deepcopy(original_keys) + + layer_keys = convert_basic_c2_names(layer_keys) + + # -------------------------------------------------------------------------- + # RPN hidden representation conv + # -------------------------------------------------------------------------- + # FPN case + # In the C2 model, the RPN hidden layer conv is defined for FPN level 2 and then + # shared for all other levels, hence the appearance of "fpn2" + layer_keys = [ + k.replace("conv.rpn.fpn2", "proposal_generator.rpn_head.conv") for k in layer_keys + ] + # Non-FPN case + layer_keys = [k.replace("conv.rpn", "proposal_generator.rpn_head.conv") for k in layer_keys] + + # -------------------------------------------------------------------------- + # RPN box transformation conv + # -------------------------------------------------------------------------- + # FPN case (see note above about "fpn2") + layer_keys = [ + k.replace("rpn.bbox.pred.fpn2", "proposal_generator.rpn_head.anchor_deltas") + for k in layer_keys + ] + layer_keys = [ + k.replace("rpn.cls.logits.fpn2", "proposal_generator.rpn_head.objectness_logits") + for k in layer_keys + ] + # Non-FPN case + layer_keys = [ + k.replace("rpn.bbox.pred", "proposal_generator.rpn_head.anchor_deltas") for k in layer_keys + ] + layer_keys = [ + k.replace("rpn.cls.logits", "proposal_generator.rpn_head.objectness_logits") + for k in layer_keys + ] + + # -------------------------------------------------------------------------- + # Fast R-CNN box head + # -------------------------------------------------------------------------- + layer_keys = [re.sub("^bbox\\.pred", "bbox_pred", k) for k in layer_keys] + layer_keys = [re.sub("^cls\\.score", "cls_score", k) for k in layer_keys] + layer_keys = [re.sub("^fc6\\.", "box_head.fc1.", k) for k in layer_keys] + layer_keys = [re.sub("^fc7\\.", "box_head.fc2.", k) for k in layer_keys] + # 4conv1fc head tensor names: head_conv1_w, head_conv1_gn_s + layer_keys = [re.sub("^head\\.conv", "box_head.conv", k) for k in layer_keys] + + # -------------------------------------------------------------------------- + # FPN lateral and output convolutions + # -------------------------------------------------------------------------- + def fpn_map(name): + """ + Look for keys with the following patterns: + 1) Starts with "fpn.inner." + Example: "fpn.inner.res2.2.sum.lateral.weight" + Meaning: These are lateral pathway convolutions + 2) Starts with "fpn.res" + Example: "fpn.res2.2.sum.weight" + Meaning: These are FPN output convolutions + """ + splits = name.split(".") + norm = ".norm" if "norm" in splits else "" + if name.startswith("fpn.inner."): + # splits example: ['fpn', 'inner', 'res2', '2', 'sum', 'lateral', 'weight'] + stage = int(splits[2][len("res") :]) + return "fpn_lateral{}{}.{}".format(stage, norm, splits[-1]) + elif name.startswith("fpn.res"): + # splits example: ['fpn', 'res2', '2', 'sum', 'weight'] + stage = int(splits[1][len("res") :]) + return "fpn_output{}{}.{}".format(stage, norm, splits[-1]) + return name + + layer_keys = [fpn_map(k) for k in layer_keys] + + # -------------------------------------------------------------------------- + # Mask R-CNN mask head + # -------------------------------------------------------------------------- + # roi_heads.StandardROIHeads case + layer_keys = [k.replace(".[mask].fcn", "mask_head.mask_fcn") for k in layer_keys] + layer_keys = [re.sub("^\\.mask\\.fcn", "mask_head.mask_fcn", k) for k in layer_keys] + layer_keys = [k.replace("mask.fcn.logits", "mask_head.predictor") for k in layer_keys] + # roi_heads.Res5ROIHeads case + layer_keys = [k.replace("conv5.mask", "mask_head.deconv") for k in layer_keys] + + # -------------------------------------------------------------------------- + # Keypoint R-CNN head + # -------------------------------------------------------------------------- + # interestingly, the keypoint head convs have blob names that are simply "conv_fcnX" + layer_keys = [k.replace("conv.fcn", "roi_heads.keypoint_head.conv_fcn") for k in layer_keys] + layer_keys = [ + k.replace("kps.score.lowres", "roi_heads.keypoint_head.score_lowres") for k in layer_keys + ] + layer_keys = [k.replace("kps.score.", "roi_heads.keypoint_head.score.") for k in layer_keys] + + # -------------------------------------------------------------------------- + # Done with replacements + # -------------------------------------------------------------------------- + assert len(set(layer_keys)) == len(layer_keys) + assert len(original_keys) == len(layer_keys) + + new_weights = {} + new_keys_to_original_keys = {} + for orig, renamed in zip(original_keys, layer_keys): + new_keys_to_original_keys[renamed] = orig + if renamed.startswith("bbox_pred.") or renamed.startswith("mask_head.predictor."): + # remove the meaningless prediction weight for background class + new_start_idx = 4 if renamed.startswith("bbox_pred.") else 1 + new_weights[renamed] = weights[orig][new_start_idx:] + logger.info( + "Remove prediction weight for background class in {}. The shape changes from " + "{} to {}.".format( + renamed, tuple(weights[orig].shape), tuple(new_weights[renamed].shape) + ) + ) + elif renamed.startswith("cls_score."): + # move weights of bg class from original index 0 to last index + logger.info( + "Move classification weights for background class in {} from index 0 to " + "index {}.".format(renamed, weights[orig].shape[0] - 1) + ) + new_weights[renamed] = torch.cat([weights[orig][1:], weights[orig][:1]]) + else: + new_weights[renamed] = weights[orig] + + return new_weights, new_keys_to_original_keys + + +# Note the current matching is not symmetric. +# it assumes model_state_dict will have longer names. +def align_and_update_state_dicts(model_state_dict, ckpt_state_dict, c2_conversion=True): + """ + Match names between the two state-dict, and update the values of model_state_dict in-place with + copies of the matched tensor in ckpt_state_dict. + If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2 + model and will be renamed at first. + + Strategy: suppose that the models that we will create will have prefixes appended + to each of its keys, for example due to an extra level of nesting that the original + pre-trained weights from ImageNet won't contain. For example, model.state_dict() + might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains + res2.conv1.weight. We thus want to match both parameters together. + For that, we look for each model weight, look among all loaded keys if there is one + that is a suffix of the current weight name, and use it if that's the case. + If multiple matches exist, take the one with longest size + of the corresponding name. For example, for the same model as before, the pretrained + weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case, + we want to match backbone[0].body.conv1.weight to conv1.weight, and + backbone[0].body.res2.conv1.weight to res2.conv1.weight. + """ + model_keys = sorted(list(model_state_dict.keys())) + if c2_conversion: + ckpt_state_dict, original_keys = convert_c2_detectron_names(ckpt_state_dict) + # original_keys: the name in the original dict (before renaming) + else: + original_keys = {x: x for x in ckpt_state_dict.keys()} + ckpt_keys = sorted(list(ckpt_state_dict.keys())) + + def match(a, b): + # Matched ckpt_key should be a complete (starts with '.') suffix. + # For example, roi_heads.mesh_head.whatever_conv1 does not match conv1, + # but matches whatever_conv1 or mesh_head.whatever_conv1. + return a == b or a.endswith("." + b) + + # get a matrix of string matches, where each (i, j) entry correspond to the size of the + # ckpt_key string, if it matches + match_matrix = [len(j) if match(i, j) else 0 for i in model_keys for j in ckpt_keys] + match_matrix = torch.as_tensor(match_matrix).view(len(model_keys), len(ckpt_keys)) + # use the matched one with longest size in case of multiple matches + max_match_size, idxs = match_matrix.max(1) + # remove indices that correspond to no-match + idxs[max_match_size == 0] = -1 + + # used for logging + max_len_model = max(len(key) for key in model_keys) if model_keys else 1 + max_len_ckpt = max(len(key) for key in ckpt_keys) if ckpt_keys else 1 + log_str_template = "{: <{}} loaded from {: <{}} of shape {}" + logger = logging.getLogger(__name__) + # matched_pairs (matched checkpoint key --> matched model key) + matched_keys = {} + for idx_model, idx_ckpt in enumerate(idxs.tolist()): + if idx_ckpt == -1: + continue + key_model = model_keys[idx_model] + key_ckpt = ckpt_keys[idx_ckpt] + value_ckpt = ckpt_state_dict[key_ckpt] + shape_in_model = model_state_dict[key_model].shape + + if shape_in_model != value_ckpt.shape: + logger.warning( + "Shape of {} in checkpoint is {}, while shape of {} in model is {}.".format( + key_ckpt, value_ckpt.shape, key_model, shape_in_model + ) + ) + logger.warning( + "{} will not be loaded. Please double check and see if this is desired.".format( + key_ckpt + ) + ) + continue + + model_state_dict[key_model] = value_ckpt.clone() + if key_ckpt in matched_keys: # already added to matched_keys + logger.error( + "Ambiguity found for {} in checkpoint!" + "It matches at least two keys in the model ({} and {}).".format( + key_ckpt, key_model, matched_keys[key_ckpt] + ) + ) + raise ValueError("Cannot match one checkpoint key to multiple keys in the model.") + + matched_keys[key_ckpt] = key_model + logger.info( + log_str_template.format( + key_model, + max_len_model, + original_keys[key_ckpt], + max_len_ckpt, + tuple(shape_in_model), + ) + ) + matched_model_keys = matched_keys.values() + matched_ckpt_keys = matched_keys.keys() + # print warnings about unmatched keys on both side + unmatched_model_keys = [k for k in model_keys if k not in matched_model_keys] + if len(unmatched_model_keys): + logger.info(get_missing_parameters_message(unmatched_model_keys)) + + unmatched_ckpt_keys = [k for k in ckpt_keys if k not in matched_ckpt_keys] + if len(unmatched_ckpt_keys): + logger.info( + get_unexpected_parameters_message(original_keys[x] for x in unmatched_ckpt_keys) + ) diff --git a/detectron2/checkpoint/detection_checkpoint.py b/detectron2/checkpoint/detection_checkpoint.py new file mode 100644 index 0000000000..4e0a35e04f --- /dev/null +++ b/detectron2/checkpoint/detection_checkpoint.py @@ -0,0 +1,59 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import pickle +from fvcore.common.checkpoint import Checkpointer +from fvcore.common.file_io import PathManager + +import detectron2.utils.comm as comm + +from .c2_model_loading import align_and_update_state_dicts + + +class DetectionCheckpointer(Checkpointer): + """ + Same as :class:`Checkpointer`, but is able to handle models in detectron & detectron2 + model zoo, and apply conversions for legacy models. + """ + + def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables): + is_main_process = comm.is_main_process() + super().__init__( + model, + save_dir, + save_to_disk=is_main_process if save_to_disk is None else save_to_disk, + **checkpointables, + ) + + def _load_file(self, filename): + if filename.endswith(".pkl"): + with PathManager.open(filename, "rb") as f: + data = pickle.load(f, encoding="latin1") + if "model" in data and "__author__" in data: + # file is in Detectron2 model zoo format + self.logger.info("Reading a file from '{}'".format(data["__author__"])) + return data + else: + # assume file is from Caffe2 / Detectron1 model zoo + if "blobs" in data: + # Detection models have "blobs", but ImageNet models don't + data = data["blobs"] + data = {k: v for k, v in data.items() if not k.endswith("_momentum")} + return {"model": data, "__author__": "Caffe2", "matching_heuristics": True} + + loaded = super()._load_file(filename) # load native pth checkpoint + if "model" not in loaded: + loaded = {"model": loaded} + return loaded + + def _load_model(self, checkpoint): + if checkpoint.get("matching_heuristics", False): + self._convert_ndarray_to_tensor(checkpoint["model"]) + # convert weights by name-matching heuristics + model_state_dict = self.model.state_dict() + align_and_update_state_dicts( + model_state_dict, + checkpoint["model"], + c2_conversion=checkpoint.get("__author__", None) == "Caffe2", + ) + checkpoint["model"] = model_state_dict + # for non-caffe2 models, use standard ways to load it + super()._load_model(checkpoint) diff --git a/detectron2/checkpoint/model_zoo.py b/detectron2/checkpoint/model_zoo.py new file mode 100644 index 0000000000..42591e7207 --- /dev/null +++ b/detectron2/checkpoint/model_zoo.py @@ -0,0 +1,132 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +from fvcore.common.file_io import PathHandler, PathManager + + +class ModelCatalog(object): + """ + Store mappings from names to third-party models. + """ + + S3_C2_DETECTRON_PREFIX = "https://dl.fbaipublicfiles.com/detectron" + + # MSRA models have STRIDE_IN_1X1=True. False otherwise. + # NOTE: all BN models here have fused BN into an affine layer. + # As a result, you should only load them to a model with "FrozenBN". + # Loading them to a model with regular BN or SyncBN is wrong. + # Even when loaded to FrozenBN, it is still different from affine by an epsilon, + # which should be negligible for training. + # NOTE: all models here uses PIXEL_STD=[1,1,1] + C2_IMAGENET_MODELS = { + "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl", + "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl", + "FAIR/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl", + "FAIR/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl", + "FAIR/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl", + "FAIR/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl", + "FAIR/X-152-32x8d-IN5k": "ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl", + } + + C2_DETECTRON_PATH_FORMAT = ( + "{prefix}/{url}/output/train/{dataset}/{type}/model_final.pkl" + ) # noqa B950 + + C2_DATASET_COCO = "coco_2014_train%3Acoco_2014_valminusminival" + C2_DATASET_COCO_KEYPOINTS = "keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival" + + # format: {model_name} -> part of the url + C2_DETECTRON_MODELS = { + "35857197/e2e_faster_rcnn_R-50-C4_1x": "35857197/12_2017_baselines/e2e_faster_rcnn_R-50-C4_1x.yaml.01_33_49.iAX0mXvW", # noqa B950 + "35857345/e2e_faster_rcnn_R-50-FPN_1x": "35857345/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_1x.yaml.01_36_30.cUF7QR7I", # noqa B950 + "35857890/e2e_faster_rcnn_R-101-FPN_1x": "35857890/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_1x.yaml.01_38_50.sNxI7sX7", # noqa B950 + "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "36761737/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_1x.yaml.06_31_39.5MIHi1fZ", # noqa B950 + "35858791/e2e_mask_rcnn_R-50-C4_1x": "35858791/12_2017_baselines/e2e_mask_rcnn_R-50-C4_1x.yaml.01_45_57.ZgkA7hPB", # noqa B950 + "35858933/e2e_mask_rcnn_R-50-FPN_1x": "35858933/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml.01_48_14.DzEQe4wC", # noqa B950 + "35861795/e2e_mask_rcnn_R-101-FPN_1x": "35861795/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT", # noqa B950 + "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "36761843/12_2017_baselines/e2e_mask_rcnn_X-101-32x8d-FPN_1x.yaml.06_35_59.RZotkLKI", # noqa B950 + "48616381/e2e_mask_rcnn_R-50-FPN_2x_gn": "GN/48616381/04_2018_gn_baselines/e2e_mask_rcnn_R-50-FPN_2x_gn_0416.13_23_38.bTlTI97Q", # noqa B950 + "37697547/e2e_keypoint_rcnn_R-50-FPN_1x": "37697547/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_1x.yaml.08_42_54.kdzV35ao", # noqa B950 + "35998355/rpn_R-50-C4_1x": "35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L", # noqa B950 + "35998814/rpn_R-50-FPN_1x": "35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179", # noqa B950 + "36225147/fast_R-50-FPN_1x": "36225147/12_2017_baselines/fast_rcnn_R-50-FPN_1x.yaml.08_39_09.L3obSdQ2", # noqa B950 + } + + @staticmethod + def get(name): + if name.startswith("Caffe2Detectron/COCO"): + return ModelCatalog._get_c2_detectron_baseline(name) + if name.startswith("ImageNetPretrained/"): + return ModelCatalog._get_c2_imagenet_pretrained(name) + raise RuntimeError("model not present in the catalog: {}".format(name)) + + @staticmethod + def _get_c2_imagenet_pretrained(name): + prefix = ModelCatalog.S3_C2_DETECTRON_PREFIX + name = name[len("ImageNetPretrained/") :] + name = ModelCatalog.C2_IMAGENET_MODELS[name] + url = "/".join([prefix, name]) + return url + + @staticmethod + def _get_c2_detectron_baseline(name): + name = name[len("Caffe2Detectron/COCO/") :] + url = ModelCatalog.C2_DETECTRON_MODELS[name] + if "keypoint_rcnn" in name: + dataset = ModelCatalog.C2_DATASET_COCO_KEYPOINTS + else: + dataset = ModelCatalog.C2_DATASET_COCO + + if "35998355/rpn_R-50-C4_1x" in name: + # this one model is somehow different from others .. + type = "rpn" + else: + type = "generalized_rcnn" + + # Detectron C2 models are stored in the structure defined in `C2_DETECTRON_PATH_FORMAT`. + url = ModelCatalog.C2_DETECTRON_PATH_FORMAT.format( + prefix=ModelCatalog.S3_C2_DETECTRON_PREFIX, url=url, type=type, dataset=dataset + ) + return url + + +class ModelCatalogHandler(PathHandler): + """ + Resolve URL like catalog://. + """ + + PREFIX = "catalog://" + + def _get_supported_prefixes(self): + return [self.PREFIX] + + def _get_local_path(self, path): + logger = logging.getLogger(__name__) + catalog_path = ModelCatalog.get(path[len(self.PREFIX) :]) + logger.info("Catalog entry {} points to {}".format(path, catalog_path)) + return PathManager.get_local_path(catalog_path) + + def _open(self, path, mode="r"): + return PathManager.open(self._get_local_path(path), mode) + + +class Detectron2Handler(PathHandler): + """ + Resolve anything that's in Detectron2 model zoo. + """ + + PREFIX = "detectron2://" + S3_DETECTRON2_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/" + + def _get_supported_prefixes(self): + return [self.PREFIX] + + def _get_local_path(self, path): + name = path[len(self.PREFIX) :] + return PathManager.get_local_path(self.S3_DETECTRON2_PREFIX + name) + + def _open(self, path, mode="r"): + return PathManager.open(self._get_local_path(path), mode) + + +PathManager.register_handler(ModelCatalogHandler()) +PathManager.register_handler(Detectron2Handler()) diff --git a/detectron2/config/__init__.py b/detectron2/config/__init__.py new file mode 100644 index 0000000000..e6f3f5eee5 --- /dev/null +++ b/detectron2/config/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .compat import downgrade_config, upgrade_config +from .config import CfgNode, get_cfg, global_cfg, set_global_cfg + +__all__ = [ + "CfgNode", + "get_cfg", + "global_cfg", + "set_global_cfg", + "downgrade_config", + "upgrade_config", +] diff --git a/detectron2/config/compat.py b/detectron2/config/compat.py new file mode 100644 index 0000000000..1e4be0390f --- /dev/null +++ b/detectron2/config/compat.py @@ -0,0 +1,229 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Backward compatibility of configs. + +Instructions to bump version: ++ It's not needed to bump version if new keys are added. + It's only needed when backward-incompatible changes happen + (i.e., some existing keys disappear, or the meaning of a key changes) ++ To bump version, do the following: + 1. Increment _C.VERSION in defaults.py + 2. Add a converter in this file. + + Each ConverterVX has a function "upgrade" which in-place upgrades config from X-1 to X, + and a function "downgrade" which in-place downgrades config from X to X-1 + + In each function, VERSION is left unchanged. + + Each converter assumes that its input has the relevant keys + (i.e., the input is not a partial config). + 3. Run the tests (test_config.py) to make sure the upgrade & downgrade + functions are consistent. +""" + +import logging +from typing import List, Optional, Tuple + +from .config import CfgNode as CN +from .defaults import _C + +__all__ = ["upgrade_config", "downgrade_config"] + + +def upgrade_config(cfg: CN, to_version: Optional[int] = None) -> CN: + """ + Upgrade a config from its current version to a newer version. + + Args: + cfg (CfgNode): + to_version (int): defaults to the latest version. + """ + cfg = cfg.clone() + if to_version is None: + to_version = _C.VERSION + + assert cfg.VERSION <= to_version, "Cannot upgrade from v{} to v{}!".format( + cfg.VERSION, to_version + ) + for k in range(cfg.VERSION, to_version): + converter = globals()["ConverterV" + str(k + 1)] + converter.upgrade(cfg) + cfg.VERSION = k + 1 + return cfg + + +def downgrade_config(cfg: CN, to_version: int) -> CN: + """ + Downgrade a config from its current version to an older version. + + Args: + cfg (CfgNode): + to_version (int): + + Note: + A general downgrade of arbitrary configs is not always possible due to the + different functionailities in different versions. + The purpose of downgrade is only to recover the defaults in old versions, + allowing it to load an old partial yaml config. + Therefore, the implementation only needs to fill in the default values + in the old version when a general downgrade is not possible. + """ + cfg = cfg.clone() + assert cfg.VERSION >= to_version, "Cannot downgrade from v{} to v{}!".format( + cfg.VERSION, to_version + ) + for k in range(cfg.VERSION, to_version, -1): + converter = globals()["ConverterV" + str(k)] + converter.downgrade(cfg) + cfg.VERSION = k - 1 + return cfg + + +def guess_version(cfg: CN, filename: str) -> int: + """ + Guess the version of a partial config where the VERSION field is not specified. + Returns the version, or the latest if cannot make a guess. + + This makes it easier for users to migrate. + """ + logger = logging.getLogger(__name__) + + def _has(name: str) -> bool: + cur = cfg + for n in name.split("."): + if n not in cur: + return False + cur = cur[n] + return True + + # Most users' partial configs have "MODEL.WEIGHT", so guess on it + ret = None + if _has("MODEL.WEIGHT") or _has("TEST.AUG_ON"): + ret = 1 + + if ret is not None: + logger.warning("Config '{}' has no VERSION. Assuming it to be v{}.".format(filename, ret)) + else: + ret = _C.VERSION + logger.warning( + "Config '{}' has no VERSION. Assuming it to be compatible with latest v{}.".format( + filename, ret + ) + ) + return ret + + +def _rename(cfg: CN, old: str, new: str) -> None: + old_keys = old.split(".") + new_keys = new.split(".") + + def _set(key_seq: List[str], val: str) -> None: + cur = cfg + for k in key_seq[:-1]: + if k not in cur: + cur[k] = CN() + cur = cur[k] + cur[key_seq[-1]] = val + + def _get(key_seq: List[str]) -> CN: + cur = cfg + for k in key_seq: + cur = cur[k] + return cur + + def _del(key_seq: List[str]) -> None: + cur = cfg + for k in key_seq[:-1]: + cur = cur[k] + del cur[key_seq[-1]] + if len(cur) == 0 and len(key_seq) > 1: + _del(key_seq[:-1]) + + _set(new_keys, _get(old_keys)) + _del(old_keys) + + +class _RenameConverter: + """ + A converter that handles simple rename. + """ + + RENAME: List[Tuple[str, str]] = [] # list of tuples of (old name, new name) + + @classmethod + def upgrade(cls, cfg: CN) -> None: + for old, new in cls.RENAME: + _rename(cfg, old, new) + + @classmethod + def downgrade(cls, cfg: CN) -> None: + for old, new in cls.RENAME[::-1]: + _rename(cfg, new, old) + + +class ConverterV1(_RenameConverter): + RENAME = [("MODEL.RPN_HEAD.NAME", "MODEL.RPN.HEAD_NAME")] + + +class ConverterV2(_RenameConverter): + """ + A large bulk of rename, before public release. + """ + + RENAME = [ + ("MODEL.WEIGHT", "MODEL.WEIGHTS"), + ("MODEL.PANOPTIC_FPN.SEMANTIC_LOSS_SCALE", "MODEL.SEM_SEG_HEAD.LOSS_WEIGHT"), + ("MODEL.PANOPTIC_FPN.RPN_LOSS_SCALE", "MODEL.RPN.LOSS_WEIGHT"), + ("MODEL.PANOPTIC_FPN.INSTANCE_LOSS_SCALE", "MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT"), + ("MODEL.PANOPTIC_FPN.COMBINE_ON", "MODEL.PANOPTIC_FPN.COMBINE.ENABLED"), + ( + "MODEL.PANOPTIC_FPN.COMBINE_OVERLAP_THRESHOLD", + "MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH", + ), + ( + "MODEL.PANOPTIC_FPN.COMBINE_STUFF_AREA_LIMIT", + "MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT", + ), + ( + "MODEL.PANOPTIC_FPN.COMBINE_INSTANCES_CONFIDENCE_THRESHOLD", + "MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH", + ), + ("MODEL.ROI_HEADS.SCORE_THRESH", "MODEL.ROI_HEADS.SCORE_THRESH_TEST"), + ("MODEL.ROI_HEADS.NMS", "MODEL.ROI_HEADS.NMS_THRESH_TEST"), + ("MODEL.RETINANET.INFERENCE_SCORE_THRESHOLD", "MODEL.RETINANET.SCORE_THRESH_TEST"), + ("MODEL.RETINANET.INFERENCE_TOPK_CANDIDATES", "MODEL.RETINANET.TOPK_CANDIDATES_TEST"), + ("MODEL.RETINANET.INFERENCE_NMS_THRESHOLD", "MODEL.RETINANET.NMS_THRESH_TEST"), + ("TEST.DETECTIONS_PER_IMG", "TEST.DETECTIONS_PER_IMAGE"), + ("TEST.AUG_ON", "TEST.AUG.ENABLED"), + ("TEST.AUG_MIN_SIZES", "TEST.AUG.MIN_SIZES"), + ("TEST.AUG_MAX_SIZE", "TEST.AUG.MAX_SIZE"), + ("TEST.AUG_FLIP", "TEST.AUG.FLIP"), + ] + + @classmethod + def upgrade(cls, cfg: CN) -> None: + super().upgrade(cfg) + + if cfg.MODEL.META_ARCHITECTURE == "RetinaNet": + _rename( + cfg, "MODEL.RETINANET.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS" + ) + _rename(cfg, "MODEL.RETINANET.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES") + del cfg["MODEL"]["RPN"]["ANCHOR_SIZES"] + del cfg["MODEL"]["RPN"]["ANCHOR_ASPECT_RATIOS"] + else: + _rename(cfg, "MODEL.RPN.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS") + _rename(cfg, "MODEL.RPN.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES") + del cfg["MODEL"]["RETINANET"]["ANCHOR_SIZES"] + del cfg["MODEL"]["RETINANET"]["ANCHOR_ASPECT_RATIOS"] + del cfg["MODEL"]["RETINANET"]["ANCHOR_STRIDES"] + + @classmethod + def downgrade(cls, cfg: CN) -> None: + super().downgrade(cfg) + + _rename(cfg, "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS", "MODEL.RPN.ANCHOR_ASPECT_RATIOS") + _rename(cfg, "MODEL.ANCHOR_GENERATOR.SIZES", "MODEL.RPN.ANCHOR_SIZES") + cfg.MODEL.RETINANET.ANCHOR_ASPECT_RATIOS = cfg.MODEL.RPN.ANCHOR_ASPECT_RATIOS + cfg.MODEL.RETINANET.ANCHOR_SIZES = cfg.MODEL.RPN.ANCHOR_SIZES + cfg.MODEL.RETINANET.ANCHOR_STRIDES = [] # this is not used anywhere in any version diff --git a/detectron2/config/config.py b/detectron2/config/config.py new file mode 100644 index 0000000000..f9d01cb42c --- /dev/null +++ b/detectron2/config/config.py @@ -0,0 +1,98 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import logging +from fvcore.common.config import CfgNode as _CfgNode + + +class CfgNode(_CfgNode): + """ + The same as `fvcore.common.config.CfgNode`, but different in: + + 1. Use unsafe yaml loading by default. + Note that this may lead to arbitrary code execution: you must not + load a config file from untrusted sources before manually inspecting + the content of the file. + 2. Support config versioning. + When attempting to merge an old config, it will convert the old config automatically. + + """ + + # Note that the default value of allow_unsafe is changed to True + def merge_from_file(self, cfg_filename: str, allow_unsafe: bool = True) -> None: + loaded_cfg = _CfgNode.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe) + loaded_cfg = type(self)(loaded_cfg) + + # defaults.py needs to import CfgNode + from .defaults import _C + + latest_ver = _C.VERSION + assert ( + latest_ver == self.VERSION + ), "CfgNode.merge_from_file is only allowed on a config of latest version!" + + logger = logging.getLogger(__name__) + + loaded_ver = loaded_cfg.get("VERSION", None) + if loaded_ver is None: + from .compat import guess_version + + loaded_ver = guess_version(loaded_cfg, cfg_filename) + assert loaded_ver <= self.VERSION, "Cannot merge a v{} config into a v{} config.".format( + loaded_ver, self.VERSION + ) + + if loaded_ver == self.VERSION: + self.merge_from_other_cfg(loaded_cfg) + else: + # compat.py needs to import CfgNode + from .compat import upgrade_config, downgrade_config + + logger.warning( + "Loading an old v{} config file '{}' by automatically upgrading to v{}. " + "See docs/CHANGELOG.md for instructions to update your files.".format( + loaded_ver, cfg_filename, self.VERSION + ) + ) + # To convert, first obtain a full config at an old version + old_self = downgrade_config(self, to_version=loaded_ver) + old_self.merge_from_other_cfg(loaded_cfg) + new_config = upgrade_config(old_self) + self.clear() + self.update(new_config) + + +global_cfg = CfgNode() + + +def get_cfg() -> CfgNode: + """ + Get a copy of the default config. + + Returns: + a detectron2 CfgNode instance. + """ + from .defaults import _C + + return _C.clone() + + +def set_global_cfg(cfg: CfgNode) -> None: + """ + Let the global config point to the given cfg. + + Assume that the given "cfg" has the key "KEY", after calling + `set_global_cfg(cfg)`, the key can be accessed by: + + .. code-block:: python + + from detectron2.config import global_cfg + print(global_cfg.KEY) + + By using a hacky global config, you can access these configs anywhere, + without having to pass the config object or the values deep into the code. + This is a hacky feature introduced for quick prototyping / research exploration. + """ + global global_cfg + global_cfg.clear() + global_cfg.update(cfg) diff --git a/detectron2/config/defaults.py b/detectron2/config/defaults.py new file mode 100644 index 0000000000..fa1b617048 --- /dev/null +++ b/detectron2/config/defaults.py @@ -0,0 +1,561 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .config import CfgNode as CN + +# ----------------------------------------------------------------------------- +# Convention about Training / Test specific parameters +# ----------------------------------------------------------------------------- +# Whenever an argument can be either used for training or for testing, the +# corresponding name will be post-fixed by a _TRAIN for a training parameter, +# or _TEST for a test-specific parameter. +# For example, the number of images during training will be +# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be +# IMAGES_PER_BATCH_TEST + +# ----------------------------------------------------------------------------- +# Config definition +# ----------------------------------------------------------------------------- + +_C = CN() + +_C.VERSION = 2 + +_C.MODEL = CN() +_C.MODEL.LOAD_PROPOSALS = False +_C.MODEL.MASK_ON = False +_C.MODEL.KEYPOINT_ON = False +_C.MODEL.DEVICE = "cuda" +_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN" + +# If the WEIGHT starts with a catalog://, like :R-50, the code will look for +# the path in ModelCatalog. Else, it will use it as the specified absolute +# path +_C.MODEL.WEIGHTS = "" + +# Values to be used for image normalization (BGR order) +# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675] +_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675] +# When using pre-trained models in Detectron1 or any MSRA models, +# std has been absorbed into its conv1 weights, so the std needs to be set 1. +# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std) +_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0] + + +# ----------------------------------------------------------------------------- +# INPUT +# ----------------------------------------------------------------------------- +_C.INPUT = CN() +# Size of the smallest side of the image during training +_C.INPUT.MIN_SIZE_TRAIN = (800,) +# Sample size of smallest side by choice or random selection from range give by +# INPUT.MIN_SIZE_TRAIN +_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice" +# Maximum size of the side of the image during training +_C.INPUT.MAX_SIZE_TRAIN = 1333 +# Size of the smallest side of the image during testing. Set to zero to disable resize in testing. +_C.INPUT.MIN_SIZE_TEST = 800 +# Maximum size of the side of the image during testing +_C.INPUT.MAX_SIZE_TEST = 1333 + +# `True` if cropping is used for data augmentation during training +_C.INPUT.CROP = CN({"ENABLED": False}) +# Cropping type: +# - "relative" crop (H * CROP.SIZE[0], W * CROP.SIZE[1]) part of an input of size (H, W) +# - "relative_range" uniformly sample relative crop size from between [CROP.SIZE[0], [CROP.SIZE[1]]. +# and [1, 1] and use it as in "realtive" scenario. +# - "absolute" crop part of an input with absolute size: (CROP.SIZE[0], CROP.SIZE[1]). +_C.INPUT.CROP.TYPE = "relative_range" +# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of +# pixels if CROP.TYPE is "absolute" +_C.INPUT.CROP.SIZE = [0.9, 0.9] + + +# Whether the model needs RGB, YUV, HSV etc. +# Should be one of the modes defined here, as we use PIL to read the image: +# https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes +# with BGR being the one exception. One can set image format to BGR, we will +# internally use RGB for conversion and flip the channels over +_C.INPUT.FORMAT = "BGR" +_C.INPUT.MASK_FORMAT = "polygon" # alternative: "bitmask" + + +# ----------------------------------------------------------------------------- +# Dataset +# ----------------------------------------------------------------------------- +_C.DATASETS = CN() +# List of the dataset names for training. Must be registered in DatasetCatalog +_C.DATASETS.TRAIN = () +# List of the pre-computed proposal files for training, which must be consistent +# with datasets listed in DATASETS.TRAIN. +_C.DATASETS.PROPOSAL_FILES_TRAIN = () +# Number of top scoring precomputed proposals to keep for training +_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000 +# List of the dataset names for testing. Must be registered in DatasetCatalog +_C.DATASETS.TEST = () +# List of the pre-computed proposal files for test, which must be consistent +# with datasets listed in DATASETS.TEST. +_C.DATASETS.PROPOSAL_FILES_TEST = () +# Number of top scoring precomputed proposals to keep for test +_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000 + +# ----------------------------------------------------------------------------- +# DataLoader +# ----------------------------------------------------------------------------- +_C.DATALOADER = CN() +# Number of data loading threads +_C.DATALOADER.NUM_WORKERS = 4 +# If True, each batch should contain only images for which the aspect ratio +# is compatible. This groups portrait images together, and landscape images +# are not batched with portrait images. +_C.DATALOADER.ASPECT_RATIO_GROUPING = True +# Options: TrainingSampler, RepeatFactorTrainingSampler +_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler" +# Repeat threshold for RepeatFactorTrainingSampler +_C.DATALOADER.REPEAT_THRESHOLD = 0.0 + + +# ---------------------------------------------------------------------------- # +# Backbone options +# ---------------------------------------------------------------------------- # +_C.MODEL.BACKBONE = CN() + +_C.MODEL.BACKBONE.NAME = "build_resnet_backbone" +# Add StopGrad at a specified stage so the bottom layers are frozen +_C.MODEL.BACKBONE.FREEZE_AT = 2 + + +# ---------------------------------------------------------------------------- # +# FPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.FPN = CN() +# Names of the input feature maps to be used by FPN +# They must have contiguous power of 2 strides +# e.g., ["res2", "res3", "res4", "res5"] +_C.MODEL.FPN.IN_FEATURES = [] +_C.MODEL.FPN.OUT_CHANNELS = 256 + +# Options: "" (no norm), "GN" +_C.MODEL.FPN.NORM = "" + +# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg" +_C.MODEL.FPN.FUSE_TYPE = "sum" + + +# ---------------------------------------------------------------------------- # +# Proposal generator options +# ---------------------------------------------------------------------------- # +_C.MODEL.PROPOSAL_GENERATOR = CN() +# Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals" +_C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN" +# Proposal height and width both need to be greater than MIN_SIZE +# (a the scale used during training or inference) +_C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0 + + +# ---------------------------------------------------------------------------- # +# Anchor generator options +# ---------------------------------------------------------------------------- # +_C.MODEL.ANCHOR_GENERATOR = CN() +# The generator can be any name in the ANCHOR_GENERATOR registry +_C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator" +# anchor sizes given in absolute pixels w.r.t. the scaled network input. +# Format: list of lists of sizes. SIZES[i] specifies the list of sizes +# to use for IN_FEATURES[i]; len(SIZES) == len(IN_FEATURES) must be true, +# or len(SIZES) == 1 is true and size list SIZES[0] is used for all +# IN_FEATURES. +_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]] +# Anchor aspect ratios. +# Format is list of lists of sizes. ASPECT_RATIOS[i] specifies the list of aspect ratios +# to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true, +# or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used +# for all IN_FEATURES. +_C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]] +# Anchor angles. +# list[float], the angle in degrees, for each input feature map. +# ANGLES[i] specifies the list of angles for IN_FEATURES[i]. +_C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]] + + +# ---------------------------------------------------------------------------- # +# RPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.RPN = CN() +_C.MODEL.RPN.HEAD_NAME = "StandardRPNHead" # used by RPN_HEAD_REGISTRY + +# Names of the input feature maps to be used by RPN +# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN +_C.MODEL.RPN.IN_FEATURES = ["res4"] +# Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels +# Set to -1 or a large value, e.g. 100000, to disable pruning anchors +_C.MODEL.RPN.BOUNDARY_THRESH = -1 +# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD] +# Minimum overlap required between an anchor and ground-truth box for the +# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD +# ==> positive RPN example: 1) +# Maximum overlap allowed between an anchor and ground-truth box for the +# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD +# ==> negative RPN example: 0) +# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD) +# are ignored (-1) +_C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7] +_C.MODEL.RPN.IOU_LABELS = [0, -1, 1] +# Total number of RPN examples per image +_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256 +# Target fraction of foreground (positive) examples per RPN minibatch +_C.MODEL.RPN.POSITIVE_FRACTION = 0.5 +# Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets +_C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0) +# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1. +_C.MODEL.RPN.SMOOTH_L1_BETA = 0.0 +_C.MODEL.RPN.LOSS_WEIGHT = 1.0 +# Number of top scoring RPN proposals to keep before applying NMS +# When FPN is used, this is *per FPN level* (not total) +_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000 +_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000 +# Number of top scoring RPN proposals to keep after applying NMS +# When FPN is used, this limit is applied per level and then again to the union +# of proposals from all levels +# NOTE: When FPN is used, the meaning of this config is different from Detectron1. +# It means per-batch topk in Detectron1, but per-image topk here. +# See "modeling/rpn/rpn_outputs.py" for details. +_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000 +_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000 +# NMS threshold used on RPN proposals +_C.MODEL.RPN.NMS_THRESH = 0.7 + +# ---------------------------------------------------------------------------- # +# ROI HEADS options +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_HEADS = CN() +_C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads" +# Number of foreground classes +_C.MODEL.ROI_HEADS.NUM_CLASSES = 80 +# Names of the input feature maps to be used by ROI heads +# Currently all heads (box, mask, ...) use the same input feature map list +# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN +_C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"] +# IOU overlap ratios [IOU_THRESHOLD] +# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD) +# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD) +_C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5] +_C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1] +# RoI minibatch size *per image* (number of regions of interest [ROIs]) +# Total number of RoIs per training minibatch = +# ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH +# E.g., a common configuration is: 512 * 16 = 8192 +_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512 +# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0) +_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25 + +# Only used on test mode + +# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to +# balance obtaining high recall with not having too many low precision +# detections that will slow down inference post processing steps (like NMS) +# A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down +# inference. +_C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05 +# Overlap threshold used for non-maximum suppression (suppress boxes with +# IoU >= this threshold) +_C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5 +# If True, augment proposals with ground-truth boxes before sampling proposals to +# train ROI heads. +_C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True + + +# ---------------------------------------------------------------------------- # +# Box Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_BOX_HEAD = CN() +# C4 don't use head name option +# Options for non-C4 models: FastRCNNConvFCHead, +_C.MODEL.ROI_BOX_HEAD.NAME = "" +# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets +# These are empirically chosen to approximately lead to unit variance targets +_C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0) +# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1. +_C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0 +_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0 +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2" + +_C.MODEL.ROI_BOX_HEAD.NUM_FC = 0 +# Hidden layer dimension for FC layers in the RoI box head +_C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024 +_C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0 +# Channel dimension for Conv layers in the RoI box head +_C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256 +# Normalization method for the convolution layers. +# Options: "" (no norm), "GN", "SyncBN". +_C.MODEL.ROI_BOX_HEAD.NORM = "" +# Whether to use class agnostic for bbox regression +_C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False + +# ---------------------------------------------------------------------------- # +# Cascaded Box Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_BOX_CASCADE_HEAD = CN() +# The number of cascade stages is implicitly defined by the length of the following two configs. +_C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = ( + (10.0, 10.0, 5.0, 5.0), + (20.0, 20.0, 10.0, 10.0), + (30.0, 30.0, 15.0, 15.0), +) +_C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7) + + +# ---------------------------------------------------------------------------- # +# Mask Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_MASK_HEAD = CN() +_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead" +_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0 # The number of convs in the mask head +_C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256 +# Normalization method for the convolution layers. +# Options: "" (no norm), "GN", "SyncBN". +_C.MODEL.ROI_MASK_HEAD.NORM = "" +# Whether to use class agnostic for mask prediction +_C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2" + + +# ---------------------------------------------------------------------------- # +# Keypoint Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_KEYPOINT_HEAD = CN() +_C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead" +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8)) +_C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17 # 17 is the number of keypoints in COCO. + +# Images with too few (or no) keypoints are excluded from training. +_C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1 +# Normalize by the total number of visible keypoints in the minibatch if True. +# Otherwise, normalize by the total number of keypoints that could ever exist +# in the minibatch. +# The keypoint softmax loss is only calculated on visible keypoints. +# Since the number of visible keypoints can vary significantly between +# minibatches, this has the effect of up-weighting the importance of +# minibatches with few visible keypoints. (Imagine the extreme case of +# only one visible keypoint versus N: in the case of N, each one +# contributes 1/N to the gradient compared to the single keypoint +# determining the gradient direction). Instead, we can normalize the +# loss by the total number of keypoints, if it were the case that all +# keypoints were visible in a full minibatch. (Returning to the example, +# this means that the one visible keypoint contributes as much as each +# of the N keypoints.) +_C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True +# Multi-task loss weight to use for keypoints +# Recommended values: +# - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True +# - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False +_C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0 +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2" + +# ---------------------------------------------------------------------------- # +# Semantic Segmenation Head +# ---------------------------------------------------------------------------- # +_C.MODEL.SEM_SEG_HEAD = CN() +_C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead" +_C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"] +# Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for +# the correposnding pixel. +_C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255 +# Number of classes in the semantic segmentation head +_C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54 +# Number of channels in the 3x3 convs inside semantic-FPN heads. +_C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128 +# Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride. +_C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4 +# Normalization method for the convolution layers. Options: "" (no norm), "GN". +_C.MODEL.SEM_SEG_HEAD.NORM = "GN" +_C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0 + +_C.MODEL.PANOPTIC_FPN = CN() +# Scaling of all losses from instance detection / segmentation head. +_C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0 + +# options when combining instance & semantic segmentation outputs +_C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True}) +_C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5 +_C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096 +_C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5 + + +# ---------------------------------------------------------------------------- # +# RetinaNet Head +# ---------------------------------------------------------------------------- # +_C.MODEL.RETINANET = CN() + +# This is the number of foreground classes. +_C.MODEL.RETINANET.NUM_CLASSES = 80 + +_C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"] + +# Convolutions to use in the cls and bbox tower +# NOTE: this doesn't include the last conv for logits +_C.MODEL.RETINANET.NUM_CONVS = 4 + +# IoU overlap ratio [bg, fg] for labeling anchors. +# Anchors with < bg are labeled negative (0) +# Anchors with >= bg and < fg are ignored (-1) +# Anchors with >= fg are labeled positive (1) +_C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5] +_C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1] + +# Prior prob for rare case (i.e. foreground) at the beginning of training. +# This is used to set the bias for the logits layer of the classifier subnet. +# This improves training stability in the case of heavy class imbalance. +_C.MODEL.RETINANET.PRIOR_PROB = 0.01 + +# Inference cls score threshold, only anchors with score > INFERENCE_TH are +# considered for inference (to improve speed) +_C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05 +_C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000 +_C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5 + +# Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets +_C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0) + +# Loss parameters +_C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0 +_C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25 +_C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1 + + +# ---------------------------------------------------------------------------- # +# ResNe[X]t options (ResNets = {ResNet, ResNeXt} +# Note that parts of a resnet may be used for both the backbone and the head +# These options apply to both +# ---------------------------------------------------------------------------- # +_C.MODEL.RESNETS = CN() + +_C.MODEL.RESNETS.DEPTH = 50 +_C.MODEL.RESNETS.OUT_FEATURES = ["res4"] # res4 for C4 backbone, res2..5 for FPN backbone + +# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt +_C.MODEL.RESNETS.NUM_GROUPS = 1 + +# Options: FrozenBN, GN, "SyncBN", "BN" +_C.MODEL.RESNETS.NORM = "FrozenBN" + +# Baseline width of each group. +# Scaling this parameters will scale the width of all bottleneck layers. +_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64 + +# Place the stride 2 conv on the 1x1 filter +# Use True only for the original MSRA ResNet; use False for C2 and Torch models +_C.MODEL.RESNETS.STRIDE_IN_1X1 = True + +# Apply dilation in stage "res5" +_C.MODEL.RESNETS.RES5_DILATION = 1 + +# Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet +_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256 +_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64 + +# Apply Deformable Convolution in stages +# Specify if apply deform_conv on Res2, Res3, Res4, Res5 +_C.MODEL.RESNETS.DEFORM_ON_PER_STAGE = [False, False, False, False] +# Use True to use modulated deform_conv (DeformableV2, https://arxiv.org/abs/1811.11168); +# Use False for DeformableV1. +_C.MODEL.RESNETS.DEFORM_MODULATED = False +# Number of groups in deformable conv. +_C.MODEL.RESNETS.DEFORM_NUM_GROUPS = 1 + + +# ---------------------------------------------------------------------------- # +# Solver +# ---------------------------------------------------------------------------- # +_C.SOLVER = CN() + +# See detectron2/solver/build.py for LR scheduler options +_C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR" + +_C.SOLVER.MAX_ITER = 40000 + +_C.SOLVER.BASE_LR = 0.001 + +_C.SOLVER.MOMENTUM = 0.9 + +_C.SOLVER.WEIGHT_DECAY = 0.0001 +# The weight decay that's applied to parameters of normalization layers +# (typically the affine transformation) +_C.SOLVER.WEIGHT_DECAY_NORM = 0.0 + +_C.SOLVER.GAMMA = 0.1 +_C.SOLVER.STEPS = (30000,) + +_C.SOLVER.WARMUP_FACTOR = 1.0 / 1000 +_C.SOLVER.WARMUP_ITERS = 1000 +_C.SOLVER.WARMUP_METHOD = "linear" + +_C.SOLVER.CHECKPOINT_PERIOD = 5000 + +# Number of images per batch across all machines. +# If we have 16 GPUs and IMS_PER_BATCH = 32, +# each GPU will see 2 images per batch. +_C.SOLVER.IMS_PER_BATCH = 16 + +# Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for +# biases. This is not useful (at least for recent models). You should avoid +# changing these and they exist only to reproduce Detectron v1 training if +# desired. +_C.SOLVER.BIAS_LR_FACTOR = 1.0 +_C.SOLVER.WEIGHT_DECAY_BIAS = _C.SOLVER.WEIGHT_DECAY + +# ---------------------------------------------------------------------------- # +# Specific test options +# ---------------------------------------------------------------------------- # +_C.TEST = CN() +# For end-to-end tests to verify the expected accuracy. +# Each item is [task, metric, value, tolerance] +# e.g.: [['bbox', 'AP', 38.5, 0.2]] +_C.TEST.EXPECTED_RESULTS = [] +# The period (in terms of steps) to evaluate the model during training. +# Set to 0 to disable. +_C.TEST.EVAL_PERIOD = 0 +# The sigmas used to calculate keypoint OKS. +# When empty it will use the defaults in COCO. +# Otherwise it should have the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. +_C.TEST.KEYPOINT_OKS_SIGMAS = [] +# Maximum number of detections to return per image during inference (100 is +# based on the limit established for the COCO dataset). +_C.TEST.DETECTIONS_PER_IMAGE = 100 + +_C.TEST.AUG = CN({"ENABLED": False}) +_C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200) +_C.TEST.AUG.MAX_SIZE = 4000 +_C.TEST.AUG.FLIP = True + +_C.TEST.PRECISE_BN = CN({"ENABLED": False}) +_C.TEST.PRECISE_BN.NUM_ITER = 200 + +# ---------------------------------------------------------------------------- # +# Misc options +# ---------------------------------------------------------------------------- # +# Directory where output files are written +_C.OUTPUT_DIR = "./output" +# Set seed to negative to fully randomize everything. +# Set seed to positive to use a fixed seed. Note that a fixed seed does not +# guarantee fully deterministic behavior. +_C.SEED = -1 +# Benchmark different cudnn algorithms. It has large overhead for about 10k +# iterations. It usually hurts total time, but can benefit for certain models. +_C.CUDNN_BENCHMARK = False + +# global config is for quick hack purposes. +# You can set them in command line or config files, +# and access it with: +# +# from detectron2.config import global_cfg +# print(global_cfg.HACK) +# +# Do not commit any configs into it. +_C.GLOBAL = CN() +_C.GLOBAL.HACK = 1.0 diff --git a/detectron2/data/__init__.py b/detectron2/data/__init__.py new file mode 100644 index 0000000000..b04d1dc275 --- /dev/null +++ b/detectron2/data/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from . import transforms # isort:skip + +from .build import ( + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, + load_proposals_into_dataset, + print_instances_class_histogram, +) +from .catalog import DatasetCatalog, MetadataCatalog +from .common import DatasetFromList, MapDataset +from .dataset_mapper import DatasetMapper + +# ensure the builtin datasets are registered +from . import datasets, samplers # isort:skip + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/detectron2/data/build.py b/detectron2/data/build.py new file mode 100644 index 0000000000..730d26093a --- /dev/null +++ b/detectron2/data/build.py @@ -0,0 +1,417 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import bisect +import copy +import itertools +import logging +import numpy as np +import pickle +import torch.utils.data +from fvcore.common.file_io import PathManager +from tabulate import tabulate +from termcolor import colored + +from detectron2.structures import BoxMode +from detectron2.utils.comm import get_world_size +from detectron2.utils.env import seed_all_rng +from detectron2.utils.logger import log_first_n + +from . import samplers +from .catalog import DatasetCatalog, MetadataCatalog +from .common import DatasetFromList, MapDataset +from .dataset_mapper import DatasetMapper +from .detection_utils import check_metadata_consistency + +""" +This file contains the default logic to build a dataloader for training or testing. +""" + +__all__ = [ + "build_detection_train_loader", + "build_detection_test_loader", + "get_detection_dataset_dicts", + "load_proposals_into_dataset", + "print_instances_class_histogram", +] + + +def filter_images_with_only_crowd_annotations(dataset_dicts): + """ + Filter out images with none annotations or only crowd annotations + (i.e., images without non-crowd annotations). + A common training-time preprocessing on COCO dataset. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format, but filtered. + """ + num_before = len(dataset_dicts) + + def valid(anns): + for ann in anns: + if ann.get("iscrowd", 0) == 0: + return True + return False + + dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with no usable annotations. {} images left.".format( + num_before - num_after, num_after + ) + ) + return dataset_dicts + + +def filter_images_with_few_keypoints(dataset_dicts, min_keypoints_per_image): + """ + Filter out images with too few number of keypoints. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format as dataset_dicts, but filtered. + """ + num_before = len(dataset_dicts) + + def visible_keypoints_in_image(dic): + # Each keypoints field has the format [x1, y1, v1, ...], where v is visibility + annotations = dic["annotations"] + return sum( + (np.array(ann["keypoints"][2::3]) > 0).sum() + for ann in annotations + if "keypoints" in ann + ) + + dataset_dicts = [ + x for x in dataset_dicts if visible_keypoints_in_image(x) >= min_keypoints_per_image + ] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with fewer than {} keypoints.".format( + num_before - num_after, min_keypoints_per_image + ) + ) + return dataset_dicts + + +def load_proposals_into_dataset(dataset_dicts, proposal_file): + """ + Load precomputed object proposals into the dataset. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + proposal_file (str): file path of pre-computed proposals, in pkl format. + + Returns: + list[dict]: the same format as dataset_dicts, but added proposal field. + """ + logger = logging.getLogger(__name__) + logger.info("Loading proposals from: {}".format(proposal_file)) + + with PathManager.open(proposal_file, "rb") as f: + proposals = pickle.load(f, encoding="latin1") + + # Rename the key names in D1 proposal files + rename_keys = {"indexes": "ids", "scores": "objectness_logits"} + for key in rename_keys: + if key in proposals: + proposals[rename_keys[key]] = proposals.pop(key) + + # Remove proposals whose ids are not in dataset + img_ids = set({entry["image_id"] for entry in dataset_dicts}) + keep = [i for i, id in enumerate(proposals["ids"]) if id in img_ids] + # Sort proposals by ids following the image order in dataset + keep = sorted(keep) + for key in ["boxes", "ids", "objectness_logits"]: + proposals[key] = [proposals[key][i] for i in keep] + # Assuming default bbox_mode of precomputed proposals are 'XYXY_ABS' + bbox_mode = BoxMode(proposals["bbox_mode"]) if "bbox_mode" in proposals else BoxMode.XYXY_ABS + + for i, record in enumerate(dataset_dicts): + # Sanity check that these proposals are for the correct image id + assert record["image_id"] == proposals["ids"][i] + + boxes = proposals["boxes"][i] + objectness_logits = proposals["objectness_logits"][i] + # Sort the proposals in descending order of the scores + inds = objectness_logits.argsort()[::-1] + record["proposal_boxes"] = boxes[inds] + record["proposal_objectness_logits"] = objectness_logits[inds] + record["proposal_bbox_mode"] = bbox_mode + + return dataset_dicts + + +def _quantize(x, bin_edges): + bin_edges = copy.copy(bin_edges) + bin_edges = sorted(bin_edges) + quantized = list(map(lambda y: bisect.bisect_right(bin_edges, y), x)) + return quantized + + +def print_instances_class_histogram(dataset_dicts, class_names): + """ + Args: + dataset_dicts (list[dict]): list of dataset dicts. + class_names (list[str]): list of class names (zero-indexed). + """ + num_classes = len(class_names) + hist_bins = np.arange(num_classes + 1) + histogram = np.zeros((num_classes,), dtype=np.int) + for entry in dataset_dicts: + annos = entry["annotations"] + classes = [x["category_id"] for x in annos if not x.get("iscrowd", 0)] + histogram += np.histogram(classes, bins=hist_bins)[0] + + N_COLS = min(6, len(class_names) * 2) + + def short_name(x): + # make long class names shorter. useful for lvis + if len(x) > 13: + return x[:11] + ".." + return x + + data = list( + itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)]) + ) + total_num_instances = sum(data[1::2]) + data.extend([None] * (N_COLS - (len(data) % N_COLS))) + if num_classes > 1: + data.extend(["total", total_num_instances]) + data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + data, + headers=["category", "#instances"] * (N_COLS // 2), + tablefmt="pipe", + numalign="left", + stralign="center", + ) + log_first_n( + logging.INFO, + "Distribution of training instances among all {} categories:\n".format(num_classes) + + colored(table, "cyan"), + key="message", + ) + + +def build_batch_data_sampler( + sampler, images_per_batch, group_bin_edges=None, grouping_features=None +): + """ + Return a dataset index sampler that batches dataset indices possibly with + grouping to improve training efficiency. + + Args: + sampler (torch.utils.data.sampler.Sampler): any subclass of + :class:`torch.utils.data.sampler.Sampler`. + images_per_batch (int): the batch size. Note that the sampler may return + batches that have between 1 and images_per_batch (inclusive) elements + because the underlying index set (and grouping partitions, if grouping + is used) may not be divisible by images_per_batch. + group_bin_edges (None, list[number], tuple[number]): If None, then grouping + is disabled. If a list or tuple is given, the values are used as bin + edges for defining len(group_bin_edges) + 1 groups. When batches are + sampled, only elements from the same group are returned together. + grouping_features (None, list[number], tuple[number]): If None, then grouping + is disabled. If a list or tuple is given, it must specify for each index + in the underlying dataset the value to be used for placing that dataset + index into one of the grouping bins. + + Returns: + A BatchSampler or subclass of BatchSampler. + """ + if group_bin_edges and grouping_features: + assert isinstance(group_bin_edges, (list, tuple)) + assert isinstance(grouping_features, (list, tuple)) + group_ids = _quantize(grouping_features, group_bin_edges) + batch_sampler = samplers.GroupedBatchSampler(sampler, group_ids, images_per_batch) + else: + batch_sampler = torch.utils.data.sampler.BatchSampler( + sampler, images_per_batch, drop_last=True + ) # drop last so the batch always have the same size + # NOTE when we add batch inference support, make sure not to use this. + return batch_sampler + + +def get_detection_dataset_dicts( + dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None +): + """ + Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. + + Args: + dataset_names (list[str]): a list of dataset names + filter_empty (bool): whether to filter out images without instance annotations + min_keypoints (int): filter out images with fewer keypoints than + `min_keypoints`. Set to 0 to do nothing. + proposal_files (list[str]): if given, a list of object proposal files + that match each dataset in `dataset_names`. + """ + assert len(dataset_names) + dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names] + + if proposal_files is not None: + assert len(dataset_names) == len(proposal_files) + # load precomputed proposals from proposal files + dataset_dicts = [ + load_proposals_into_dataset(dataset_i_dicts, proposal_file) + for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files) + ] + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + # Keep images without instance-level GT if the dataset has semantic labels. + if filter_empty and has_instances and "sem_seg_file_name" not in dataset_dicts[0]: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) + + if min_keypoints > 0 and has_instances: + dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) + + if has_instances: + try: + class_names = MetadataCatalog.get(dataset_names[0]).thing_classes + check_metadata_consistency("thing_classes", dataset_names) + print_instances_class_histogram(dataset_dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + return dataset_dicts + + +def build_detection_train_loader(cfg, mapper=None): + """ + A data loader is created by the following steps: + + 1. Use the dataset names in config to query :class:`DatasetCatalog`, and obtain a list of dicts. + 2. Start workers to work on the dicts. Each worker will: + * Map each metadata dict into another format to be consumed by the model. + * Batch them by simply putting dicts into a list. + The batched ``list[mapped_dict]`` is what this dataloader will return. + + Args: + cfg (CfgNode): the config + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + By default it will be `DatasetMapper(cfg, True)`. + + Returns: + a torch DataLoader object + """ + num_workers = get_world_size() + images_per_batch = cfg.SOLVER.IMS_PER_BATCH + assert ( + images_per_batch % num_workers == 0 + ), "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of workers ({}).".format( + images_per_batch, num_workers + ) + assert ( + images_per_batch >= num_workers + ), "SOLVER.IMS_PER_BATCH ({}) must be larger than the number of workers ({}).".format( + images_per_batch, num_workers + ) + images_per_worker = images_per_batch // num_workers + + dataset_dicts = get_detection_dataset_dicts( + cfg.DATASETS.TRAIN, + filter_empty=True, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + dataset = DatasetFromList(dataset_dicts, copy=False) + + # Bin edges for batching images with similar aspect ratios. If ASPECT_RATIO_GROUPING + # is enabled, we define two bins with an edge at height / width = 1. + group_bin_edges = [1] if cfg.DATALOADER.ASPECT_RATIO_GROUPING else [] + aspect_ratios = [float(img["height"]) / float(img["width"]) for img in dataset] + + if mapper is None: + mapper = DatasetMapper(cfg, True) + dataset = MapDataset(dataset, mapper) + + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + logger.info("Using training sampler {}".format(sampler_name)) + if sampler_name == "TrainingSampler": + sampler = samplers.TrainingSampler(len(dataset)) + elif sampler_name == "RepeatFactorTrainingSampler": + sampler = samplers.RepeatFactorTrainingSampler( + dataset_dicts, cfg.DATALOADER.REPEAT_THRESHOLD + ) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + batch_sampler = build_batch_data_sampler( + sampler, images_per_worker, group_bin_edges, aspect_ratios + ) + + data_loader = torch.utils.data.DataLoader( + dataset, + num_workers=cfg.DATALOADER.NUM_WORKERS, + batch_sampler=batch_sampler, + collate_fn=trivial_batch_collator, + worker_init_fn=worker_init_reset_seed, + ) + return data_loader + + +def build_detection_test_loader(cfg, dataset_name, mapper=None): + """ + Similar to `build_detection_train_loader`. + But this function uses the given `dataset_name` argument (instead of the names in cfg), + and uses batch size 1. + + Args: + cfg: a detectron2 CfgNode + dataset_name (str): a name of the dataset that's available in the DatasetCatalog + mapper (callable): a callable which takes a sample (dict) from dataset + and returns the format to be consumed by the model. + By default it will be `DatasetMapper(cfg, False)`. + + Returns: + DataLoader: a torch DataLoader, that loads the given detection + dataset, with test-time transformation and batching. + """ + dataset_dicts = get_detection_dataset_dicts( + [dataset_name], + filter_empty=False, + proposal_files=[ + cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(dataset_name)] + ] + if cfg.MODEL.LOAD_PROPOSALS + else None, + ) + + dataset = DatasetFromList(dataset_dicts) + if mapper is None: + mapper = DatasetMapper(cfg, False) + dataset = MapDataset(dataset, mapper) + + sampler = samplers.InferenceSampler(len(dataset)) + # Always use 1 image per worker during inference since this is the + # standard when reporting inference time in papers. + batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, 1, drop_last=False) + + data_loader = torch.utils.data.DataLoader( + dataset, + num_workers=cfg.DATALOADER.NUM_WORKERS, + batch_sampler=batch_sampler, + collate_fn=trivial_batch_collator, + ) + return data_loader + + +def trivial_batch_collator(batch): + """ + A batch collator that does nothing. + """ + return batch + + +def worker_init_reset_seed(worker_id): + seed_all_rng(np.random.randint(2 ** 31) + worker_id) diff --git a/detectron2/data/catalog.py b/detectron2/data/catalog.py new file mode 100644 index 0000000000..9019d79784 --- /dev/null +++ b/detectron2/data/catalog.py @@ -0,0 +1,207 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy +import logging +import types +from typing import List + +from detectron2.utils.logger import log_first_n + +__all__ = ["DatasetCatalog", "MetadataCatalog"] + + +class DatasetCatalog(object): + """ + A catalog that stores information about the datasets and how to obtain them. + + It contains a mapping from strings + (which are names that identify a dataset, e.g. "coco_2014_train") + to a function which parses the dataset and returns the samples in the + format of `list[dict]`. + + The returned dicts should be in Detectron2 Dataset format (See DATASETS.md for details) + if used with the data loader functionatilities in `data/build.py,data/detection_transform.py`. + + The purpose of having this catalog is to make it easy to choose + different datasets, by just using the strings in the config. + """ + + _REGISTERED = {} + + @staticmethod + def register(name, func): + """ + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + func (callable): a callable which takes no arguments and returns a list of dicts. + """ + DatasetCatalog._REGISTERED[name] = func + + @staticmethod + def get(name): + """ + Call the registered function and return its results. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + + Returns: + list[dict]: dataset annotations.0 + """ + try: + f = DatasetCatalog._REGISTERED[name] + except KeyError: + raise KeyError( + "Dataset '{}' is not registered! Available datasets are: {}".format( + name, ", ".join(DatasetCatalog._REGISTERED.keys()) + ) + ) + return f() + + @staticmethod + def list() -> List[str]: + """ + List all registered datasets. + + Returns: + list[str] + """ + return list(DatasetCatalog._REGISTERED.keys()) + + @staticmethod + def clear(): + """ + Remove all registered dataset. + """ + DatasetCatalog._REGISTERED.clear() + + +class Metadata(types.SimpleNamespace): + """ + A class that supports simple attribute setter/getter. + It is intended for storing metadata of a dataset and make it accessible globally. + + Examples: + + .. code-block:: python + + # somewhere when you load the data: + MetadataCatalog.get("mydataset").thing_classes = ["person", "dog"] + + # somewhere when you print statistics or visualize: + classes = MetadataCatalog.get("mydataset").thing_classes + """ + + # the name of the dataset + # set default to N/A so that `self.name` in the errors will not trigger getattr again + name: str = "N/A" + + _RENAMED = { + "class_names": "thing_classes", + "dataset_id_to_contiguous_id": "thing_dataset_id_to_contiguous_id", + "stuff_class_names": "stuff_classes", + } + + def __getattr__(self, key): + if key in self._RENAMED: + log_first_n( + logging.WARNING, + "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]), + n=10, + ) + return getattr(self, self._RENAMED[key]) + + raise AttributeError( + "Attribute '{}' does not exist in the metadata of '{}'. Available keys are {}.".format( + key, self.name, str(self.__dict__.keys()) + ) + ) + + def __setattr__(self, key, val): + if key in self._RENAMED: + log_first_n( + logging.WARNING, + "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]), + n=10, + ) + setattr(self, self._RENAMED[key], val) + + # Ensure that metadata of the same name stays consistent + try: + oldval = getattr(self, key) + assert oldval == val, ( + "Attribute '{}' in the metadata of '{}' cannot be set " + "to a different value!\n{} != {}".format(key, self.name, oldval, val) + ) + except AttributeError: + super().__setattr__(key, val) + + def as_dict(self): + """ + Returns all the metadata as a dict. + Note that modifications to the returned dict will not reflect on the Metadata object. + """ + return copy.copy(self.__dict__) + + def set(self, **kwargs): + """ + Set multiple metadata with kwargs. + """ + for k, v in kwargs.items(): + setattr(self, k, v) + return self + + def get(self, key, default=None): + """ + Access an attribute and return its value if exists. + Otherwise return default. + """ + try: + return getattr(self, key) + except AttributeError: + return default + + +class MetadataCatalog: + """ + MetadataCatalog provides access to "Metadata" of a given dataset. + + The metadata associated with a certain name is a singleton: once created, + the metadata will stay alive and will be returned by future calls to `get(name)`. + + It's like global variables, so don't abuse it. + It's meant for storing knowledge that's constant and shared across the execution + of the program, e.g.: the class names in COCO. + """ + + _NAME_TO_META = {} + + @staticmethod + def get(name): + """ + Args: + name (str): name of a dataset (e.g. coco_2014_train). + + Returns: + Metadata: The :class:`Metadata` instance associated with this name, + or create an empty one if none is available. + """ + assert len(name) + if name in MetadataCatalog._NAME_TO_META: + ret = MetadataCatalog._NAME_TO_META[name] + # TODO this is for the BC breaking change in D15247032. + # Remove this in the future. + if hasattr(ret, "dataset_name"): + logger = logging.getLogger() + logger.warning( + """ +The 'dataset_name' key in metadata is no longer used for +sharing metadata among splits after D15247032! Add +metadata to each split (now called dataset) separately! + """ + ) + parent_meta = MetadataCatalog.get(ret.dataset_name).as_dict() + ret.set(**parent_meta) + return ret + else: + m = MetadataCatalog._NAME_TO_META[name] = Metadata(name=name) + return m diff --git a/detectron2/data/common.py b/detectron2/data/common.py new file mode 100644 index 0000000000..6512c3d04a --- /dev/null +++ b/detectron2/data/common.py @@ -0,0 +1,81 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy +import logging +import random +import torch.utils.data as data + +from detectron2.utils.serialize import PicklableWrapper + +__all__ = ["MapDataset", "DatasetFromList"] + + +class MapDataset(data.Dataset): + """ + Map a function over the elements in a dataset. + + Args: + dataset: a dataset where map function is applied. + map_func: a callable which maps the element in dataset. map_func is + responsible for error handling, when error happens, it needs to + return None so the MapDataset will randomly use other + elements from the dataset. + """ + + def __init__(self, dataset, map_func): + self._dataset = dataset + self._map_func = PicklableWrapper(map_func) # wrap so that a lambda will work + + self._rng = random.Random(42) + self._fallback_candidates = set(range(len(dataset))) + + def __len__(self): + return len(self._dataset) + + def __getitem__(self, idx): + retry_count = 0 + cur_idx = int(idx) + + while True: + data = self._map_func(self._dataset[cur_idx]) + if data is not None: + self._fallback_candidates.add(cur_idx) + return data + + # _map_func fails for this idx, use a random new index from the pool + retry_count += 1 + self._fallback_candidates.discard(cur_idx) + cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0] + + if retry_count >= 3: + logger = logging.getLogger(__name__) + logger.warning( + "Failed to apply `_map_func` for idx: {}, retry count: {}".format( + idx, retry_count + ) + ) + + +class DatasetFromList(data.Dataset): + """ + Wrap a list to a torch Dataset. It produces elements of the list as data. + """ + + def __init__(self, lst: list, copy: bool = True): + """ + Args: + lst (list): a list which contains elements to produce. + copy (bool): whether to deepcopy the element when producing it, + so that the result can be modified in place without affecting the + source in the list. + """ + self._lst = lst + self._copy = copy + + def __len__(self): + return len(self._lst) + + def __getitem__(self, idx): + if self._copy: + return copy.deepcopy(self._lst[idx]) + else: + return self._lst[idx] diff --git a/detectron2/data/dataset_mapper.py b/detectron2/data/dataset_mapper.py new file mode 100644 index 0000000000..0ca4dee284 --- /dev/null +++ b/detectron2/data/dataset_mapper.py @@ -0,0 +1,144 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy +import numpy as np +import torch +from fvcore.common.file_io import PathManager +from PIL import Image + +from . import detection_utils as utils +from . import transforms as T + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper"] + + +class DatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic. + + The callable currently does the following: + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + def __init__(self, cfg, is_train=True): + self.tfm_gens = utils.build_transform_gen(cfg, is_train) + + if cfg.INPUT.CROP.ENABLED and is_train: + self.crop_gen = T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE) + else: + self.crop_gen = None + + # fmt: off + self.img_format = cfg.INPUT.FORMAT + self.mask_on = cfg.MODEL.MASK_ON + self.mask_format = cfg.INPUT.MASK_FORMAT + self.keypoint_on = cfg.MODEL.KEYPOINT_ON + self.load_proposals = cfg.MODEL.LOAD_PROPOSALS + # fmt: on + if self.keypoint_on and is_train: + # Flip only makes sense in training + self.keypoint_hflip_indices = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) + else: + self.keypoint_hflip_indices = None + + if self.load_proposals: + self.min_box_side_len = cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE + self.proposal_topk = ( + cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN + if is_train + else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST + ) + self.is_train = is_train + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + if "annotations" not in dataset_dict: + image, transforms = T.apply_transform_gens( + ([self.crop_gen] if self.crop_gen else []) + self.tfm_gens, image + ) + else: + # Crop around an instance if there are instances in the image. + # USER: Remove if you don't use cropping + if self.crop_gen: + crop_tfm = utils.gen_crop_transform_with_instance( + self.crop_gen.get_crop_size(image.shape[:2]), + image.shape[:2], + np.random.choice(dataset_dict["annotations"]), + ) + image = crop_tfm.apply_image(image) + image, transforms = T.apply_transform_gens(self.tfm_gens, image) + if self.crop_gen: + transforms = crop_tfm + transforms + + image_shape = image.shape[:2] # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) + # Can use uint8 if it turns out to be slow some day + + # USER: Remove if you don't use pre-computed proposals. + if self.load_proposals: + utils.transform_proposals( + dataset_dict, image_shape, transforms, self.min_box_side_len, self.proposal_topk + ) + + if not self.is_train: + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + return dataset_dict + + if "annotations" in dataset_dict: + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.mask_on: + anno.pop("segmentation", None) + if not self.keypoint_on: + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.mask_format + ) + # Create a tight bounding box from masks, useful when image is cropped + if self.crop_gen and instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + with PathManager.open(dataset_dict.pop("sem_seg_file_name"), "rb") as f: + sem_seg_gt = Image.open(f) + sem_seg_gt = np.asarray(sem_seg_gt, dtype="uint8") + sem_seg_gt = transforms.apply_segmentation(sem_seg_gt) + sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long")) + dataset_dict["sem_seg"] = sem_seg_gt + return dataset_dict diff --git a/detectron2/data/datasets/README.md b/detectron2/data/datasets/README.md new file mode 100644 index 0000000000..17730263a6 --- /dev/null +++ b/detectron2/data/datasets/README.md @@ -0,0 +1,9 @@ + + +### Common Datasets + +The dataset implemented here do not need to load the data into the final format. +It should provide the minimal data strcture needed to use the dataset, so it can be very efficient. + +For example, for an image dataset, just provide the file names and labels, but don't read the images. +Let the downstream decide how to read. diff --git a/detectron2/data/datasets/__init__.py b/detectron2/data/datasets/__init__.py new file mode 100644 index 0000000000..a2bfbea6bc --- /dev/null +++ b/detectron2/data/datasets/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .cityscapes import load_cityscapes_instances +from .coco import load_coco_json, load_sem_seg +from .lvis import load_lvis_json, register_lvis_instances, get_lvis_instances_meta +from .register_coco import register_coco_instances, register_coco_panoptic_separated +from . import builtin # ensure the builtin datasets are registered + + +__all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")] diff --git a/detectron2/data/datasets/builtin.py b/detectron2/data/datasets/builtin.py new file mode 100644 index 0000000000..9969e878ad --- /dev/null +++ b/detectron2/data/datasets/builtin.py @@ -0,0 +1,213 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + + +""" +This file registers pre-defined datasets at hard-coded paths, and their metadata. + +We hard-code metadata for common datasets. This will enable: +1. Consistency check when loading the datasets +2. Use models on these standard datasets directly and run demos, + without having to download the dataset annotations + +We hard-code some paths to the dataset that's assumed to +exist in "./datasets/". + +Users SHOULD NOT use this file to create new dataset / metadata for new dataset. +To add new dataset, refer to the tutorial "docs/DATASETS.md". +""" + +import os + +from detectron2.data import MetadataCatalog, DatasetCatalog +from .register_coco import register_coco_instances, register_coco_panoptic_separated +from .lvis import register_lvis_instances, get_lvis_instances_meta +from .cityscapes import load_cityscapes_instances, load_cityscapes_semantic +from .pascal_voc import register_pascal_voc +from .builtin_meta import _get_builtin_metadata + + +# ==== Predefined datasets and splits for COCO ========== + +_PREDEFINED_SPLITS_COCO = {} +_PREDEFINED_SPLITS_COCO["coco"] = { + "coco_2014_train": ("coco/train2014", "coco/annotations/instances_train2014.json"), + "coco_2014_val": ("coco/val2014", "coco/annotations/instances_val2014.json"), + "coco_2014_minival": ("coco/val2014", "coco/annotations/instances_minival2014.json"), + "coco_2014_minival_100": ("coco/val2014", "coco/annotations/instances_minival2014_100.json"), + "coco_2014_valminusminival": ( + "coco/val2014", + "coco/annotations/instances_valminusminival2014.json", + ), + "coco_2017_train": ("coco/train2017", "coco/annotations/instances_train2017.json"), + "coco_2017_val": ("coco/val2017", "coco/annotations/instances_val2017.json"), + "coco_2017_val_100": ("coco/val2017", "coco/annotations/instances_val2017_100.json"), +} + +_PREDEFINED_SPLITS_COCO["coco_person"] = { + "keypoints_coco_2014_train": ( + "coco/train2014", + "coco/annotations/person_keypoints_train2014.json", + ), + "keypoints_coco_2014_val": ("coco/val2014", "coco/annotations/person_keypoints_val2014.json"), + "keypoints_coco_2014_minival": ( + "coco/val2014", + "coco/annotations/person_keypoints_minival2014.json", + ), + "keypoints_coco_2014_valminusminival": ( + "coco/val2014", + "coco/annotations/person_keypoints_valminusminival2014.json", + ), + "keypoints_coco_2014_minival_100": ( + "coco/val2014", + "coco/annotations/person_keypoints_minival2014_100.json", + ), + "keypoints_coco_2017_train": ( + "coco/train2017", + "coco/annotations/person_keypoints_train2017.json", + ), + "keypoints_coco_2017_val": ("coco/val2017", "coco/annotations/person_keypoints_val2017.json"), + "keypoints_coco_2017_val_100": ( + "coco/val2017", + "coco/annotations/person_keypoints_val2017_100.json", + ), +} + + +_PREDEFINED_SPLITS_COCO_PANOPTIC = { + "coco_2017_train_panoptic": ( + # This is the original panoptic annotation directory + "coco/panoptic_train2017", + "coco/annotations/panoptic_train2017.json", + # This directory contains semantic annotations that are + # converted from panoptic annotations. + # It is used by PanopticFPN. + # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py + # to create these directories. + "coco/panoptic_stuff_train2017", + ), + "coco_2017_val_panoptic": ( + "coco/panoptic_val2017", + "coco/annotations/panoptic_val2017.json", + "coco/panoptic_stuff_val2017", + ), + "coco_2017_val_100_panoptic": ( + "coco/panoptic_val2017_100", + "coco/annotations/panoptic_val2017_100.json", + "coco/panoptic_stuff_val2017_100", + ), +} + + +def register_all_coco(root="datasets"): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_COCO.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + for ( + prefix, + (panoptic_root, panoptic_json, semantic_root), + ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items(): + prefix_instances = prefix[: -len("_panoptic")] + instances_meta = MetadataCatalog.get(prefix_instances) + image_root, instances_json = instances_meta.image_root, instances_meta.json_file + register_coco_panoptic_separated( + prefix, + _get_builtin_metadata("coco_panoptic_separated"), + image_root, + os.path.join(root, panoptic_root), + os.path.join(root, panoptic_json), + os.path.join(root, semantic_root), + instances_json, + ) + + +# ==== Predefined datasets and splits for LVIS ========== + + +_PREDEFINED_SPLITS_LVIS = { + "lvis_v0.5": { + "lvis_v0.5_train": ("coco/train2017", "lvis/lvis_v0.5_train.json"), + "lvis_v0.5_val": ("coco/val2017", "lvis/lvis_v0.5_val.json"), + "lvis_v0.5_val_rand_100": ("coco/val2017", "lvis/lvis_v0.5_val_rand_100.json"), + "lvis_v0.5_test": ("coco/test2017", "lvis/lvis_v0.5_image_info_test.json"), + } +} + + +def register_all_lvis(root="datasets"): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_LVIS.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_lvis_instances( + key, + get_lvis_instances_meta(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +# ==== Predefined splits for raw cityscapes images =========== + + +_RAW_CITYSCAPES_SPLITS = { + "cityscapes_fine_{task}_train": ("cityscapes/leftImg8bit/train", "cityscapes/gtFine/train"), + "cityscapes_fine_{task}_val": ("cityscapes/leftImg8bit/val", "cityscapes/gtFine/val"), + "cityscapes_fine_{task}_test": ("cityscapes/leftImg8bit/test", "cityscapes/gtFine/test"), +} + + +def register_all_cityscapes(root="datasets"): + for key, (image_dir, gt_dir) in _RAW_CITYSCAPES_SPLITS.items(): + meta = _get_builtin_metadata("cityscapes") + image_dir = os.path.join(root, image_dir) + gt_dir = os.path.join(root, gt_dir) + + inst_key = key.format(task="instance_seg") + DatasetCatalog.register( + inst_key, + lambda x=image_dir, y=gt_dir: load_cityscapes_instances( + x, y, from_json=True, to_polygons=True + ), + ) + MetadataCatalog.get(inst_key).set( + image_dir=image_dir, gt_dir=gt_dir, evaluator_type="cityscapes", **meta + ) + + sem_key = key.format(task="sem_seg") + DatasetCatalog.register( + sem_key, lambda x=image_dir, y=gt_dir: load_cityscapes_semantic(x, y) + ) + MetadataCatalog.get(sem_key).set( + image_dir=image_dir, gt_dir=gt_dir, evaluator_type="sem_seg", **meta + ) + + +# ==== Predefined splits for PASCAL VOC =========== +def register_all_pascal_voc(root="datasets"): + SPLITS = [ + ("voc_2007_trainval", "VOC2007", "trainval"), + ("voc_2007_train", "VOC2007", "train"), + ("voc_2007_val", "VOC2007", "val"), + ("voc_2007_test", "VOC2007", "test"), + ("voc_2012_trainval", "VOC2012", "trainval"), + ("voc_2012_train", "VOC2012", "train"), + ("voc_2012_val", "VOC2012", "val"), + ] + for name, dirname, split in SPLITS: + year = 2007 if "2007" in name else 2012 + register_pascal_voc(name, os.path.join(root, dirname), split, year) + MetadataCatalog.get(name).evaluator_type = "pascal_voc" + + +# Register them all under "./datasets" +register_all_coco() +register_all_lvis() +register_all_cityscapes() +register_all_pascal_voc() diff --git a/detectron2/data/datasets/builtin_meta.py b/detectron2/data/datasets/builtin_meta.py new file mode 100644 index 0000000000..74c79863a9 --- /dev/null +++ b/detectron2/data/datasets/builtin_meta.py @@ -0,0 +1,267 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + + +# All coco categories, together with their nice-looking visualization colors +# It's from https://github.com/cocodataset/panopticapi/blob/master/panoptic_coco_categories.json +COCO_CATEGORIES = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, + {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, + {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, + {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, + {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, + {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, + {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, + {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, + {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, + {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, + {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, + {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, + {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, + {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, + {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, + {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, + {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, + {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, + {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, + {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, + {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, + {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, + {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, + {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, + {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, + {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, + {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, + {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, + {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, + {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, + {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, + {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, + {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, + {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, + {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, + {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, + {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, + {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, + {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, + {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, + {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, + {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"}, + {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"}, + {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"}, + {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"}, + {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"}, + {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"}, + {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"}, + {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"}, + {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"}, + {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"}, + {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"}, + {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"}, + {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"}, + {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"}, + {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"}, + {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"}, + {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"}, + {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"}, + {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"}, + {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"}, + {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"}, + {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"}, + {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"}, + {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"}, + {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"}, + {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"}, + {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"}, + {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"}, + {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"}, + {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"}, + {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"}, + {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"}, + {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"}, + {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"}, + {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"}, + {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"}, + {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"}, + {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"}, + {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"}, + {"color": [255, 255, 128], "isthing": 0, "id": 92, "name": "banner"}, + {"color": [147, 211, 203], "isthing": 0, "id": 93, "name": "blanket"}, + {"color": [150, 100, 100], "isthing": 0, "id": 95, "name": "bridge"}, + {"color": [168, 171, 172], "isthing": 0, "id": 100, "name": "cardboard"}, + {"color": [146, 112, 198], "isthing": 0, "id": 107, "name": "counter"}, + {"color": [210, 170, 100], "isthing": 0, "id": 109, "name": "curtain"}, + {"color": [92, 136, 89], "isthing": 0, "id": 112, "name": "door-stuff"}, + {"color": [218, 88, 184], "isthing": 0, "id": 118, "name": "floor-wood"}, + {"color": [241, 129, 0], "isthing": 0, "id": 119, "name": "flower"}, + {"color": [217, 17, 255], "isthing": 0, "id": 122, "name": "fruit"}, + {"color": [124, 74, 181], "isthing": 0, "id": 125, "name": "gravel"}, + {"color": [70, 70, 70], "isthing": 0, "id": 128, "name": "house"}, + {"color": [255, 228, 255], "isthing": 0, "id": 130, "name": "light"}, + {"color": [154, 208, 0], "isthing": 0, "id": 133, "name": "mirror-stuff"}, + {"color": [193, 0, 92], "isthing": 0, "id": 138, "name": "net"}, + {"color": [76, 91, 113], "isthing": 0, "id": 141, "name": "pillow"}, + {"color": [255, 180, 195], "isthing": 0, "id": 144, "name": "platform"}, + {"color": [106, 154, 176], "isthing": 0, "id": 145, "name": "playingfield"}, + {"color": [230, 150, 140], "isthing": 0, "id": 147, "name": "railroad"}, + {"color": [60, 143, 255], "isthing": 0, "id": 148, "name": "river"}, + {"color": [128, 64, 128], "isthing": 0, "id": 149, "name": "road"}, + {"color": [92, 82, 55], "isthing": 0, "id": 151, "name": "roof"}, + {"color": [254, 212, 124], "isthing": 0, "id": 154, "name": "sand"}, + {"color": [73, 77, 174], "isthing": 0, "id": 155, "name": "sea"}, + {"color": [255, 160, 98], "isthing": 0, "id": 156, "name": "shelf"}, + {"color": [255, 255, 255], "isthing": 0, "id": 159, "name": "snow"}, + {"color": [104, 84, 109], "isthing": 0, "id": 161, "name": "stairs"}, + {"color": [169, 164, 131], "isthing": 0, "id": 166, "name": "tent"}, + {"color": [225, 199, 255], "isthing": 0, "id": 168, "name": "towel"}, + {"color": [137, 54, 74], "isthing": 0, "id": 171, "name": "wall-brick"}, + {"color": [135, 158, 223], "isthing": 0, "id": 175, "name": "wall-stone"}, + {"color": [7, 246, 231], "isthing": 0, "id": 176, "name": "wall-tile"}, + {"color": [107, 255, 200], "isthing": 0, "id": 177, "name": "wall-wood"}, + {"color": [58, 41, 149], "isthing": 0, "id": 178, "name": "water-other"}, + {"color": [183, 121, 142], "isthing": 0, "id": 180, "name": "window-blind"}, + {"color": [255, 73, 97], "isthing": 0, "id": 181, "name": "window-other"}, + {"color": [107, 142, 35], "isthing": 0, "id": 184, "name": "tree-merged"}, + {"color": [190, 153, 153], "isthing": 0, "id": 185, "name": "fence-merged"}, + {"color": [146, 139, 141], "isthing": 0, "id": 186, "name": "ceiling-merged"}, + {"color": [70, 130, 180], "isthing": 0, "id": 187, "name": "sky-other-merged"}, + {"color": [134, 199, 156], "isthing": 0, "id": 188, "name": "cabinet-merged"}, + {"color": [209, 226, 140], "isthing": 0, "id": 189, "name": "table-merged"}, + {"color": [96, 36, 108], "isthing": 0, "id": 190, "name": "floor-other-merged"}, + {"color": [96, 96, 96], "isthing": 0, "id": 191, "name": "pavement-merged"}, + {"color": [64, 170, 64], "isthing": 0, "id": 192, "name": "mountain-merged"}, + {"color": [152, 251, 152], "isthing": 0, "id": 193, "name": "grass-merged"}, + {"color": [208, 229, 228], "isthing": 0, "id": 194, "name": "dirt-merged"}, + {"color": [206, 186, 171], "isthing": 0, "id": 195, "name": "paper-merged"}, + {"color": [152, 161, 64], "isthing": 0, "id": 196, "name": "food-other-merged"}, + {"color": [116, 112, 0], "isthing": 0, "id": 197, "name": "building-other-merged"}, + {"color": [0, 114, 143], "isthing": 0, "id": 198, "name": "rock-merged"}, + {"color": [102, 102, 156], "isthing": 0, "id": 199, "name": "wall-other-merged"}, + {"color": [250, 141, 255], "isthing": 0, "id": 200, "name": "rug-merged"}, +] + +# fmt: off +COCO_PERSON_KEYPOINT_NAMES = ( + "nose", + "left_eye", "right_eye", + "left_ear", "right_ear", + "left_shoulder", "right_shoulder", + "left_elbow", "right_elbow", + "left_wrist", "right_wrist", + "left_hip", "right_hip", + "left_knee", "right_knee", + "left_ankle", "right_ankle", +) +# fmt: on + +# Pairs of keypoints that should be exchanged under horizontal flipping +COCO_PERSON_KEYPOINT_FLIP_MAP = ( + ("left_eye", "right_eye"), + ("left_ear", "right_ear"), + ("left_shoulder", "right_shoulder"), + ("left_elbow", "right_elbow"), + ("left_wrist", "right_wrist"), + ("left_hip", "right_hip"), + ("left_knee", "right_knee"), + ("left_ankle", "right_ankle"), +) + +# rules for pairs of keypoints to draw a line between, and the line color to use. +KEYPOINT_CONNECTION_RULES = [ + # face + ("left_ear", "left_eye", (102, 204, 255)), + ("right_ear", "right_eye", (51, 153, 255)), + ("left_eye", "nose", (102, 0, 204)), + ("nose", "right_eye", (51, 102, 255)), + # upper-body + ("left_shoulder", "right_shoulder", (255, 128, 0)), + ("left_shoulder", "left_elbow", (153, 255, 204)), + ("right_shoulder", "right_elbow", (128, 229, 255)), + ("left_elbow", "left_wrist", (153, 255, 153)), + ("right_elbow", "right_wrist", (102, 255, 224)), + # lower-body + ("left_hip", "right_hip", (255, 102, 0)), + ("left_hip", "left_knee", (255, 255, 77)), + ("right_hip", "right_knee", (153, 255, 204)), + ("left_knee", "left_ankle", (191, 255, 128)), + ("right_knee", "right_ankle", (255, 195, 77)), +] + + +def _get_coco_instances_meta(): + thing_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1] + assert len(thing_ids) == 80, len(thing_ids) + # Mapping from the incontiguous COCO category id to an id in [0, 79] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + + +def _get_coco_panoptic_separated_meta(): + """ + Returns metadata for "separated" version of the panoptic segmentation dataset. + """ + stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0] + assert len(stuff_ids) == 53, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 53], used in models) to ids in the dataset (used for processing results) + # The id 0 is mapped to an extra category "thing". + stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)} + # When converting COCO panoptic annotations to semantic annotations + # We label the "thing" category to 0 + stuff_dataset_id_to_contiguous_id[0] = 0 + + # 54 names for COCO stuff categories (including "things") + stuff_classes = ["things"] + [ + k["name"].replace("-other", "").replace("-merged", "") + for k in COCO_CATEGORIES + if k["isthing"] == 0 + ] + + # NOTE: I randomly picked a color for things + stuff_colors = [[82, 18, 128]] + [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 0] + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + "stuff_colors": stuff_colors, + } + ret.update(_get_coco_instances_meta()) + return ret + + +def _get_builtin_metadata(dataset_name): + if dataset_name == "coco": + return _get_coco_instances_meta() + if dataset_name == "coco_panoptic_separated": + return _get_coco_panoptic_separated_meta() + elif dataset_name == "coco_person": + return { + "thing_classes": ["person"], + "keypoint_names": COCO_PERSON_KEYPOINT_NAMES, + "keypoint_flip_map": COCO_PERSON_KEYPOINT_FLIP_MAP, + "keypoint_connection_rules": KEYPOINT_CONNECTION_RULES, + } + elif dataset_name == "cityscapes": + # fmt: off + CITYSCAPES_THING_CLASSES = [ + "person", "rider", "car", "truck", + "bus", "train", "motorcycle", "bicycle", + ] + CITYSCAPES_STUFF_CLASSES = [ + "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light", + "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car", + "truck", "bus", "train", "motorcycle", "bicycle", "license plate", + ] + # fmt: on + return { + "thing_classes": CITYSCAPES_THING_CLASSES, + "stuff_classes": CITYSCAPES_STUFF_CLASSES, + } + raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) diff --git a/detectron2/data/datasets/cityscapes.py b/detectron2/data/datasets/cityscapes.py new file mode 100644 index 0000000000..f649dfa613 --- /dev/null +++ b/detectron2/data/datasets/cityscapes.py @@ -0,0 +1,317 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import functools +import glob +import json +import logging +import multiprocessing as mp +import numpy as np +import os +from itertools import chain +import pycocotools.mask as mask_util +from PIL import Image + +from detectron2.structures import BoxMode +from detectron2.utils.logger import setup_logger +from detectron2.utils.comm import get_world_size +from fvcore.common.file_io import PathManager + +try: + import cv2 # noqa +except ImportError: + # OpenCV is an optional dependency at the moment + pass + + +def load_cityscapes_instances(image_dir, gt_dir, from_json=True, to_polygons=True): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". + gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train". + from_json (bool): whether to read annotations from the raw json file or the png files. + to_polygons (bool): whether to represent the segmentation as polygons + (COCO's format) instead of masks (cityscapes's format). + + Returns: + list[dict]: a list of dicts in "Detectron2 Dataset" format. (See DATASETS.md) + """ + if from_json: + assert to_polygons, ( + "Cityscapes's json annotations are in polygon format. " + "Converting to mask format is not supported now." + ) + files = [] + for image_file in glob.glob(os.path.join(image_dir, "**/*.png")): + suffix = "leftImg8bit.png" + assert image_file.endswith(suffix) + prefix = image_dir + instance_file = gt_dir + image_file[len(prefix) : -len(suffix)] + "gtFine_instanceIds.png" + assert os.path.isfile(instance_file), instance_file + + label_file = gt_dir + image_file[len(prefix) : -len(suffix)] + "gtFine_labelIds.png" + assert os.path.isfile(label_file), label_file + + json_file = gt_dir + image_file[len(prefix) : -len(suffix)] + "gtFine_polygons.json" + files.append((image_file, instance_file, label_file, json_file)) + assert len(files), "No images found in {}".format(image_dir) + + logger = logging.getLogger(__name__) + logger.info("Preprocessing cityscapes annotations ...") + # This is still not fast: all workers will execute duplicate works and will + # take up to 10m on a 8GPU server. + pool = mp.Pool(processes=max(mp.cpu_count() // get_world_size() // 2, 4)) + + ret = pool.map( + functools.partial(cityscapes_files_to_dict, from_json=from_json, to_polygons=to_polygons), + files, + ) + logger.info("Loaded {} images from {}".format(len(ret), image_dir)) + + # Map cityscape ids to contiguous ids + from cityscapesscripts.helpers.labels import labels + + labels = [l for l in labels if l.hasInstances and not l.ignoreInEval] + dataset_id_to_contiguous_id = {l.id: idx for idx, l in enumerate(labels)} + for dict_per_image in ret: + for anno in dict_per_image["annotations"]: + anno["category_id"] = dataset_id_to_contiguous_id[anno["category_id"]] + return ret + + +def load_cityscapes_semantic(image_dir, gt_dir): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". + gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train". + + Returns: + list[dict]: a list of dict, each has "file_name" and + "sem_seg_file_name". + """ + ret = [] + for image_file in glob.glob(os.path.join(image_dir, "**/*.png")): + suffix = "leftImg8bit.png" + assert image_file.endswith(suffix) + prefix = image_dir + + label_file = gt_dir + image_file[len(prefix) : -len(suffix)] + "gtFine_labelTrainIds.png" + assert os.path.isfile( + label_file + ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py" # noqa + + json_file = gt_dir + image_file[len(prefix) : -len(suffix)] + "gtFine_polygons.json" + + with PathManager.open(json_file, "r") as f: + jsonobj = json.load(f) + ret.append( + { + "file_name": image_file, + "sem_seg_file_name": label_file, + "height": jsonobj["imgHeight"], + "width": jsonobj["imgWidth"], + } + ) + return ret + + +def cityscapes_files_to_dict(files, from_json, to_polygons): + """ + Parse cityscapes annotation files to a dict. + + Args: + files (tuple): consists of (image_file, instance_id_file, label_id_file, json_file) + from_json (bool): whether to read annotations from the raw json file or the png files. + to_polygons (bool): whether to represent the segmentation as polygons + (COCO's format) instead of masks (cityscapes's format). + + Returns: + A dict in Detectron2 Dataset format. + """ + from cityscapesscripts.helpers.labels import id2label, name2label + + image_file, instance_id_file, _, json_file = files + + annos = [] + + if from_json: + from shapely.geometry import MultiPolygon, Polygon + + with PathManager.open(json_file, "r") as f: + jsonobj = json.load(f) + ret = { + "file_name": image_file, + "image_id": os.path.basename(image_file), + "height": jsonobj["imgHeight"], + "width": jsonobj["imgWidth"], + } + + # `polygons_union` contains the union of all valid polygons. + polygons_union = Polygon() + + # CityscapesScripts draw the polygons in sequential order + # and each polygon *overwrites* existing ones. See + # (https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/json2instanceImg.py) # noqa + # We use reverse order, and each polygon *avoids* early ones. + # This will resolve the ploygon overlaps in the same way as CityscapesScripts. + for obj in jsonobj["objects"][::-1]: + if "deleted" in obj: # cityscapes data format specific + continue + label_name = obj["label"] + + try: + label = name2label[label_name] + except KeyError: + if label_name.endswith("group"): # crowd area + label = name2label[label_name[: -len("group")]] + else: + raise + if label.id < 0: # cityscapes data format + continue + + # Cityscapes's raw annotations uses integer coordinates + # Therefore +0.5 here + poly_coord = np.asarray(obj["polygon"], dtype="f4") + 0.5 + # CityscapesScript uses PIL.ImageDraw.polygon to rasterize + # polygons for evaluation. This function operates in integer space + # and draws each pixel whose center falls into the polygon. + # Therefore it draws a polygon which is 0.5 "fatter" in expectation. + # We therefore dilate the input polygon by 0.5 as our input. + poly = Polygon(poly_coord).buffer(0.5, resolution=4) + + if not label.hasInstances or label.ignoreInEval: + # even if we won't store the polygon it still contributes to overlaps resolution + polygons_union = polygons_union.union(poly) + continue + + # Take non-overlapping part of the polygon + poly_wo_overlaps = poly.difference(polygons_union) + if poly_wo_overlaps.is_empty: + continue + polygons_union = polygons_union.union(poly) + + anno = {} + anno["iscrowd"] = label_name.endswith("group") + anno["category_id"] = label.id + + if isinstance(poly_wo_overlaps, Polygon): + poly_list = [poly_wo_overlaps] + elif isinstance(poly_wo_overlaps, MultiPolygon): + poly_list = poly_wo_overlaps.geoms + else: + raise NotImplementedError("Unknown geometric structure {}".format(poly_wo_overlaps)) + + poly_coord = [] + for poly_el in poly_list: + # COCO API can work only with exterior boundaries now, hence we store only them. + # TODO: store both exterior and interior boundaries once other parts of the + # codebase support holes in polygons. + poly_coord.append(list(chain(*poly_el.exterior.coords))) + anno["segmentation"] = poly_coord + (xmin, ymin, xmax, ymax) = poly_wo_overlaps.bounds + + anno["bbox"] = (xmin, ymin, xmax, ymax) + anno["bbox_mode"] = BoxMode.XYXY_ABS + + annos.append(anno) + else: + # See also the official annotation parsing scripts at + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/instances2dict.py # noqa + with PathManager.open(instance_id_file, "rb") as f: + inst_image = np.asarray(Image.open(f), order="F") + # ids < 24 are stuff labels (filtering them first is about 5% faster) + flattened_ids = np.unique(inst_image[inst_image >= 24]) + + ret = { + "file_name": image_file, + "image_id": os.path.basename(image_file), + "height": inst_image.shape[0], + "width": inst_image.shape[1], + } + + for instance_id in flattened_ids: + # For non-crowd annotations, instance_id // 1000 is the label_id + # Crowd annotations have <1000 instance ids + label_id = instance_id // 1000 if instance_id >= 1000 else instance_id + label = id2label[label_id] + if not label.hasInstances or label.ignoreInEval: + continue + + anno = {} + anno["iscrowd"] = instance_id < 1000 + anno["category_id"] = label.id + + mask = np.asarray(inst_image == instance_id, dtype=np.uint8, order="F") + + inds = np.nonzero(mask) + ymin, ymax = inds[0].min(), inds[0].max() + xmin, xmax = inds[1].min(), inds[1].max() + anno["bbox"] = (xmin, ymin, xmax, ymax) + if xmax <= xmin or ymax <= ymin: + continue + anno["bbox_mode"] = BoxMode.XYXY_ABS + if to_polygons: + # This conversion comes from D4809743 and D5171122, + # when Mask-RCNN was first developed. + contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[ + -2 + ] + polygons = [c.reshape(-1).tolist() for c in contours if len(c) >= 3] + # opencv's can produce invalid polygons + if len(polygons) == 0: + continue + anno["segmentation"] = polygons + else: + anno["segmentation"] = mask_util.encode(mask[:, :, None])[0] + annos.append(anno) + ret["annotations"] = annos + return ret + + +if __name__ == "__main__": + """ + Test the cityscapes dataset loader. + + Usage: + python -m detectron2.data.datasets.cityscapes \ + cityscapes/leftImg8bit/train cityscapes/gtFine/train + """ + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("image_dir") + parser.add_argument("gt_dir") + parser.add_argument("--type", choices=["instance", "semantic"], default="instance") + args = parser.parse_args() + from detectron2.data.catalog import Metadata + from detectron2.utils.visualizer import Visualizer + from cityscapesscripts.helpers.labels import labels + + logger = setup_logger(name=__name__) + + dirname = "cityscapes-data-vis" + os.makedirs(dirname, exist_ok=True) + + if args.type == "instance": + dicts = load_cityscapes_instances( + args.image_dir, args.gt_dir, from_json=True, to_polygons=True + ) + logger.info("Done loading {} samples.".format(len(dicts))) + + thing_classes = [k.name for k in labels if k.hasInstances and not k.ignoreInEval] + meta = Metadata().set(thing_classes=thing_classes) + + else: + dicts = load_cityscapes_semantic(args.image_dir, args.gt_dir) + logger.info("Done loading {} samples.".format(len(dicts))) + + stuff_names = [k.name for k in labels if k.trainId != 255] + stuff_colors = [k.color for k in labels if k.trainId != 255] + meta = Metadata().set(stuff_names=stuff_names, stuff_colors=stuff_colors) + + for d in dicts: + img = np.array(Image.open(d["file_name"])) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + # cv2.imshow("a", vis.get_image()[:, :, ::-1]) + # cv2.waitKey() + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) diff --git a/detectron2/data/datasets/coco.py b/detectron2/data/datasets/coco.py new file mode 100644 index 0000000000..3b17a6f408 --- /dev/null +++ b/detectron2/data/datasets/coco.py @@ -0,0 +1,315 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import io +import logging +import contextlib +import os +from PIL import Image + +from fvcore.common.timer import Timer +from detectron2.structures import BoxMode +from fvcore.common.file_io import PathManager + +from .. import MetadataCatalog, DatasetCatalog + +""" +This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format". +""" + + +logger = logging.getLogger(__name__) + +__all__ = ["load_coco_json", "load_sem_seg"] + + +def load_coco_json(json_file, image_root, dataset_name=None): + """ + Load a json file with COCO's instances annotation format. + Currently supports instance detection, instance segmentation, + person keypoints and densepose annotations. + + Args: + json_file (str): full path to the json file in COCO instances annotation format. + image_root (str): the directory where the images in this json file exists. + dataset_name (str): the name of the dataset (e.g., coco_2017_train). + If provided, this function will also put "thing_classes" into + the metadata associated with this dataset. + + Returns: + list[dict]: a list of dicts in "Detectron2 Dataset" format. (See DATASETS.md) + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from pycocotools.coco import COCO + + timer = Timer() + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + coco_api = COCO(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(coco_api.getCatIds()) + cats = coco_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + # sort indices for reproducible results + img_ids = sorted(list(coco_api.imgs.keys())) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = coco_api.loadImgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'iscrowd': 0, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] + + if "minival" not in json_file: + # The popular valminusminival & minival annotations for COCO2014 contain this bug. + # However the ratio of buggy annotations there is tiny and does not affect accuracy. + # Therefore we explicitly white-list them. + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + + logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) + + dataset_dicts = [] + + # TODO: refactoring candidate, one should not have to alter DB reader + # every time new data type is added + DENSEPOSE_KEYS = ["dp_x", "dp_y", "dp_I", "dp_U", "dp_V", "dp_masks"] + + num_instances_without_valid_segmentation = 0 + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = os.path.join(image_root, img_dict["file_name"]) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + + # The original COCO valminusminival2014 & minival2014 annotation files + # actually contains bugs that, together with certain ways of using COCO API, + # can trigger this assertion. + assert anno["image_id"] == image_id + + assert anno.get("ignore", 0) == 0 + + obj = { + field: anno[field] + for field in ["iscrowd", "bbox", "keypoints", "category_id"] + DENSEPOSE_KEYS + if field in anno + } + + segm = anno.get("segmentation", None) + if segm: # either list[list[float]] or dict(RLE) + if not isinstance(segm, dict): + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + keypts = anno.get("keypoints", None) + if keypts: # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + obj["bbox_mode"] = BoxMode.XYWH_ABS + if id_map: + obj["category_id"] = id_map[obj["category_id"]] + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warn( + "Filtered out {} instances without valid segmentation. " + "There might be issues in your dataset generation process.".format( + num_instances_without_valid_segmentation + ) + ) + return dataset_dicts + + +# TODO this function is not specific to COCO, except for the "image_id" logic. +def load_sem_seg(gt_root, image_root, gt_ext="png", image_ext="jpg"): + """ + Load semantic segmenation datasets. All files under "gt_root" with "gt_ext" extension are + treated as ground truth annotations and all files under "image_root" with "image_ext" extension + as input images. Ground truth and input images are matched using file paths relative to + "gt_root" and "image_root" respectively without taking into account file extensions. + + Args: + gt_root (str): full path to ground truth semantic segmentation files. Semantic segmentation + annotations are stored as images with integer values in pixels that represent + corresponding semantic labels. + image_root (str): the directory where the input images are. + gt_ext (str): file extension for ground truth annotations. + image_ext (str): file extension for input images. + + Returns: + list[dict]: + a list of dicts in detectron2 standard format without instance-level + annotation. + + Notes: + 1. This function does not read the image and ground truth files. + The results do not have the "image" and "sem_seg" fields. + """ + + # We match input images with ground truth based on their raltive filepaths (without file + # extensions) starting from 'image_root' and 'gt_root' respectively. COCO API works with integer + # IDs, hence, we try to convert these paths to int if possible. + def file2id(folder_path, file_path): + # TODO id is not used. + # extract realtive path starting from `folder_path` + image_id = os.path.normpath(os.path.relpath(file_path, start=folder_path)) + # remove file extension + image_id = os.path.splitext(image_id)[0] + try: + image_id = int(image_id) + except ValueError: + pass + return image_id + + input_files = sorted( + (os.path.join(image_root, f) for f in PathManager.ls(image_root) if f.endswith(image_ext)), + key=lambda file_path: file2id(image_root, file_path), + ) + gt_files = sorted( + (os.path.join(gt_root, f) for f in PathManager.ls(gt_root) if f.endswith(gt_ext)), + key=lambda file_path: file2id(gt_root, file_path), + ) + + assert len(gt_files) > 0, "No annotations found in {}.".format(gt_root) + + # Use the intersection, so that val2017_100 annotations can run smoothly with val2017 images + if len(input_files) != len(gt_files): + logger.warn( + "Directory {} and {} has {} and {} files, respectively.".format( + image_root, gt_root, len(input_files), len(gt_files) + ) + ) + input_basenames = [os.path.basename(f)[: -len(image_ext)] for f in input_files] + gt_basenames = [os.path.basename(f)[: -len(gt_ext)] for f in gt_files] + intersect = list(set(input_basenames) & set(gt_basenames)) + # sort, otherwise each worker may obtain a list[dict] in different order + intersect = sorted(intersect) + logger.warn("Will use their intersection of {} files.".format(len(intersect))) + input_files = [os.path.join(image_root, f + image_ext) for f in intersect] + gt_files = [os.path.join(gt_root, f + gt_ext) for f in intersect] + + logger.info( + "Loaded {} images with semantic segmentation from {}".format(len(input_files), image_root) + ) + + dataset_dicts = [] + for (img_path, gt_path) in zip(input_files, gt_files): + record = {} + record["file_name"] = img_path + record["sem_seg_file_name"] = gt_path + record["image_id"] = file2id(image_root, img_path) + assert record["image_id"] == file2id( + gt_root, gt_path + ), "there is no ground truth for {}".format(img_path) + with PathManager.open(gt_path, "rb") as f: + img = Image.open(f) + w, h = img.size + record["height"] = h + record["width"] = w + dataset_dicts.append(record) + + return dataset_dicts + + +if __name__ == "__main__": + """ + Test the COCO json dataset loader. + + Usage: + python -m detectron2.data.datasets.coco \ + path/to/json path/to/image_root dataset_name + + "dataset_name" can be "coco_2014_minival_100", or other + pre-registered ones + """ + import numpy as np + from detectron2.utils.logger import setup_logger + from detectron2.utils.visualizer import Visualizer + import detectron2.data.datasets # noqa # add pre-defined metadata + import sys + + logger = setup_logger(name=__name__) + assert sys.argv[3] in DatasetCatalog.list() + meta = MetadataCatalog.get(sys.argv[3]) + + dicts = load_coco_json(sys.argv[1], sys.argv[2], sys.argv[3]) + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "coco-data-vis" + os.makedirs(dirname, exist_ok=True) + for d in dicts: + img = np.array(Image.open(d["file_name"])) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) diff --git a/detectron2/data/datasets/lvis.py b/detectron2/data/datasets/lvis.py new file mode 100644 index 0000000000..9fc39b1ead --- /dev/null +++ b/detectron2/data/datasets/lvis.py @@ -0,0 +1,205 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import os + +from fvcore.common.timer import Timer +from detectron2.structures import BoxMode +from fvcore.common.file_io import PathManager +from detectron2.data import DatasetCatalog, MetadataCatalog + +from .lvis_v0_5_categories import LVIS_CATEGORIES + +""" +This file contains functions to parse LVIS-format annotations into dicts in the +"Detectron2 format". +""" + +logger = logging.getLogger(__name__) + +__all__ = ["load_lvis_json", "register_lvis_instances", "get_lvis_instances_meta"] + + +def register_lvis_instances(name, metadata, json_file, image_root): + """ + Register a dataset in LVIS's json annotation format for instance detection and segmentation. + + Args: + name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train". + metadata (dict): extra metadata associated with this dataset. It can be an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str): directory which contains all the images. + """ + DatasetCatalog.register(name, lambda: load_lvis_json(json_file, image_root, name)) + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="lvis", **metadata + ) + + +def load_lvis_json(json_file, image_root, dataset_name=None): + """ + Load a json file in LVIS's annotation format. + + Args: + json_file (str): full path to the LVIS json annotation file. + image_root (str): the directory where the images in this json file exists. + dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train"). + If provided, this function will put "thing_classes" into the metadata + associated with this dataset. + + Returns: + list[dict]: a list of dicts in "Detectron2 Dataset" format. (See DATASETS.md) + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from lvis import LVIS + + json_file = PathManager.get_local_path(json_file) + + timer = Timer() + lvis_api = LVIS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + if dataset_name is not None: + meta = get_lvis_instances_meta(dataset_name) + MetadataCatalog.get(dataset_name).set(**meta) + + # sort indices for reproducible results + img_ids = sorted(list(lvis_api.imgs.keys())) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = lvis_api.load_imgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] + + # Sanity check that each annotation has a unique id + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + + logger.info("Loaded {} images in the LVIS format from {}".format(len(imgs_anns), json_file)) + + dataset_dicts = [] + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + file_name = img_dict["file_name"] + if img_dict["file_name"].startswith("COCO"): + # Convert form the COCO 2014 file naming convention of + # COCO_[train/val/test]2014_000000000000.jpg to the 2017 naming convention of + # 000000000000.jpg (LVIS v1 will fix this naming issue) + file_name = file_name[-16:] + record["file_name"] = os.path.join(image_root, file_name) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", []) + record["neg_category_ids"] = img_dict.get("neg_category_ids", []) + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + assert anno["image_id"] == image_id + obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS} + obj["category_id"] = anno["category_id"] - 1 # Convert 1-indexed to 0-indexed + segm = anno["segmentation"] # list[list[float]] + # filter out invalid polygons (< 3 points) + valid_segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + assert len(segm) == len( + valid_segm + ), "Annotation contains an invalid polygon with < 3 points" + assert len(segm) > 0 + obj["segmentation"] = segm + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + return dataset_dicts + + +def get_lvis_instances_meta(dataset_name): + """ + Load LVIS metadata. + + Args: + dataset_name (str): LVIS dataset name without the split name (e.g., "lvis_v0.5"). + + Returns: + dict: LVIS metadata with keys: thing_classes + """ + if "v0.5" in dataset_name: + return _get_lvis_instances_meta_v0_5() + # There will be a v1 in the future + # elif dataset_name == "lvis_v1": + # return get_lvis_instances_meta_v1() + raise ValueError("No built-in metadata for dataset {}".format(dataset_name)) + + +def _get_lvis_instances_meta_v0_5(): + assert len(LVIS_CATEGORIES) == 1230 + cat_ids = [k["id"] for k in LVIS_CATEGORIES] + assert min(cat_ids) == 1 and max(cat_ids) == len( + cat_ids + ), "Category ids are not in [1, #categories], as expected" + # Ensure that the category list is sorted by id + lvis_categories = [k for k in sorted(LVIS_CATEGORIES, key=lambda x: x["id"])] + thing_classes = [k["synonyms"][0] for k in lvis_categories] + meta = {"thing_classes": thing_classes} + return meta + + +if __name__ == "__main__": + """ + Test the LVIS json dataset loader. + + Usage: + python -m detectron2.data.datasets.lvis \ + path/to/json path/to/image_root dataset_name vis_limit + """ + import sys + import numpy as np + from detectron2.utils.logger import setup_logger + from PIL import Image + import detectron2.data.datasets # noqa # add pre-defined metadata + from detectron2.utils.visualizer import Visualizer + + logger = setup_logger(name=__name__) + meta = MetadataCatalog.get(sys.argv[3]) + + dicts = load_lvis_json(sys.argv[1], sys.argv[2], sys.argv[3]) + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "lvis-data-vis" + os.makedirs(dirname, exist_ok=True) + for d in dicts[: int(sys.argv[4])]: + img = np.array(Image.open(d["file_name"])) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) diff --git a/detectron2/data/datasets/lvis_v0_5_categories.py b/detectron2/data/datasets/lvis_v0_5_categories.py new file mode 100644 index 0000000000..8205e605f8 --- /dev/null +++ b/detectron2/data/datasets/lvis_v0_5_categories.py @@ -0,0 +1,13 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Autogen with +# with open("lvis_v0.5_val.json", "r") as f: +# a = json.load(f) +# c = a["categories"] +# for x in c: +# del x["image_count"] +# del x["instance_count"] +# LVIS_CATEGORIES = repr(c) + " # noqa" + +# fmt: off +LVIS_CATEGORIES = [{'frequency': 'r', 'id': 1, 'synset': 'acorn.n.01', 'synonyms': ['acorn'], 'def': 'nut from an oak tree', 'name': 'acorn'}, {'frequency': 'c', 'id': 2, 'synset': 'aerosol.n.02', 'synonyms': ['aerosol_can', 'spray_can'], 'def': 'a dispenser that holds a substance under pressure', 'name': 'aerosol_can'}, {'frequency': 'f', 'id': 3, 'synset': 'air_conditioner.n.01', 'synonyms': ['air_conditioner'], 'def': 'a machine that keeps air cool and dry', 'name': 'air_conditioner'}, {'frequency': 'f', 'id': 4, 'synset': 'airplane.n.01', 'synonyms': ['airplane', 'aeroplane'], 'def': 'an aircraft that has a fixed wing and is powered by propellers or jets', 'name': 'airplane'}, {'frequency': 'c', 'id': 5, 'synset': 'alarm_clock.n.01', 'synonyms': ['alarm_clock'], 'def': 'a clock that wakes a sleeper at some preset time', 'name': 'alarm_clock'}, {'frequency': 'c', 'id': 6, 'synset': 'alcohol.n.01', 'synonyms': ['alcohol', 'alcoholic_beverage'], 'def': 'a liquor or brew containing alcohol as the active agent', 'name': 'alcohol'}, {'frequency': 'r', 'id': 7, 'synset': 'alligator.n.02', 'synonyms': ['alligator', 'gator'], 'def': 'amphibious reptiles related to crocodiles but with shorter broader snouts', 'name': 'alligator'}, {'frequency': 'c', 'id': 8, 'synset': 'almond.n.02', 'synonyms': ['almond'], 'def': 'oval-shaped edible seed of the almond tree', 'name': 'almond'}, {'frequency': 'c', 'id': 9, 'synset': 'ambulance.n.01', 'synonyms': ['ambulance'], 'def': 'a vehicle that takes people to and from hospitals', 'name': 'ambulance'}, {'frequency': 'r', 'id': 10, 'synset': 'amplifier.n.01', 'synonyms': ['amplifier'], 'def': 'electronic equipment that increases strength of signals', 'name': 'amplifier'}, {'frequency': 'c', 'id': 11, 'synset': 'anklet.n.03', 'synonyms': ['anklet', 'ankle_bracelet'], 'def': 'an ornament worn around the ankle', 'name': 'anklet'}, {'frequency': 'f', 'id': 12, 'synset': 'antenna.n.01', 'synonyms': ['antenna', 'aerial', 'transmitting_aerial'], 'def': 'an electrical device that sends or receives radio or television signals', 'name': 'antenna'}, {'frequency': 'f', 'id': 13, 'synset': 'apple.n.01', 'synonyms': ['apple'], 'def': 'fruit with red or yellow or green skin and sweet to tart crisp whitish flesh', 'name': 'apple'}, {'frequency': 'r', 'id': 14, 'synset': 'apple_juice.n.01', 'synonyms': ['apple_juice'], 'def': 'the juice of apples', 'name': 'apple_juice'}, {'frequency': 'r', 'id': 15, 'synset': 'applesauce.n.01', 'synonyms': ['applesauce'], 'def': 'puree of stewed apples usually sweetened and spiced', 'name': 'applesauce'}, {'frequency': 'r', 'id': 16, 'synset': 'apricot.n.02', 'synonyms': ['apricot'], 'def': 'downy yellow to rosy-colored fruit resembling a small peach', 'name': 'apricot'}, {'frequency': 'f', 'id': 17, 'synset': 'apron.n.01', 'synonyms': ['apron'], 'def': 'a garment of cloth that is tied about the waist and worn to protect clothing', 'name': 'apron'}, {'frequency': 'c', 'id': 18, 'synset': 'aquarium.n.01', 'synonyms': ['aquarium', 'fish_tank'], 'def': 'a tank/pool/bowl filled with water for keeping live fish and underwater animals', 'name': 'aquarium'}, {'frequency': 'c', 'id': 19, 'synset': 'armband.n.02', 'synonyms': ['armband'], 'def': 'a band worn around the upper arm', 'name': 'armband'}, {'frequency': 'f', 'id': 20, 'synset': 'armchair.n.01', 'synonyms': ['armchair'], 'def': 'chair with a support on each side for arms', 'name': 'armchair'}, {'frequency': 'r', 'id': 21, 'synset': 'armoire.n.01', 'synonyms': ['armoire'], 'def': 'a large wardrobe or cabinet', 'name': 'armoire'}, {'frequency': 'r', 'id': 22, 'synset': 'armor.n.01', 'synonyms': ['armor', 'armour'], 'def': 'protective covering made of metal and used in combat', 'name': 'armor'}, {'frequency': 'c', 'id': 23, 'synset': 'artichoke.n.02', 'synonyms': ['artichoke'], 'def': 'a thistlelike flower head with edible fleshy leaves and heart', 'name': 'artichoke'}, {'frequency': 'f', 'id': 24, 'synset': 'ashcan.n.01', 'synonyms': ['trash_can', 'garbage_can', 'wastebin', 'dustbin', 'trash_barrel', 'trash_bin'], 'def': 'a bin that holds rubbish until it is collected', 'name': 'trash_can'}, {'frequency': 'c', 'id': 25, 'synset': 'ashtray.n.01', 'synonyms': ['ashtray'], 'def': "a receptacle for the ash from smokers' cigars or cigarettes", 'name': 'ashtray'}, {'frequency': 'c', 'id': 26, 'synset': 'asparagus.n.02', 'synonyms': ['asparagus'], 'def': 'edible young shoots of the asparagus plant', 'name': 'asparagus'}, {'frequency': 'c', 'id': 27, 'synset': 'atomizer.n.01', 'synonyms': ['atomizer', 'atomiser', 'spray', 'sprayer', 'nebulizer', 'nebuliser'], 'def': 'a dispenser that turns a liquid (such as perfume) into a fine mist', 'name': 'atomizer'}, {'frequency': 'c', 'id': 28, 'synset': 'avocado.n.01', 'synonyms': ['avocado'], 'def': 'a pear-shaped fruit with green or blackish skin and rich yellowish pulp enclosing a single large seed', 'name': 'avocado'}, {'frequency': 'c', 'id': 29, 'synset': 'award.n.02', 'synonyms': ['award', 'accolade'], 'def': 'a tangible symbol signifying approval or distinction', 'name': 'award'}, {'frequency': 'f', 'id': 30, 'synset': 'awning.n.01', 'synonyms': ['awning'], 'def': 'a canopy made of canvas to shelter people or things from rain or sun', 'name': 'awning'}, {'frequency': 'r', 'id': 31, 'synset': 'ax.n.01', 'synonyms': ['ax', 'axe'], 'def': 'an edge tool with a heavy bladed head mounted across a handle', 'name': 'ax'}, {'frequency': 'f', 'id': 32, 'synset': 'baby_buggy.n.01', 'synonyms': ['baby_buggy', 'baby_carriage', 'perambulator', 'pram', 'stroller'], 'def': 'a small vehicle with four wheels in which a baby or child is pushed around', 'name': 'baby_buggy'}, {'frequency': 'c', 'id': 33, 'synset': 'backboard.n.01', 'synonyms': ['basketball_backboard'], 'def': 'a raised vertical board with basket attached; used to play basketball', 'name': 'basketball_backboard'}, {'frequency': 'f', 'id': 34, 'synset': 'backpack.n.01', 'synonyms': ['backpack', 'knapsack', 'packsack', 'rucksack', 'haversack'], 'def': 'a bag carried by a strap on your back or shoulder', 'name': 'backpack'}, {'frequency': 'f', 'id': 35, 'synset': 'bag.n.04', 'synonyms': ['handbag', 'purse', 'pocketbook'], 'def': 'a container used for carrying money and small personal items or accessories', 'name': 'handbag'}, {'frequency': 'f', 'id': 36, 'synset': 'bag.n.06', 'synonyms': ['suitcase', 'baggage', 'luggage'], 'def': 'cases used to carry belongings when traveling', 'name': 'suitcase'}, {'frequency': 'c', 'id': 37, 'synset': 'bagel.n.01', 'synonyms': ['bagel', 'beigel'], 'def': 'glazed yeast-raised doughnut-shaped roll with hard crust', 'name': 'bagel'}, {'frequency': 'r', 'id': 38, 'synset': 'bagpipe.n.01', 'synonyms': ['bagpipe'], 'def': 'a tubular wind instrument; the player blows air into a bag and squeezes it out', 'name': 'bagpipe'}, {'frequency': 'r', 'id': 39, 'synset': 'baguet.n.01', 'synonyms': ['baguet', 'baguette'], 'def': 'narrow French stick loaf', 'name': 'baguet'}, {'frequency': 'r', 'id': 40, 'synset': 'bait.n.02', 'synonyms': ['bait', 'lure'], 'def': 'something used to lure fish or other animals into danger so they can be trapped or killed', 'name': 'bait'}, {'frequency': 'f', 'id': 41, 'synset': 'ball.n.06', 'synonyms': ['ball'], 'def': 'a spherical object used as a plaything', 'name': 'ball'}, {'frequency': 'r', 'id': 42, 'synset': 'ballet_skirt.n.01', 'synonyms': ['ballet_skirt', 'tutu'], 'def': 'very short skirt worn by ballerinas', 'name': 'ballet_skirt'}, {'frequency': 'f', 'id': 43, 'synset': 'balloon.n.01', 'synonyms': ['balloon'], 'def': 'large tough nonrigid bag filled with gas or heated air', 'name': 'balloon'}, {'frequency': 'c', 'id': 44, 'synset': 'bamboo.n.02', 'synonyms': ['bamboo'], 'def': 'woody tropical grass having hollow woody stems', 'name': 'bamboo'}, {'frequency': 'f', 'id': 45, 'synset': 'banana.n.02', 'synonyms': ['banana'], 'def': 'elongated crescent-shaped yellow fruit with soft sweet flesh', 'name': 'banana'}, {'frequency': 'r', 'id': 46, 'synset': 'band_aid.n.01', 'synonyms': ['Band_Aid'], 'def': 'trade name for an adhesive bandage to cover small cuts or blisters', 'name': 'Band_Aid'}, {'frequency': 'c', 'id': 47, 'synset': 'bandage.n.01', 'synonyms': ['bandage'], 'def': 'a piece of soft material that covers and protects an injured part of the body', 'name': 'bandage'}, {'frequency': 'c', 'id': 48, 'synset': 'bandanna.n.01', 'synonyms': ['bandanna', 'bandana'], 'def': 'large and brightly colored handkerchief; often used as a neckerchief', 'name': 'bandanna'}, {'frequency': 'r', 'id': 49, 'synset': 'banjo.n.01', 'synonyms': ['banjo'], 'def': 'a stringed instrument of the guitar family with a long neck and circular body', 'name': 'banjo'}, {'frequency': 'f', 'id': 50, 'synset': 'banner.n.01', 'synonyms': ['banner', 'streamer'], 'def': 'long strip of cloth or paper used for decoration or advertising', 'name': 'banner'}, {'frequency': 'r', 'id': 51, 'synset': 'barbell.n.01', 'synonyms': ['barbell'], 'def': 'a bar to which heavy discs are attached at each end; used in weightlifting', 'name': 'barbell'}, {'frequency': 'r', 'id': 52, 'synset': 'barge.n.01', 'synonyms': ['barge'], 'def': 'a flatbottom boat for carrying heavy loads (especially on canals)', 'name': 'barge'}, {'frequency': 'f', 'id': 53, 'synset': 'barrel.n.02', 'synonyms': ['barrel', 'cask'], 'def': 'a cylindrical container that holds liquids', 'name': 'barrel'}, {'frequency': 'c', 'id': 54, 'synset': 'barrette.n.01', 'synonyms': ['barrette'], 'def': "a pin for holding women's hair in place", 'name': 'barrette'}, {'frequency': 'c', 'id': 55, 'synset': 'barrow.n.03', 'synonyms': ['barrow', 'garden_cart', 'lawn_cart', 'wheelbarrow'], 'def': 'a cart for carrying small loads; has handles and one or more wheels', 'name': 'barrow'}, {'frequency': 'f', 'id': 56, 'synset': 'base.n.03', 'synonyms': ['baseball_base'], 'def': 'a place that the runner must touch before scoring', 'name': 'baseball_base'}, {'frequency': 'f', 'id': 57, 'synset': 'baseball.n.02', 'synonyms': ['baseball'], 'def': 'a ball used in playing baseball', 'name': 'baseball'}, {'frequency': 'f', 'id': 58, 'synset': 'baseball_bat.n.01', 'synonyms': ['baseball_bat'], 'def': 'an implement used in baseball by the batter', 'name': 'baseball_bat'}, {'frequency': 'f', 'id': 59, 'synset': 'baseball_cap.n.01', 'synonyms': ['baseball_cap', 'jockey_cap', 'golf_cap'], 'def': 'a cap with a bill', 'name': 'baseball_cap'}, {'frequency': 'f', 'id': 60, 'synset': 'baseball_glove.n.01', 'synonyms': ['baseball_glove', 'baseball_mitt'], 'def': 'the handwear used by fielders in playing baseball', 'name': 'baseball_glove'}, {'frequency': 'f', 'id': 61, 'synset': 'basket.n.01', 'synonyms': ['basket', 'handbasket'], 'def': 'a container that is usually woven and has handles', 'name': 'basket'}, {'frequency': 'c', 'id': 62, 'synset': 'basket.n.03', 'synonyms': ['basketball_hoop'], 'def': 'metal hoop supporting a net through which players try to throw the basketball', 'name': 'basketball_hoop'}, {'frequency': 'c', 'id': 63, 'synset': 'basketball.n.02', 'synonyms': ['basketball'], 'def': 'an inflated ball used in playing basketball', 'name': 'basketball'}, {'frequency': 'r', 'id': 64, 'synset': 'bass_horn.n.01', 'synonyms': ['bass_horn', 'sousaphone', 'tuba'], 'def': 'the lowest brass wind instrument', 'name': 'bass_horn'}, {'frequency': 'r', 'id': 65, 'synset': 'bat.n.01', 'synonyms': ['bat_(animal)'], 'def': 'nocturnal mouselike mammal with forelimbs modified to form membranous wings', 'name': 'bat_(animal)'}, {'frequency': 'f', 'id': 66, 'synset': 'bath_mat.n.01', 'synonyms': ['bath_mat'], 'def': 'a heavy towel or mat to stand on while drying yourself after a bath', 'name': 'bath_mat'}, {'frequency': 'f', 'id': 67, 'synset': 'bath_towel.n.01', 'synonyms': ['bath_towel'], 'def': 'a large towel; to dry yourself after a bath', 'name': 'bath_towel'}, {'frequency': 'c', 'id': 68, 'synset': 'bathrobe.n.01', 'synonyms': ['bathrobe'], 'def': 'a loose-fitting robe of towelling; worn after a bath or swim', 'name': 'bathrobe'}, {'frequency': 'f', 'id': 69, 'synset': 'bathtub.n.01', 'synonyms': ['bathtub', 'bathing_tub'], 'def': 'a large open container that you fill with water and use to wash the body', 'name': 'bathtub'}, {'frequency': 'r', 'id': 70, 'synset': 'batter.n.02', 'synonyms': ['batter_(food)'], 'def': 'a liquid or semiliquid mixture, as of flour, eggs, and milk, used in cooking', 'name': 'batter_(food)'}, {'frequency': 'c', 'id': 71, 'synset': 'battery.n.02', 'synonyms': ['battery'], 'def': 'a portable device that produces electricity', 'name': 'battery'}, {'frequency': 'r', 'id': 72, 'synset': 'beach_ball.n.01', 'synonyms': ['beachball'], 'def': 'large and light ball; for play at the seaside', 'name': 'beachball'}, {'frequency': 'c', 'id': 73, 'synset': 'bead.n.01', 'synonyms': ['bead'], 'def': 'a small ball with a hole through the middle used for ornamentation, jewellery, etc.', 'name': 'bead'}, {'frequency': 'r', 'id': 74, 'synset': 'beaker.n.01', 'synonyms': ['beaker'], 'def': 'a flatbottomed jar made of glass or plastic; used for chemistry', 'name': 'beaker'}, {'frequency': 'c', 'id': 75, 'synset': 'bean_curd.n.01', 'synonyms': ['bean_curd', 'tofu'], 'def': 'cheeselike food made of curdled soybean milk', 'name': 'bean_curd'}, {'frequency': 'c', 'id': 76, 'synset': 'beanbag.n.01', 'synonyms': ['beanbag'], 'def': 'a bag filled with dried beans or similar items; used in games or to sit on', 'name': 'beanbag'}, {'frequency': 'f', 'id': 77, 'synset': 'beanie.n.01', 'synonyms': ['beanie', 'beany'], 'def': 'a small skullcap; formerly worn by schoolboys and college freshmen', 'name': 'beanie'}, {'frequency': 'f', 'id': 78, 'synset': 'bear.n.01', 'synonyms': ['bear'], 'def': 'large carnivorous or omnivorous mammals with shaggy coats and claws', 'name': 'bear'}, {'frequency': 'f', 'id': 79, 'synset': 'bed.n.01', 'synonyms': ['bed'], 'def': 'a piece of furniture that provides a place to sleep', 'name': 'bed'}, {'frequency': 'c', 'id': 80, 'synset': 'bedspread.n.01', 'synonyms': ['bedspread', 'bedcover', 'bed_covering', 'counterpane', 'spread'], 'def': 'decorative cover for a bed', 'name': 'bedspread'}, {'frequency': 'f', 'id': 81, 'synset': 'beef.n.01', 'synonyms': ['cow'], 'def': 'cattle that are reared for their meat', 'name': 'cow'}, {'frequency': 'c', 'id': 82, 'synset': 'beef.n.02', 'synonyms': ['beef_(food)', 'boeuf_(food)'], 'def': 'meat from an adult domestic bovine', 'name': 'beef_(food)'}, {'frequency': 'r', 'id': 83, 'synset': 'beeper.n.01', 'synonyms': ['beeper', 'pager'], 'def': 'an device that beeps when the person carrying it is being paged', 'name': 'beeper'}, {'frequency': 'f', 'id': 84, 'synset': 'beer_bottle.n.01', 'synonyms': ['beer_bottle'], 'def': 'a bottle that holds beer', 'name': 'beer_bottle'}, {'frequency': 'c', 'id': 85, 'synset': 'beer_can.n.01', 'synonyms': ['beer_can'], 'def': 'a can that holds beer', 'name': 'beer_can'}, {'frequency': 'r', 'id': 86, 'synset': 'beetle.n.01', 'synonyms': ['beetle'], 'def': 'insect with hard wing covers', 'name': 'beetle'}, {'frequency': 'f', 'id': 87, 'synset': 'bell.n.01', 'synonyms': ['bell'], 'def': 'a hollow device made of metal that makes a ringing sound when struck', 'name': 'bell'}, {'frequency': 'f', 'id': 88, 'synset': 'bell_pepper.n.02', 'synonyms': ['bell_pepper', 'capsicum'], 'def': 'large bell-shaped sweet pepper in green or red or yellow or orange or black varieties', 'name': 'bell_pepper'}, {'frequency': 'f', 'id': 89, 'synset': 'belt.n.02', 'synonyms': ['belt'], 'def': 'a band to tie or buckle around the body (usually at the waist)', 'name': 'belt'}, {'frequency': 'f', 'id': 90, 'synset': 'belt_buckle.n.01', 'synonyms': ['belt_buckle'], 'def': 'the buckle used to fasten a belt', 'name': 'belt_buckle'}, {'frequency': 'f', 'id': 91, 'synset': 'bench.n.01', 'synonyms': ['bench'], 'def': 'a long seat for more than one person', 'name': 'bench'}, {'frequency': 'c', 'id': 92, 'synset': 'beret.n.01', 'synonyms': ['beret'], 'def': 'a cap with no brim or bill; made of soft cloth', 'name': 'beret'}, {'frequency': 'c', 'id': 93, 'synset': 'bib.n.02', 'synonyms': ['bib'], 'def': 'a napkin tied under the chin of a child while eating', 'name': 'bib'}, {'frequency': 'r', 'id': 94, 'synset': 'bible.n.01', 'synonyms': ['Bible'], 'def': 'the sacred writings of the Christian religions', 'name': 'Bible'}, {'frequency': 'f', 'id': 95, 'synset': 'bicycle.n.01', 'synonyms': ['bicycle', 'bike_(bicycle)'], 'def': 'a wheeled vehicle that has two wheels and is moved by foot pedals', 'name': 'bicycle'}, {'frequency': 'f', 'id': 96, 'synset': 'bill.n.09', 'synonyms': ['visor', 'vizor'], 'def': 'a brim that projects to the front to shade the eyes', 'name': 'visor'}, {'frequency': 'c', 'id': 97, 'synset': 'binder.n.03', 'synonyms': ['binder', 'ring-binder'], 'def': 'holds loose papers or magazines', 'name': 'binder'}, {'frequency': 'c', 'id': 98, 'synset': 'binoculars.n.01', 'synonyms': ['binoculars', 'field_glasses', 'opera_glasses'], 'def': 'an optical instrument designed for simultaneous use by both eyes', 'name': 'binoculars'}, {'frequency': 'f', 'id': 99, 'synset': 'bird.n.01', 'synonyms': ['bird'], 'def': 'animal characterized by feathers and wings', 'name': 'bird'}, {'frequency': 'r', 'id': 100, 'synset': 'bird_feeder.n.01', 'synonyms': ['birdfeeder'], 'def': 'an outdoor device that supplies food for wild birds', 'name': 'birdfeeder'}, {'frequency': 'r', 'id': 101, 'synset': 'birdbath.n.01', 'synonyms': ['birdbath'], 'def': 'an ornamental basin (usually in a garden) for birds to bathe in', 'name': 'birdbath'}, {'frequency': 'c', 'id': 102, 'synset': 'birdcage.n.01', 'synonyms': ['birdcage'], 'def': 'a cage in which a bird can be kept', 'name': 'birdcage'}, {'frequency': 'c', 'id': 103, 'synset': 'birdhouse.n.01', 'synonyms': ['birdhouse'], 'def': 'a shelter for birds', 'name': 'birdhouse'}, {'frequency': 'f', 'id': 104, 'synset': 'birthday_cake.n.01', 'synonyms': ['birthday_cake'], 'def': 'decorated cake served at a birthday party', 'name': 'birthday_cake'}, {'frequency': 'r', 'id': 105, 'synset': 'birthday_card.n.01', 'synonyms': ['birthday_card'], 'def': 'a card expressing a birthday greeting', 'name': 'birthday_card'}, {'frequency': 'r', 'id': 106, 'synset': 'biscuit.n.01', 'synonyms': ['biscuit_(bread)'], 'def': 'small round bread leavened with baking-powder or soda', 'name': 'biscuit_(bread)'}, {'frequency': 'r', 'id': 107, 'synset': 'black_flag.n.01', 'synonyms': ['pirate_flag'], 'def': 'a flag usually bearing a white skull and crossbones on a black background', 'name': 'pirate_flag'}, {'frequency': 'c', 'id': 108, 'synset': 'black_sheep.n.02', 'synonyms': ['black_sheep'], 'def': 'sheep with a black coat', 'name': 'black_sheep'}, {'frequency': 'c', 'id': 109, 'synset': 'blackboard.n.01', 'synonyms': ['blackboard', 'chalkboard'], 'def': 'sheet of slate; for writing with chalk', 'name': 'blackboard'}, {'frequency': 'f', 'id': 110, 'synset': 'blanket.n.01', 'synonyms': ['blanket'], 'def': 'bedding that keeps a person warm in bed', 'name': 'blanket'}, {'frequency': 'c', 'id': 111, 'synset': 'blazer.n.01', 'synonyms': ['blazer', 'sport_jacket', 'sport_coat', 'sports_jacket', 'sports_coat'], 'def': 'lightweight jacket; often striped in the colors of a club or school', 'name': 'blazer'}, {'frequency': 'f', 'id': 112, 'synset': 'blender.n.01', 'synonyms': ['blender', 'liquidizer', 'liquidiser'], 'def': 'an electrically powered mixer that mix or chop or liquefy foods', 'name': 'blender'}, {'frequency': 'r', 'id': 113, 'synset': 'blimp.n.02', 'synonyms': ['blimp'], 'def': 'a small nonrigid airship used for observation or as a barrage balloon', 'name': 'blimp'}, {'frequency': 'c', 'id': 114, 'synset': 'blinker.n.01', 'synonyms': ['blinker', 'flasher'], 'def': 'a light that flashes on and off; used as a signal or to send messages', 'name': 'blinker'}, {'frequency': 'c', 'id': 115, 'synset': 'blueberry.n.02', 'synonyms': ['blueberry'], 'def': 'sweet edible dark-blue berries of blueberry plants', 'name': 'blueberry'}, {'frequency': 'r', 'id': 116, 'synset': 'boar.n.02', 'synonyms': ['boar'], 'def': 'an uncastrated male hog', 'name': 'boar'}, {'frequency': 'r', 'id': 117, 'synset': 'board.n.09', 'synonyms': ['gameboard'], 'def': 'a flat portable surface (usually rectangular) designed for board games', 'name': 'gameboard'}, {'frequency': 'f', 'id': 118, 'synset': 'boat.n.01', 'synonyms': ['boat', 'ship_(boat)'], 'def': 'a vessel for travel on water', 'name': 'boat'}, {'frequency': 'c', 'id': 119, 'synset': 'bobbin.n.01', 'synonyms': ['bobbin', 'spool', 'reel'], 'def': 'a thing around which thread/tape/film or other flexible materials can be wound', 'name': 'bobbin'}, {'frequency': 'r', 'id': 120, 'synset': 'bobby_pin.n.01', 'synonyms': ['bobby_pin', 'hairgrip'], 'def': 'a flat wire hairpin used to hold bobbed hair in place', 'name': 'bobby_pin'}, {'frequency': 'c', 'id': 121, 'synset': 'boiled_egg.n.01', 'synonyms': ['boiled_egg', 'coddled_egg'], 'def': 'egg cooked briefly in the shell in gently boiling water', 'name': 'boiled_egg'}, {'frequency': 'r', 'id': 122, 'synset': 'bolo_tie.n.01', 'synonyms': ['bolo_tie', 'bolo', 'bola_tie', 'bola'], 'def': 'a cord fastened around the neck with an ornamental clasp and worn as a necktie', 'name': 'bolo_tie'}, {'frequency': 'c', 'id': 123, 'synset': 'bolt.n.03', 'synonyms': ['deadbolt'], 'def': 'the part of a lock that is engaged or withdrawn with a key', 'name': 'deadbolt'}, {'frequency': 'f', 'id': 124, 'synset': 'bolt.n.06', 'synonyms': ['bolt'], 'def': 'a screw that screws into a nut to form a fastener', 'name': 'bolt'}, {'frequency': 'r', 'id': 125, 'synset': 'bonnet.n.01', 'synonyms': ['bonnet'], 'def': 'a hat tied under the chin', 'name': 'bonnet'}, {'frequency': 'f', 'id': 126, 'synset': 'book.n.01', 'synonyms': ['book'], 'def': 'a written work or composition that has been published', 'name': 'book'}, {'frequency': 'r', 'id': 127, 'synset': 'book_bag.n.01', 'synonyms': ['book_bag'], 'def': 'a bag in which students carry their books', 'name': 'book_bag'}, {'frequency': 'c', 'id': 128, 'synset': 'bookcase.n.01', 'synonyms': ['bookcase'], 'def': 'a piece of furniture with shelves for storing books', 'name': 'bookcase'}, {'frequency': 'c', 'id': 129, 'synset': 'booklet.n.01', 'synonyms': ['booklet', 'brochure', 'leaflet', 'pamphlet'], 'def': 'a small book usually having a paper cover', 'name': 'booklet'}, {'frequency': 'r', 'id': 130, 'synset': 'bookmark.n.01', 'synonyms': ['bookmark', 'bookmarker'], 'def': 'a marker (a piece of paper or ribbon) placed between the pages of a book', 'name': 'bookmark'}, {'frequency': 'r', 'id': 131, 'synset': 'boom.n.04', 'synonyms': ['boom_microphone', 'microphone_boom'], 'def': 'a pole carrying an overhead microphone projected over a film or tv set', 'name': 'boom_microphone'}, {'frequency': 'f', 'id': 132, 'synset': 'boot.n.01', 'synonyms': ['boot'], 'def': 'footwear that covers the whole foot and lower leg', 'name': 'boot'}, {'frequency': 'f', 'id': 133, 'synset': 'bottle.n.01', 'synonyms': ['bottle'], 'def': 'a glass or plastic vessel used for storing drinks or other liquids', 'name': 'bottle'}, {'frequency': 'c', 'id': 134, 'synset': 'bottle_opener.n.01', 'synonyms': ['bottle_opener'], 'def': 'an opener for removing caps or corks from bottles', 'name': 'bottle_opener'}, {'frequency': 'c', 'id': 135, 'synset': 'bouquet.n.01', 'synonyms': ['bouquet'], 'def': 'an arrangement of flowers that is usually given as a present', 'name': 'bouquet'}, {'frequency': 'r', 'id': 136, 'synset': 'bow.n.04', 'synonyms': ['bow_(weapon)'], 'def': 'a weapon for shooting arrows', 'name': 'bow_(weapon)'}, {'frequency': 'f', 'id': 137, 'synset': 'bow.n.08', 'synonyms': ['bow_(decorative_ribbons)'], 'def': 'a decorative interlacing of ribbons', 'name': 'bow_(decorative_ribbons)'}, {'frequency': 'f', 'id': 138, 'synset': 'bow_tie.n.01', 'synonyms': ['bow-tie', 'bowtie'], 'def': "a man's tie that ties in a bow", 'name': 'bow-tie'}, {'frequency': 'f', 'id': 139, 'synset': 'bowl.n.03', 'synonyms': ['bowl'], 'def': 'a dish that is round and open at the top for serving foods', 'name': 'bowl'}, {'frequency': 'r', 'id': 140, 'synset': 'bowl.n.08', 'synonyms': ['pipe_bowl'], 'def': 'a small round container that is open at the top for holding tobacco', 'name': 'pipe_bowl'}, {'frequency': 'c', 'id': 141, 'synset': 'bowler_hat.n.01', 'synonyms': ['bowler_hat', 'bowler', 'derby_hat', 'derby', 'plug_hat'], 'def': 'a felt hat that is round and hard with a narrow brim', 'name': 'bowler_hat'}, {'frequency': 'r', 'id': 142, 'synset': 'bowling_ball.n.01', 'synonyms': ['bowling_ball'], 'def': 'a large ball with finger holes used in the sport of bowling', 'name': 'bowling_ball'}, {'frequency': 'r', 'id': 143, 'synset': 'bowling_pin.n.01', 'synonyms': ['bowling_pin'], 'def': 'a club-shaped wooden object used in bowling', 'name': 'bowling_pin'}, {'frequency': 'r', 'id': 144, 'synset': 'boxing_glove.n.01', 'synonyms': ['boxing_glove'], 'def': 'large glove coverings the fists of a fighter worn for the sport of boxing', 'name': 'boxing_glove'}, {'frequency': 'c', 'id': 145, 'synset': 'brace.n.06', 'synonyms': ['suspenders'], 'def': 'elastic straps that hold trousers up (usually used in the plural)', 'name': 'suspenders'}, {'frequency': 'f', 'id': 146, 'synset': 'bracelet.n.02', 'synonyms': ['bracelet', 'bangle'], 'def': 'jewelry worn around the wrist for decoration', 'name': 'bracelet'}, {'frequency': 'r', 'id': 147, 'synset': 'brass.n.07', 'synonyms': ['brass_plaque'], 'def': 'a memorial made of brass', 'name': 'brass_plaque'}, {'frequency': 'c', 'id': 148, 'synset': 'brassiere.n.01', 'synonyms': ['brassiere', 'bra', 'bandeau'], 'def': 'an undergarment worn by women to support their breasts', 'name': 'brassiere'}, {'frequency': 'c', 'id': 149, 'synset': 'bread-bin.n.01', 'synonyms': ['bread-bin', 'breadbox'], 'def': 'a container used to keep bread or cake in', 'name': 'bread-bin'}, {'frequency': 'r', 'id': 150, 'synset': 'breechcloth.n.01', 'synonyms': ['breechcloth', 'breechclout', 'loincloth'], 'def': 'a garment that provides covering for the loins', 'name': 'breechcloth'}, {'frequency': 'c', 'id': 151, 'synset': 'bridal_gown.n.01', 'synonyms': ['bridal_gown', 'wedding_gown', 'wedding_dress'], 'def': 'a gown worn by the bride at a wedding', 'name': 'bridal_gown'}, {'frequency': 'c', 'id': 152, 'synset': 'briefcase.n.01', 'synonyms': ['briefcase'], 'def': 'a case with a handle; for carrying papers or files or books', 'name': 'briefcase'}, {'frequency': 'c', 'id': 153, 'synset': 'bristle_brush.n.01', 'synonyms': ['bristle_brush'], 'def': 'a brush that is made with the short stiff hairs of an animal or plant', 'name': 'bristle_brush'}, {'frequency': 'f', 'id': 154, 'synset': 'broccoli.n.01', 'synonyms': ['broccoli'], 'def': 'plant with dense clusters of tight green flower buds', 'name': 'broccoli'}, {'frequency': 'r', 'id': 155, 'synset': 'brooch.n.01', 'synonyms': ['broach'], 'def': 'a decorative pin worn by women', 'name': 'broach'}, {'frequency': 'c', 'id': 156, 'synset': 'broom.n.01', 'synonyms': ['broom'], 'def': 'bundle of straws or twigs attached to a long handle; used for cleaning', 'name': 'broom'}, {'frequency': 'c', 'id': 157, 'synset': 'brownie.n.03', 'synonyms': ['brownie'], 'def': 'square or bar of very rich chocolate cake usually with nuts', 'name': 'brownie'}, {'frequency': 'c', 'id': 158, 'synset': 'brussels_sprouts.n.01', 'synonyms': ['brussels_sprouts'], 'def': 'the small edible cabbage-like buds growing along a stalk', 'name': 'brussels_sprouts'}, {'frequency': 'r', 'id': 159, 'synset': 'bubble_gum.n.01', 'synonyms': ['bubble_gum'], 'def': 'a kind of chewing gum that can be blown into bubbles', 'name': 'bubble_gum'}, {'frequency': 'f', 'id': 160, 'synset': 'bucket.n.01', 'synonyms': ['bucket', 'pail'], 'def': 'a roughly cylindrical vessel that is open at the top', 'name': 'bucket'}, {'frequency': 'r', 'id': 161, 'synset': 'buggy.n.01', 'synonyms': ['horse_buggy'], 'def': 'a small lightweight carriage; drawn by a single horse', 'name': 'horse_buggy'}, {'frequency': 'c', 'id': 162, 'synset': 'bull.n.11', 'synonyms': ['bull'], 'def': 'mature male cow', 'name': 'bull'}, {'frequency': 'r', 'id': 163, 'synset': 'bulldog.n.01', 'synonyms': ['bulldog'], 'def': 'a thickset short-haired dog with a large head and strong undershot lower jaw', 'name': 'bulldog'}, {'frequency': 'r', 'id': 164, 'synset': 'bulldozer.n.01', 'synonyms': ['bulldozer', 'dozer'], 'def': 'large powerful tractor; a large blade in front flattens areas of ground', 'name': 'bulldozer'}, {'frequency': 'c', 'id': 165, 'synset': 'bullet_train.n.01', 'synonyms': ['bullet_train'], 'def': 'a high-speed passenger train', 'name': 'bullet_train'}, {'frequency': 'c', 'id': 166, 'synset': 'bulletin_board.n.02', 'synonyms': ['bulletin_board', 'notice_board'], 'def': 'a board that hangs on a wall; displays announcements', 'name': 'bulletin_board'}, {'frequency': 'r', 'id': 167, 'synset': 'bulletproof_vest.n.01', 'synonyms': ['bulletproof_vest'], 'def': 'a vest capable of resisting the impact of a bullet', 'name': 'bulletproof_vest'}, {'frequency': 'c', 'id': 168, 'synset': 'bullhorn.n.01', 'synonyms': ['bullhorn', 'megaphone'], 'def': 'a portable loudspeaker with built-in microphone and amplifier', 'name': 'bullhorn'}, {'frequency': 'r', 'id': 169, 'synset': 'bully_beef.n.01', 'synonyms': ['corned_beef', 'corn_beef'], 'def': 'beef cured or pickled in brine', 'name': 'corned_beef'}, {'frequency': 'f', 'id': 170, 'synset': 'bun.n.01', 'synonyms': ['bun', 'roll'], 'def': 'small rounded bread either plain or sweet', 'name': 'bun'}, {'frequency': 'c', 'id': 171, 'synset': 'bunk_bed.n.01', 'synonyms': ['bunk_bed'], 'def': 'beds built one above the other', 'name': 'bunk_bed'}, {'frequency': 'f', 'id': 172, 'synset': 'buoy.n.01', 'synonyms': ['buoy'], 'def': 'a float attached by rope to the seabed to mark channels in a harbor or underwater hazards', 'name': 'buoy'}, {'frequency': 'r', 'id': 173, 'synset': 'burrito.n.01', 'synonyms': ['burrito'], 'def': 'a flour tortilla folded around a filling', 'name': 'burrito'}, {'frequency': 'f', 'id': 174, 'synset': 'bus.n.01', 'synonyms': ['bus_(vehicle)', 'autobus', 'charabanc', 'double-decker', 'motorbus', 'motorcoach'], 'def': 'a vehicle carrying many passengers; used for public transport', 'name': 'bus_(vehicle)'}, {'frequency': 'c', 'id': 175, 'synset': 'business_card.n.01', 'synonyms': ['business_card'], 'def': "a card on which are printed the person's name and business affiliation", 'name': 'business_card'}, {'frequency': 'c', 'id': 176, 'synset': 'butcher_knife.n.01', 'synonyms': ['butcher_knife'], 'def': 'a large sharp knife for cutting or trimming meat', 'name': 'butcher_knife'}, {'frequency': 'c', 'id': 177, 'synset': 'butter.n.01', 'synonyms': ['butter'], 'def': 'an edible emulsion of fat globules made by churning milk or cream; for cooking and table use', 'name': 'butter'}, {'frequency': 'c', 'id': 178, 'synset': 'butterfly.n.01', 'synonyms': ['butterfly'], 'def': 'insect typically having a slender body with knobbed antennae and broad colorful wings', 'name': 'butterfly'}, {'frequency': 'f', 'id': 179, 'synset': 'button.n.01', 'synonyms': ['button'], 'def': 'a round fastener sewn to shirts and coats etc to fit through buttonholes', 'name': 'button'}, {'frequency': 'f', 'id': 180, 'synset': 'cab.n.03', 'synonyms': ['cab_(taxi)', 'taxi', 'taxicab'], 'def': 'a car that takes passengers where they want to go in exchange for money', 'name': 'cab_(taxi)'}, {'frequency': 'r', 'id': 181, 'synset': 'cabana.n.01', 'synonyms': ['cabana'], 'def': 'a small tent used as a dressing room beside the sea or a swimming pool', 'name': 'cabana'}, {'frequency': 'r', 'id': 182, 'synset': 'cabin_car.n.01', 'synonyms': ['cabin_car', 'caboose'], 'def': 'a car on a freight train for use of the train crew; usually the last car on the train', 'name': 'cabin_car'}, {'frequency': 'f', 'id': 183, 'synset': 'cabinet.n.01', 'synonyms': ['cabinet'], 'def': 'a piece of furniture resembling a cupboard with doors and shelves and drawers', 'name': 'cabinet'}, {'frequency': 'r', 'id': 184, 'synset': 'cabinet.n.03', 'synonyms': ['locker', 'storage_locker'], 'def': 'a storage compartment for clothes and valuables; usually it has a lock', 'name': 'locker'}, {'frequency': 'f', 'id': 185, 'synset': 'cake.n.03', 'synonyms': ['cake'], 'def': 'baked goods made from or based on a mixture of flour, sugar, eggs, and fat', 'name': 'cake'}, {'frequency': 'c', 'id': 186, 'synset': 'calculator.n.02', 'synonyms': ['calculator'], 'def': 'a small machine that is used for mathematical calculations', 'name': 'calculator'}, {'frequency': 'f', 'id': 187, 'synset': 'calendar.n.02', 'synonyms': ['calendar'], 'def': 'a list or register of events (appointments/social events/court cases, etc)', 'name': 'calendar'}, {'frequency': 'c', 'id': 188, 'synset': 'calf.n.01', 'synonyms': ['calf'], 'def': 'young of domestic cattle', 'name': 'calf'}, {'frequency': 'c', 'id': 189, 'synset': 'camcorder.n.01', 'synonyms': ['camcorder'], 'def': 'a portable television camera and videocassette recorder', 'name': 'camcorder'}, {'frequency': 'c', 'id': 190, 'synset': 'camel.n.01', 'synonyms': ['camel'], 'def': 'cud-chewing mammal used as a draft or saddle animal in desert regions', 'name': 'camel'}, {'frequency': 'f', 'id': 191, 'synset': 'camera.n.01', 'synonyms': ['camera'], 'def': 'equipment for taking photographs', 'name': 'camera'}, {'frequency': 'c', 'id': 192, 'synset': 'camera_lens.n.01', 'synonyms': ['camera_lens'], 'def': 'a lens that focuses the image in a camera', 'name': 'camera_lens'}, {'frequency': 'c', 'id': 193, 'synset': 'camper.n.02', 'synonyms': ['camper_(vehicle)', 'camping_bus', 'motor_home'], 'def': 'a recreational vehicle equipped for camping out while traveling', 'name': 'camper_(vehicle)'}, {'frequency': 'f', 'id': 194, 'synset': 'can.n.01', 'synonyms': ['can', 'tin_can'], 'def': 'airtight sealed metal container for food or drink or paint etc.', 'name': 'can'}, {'frequency': 'c', 'id': 195, 'synset': 'can_opener.n.01', 'synonyms': ['can_opener', 'tin_opener'], 'def': 'a device for cutting cans open', 'name': 'can_opener'}, {'frequency': 'r', 'id': 196, 'synset': 'candelabrum.n.01', 'synonyms': ['candelabrum', 'candelabra'], 'def': 'branched candlestick; ornamental; has several lights', 'name': 'candelabrum'}, {'frequency': 'f', 'id': 197, 'synset': 'candle.n.01', 'synonyms': ['candle', 'candlestick'], 'def': 'stick of wax with a wick in the middle', 'name': 'candle'}, {'frequency': 'f', 'id': 198, 'synset': 'candlestick.n.01', 'synonyms': ['candle_holder'], 'def': 'a holder with sockets for candles', 'name': 'candle_holder'}, {'frequency': 'r', 'id': 199, 'synset': 'candy_bar.n.01', 'synonyms': ['candy_bar'], 'def': 'a candy shaped as a bar', 'name': 'candy_bar'}, {'frequency': 'c', 'id': 200, 'synset': 'candy_cane.n.01', 'synonyms': ['candy_cane'], 'def': 'a hard candy in the shape of a rod (usually with stripes)', 'name': 'candy_cane'}, {'frequency': 'c', 'id': 201, 'synset': 'cane.n.01', 'synonyms': ['walking_cane'], 'def': 'a stick that people can lean on to help them walk', 'name': 'walking_cane'}, {'frequency': 'c', 'id': 202, 'synset': 'canister.n.02', 'synonyms': ['canister', 'cannister'], 'def': 'metal container for storing dry foods such as tea or flour', 'name': 'canister'}, {'frequency': 'r', 'id': 203, 'synset': 'cannon.n.02', 'synonyms': ['cannon'], 'def': 'heavy gun fired from a tank', 'name': 'cannon'}, {'frequency': 'c', 'id': 204, 'synset': 'canoe.n.01', 'synonyms': ['canoe'], 'def': 'small and light boat; pointed at both ends; propelled with a paddle', 'name': 'canoe'}, {'frequency': 'r', 'id': 205, 'synset': 'cantaloup.n.02', 'synonyms': ['cantaloup', 'cantaloupe'], 'def': 'the fruit of a cantaloup vine; small to medium-sized melon with yellowish flesh', 'name': 'cantaloup'}, {'frequency': 'r', 'id': 206, 'synset': 'canteen.n.01', 'synonyms': ['canteen'], 'def': 'a flask for carrying water; used by soldiers or travelers', 'name': 'canteen'}, {'frequency': 'c', 'id': 207, 'synset': 'cap.n.01', 'synonyms': ['cap_(headwear)'], 'def': 'a tight-fitting headwear', 'name': 'cap_(headwear)'}, {'frequency': 'f', 'id': 208, 'synset': 'cap.n.02', 'synonyms': ['bottle_cap', 'cap_(container_lid)'], 'def': 'a top (as for a bottle)', 'name': 'bottle_cap'}, {'frequency': 'r', 'id': 209, 'synset': 'cape.n.02', 'synonyms': ['cape'], 'def': 'a sleeveless garment like a cloak but shorter', 'name': 'cape'}, {'frequency': 'c', 'id': 210, 'synset': 'cappuccino.n.01', 'synonyms': ['cappuccino', 'coffee_cappuccino'], 'def': 'equal parts of espresso and steamed milk', 'name': 'cappuccino'}, {'frequency': 'f', 'id': 211, 'synset': 'car.n.01', 'synonyms': ['car_(automobile)', 'auto_(automobile)', 'automobile'], 'def': 'a motor vehicle with four wheels', 'name': 'car_(automobile)'}, {'frequency': 'f', 'id': 212, 'synset': 'car.n.02', 'synonyms': ['railcar_(part_of_a_train)', 'railway_car_(part_of_a_train)', 'railroad_car_(part_of_a_train)'], 'def': 'a wheeled vehicle adapted to the rails of railroad', 'name': 'railcar_(part_of_a_train)'}, {'frequency': 'r', 'id': 213, 'synset': 'car.n.04', 'synonyms': ['elevator_car'], 'def': 'where passengers ride up and down', 'name': 'elevator_car'}, {'frequency': 'r', 'id': 214, 'synset': 'car_battery.n.01', 'synonyms': ['car_battery', 'automobile_battery'], 'def': 'a battery in a motor vehicle', 'name': 'car_battery'}, {'frequency': 'c', 'id': 215, 'synset': 'card.n.02', 'synonyms': ['identity_card'], 'def': 'a card certifying the identity of the bearer', 'name': 'identity_card'}, {'frequency': 'c', 'id': 216, 'synset': 'card.n.03', 'synonyms': ['card'], 'def': 'a rectangular piece of paper used to send messages (e.g. greetings or pictures)', 'name': 'card'}, {'frequency': 'r', 'id': 217, 'synset': 'cardigan.n.01', 'synonyms': ['cardigan'], 'def': 'knitted jacket that is fastened up the front with buttons or a zipper', 'name': 'cardigan'}, {'frequency': 'r', 'id': 218, 'synset': 'cargo_ship.n.01', 'synonyms': ['cargo_ship', 'cargo_vessel'], 'def': 'a ship designed to carry cargo', 'name': 'cargo_ship'}, {'frequency': 'r', 'id': 219, 'synset': 'carnation.n.01', 'synonyms': ['carnation'], 'def': 'plant with pink to purple-red spice-scented usually double flowers', 'name': 'carnation'}, {'frequency': 'c', 'id': 220, 'synset': 'carriage.n.02', 'synonyms': ['horse_carriage'], 'def': 'a vehicle with wheels drawn by one or more horses', 'name': 'horse_carriage'}, {'frequency': 'f', 'id': 221, 'synset': 'carrot.n.01', 'synonyms': ['carrot'], 'def': 'deep orange edible root of the cultivated carrot plant', 'name': 'carrot'}, {'frequency': 'c', 'id': 222, 'synset': 'carryall.n.01', 'synonyms': ['tote_bag'], 'def': 'a capacious bag or basket', 'name': 'tote_bag'}, {'frequency': 'c', 'id': 223, 'synset': 'cart.n.01', 'synonyms': ['cart'], 'def': 'a heavy open wagon usually having two wheels and drawn by an animal', 'name': 'cart'}, {'frequency': 'c', 'id': 224, 'synset': 'carton.n.02', 'synonyms': ['carton'], 'def': 'a box made of cardboard; opens by flaps on top', 'name': 'carton'}, {'frequency': 'c', 'id': 225, 'synset': 'cash_register.n.01', 'synonyms': ['cash_register', 'register_(for_cash_transactions)'], 'def': 'a cashbox with an adding machine to register transactions', 'name': 'cash_register'}, {'frequency': 'r', 'id': 226, 'synset': 'casserole.n.01', 'synonyms': ['casserole'], 'def': 'food cooked and served in a casserole', 'name': 'casserole'}, {'frequency': 'r', 'id': 227, 'synset': 'cassette.n.01', 'synonyms': ['cassette'], 'def': 'a container that holds a magnetic tape used for recording or playing sound or video', 'name': 'cassette'}, {'frequency': 'c', 'id': 228, 'synset': 'cast.n.05', 'synonyms': ['cast', 'plaster_cast', 'plaster_bandage'], 'def': 'bandage consisting of a firm covering that immobilizes broken bones while they heal', 'name': 'cast'}, {'frequency': 'f', 'id': 229, 'synset': 'cat.n.01', 'synonyms': ['cat'], 'def': 'a domestic house cat', 'name': 'cat'}, {'frequency': 'c', 'id': 230, 'synset': 'cauliflower.n.02', 'synonyms': ['cauliflower'], 'def': 'edible compact head of white undeveloped flowers', 'name': 'cauliflower'}, {'frequency': 'r', 'id': 231, 'synset': 'caviar.n.01', 'synonyms': ['caviar', 'caviare'], 'def': "salted roe of sturgeon or other large fish; usually served as an hors d'oeuvre", 'name': 'caviar'}, {'frequency': 'c', 'id': 232, 'synset': 'cayenne.n.02', 'synonyms': ['cayenne_(spice)', 'cayenne_pepper_(spice)', 'red_pepper_(spice)'], 'def': 'ground pods and seeds of pungent red peppers of the genus Capsicum', 'name': 'cayenne_(spice)'}, {'frequency': 'c', 'id': 233, 'synset': 'cd_player.n.01', 'synonyms': ['CD_player'], 'def': 'electronic equipment for playing compact discs (CDs)', 'name': 'CD_player'}, {'frequency': 'c', 'id': 234, 'synset': 'celery.n.01', 'synonyms': ['celery'], 'def': 'widely cultivated herb with aromatic leaf stalks that are eaten raw or cooked', 'name': 'celery'}, {'frequency': 'f', 'id': 235, 'synset': 'cellular_telephone.n.01', 'synonyms': ['cellular_telephone', 'cellular_phone', 'cellphone', 'mobile_phone', 'smart_phone'], 'def': 'a hand-held mobile telephone', 'name': 'cellular_telephone'}, {'frequency': 'r', 'id': 236, 'synset': 'chain_mail.n.01', 'synonyms': ['chain_mail', 'ring_mail', 'chain_armor', 'chain_armour', 'ring_armor', 'ring_armour'], 'def': '(Middle Ages) flexible armor made of interlinked metal rings', 'name': 'chain_mail'}, {'frequency': 'f', 'id': 237, 'synset': 'chair.n.01', 'synonyms': ['chair'], 'def': 'a seat for one person, with a support for the back', 'name': 'chair'}, {'frequency': 'r', 'id': 238, 'synset': 'chaise_longue.n.01', 'synonyms': ['chaise_longue', 'chaise', 'daybed'], 'def': 'a long chair; for reclining', 'name': 'chaise_longue'}, {'frequency': 'r', 'id': 239, 'synset': 'champagne.n.01', 'synonyms': ['champagne'], 'def': 'a white sparkling wine produced in Champagne or resembling that produced there', 'name': 'champagne'}, {'frequency': 'f', 'id': 240, 'synset': 'chandelier.n.01', 'synonyms': ['chandelier'], 'def': 'branched lighting fixture; often ornate; hangs from the ceiling', 'name': 'chandelier'}, {'frequency': 'r', 'id': 241, 'synset': 'chap.n.04', 'synonyms': ['chap'], 'def': 'leather leggings without a seat; worn over trousers by cowboys to protect their legs', 'name': 'chap'}, {'frequency': 'r', 'id': 242, 'synset': 'checkbook.n.01', 'synonyms': ['checkbook', 'chequebook'], 'def': 'a book issued to holders of checking accounts', 'name': 'checkbook'}, {'frequency': 'r', 'id': 243, 'synset': 'checkerboard.n.01', 'synonyms': ['checkerboard'], 'def': 'a board having 64 squares of two alternating colors', 'name': 'checkerboard'}, {'frequency': 'c', 'id': 244, 'synset': 'cherry.n.03', 'synonyms': ['cherry'], 'def': 'a red fruit with a single hard stone', 'name': 'cherry'}, {'frequency': 'r', 'id': 245, 'synset': 'chessboard.n.01', 'synonyms': ['chessboard'], 'def': 'a checkerboard used to play chess', 'name': 'chessboard'}, {'frequency': 'r', 'id': 246, 'synset': 'chest_of_drawers.n.01', 'synonyms': ['chest_of_drawers_(furniture)', 'bureau_(furniture)', 'chest_(furniture)'], 'def': 'furniture with drawers for keeping clothes', 'name': 'chest_of_drawers_(furniture)'}, {'frequency': 'c', 'id': 247, 'synset': 'chicken.n.02', 'synonyms': ['chicken_(animal)'], 'def': 'a domestic fowl bred for flesh or eggs', 'name': 'chicken_(animal)'}, {'frequency': 'c', 'id': 248, 'synset': 'chicken_wire.n.01', 'synonyms': ['chicken_wire'], 'def': 'a galvanized wire network with a hexagonal mesh; used to build fences', 'name': 'chicken_wire'}, {'frequency': 'r', 'id': 249, 'synset': 'chickpea.n.01', 'synonyms': ['chickpea', 'garbanzo'], 'def': 'the seed of the chickpea plant; usually dried', 'name': 'chickpea'}, {'frequency': 'r', 'id': 250, 'synset': 'chihuahua.n.03', 'synonyms': ['Chihuahua'], 'def': 'an old breed of tiny short-haired dog with protruding eyes from Mexico', 'name': 'Chihuahua'}, {'frequency': 'r', 'id': 251, 'synset': 'chili.n.02', 'synonyms': ['chili_(vegetable)', 'chili_pepper_(vegetable)', 'chilli_(vegetable)', 'chilly_(vegetable)', 'chile_(vegetable)'], 'def': 'very hot and finely tapering pepper of special pungency', 'name': 'chili_(vegetable)'}, {'frequency': 'r', 'id': 252, 'synset': 'chime.n.01', 'synonyms': ['chime', 'gong'], 'def': 'an instrument consisting of a set of bells that are struck with a hammer', 'name': 'chime'}, {'frequency': 'r', 'id': 253, 'synset': 'chinaware.n.01', 'synonyms': ['chinaware'], 'def': 'dishware made of high quality porcelain', 'name': 'chinaware'}, {'frequency': 'c', 'id': 254, 'synset': 'chip.n.04', 'synonyms': ['crisp_(potato_chip)', 'potato_chip'], 'def': 'a thin crisp slice of potato fried in deep fat', 'name': 'crisp_(potato_chip)'}, {'frequency': 'r', 'id': 255, 'synset': 'chip.n.06', 'synonyms': ['poker_chip'], 'def': 'a small disk-shaped counter used to represent money when gambling', 'name': 'poker_chip'}, {'frequency': 'c', 'id': 256, 'synset': 'chocolate_bar.n.01', 'synonyms': ['chocolate_bar'], 'def': 'a bar of chocolate candy', 'name': 'chocolate_bar'}, {'frequency': 'c', 'id': 257, 'synset': 'chocolate_cake.n.01', 'synonyms': ['chocolate_cake'], 'def': 'cake containing chocolate', 'name': 'chocolate_cake'}, {'frequency': 'r', 'id': 258, 'synset': 'chocolate_milk.n.01', 'synonyms': ['chocolate_milk'], 'def': 'milk flavored with chocolate syrup', 'name': 'chocolate_milk'}, {'frequency': 'r', 'id': 259, 'synset': 'chocolate_mousse.n.01', 'synonyms': ['chocolate_mousse'], 'def': 'dessert mousse made with chocolate', 'name': 'chocolate_mousse'}, {'frequency': 'f', 'id': 260, 'synset': 'choker.n.03', 'synonyms': ['choker', 'collar', 'neckband'], 'def': 'necklace that fits tightly around the neck', 'name': 'choker'}, {'frequency': 'f', 'id': 261, 'synset': 'chopping_board.n.01', 'synonyms': ['chopping_board', 'cutting_board', 'chopping_block'], 'def': 'a wooden board where meats or vegetables can be cut', 'name': 'chopping_board'}, {'frequency': 'c', 'id': 262, 'synset': 'chopstick.n.01', 'synonyms': ['chopstick'], 'def': 'one of a pair of slender sticks used as oriental tableware to eat food with', 'name': 'chopstick'}, {'frequency': 'f', 'id': 263, 'synset': 'christmas_tree.n.05', 'synonyms': ['Christmas_tree'], 'def': 'an ornamented evergreen used as a Christmas decoration', 'name': 'Christmas_tree'}, {'frequency': 'c', 'id': 264, 'synset': 'chute.n.02', 'synonyms': ['slide'], 'def': 'sloping channel through which things can descend', 'name': 'slide'}, {'frequency': 'r', 'id': 265, 'synset': 'cider.n.01', 'synonyms': ['cider', 'cyder'], 'def': 'a beverage made from juice pressed from apples', 'name': 'cider'}, {'frequency': 'r', 'id': 266, 'synset': 'cigar_box.n.01', 'synonyms': ['cigar_box'], 'def': 'a box for holding cigars', 'name': 'cigar_box'}, {'frequency': 'c', 'id': 267, 'synset': 'cigarette.n.01', 'synonyms': ['cigarette'], 'def': 'finely ground tobacco wrapped in paper; for smoking', 'name': 'cigarette'}, {'frequency': 'c', 'id': 268, 'synset': 'cigarette_case.n.01', 'synonyms': ['cigarette_case', 'cigarette_pack'], 'def': 'a small flat case for holding cigarettes', 'name': 'cigarette_case'}, {'frequency': 'f', 'id': 269, 'synset': 'cistern.n.02', 'synonyms': ['cistern', 'water_tank'], 'def': 'a tank that holds the water used to flush a toilet', 'name': 'cistern'}, {'frequency': 'r', 'id': 270, 'synset': 'clarinet.n.01', 'synonyms': ['clarinet'], 'def': 'a single-reed instrument with a straight tube', 'name': 'clarinet'}, {'frequency': 'r', 'id': 271, 'synset': 'clasp.n.01', 'synonyms': ['clasp'], 'def': 'a fastener (as a buckle or hook) that is used to hold two things together', 'name': 'clasp'}, {'frequency': 'c', 'id': 272, 'synset': 'cleansing_agent.n.01', 'synonyms': ['cleansing_agent', 'cleanser', 'cleaner'], 'def': 'a preparation used in cleaning something', 'name': 'cleansing_agent'}, {'frequency': 'r', 'id': 273, 'synset': 'clementine.n.01', 'synonyms': ['clementine'], 'def': 'a variety of mandarin orange', 'name': 'clementine'}, {'frequency': 'c', 'id': 274, 'synset': 'clip.n.03', 'synonyms': ['clip'], 'def': 'any of various small fasteners used to hold loose articles together', 'name': 'clip'}, {'frequency': 'c', 'id': 275, 'synset': 'clipboard.n.01', 'synonyms': ['clipboard'], 'def': 'a small writing board with a clip at the top for holding papers', 'name': 'clipboard'}, {'frequency': 'f', 'id': 276, 'synset': 'clock.n.01', 'synonyms': ['clock', 'timepiece', 'timekeeper'], 'def': 'a timepiece that shows the time of day', 'name': 'clock'}, {'frequency': 'f', 'id': 277, 'synset': 'clock_tower.n.01', 'synonyms': ['clock_tower'], 'def': 'a tower with a large clock visible high up on an outside face', 'name': 'clock_tower'}, {'frequency': 'c', 'id': 278, 'synset': 'clothes_hamper.n.01', 'synonyms': ['clothes_hamper', 'laundry_basket', 'clothes_basket'], 'def': 'a hamper that holds dirty clothes to be washed or wet clothes to be dried', 'name': 'clothes_hamper'}, {'frequency': 'c', 'id': 279, 'synset': 'clothespin.n.01', 'synonyms': ['clothespin', 'clothes_peg'], 'def': 'wood or plastic fastener; for holding clothes on a clothesline', 'name': 'clothespin'}, {'frequency': 'r', 'id': 280, 'synset': 'clutch_bag.n.01', 'synonyms': ['clutch_bag'], 'def': "a woman's strapless purse that is carried in the hand", 'name': 'clutch_bag'}, {'frequency': 'f', 'id': 281, 'synset': 'coaster.n.03', 'synonyms': ['coaster'], 'def': 'a covering (plate or mat) that protects the surface of a table', 'name': 'coaster'}, {'frequency': 'f', 'id': 282, 'synset': 'coat.n.01', 'synonyms': ['coat'], 'def': 'an outer garment that has sleeves and covers the body from shoulder down', 'name': 'coat'}, {'frequency': 'c', 'id': 283, 'synset': 'coat_hanger.n.01', 'synonyms': ['coat_hanger', 'clothes_hanger', 'dress_hanger'], 'def': "a hanger that is shaped like a person's shoulders", 'name': 'coat_hanger'}, {'frequency': 'r', 'id': 284, 'synset': 'coatrack.n.01', 'synonyms': ['coatrack', 'hatrack'], 'def': 'a rack with hooks for temporarily holding coats and hats', 'name': 'coatrack'}, {'frequency': 'c', 'id': 285, 'synset': 'cock.n.04', 'synonyms': ['cock', 'rooster'], 'def': 'adult male chicken', 'name': 'cock'}, {'frequency': 'c', 'id': 286, 'synset': 'coconut.n.02', 'synonyms': ['coconut', 'cocoanut'], 'def': 'large hard-shelled brown oval nut with a fibrous husk', 'name': 'coconut'}, {'frequency': 'r', 'id': 287, 'synset': 'coffee_filter.n.01', 'synonyms': ['coffee_filter'], 'def': 'filter (usually of paper) that passes the coffee and retains the coffee grounds', 'name': 'coffee_filter'}, {'frequency': 'f', 'id': 288, 'synset': 'coffee_maker.n.01', 'synonyms': ['coffee_maker', 'coffee_machine'], 'def': 'a kitchen appliance for brewing coffee automatically', 'name': 'coffee_maker'}, {'frequency': 'f', 'id': 289, 'synset': 'coffee_table.n.01', 'synonyms': ['coffee_table', 'cocktail_table'], 'def': 'low table where magazines can be placed and coffee or cocktails are served', 'name': 'coffee_table'}, {'frequency': 'c', 'id': 290, 'synset': 'coffeepot.n.01', 'synonyms': ['coffeepot'], 'def': 'tall pot in which coffee is brewed', 'name': 'coffeepot'}, {'frequency': 'r', 'id': 291, 'synset': 'coil.n.05', 'synonyms': ['coil'], 'def': 'tubing that is wound in a spiral', 'name': 'coil'}, {'frequency': 'c', 'id': 292, 'synset': 'coin.n.01', 'synonyms': ['coin'], 'def': 'a flat metal piece (usually a disc) used as money', 'name': 'coin'}, {'frequency': 'r', 'id': 293, 'synset': 'colander.n.01', 'synonyms': ['colander', 'cullender'], 'def': 'bowl-shaped strainer; used to wash or drain foods', 'name': 'colander'}, {'frequency': 'c', 'id': 294, 'synset': 'coleslaw.n.01', 'synonyms': ['coleslaw', 'slaw'], 'def': 'basically shredded cabbage', 'name': 'coleslaw'}, {'frequency': 'r', 'id': 295, 'synset': 'coloring_material.n.01', 'synonyms': ['coloring_material', 'colouring_material'], 'def': 'any material used for its color', 'name': 'coloring_material'}, {'frequency': 'r', 'id': 296, 'synset': 'combination_lock.n.01', 'synonyms': ['combination_lock'], 'def': 'lock that can be opened only by turning dials in a special sequence', 'name': 'combination_lock'}, {'frequency': 'c', 'id': 297, 'synset': 'comforter.n.04', 'synonyms': ['pacifier', 'teething_ring'], 'def': 'device used for an infant to suck or bite on', 'name': 'pacifier'}, {'frequency': 'r', 'id': 298, 'synset': 'comic_book.n.01', 'synonyms': ['comic_book'], 'def': 'a magazine devoted to comic strips', 'name': 'comic_book'}, {'frequency': 'f', 'id': 299, 'synset': 'computer_keyboard.n.01', 'synonyms': ['computer_keyboard', 'keyboard_(computer)'], 'def': 'a keyboard that is a data input device for computers', 'name': 'computer_keyboard'}, {'frequency': 'r', 'id': 300, 'synset': 'concrete_mixer.n.01', 'synonyms': ['concrete_mixer', 'cement_mixer'], 'def': 'a machine with a large revolving drum in which cement/concrete is mixed', 'name': 'concrete_mixer'}, {'frequency': 'f', 'id': 301, 'synset': 'cone.n.01', 'synonyms': ['cone', 'traffic_cone'], 'def': 'a cone-shaped object used to direct traffic', 'name': 'cone'}, {'frequency': 'f', 'id': 302, 'synset': 'control.n.09', 'synonyms': ['control', 'controller'], 'def': 'a mechanism that controls the operation of a machine', 'name': 'control'}, {'frequency': 'r', 'id': 303, 'synset': 'convertible.n.01', 'synonyms': ['convertible_(automobile)'], 'def': 'a car that has top that can be folded or removed', 'name': 'convertible_(automobile)'}, {'frequency': 'r', 'id': 304, 'synset': 'convertible.n.03', 'synonyms': ['sofa_bed'], 'def': 'a sofa that can be converted into a bed', 'name': 'sofa_bed'}, {'frequency': 'c', 'id': 305, 'synset': 'cookie.n.01', 'synonyms': ['cookie', 'cooky', 'biscuit_(cookie)'], 'def': "any of various small flat sweet cakes (`biscuit' is the British term)", 'name': 'cookie'}, {'frequency': 'r', 'id': 306, 'synset': 'cookie_jar.n.01', 'synonyms': ['cookie_jar', 'cooky_jar'], 'def': 'a jar in which cookies are kept (and sometimes money is hidden)', 'name': 'cookie_jar'}, {'frequency': 'r', 'id': 307, 'synset': 'cooking_utensil.n.01', 'synonyms': ['cooking_utensil'], 'def': 'a kitchen utensil made of material that does not melt easily; used for cooking', 'name': 'cooking_utensil'}, {'frequency': 'f', 'id': 308, 'synset': 'cooler.n.01', 'synonyms': ['cooler_(for_food)', 'ice_chest'], 'def': 'an insulated box for storing food often with ice', 'name': 'cooler_(for_food)'}, {'frequency': 'c', 'id': 309, 'synset': 'cork.n.04', 'synonyms': ['cork_(bottle_plug)', 'bottle_cork'], 'def': 'the plug in the mouth of a bottle (especially a wine bottle)', 'name': 'cork_(bottle_plug)'}, {'frequency': 'r', 'id': 310, 'synset': 'corkboard.n.01', 'synonyms': ['corkboard'], 'def': 'a sheet consisting of cork granules', 'name': 'corkboard'}, {'frequency': 'r', 'id': 311, 'synset': 'corkscrew.n.01', 'synonyms': ['corkscrew', 'bottle_screw'], 'def': 'a bottle opener that pulls corks', 'name': 'corkscrew'}, {'frequency': 'c', 'id': 312, 'synset': 'corn.n.03', 'synonyms': ['edible_corn', 'corn', 'maize'], 'def': 'ears of corn that can be prepared and served for human food', 'name': 'edible_corn'}, {'frequency': 'r', 'id': 313, 'synset': 'cornbread.n.01', 'synonyms': ['cornbread'], 'def': 'bread made primarily of cornmeal', 'name': 'cornbread'}, {'frequency': 'c', 'id': 314, 'synset': 'cornet.n.01', 'synonyms': ['cornet', 'horn', 'trumpet'], 'def': 'a brass musical instrument with a narrow tube and a flared bell and many valves', 'name': 'cornet'}, {'frequency': 'c', 'id': 315, 'synset': 'cornice.n.01', 'synonyms': ['cornice', 'valance', 'valance_board', 'pelmet'], 'def': 'a decorative framework to conceal curtain fixtures at the top of a window casing', 'name': 'cornice'}, {'frequency': 'r', 'id': 316, 'synset': 'cornmeal.n.01', 'synonyms': ['cornmeal'], 'def': 'coarsely ground corn', 'name': 'cornmeal'}, {'frequency': 'r', 'id': 317, 'synset': 'corset.n.01', 'synonyms': ['corset', 'girdle'], 'def': "a woman's close-fitting foundation garment", 'name': 'corset'}, {'frequency': 'r', 'id': 318, 'synset': 'cos.n.02', 'synonyms': ['romaine_lettuce'], 'def': 'lettuce with long dark-green leaves in a loosely packed elongated head', 'name': 'romaine_lettuce'}, {'frequency': 'c', 'id': 319, 'synset': 'costume.n.04', 'synonyms': ['costume'], 'def': 'the attire characteristic of a country or a time or a social class', 'name': 'costume'}, {'frequency': 'r', 'id': 320, 'synset': 'cougar.n.01', 'synonyms': ['cougar', 'puma', 'catamount', 'mountain_lion', 'panther'], 'def': 'large American feline resembling a lion', 'name': 'cougar'}, {'frequency': 'r', 'id': 321, 'synset': 'coverall.n.01', 'synonyms': ['coverall'], 'def': 'a loose-fitting protective garment that is worn over other clothing', 'name': 'coverall'}, {'frequency': 'r', 'id': 322, 'synset': 'cowbell.n.01', 'synonyms': ['cowbell'], 'def': 'a bell hung around the neck of cow so that the cow can be easily located', 'name': 'cowbell'}, {'frequency': 'f', 'id': 323, 'synset': 'cowboy_hat.n.01', 'synonyms': ['cowboy_hat', 'ten-gallon_hat'], 'def': 'a hat with a wide brim and a soft crown; worn by American ranch hands', 'name': 'cowboy_hat'}, {'frequency': 'r', 'id': 324, 'synset': 'crab.n.01', 'synonyms': ['crab_(animal)'], 'def': 'decapod having eyes on short stalks and a broad flattened shell and pincers', 'name': 'crab_(animal)'}, {'frequency': 'c', 'id': 325, 'synset': 'cracker.n.01', 'synonyms': ['cracker'], 'def': 'a thin crisp wafer', 'name': 'cracker'}, {'frequency': 'r', 'id': 326, 'synset': 'crape.n.01', 'synonyms': ['crape', 'crepe', 'French_pancake'], 'def': 'small very thin pancake', 'name': 'crape'}, {'frequency': 'f', 'id': 327, 'synset': 'crate.n.01', 'synonyms': ['crate'], 'def': 'a rugged box (usually made of wood); used for shipping', 'name': 'crate'}, {'frequency': 'r', 'id': 328, 'synset': 'crayon.n.01', 'synonyms': ['crayon', 'wax_crayon'], 'def': 'writing or drawing implement made of a colored stick of composition wax', 'name': 'crayon'}, {'frequency': 'r', 'id': 329, 'synset': 'cream_pitcher.n.01', 'synonyms': ['cream_pitcher'], 'def': 'a small pitcher for serving cream', 'name': 'cream_pitcher'}, {'frequency': 'r', 'id': 330, 'synset': 'credit_card.n.01', 'synonyms': ['credit_card', 'charge_card', 'debit_card'], 'def': 'a card, usually plastic, used to pay for goods and services', 'name': 'credit_card'}, {'frequency': 'c', 'id': 331, 'synset': 'crescent_roll.n.01', 'synonyms': ['crescent_roll', 'croissant'], 'def': 'very rich flaky crescent-shaped roll', 'name': 'crescent_roll'}, {'frequency': 'c', 'id': 332, 'synset': 'crib.n.01', 'synonyms': ['crib', 'cot'], 'def': 'baby bed with high sides made of slats', 'name': 'crib'}, {'frequency': 'c', 'id': 333, 'synset': 'crock.n.03', 'synonyms': ['crock_pot', 'earthenware_jar'], 'def': 'an earthen jar (made of baked clay)', 'name': 'crock_pot'}, {'frequency': 'f', 'id': 334, 'synset': 'crossbar.n.01', 'synonyms': ['crossbar'], 'def': 'a horizontal bar that goes across something', 'name': 'crossbar'}, {'frequency': 'r', 'id': 335, 'synset': 'crouton.n.01', 'synonyms': ['crouton'], 'def': 'a small piece of toasted or fried bread; served in soup or salads', 'name': 'crouton'}, {'frequency': 'r', 'id': 336, 'synset': 'crow.n.01', 'synonyms': ['crow'], 'def': 'black birds having a raucous call', 'name': 'crow'}, {'frequency': 'c', 'id': 337, 'synset': 'crown.n.04', 'synonyms': ['crown'], 'def': 'an ornamental jeweled headdress signifying sovereignty', 'name': 'crown'}, {'frequency': 'c', 'id': 338, 'synset': 'crucifix.n.01', 'synonyms': ['crucifix'], 'def': 'representation of the cross on which Jesus died', 'name': 'crucifix'}, {'frequency': 'c', 'id': 339, 'synset': 'cruise_ship.n.01', 'synonyms': ['cruise_ship', 'cruise_liner'], 'def': 'a passenger ship used commercially for pleasure cruises', 'name': 'cruise_ship'}, {'frequency': 'c', 'id': 340, 'synset': 'cruiser.n.01', 'synonyms': ['police_cruiser', 'patrol_car', 'police_car', 'squad_car'], 'def': 'a car in which policemen cruise the streets', 'name': 'police_cruiser'}, {'frequency': 'c', 'id': 341, 'synset': 'crumb.n.03', 'synonyms': ['crumb'], 'def': 'small piece of e.g. bread or cake', 'name': 'crumb'}, {'frequency': 'r', 'id': 342, 'synset': 'crutch.n.01', 'synonyms': ['crutch'], 'def': 'a wooden or metal staff that fits under the armpit and reaches to the ground', 'name': 'crutch'}, {'frequency': 'c', 'id': 343, 'synset': 'cub.n.03', 'synonyms': ['cub_(animal)'], 'def': 'the young of certain carnivorous mammals such as the bear or wolf or lion', 'name': 'cub_(animal)'}, {'frequency': 'r', 'id': 344, 'synset': 'cube.n.05', 'synonyms': ['cube', 'square_block'], 'def': 'a block in the (approximate) shape of a cube', 'name': 'cube'}, {'frequency': 'f', 'id': 345, 'synset': 'cucumber.n.02', 'synonyms': ['cucumber', 'cuke'], 'def': 'cylindrical green fruit with thin green rind and white flesh eaten as a vegetable', 'name': 'cucumber'}, {'frequency': 'c', 'id': 346, 'synset': 'cufflink.n.01', 'synonyms': ['cufflink'], 'def': 'jewelry consisting of linked buttons used to fasten the cuffs of a shirt', 'name': 'cufflink'}, {'frequency': 'f', 'id': 347, 'synset': 'cup.n.01', 'synonyms': ['cup'], 'def': 'a small open container usually used for drinking; usually has a handle', 'name': 'cup'}, {'frequency': 'c', 'id': 348, 'synset': 'cup.n.08', 'synonyms': ['trophy_cup'], 'def': 'a metal vessel with handles that is awarded as a trophy to a competition winner', 'name': 'trophy_cup'}, {'frequency': 'c', 'id': 349, 'synset': 'cupcake.n.01', 'synonyms': ['cupcake'], 'def': 'small cake baked in a muffin tin', 'name': 'cupcake'}, {'frequency': 'r', 'id': 350, 'synset': 'curler.n.01', 'synonyms': ['hair_curler', 'hair_roller', 'hair_crimper'], 'def': 'a cylindrical tube around which the hair is wound to curl it', 'name': 'hair_curler'}, {'frequency': 'r', 'id': 351, 'synset': 'curling_iron.n.01', 'synonyms': ['curling_iron'], 'def': 'a cylindrical home appliance that heats hair that has been curled around it', 'name': 'curling_iron'}, {'frequency': 'f', 'id': 352, 'synset': 'curtain.n.01', 'synonyms': ['curtain', 'drapery'], 'def': 'hanging cloth used as a blind (especially for a window)', 'name': 'curtain'}, {'frequency': 'f', 'id': 353, 'synset': 'cushion.n.03', 'synonyms': ['cushion'], 'def': 'a soft bag filled with air or padding such as feathers or foam rubber', 'name': 'cushion'}, {'frequency': 'r', 'id': 354, 'synset': 'custard.n.01', 'synonyms': ['custard'], 'def': 'sweetened mixture of milk and eggs baked or boiled or frozen', 'name': 'custard'}, {'frequency': 'c', 'id': 355, 'synset': 'cutter.n.06', 'synonyms': ['cutting_tool'], 'def': 'a cutting implement; a tool for cutting', 'name': 'cutting_tool'}, {'frequency': 'r', 'id': 356, 'synset': 'cylinder.n.04', 'synonyms': ['cylinder'], 'def': 'a cylindrical container', 'name': 'cylinder'}, {'frequency': 'r', 'id': 357, 'synset': 'cymbal.n.01', 'synonyms': ['cymbal'], 'def': 'a percussion instrument consisting of a concave brass disk', 'name': 'cymbal'}, {'frequency': 'r', 'id': 358, 'synset': 'dachshund.n.01', 'synonyms': ['dachshund', 'dachsie', 'badger_dog'], 'def': 'small long-bodied short-legged breed of dog having a short sleek coat and long drooping ears', 'name': 'dachshund'}, {'frequency': 'r', 'id': 359, 'synset': 'dagger.n.01', 'synonyms': ['dagger'], 'def': 'a short knife with a pointed blade used for piercing or stabbing', 'name': 'dagger'}, {'frequency': 'r', 'id': 360, 'synset': 'dartboard.n.01', 'synonyms': ['dartboard'], 'def': 'a circular board of wood or cork used as the target in the game of darts', 'name': 'dartboard'}, {'frequency': 'r', 'id': 361, 'synset': 'date.n.08', 'synonyms': ['date_(fruit)'], 'def': 'sweet edible fruit of the date palm with a single long woody seed', 'name': 'date_(fruit)'}, {'frequency': 'f', 'id': 362, 'synset': 'deck_chair.n.01', 'synonyms': ['deck_chair', 'beach_chair'], 'def': 'a folding chair for use outdoors; a wooden frame supports a length of canvas', 'name': 'deck_chair'}, {'frequency': 'c', 'id': 363, 'synset': 'deer.n.01', 'synonyms': ['deer', 'cervid'], 'def': "distinguished from Bovidae by the male's having solid deciduous antlers", 'name': 'deer'}, {'frequency': 'c', 'id': 364, 'synset': 'dental_floss.n.01', 'synonyms': ['dental_floss', 'floss'], 'def': 'a soft thread for cleaning the spaces between the teeth', 'name': 'dental_floss'}, {'frequency': 'f', 'id': 365, 'synset': 'desk.n.01', 'synonyms': ['desk'], 'def': 'a piece of furniture with a writing surface and usually drawers or other compartments', 'name': 'desk'}, {'frequency': 'r', 'id': 366, 'synset': 'detergent.n.01', 'synonyms': ['detergent'], 'def': 'a surface-active chemical widely used in industry and laundering', 'name': 'detergent'}, {'frequency': 'c', 'id': 367, 'synset': 'diaper.n.01', 'synonyms': ['diaper'], 'def': 'garment consisting of a folded cloth drawn up between the legs and fastened at the waist', 'name': 'diaper'}, {'frequency': 'r', 'id': 368, 'synset': 'diary.n.01', 'synonyms': ['diary', 'journal'], 'def': 'a daily written record of (usually personal) experiences and observations', 'name': 'diary'}, {'frequency': 'r', 'id': 369, 'synset': 'die.n.01', 'synonyms': ['die', 'dice'], 'def': 'a small cube with 1 to 6 spots on the six faces; used in gambling', 'name': 'die'}, {'frequency': 'r', 'id': 370, 'synset': 'dinghy.n.01', 'synonyms': ['dinghy', 'dory', 'rowboat'], 'def': 'a small boat of shallow draft with seats and oars with which it is propelled', 'name': 'dinghy'}, {'frequency': 'f', 'id': 371, 'synset': 'dining_table.n.01', 'synonyms': ['dining_table'], 'def': 'a table at which meals are served', 'name': 'dining_table'}, {'frequency': 'r', 'id': 372, 'synset': 'dinner_jacket.n.01', 'synonyms': ['tux', 'tuxedo'], 'def': 'semiformal evening dress for men', 'name': 'tux'}, {'frequency': 'c', 'id': 373, 'synset': 'dish.n.01', 'synonyms': ['dish'], 'def': 'a piece of dishware normally used as a container for holding or serving food', 'name': 'dish'}, {'frequency': 'c', 'id': 374, 'synset': 'dish.n.05', 'synonyms': ['dish_antenna'], 'def': 'directional antenna consisting of a parabolic reflector', 'name': 'dish_antenna'}, {'frequency': 'c', 'id': 375, 'synset': 'dishrag.n.01', 'synonyms': ['dishrag', 'dishcloth'], 'def': 'a cloth for washing dishes', 'name': 'dishrag'}, {'frequency': 'c', 'id': 376, 'synset': 'dishtowel.n.01', 'synonyms': ['dishtowel', 'tea_towel'], 'def': 'a towel for drying dishes', 'name': 'dishtowel'}, {'frequency': 'f', 'id': 377, 'synset': 'dishwasher.n.01', 'synonyms': ['dishwasher', 'dishwashing_machine'], 'def': 'a machine for washing dishes', 'name': 'dishwasher'}, {'frequency': 'r', 'id': 378, 'synset': 'dishwasher_detergent.n.01', 'synonyms': ['dishwasher_detergent', 'dishwashing_detergent', 'dishwashing_liquid'], 'def': 'a low-sudsing detergent designed for use in dishwashers', 'name': 'dishwasher_detergent'}, {'frequency': 'r', 'id': 379, 'synset': 'diskette.n.01', 'synonyms': ['diskette', 'floppy', 'floppy_disk'], 'def': 'a small plastic magnetic disk enclosed in a stiff envelope used to store data', 'name': 'diskette'}, {'frequency': 'c', 'id': 380, 'synset': 'dispenser.n.01', 'synonyms': ['dispenser'], 'def': 'a container so designed that the contents can be used in prescribed amounts', 'name': 'dispenser'}, {'frequency': 'c', 'id': 381, 'synset': 'dixie_cup.n.01', 'synonyms': ['Dixie_cup', 'paper_cup'], 'def': 'a disposable cup made of paper; for holding drinks', 'name': 'Dixie_cup'}, {'frequency': 'f', 'id': 382, 'synset': 'dog.n.01', 'synonyms': ['dog'], 'def': 'a common domesticated dog', 'name': 'dog'}, {'frequency': 'f', 'id': 383, 'synset': 'dog_collar.n.01', 'synonyms': ['dog_collar'], 'def': 'a collar for a dog', 'name': 'dog_collar'}, {'frequency': 'c', 'id': 384, 'synset': 'doll.n.01', 'synonyms': ['doll'], 'def': 'a toy replica of a HUMAN (NOT AN ANIMAL)', 'name': 'doll'}, {'frequency': 'r', 'id': 385, 'synset': 'dollar.n.02', 'synonyms': ['dollar', 'dollar_bill', 'one_dollar_bill'], 'def': 'a piece of paper money worth one dollar', 'name': 'dollar'}, {'frequency': 'r', 'id': 386, 'synset': 'dolphin.n.02', 'synonyms': ['dolphin'], 'def': 'any of various small toothed whales with a beaklike snout; larger than porpoises', 'name': 'dolphin'}, {'frequency': 'c', 'id': 387, 'synset': 'domestic_ass.n.01', 'synonyms': ['domestic_ass', 'donkey'], 'def': 'domestic beast of burden descended from the African wild ass; patient but stubborn', 'name': 'domestic_ass'}, {'frequency': 'r', 'id': 388, 'synset': 'domino.n.03', 'synonyms': ['eye_mask'], 'def': 'a mask covering the upper part of the face but with holes for the eyes', 'name': 'eye_mask'}, {'frequency': 'r', 'id': 389, 'synset': 'doorbell.n.01', 'synonyms': ['doorbell', 'buzzer'], 'def': 'a button at an outer door that gives a ringing or buzzing signal when pushed', 'name': 'doorbell'}, {'frequency': 'f', 'id': 390, 'synset': 'doorknob.n.01', 'synonyms': ['doorknob', 'doorhandle'], 'def': "a knob used to open a door (often called `doorhandle' in Great Britain)", 'name': 'doorknob'}, {'frequency': 'c', 'id': 391, 'synset': 'doormat.n.02', 'synonyms': ['doormat', 'welcome_mat'], 'def': 'a mat placed outside an exterior door for wiping the shoes before entering', 'name': 'doormat'}, {'frequency': 'f', 'id': 392, 'synset': 'doughnut.n.02', 'synonyms': ['doughnut', 'donut'], 'def': 'a small ring-shaped friedcake', 'name': 'doughnut'}, {'frequency': 'r', 'id': 393, 'synset': 'dove.n.01', 'synonyms': ['dove'], 'def': 'any of numerous small pigeons', 'name': 'dove'}, {'frequency': 'r', 'id': 394, 'synset': 'dragonfly.n.01', 'synonyms': ['dragonfly'], 'def': 'slender-bodied non-stinging insect having iridescent wings that are outspread at rest', 'name': 'dragonfly'}, {'frequency': 'f', 'id': 395, 'synset': 'drawer.n.01', 'synonyms': ['drawer'], 'def': 'a boxlike container in a piece of furniture; made so as to slide in and out', 'name': 'drawer'}, {'frequency': 'c', 'id': 396, 'synset': 'drawers.n.01', 'synonyms': ['underdrawers', 'boxers', 'boxershorts'], 'def': 'underpants worn by men', 'name': 'underdrawers'}, {'frequency': 'f', 'id': 397, 'synset': 'dress.n.01', 'synonyms': ['dress', 'frock'], 'def': 'a one-piece garment for a woman; has skirt and bodice', 'name': 'dress'}, {'frequency': 'c', 'id': 398, 'synset': 'dress_hat.n.01', 'synonyms': ['dress_hat', 'high_hat', 'opera_hat', 'silk_hat', 'top_hat'], 'def': "a man's hat with a tall crown; usually covered with silk or with beaver fur", 'name': 'dress_hat'}, {'frequency': 'c', 'id': 399, 'synset': 'dress_suit.n.01', 'synonyms': ['dress_suit'], 'def': 'formalwear consisting of full evening dress for men', 'name': 'dress_suit'}, {'frequency': 'c', 'id': 400, 'synset': 'dresser.n.05', 'synonyms': ['dresser'], 'def': 'a cabinet with shelves', 'name': 'dresser'}, {'frequency': 'c', 'id': 401, 'synset': 'drill.n.01', 'synonyms': ['drill'], 'def': 'a tool with a sharp rotating point for making holes in hard materials', 'name': 'drill'}, {'frequency': 'r', 'id': 402, 'synset': 'drinking_fountain.n.01', 'synonyms': ['drinking_fountain'], 'def': 'a public fountain to provide a jet of drinking water', 'name': 'drinking_fountain'}, {'frequency': 'r', 'id': 403, 'synset': 'drone.n.04', 'synonyms': ['drone'], 'def': 'an aircraft without a pilot that is operated by remote control', 'name': 'drone'}, {'frequency': 'r', 'id': 404, 'synset': 'dropper.n.01', 'synonyms': ['dropper', 'eye_dropper'], 'def': 'pipet consisting of a small tube with a vacuum bulb at one end for drawing liquid in and releasing it a drop at a time', 'name': 'dropper'}, {'frequency': 'c', 'id': 405, 'synset': 'drum.n.01', 'synonyms': ['drum_(musical_instrument)'], 'def': 'a musical percussion instrument; usually consists of a hollow cylinder with a membrane stretched across each end', 'name': 'drum_(musical_instrument)'}, {'frequency': 'r', 'id': 406, 'synset': 'drumstick.n.02', 'synonyms': ['drumstick'], 'def': 'a stick used for playing a drum', 'name': 'drumstick'}, {'frequency': 'f', 'id': 407, 'synset': 'duck.n.01', 'synonyms': ['duck'], 'def': 'small web-footed broad-billed swimming bird', 'name': 'duck'}, {'frequency': 'r', 'id': 408, 'synset': 'duckling.n.02', 'synonyms': ['duckling'], 'def': 'young duck', 'name': 'duckling'}, {'frequency': 'c', 'id': 409, 'synset': 'duct_tape.n.01', 'synonyms': ['duct_tape'], 'def': 'a wide silvery adhesive tape', 'name': 'duct_tape'}, {'frequency': 'f', 'id': 410, 'synset': 'duffel_bag.n.01', 'synonyms': ['duffel_bag', 'duffle_bag', 'duffel', 'duffle'], 'def': 'a large cylindrical bag of heavy cloth', 'name': 'duffel_bag'}, {'frequency': 'r', 'id': 411, 'synset': 'dumbbell.n.01', 'synonyms': ['dumbbell'], 'def': 'an exercising weight with two ball-like ends connected by a short handle', 'name': 'dumbbell'}, {'frequency': 'c', 'id': 412, 'synset': 'dumpster.n.01', 'synonyms': ['dumpster'], 'def': 'a container designed to receive and transport and dump waste', 'name': 'dumpster'}, {'frequency': 'r', 'id': 413, 'synset': 'dustpan.n.02', 'synonyms': ['dustpan'], 'def': 'a short-handled receptacle into which dust can be swept', 'name': 'dustpan'}, {'frequency': 'r', 'id': 414, 'synset': 'dutch_oven.n.02', 'synonyms': ['Dutch_oven'], 'def': 'iron or earthenware cooking pot; used for stews', 'name': 'Dutch_oven'}, {'frequency': 'c', 'id': 415, 'synset': 'eagle.n.01', 'synonyms': ['eagle'], 'def': 'large birds of prey noted for their broad wings and strong soaring flight', 'name': 'eagle'}, {'frequency': 'f', 'id': 416, 'synset': 'earphone.n.01', 'synonyms': ['earphone', 'earpiece', 'headphone'], 'def': 'device for listening to audio that is held over or inserted into the ear', 'name': 'earphone'}, {'frequency': 'r', 'id': 417, 'synset': 'earplug.n.01', 'synonyms': ['earplug'], 'def': 'a soft plug that is inserted into the ear canal to block sound', 'name': 'earplug'}, {'frequency': 'f', 'id': 418, 'synset': 'earring.n.01', 'synonyms': ['earring'], 'def': 'jewelry to ornament the ear', 'name': 'earring'}, {'frequency': 'c', 'id': 419, 'synset': 'easel.n.01', 'synonyms': ['easel'], 'def': "an upright tripod for displaying something (usually an artist's canvas)", 'name': 'easel'}, {'frequency': 'r', 'id': 420, 'synset': 'eclair.n.01', 'synonyms': ['eclair'], 'def': 'oblong cream puff', 'name': 'eclair'}, {'frequency': 'r', 'id': 421, 'synset': 'eel.n.01', 'synonyms': ['eel'], 'def': 'an elongate fish with fatty flesh', 'name': 'eel'}, {'frequency': 'f', 'id': 422, 'synset': 'egg.n.02', 'synonyms': ['egg', 'eggs'], 'def': 'oval reproductive body of a fowl (especially a hen) used as food', 'name': 'egg'}, {'frequency': 'r', 'id': 423, 'synset': 'egg_roll.n.01', 'synonyms': ['egg_roll', 'spring_roll'], 'def': 'minced vegetables and meat wrapped in a pancake and fried', 'name': 'egg_roll'}, {'frequency': 'c', 'id': 424, 'synset': 'egg_yolk.n.01', 'synonyms': ['egg_yolk', 'yolk_(egg)'], 'def': 'the yellow spherical part of an egg', 'name': 'egg_yolk'}, {'frequency': 'c', 'id': 425, 'synset': 'eggbeater.n.02', 'synonyms': ['eggbeater', 'eggwhisk'], 'def': 'a mixer for beating eggs or whipping cream', 'name': 'eggbeater'}, {'frequency': 'c', 'id': 426, 'synset': 'eggplant.n.01', 'synonyms': ['eggplant', 'aubergine'], 'def': 'egg-shaped vegetable having a shiny skin typically dark purple', 'name': 'eggplant'}, {'frequency': 'r', 'id': 427, 'synset': 'electric_chair.n.01', 'synonyms': ['electric_chair'], 'def': 'a chair-shaped instrument of execution by electrocution', 'name': 'electric_chair'}, {'frequency': 'f', 'id': 428, 'synset': 'electric_refrigerator.n.01', 'synonyms': ['refrigerator'], 'def': 'a refrigerator in which the coolant is pumped around by an electric motor', 'name': 'refrigerator'}, {'frequency': 'f', 'id': 429, 'synset': 'elephant.n.01', 'synonyms': ['elephant'], 'def': 'a common elephant', 'name': 'elephant'}, {'frequency': 'r', 'id': 430, 'synset': 'elk.n.01', 'synonyms': ['elk', 'moose'], 'def': 'large northern deer with enormous flattened antlers in the male', 'name': 'elk'}, {'frequency': 'c', 'id': 431, 'synset': 'envelope.n.01', 'synonyms': ['envelope'], 'def': 'a flat (usually rectangular) container for a letter, thin package, etc.', 'name': 'envelope'}, {'frequency': 'c', 'id': 432, 'synset': 'eraser.n.01', 'synonyms': ['eraser'], 'def': 'an implement used to erase something', 'name': 'eraser'}, {'frequency': 'r', 'id': 433, 'synset': 'escargot.n.01', 'synonyms': ['escargot'], 'def': 'edible snail usually served in the shell with a sauce of melted butter and garlic', 'name': 'escargot'}, {'frequency': 'r', 'id': 434, 'synset': 'eyepatch.n.01', 'synonyms': ['eyepatch'], 'def': 'a protective cloth covering for an injured eye', 'name': 'eyepatch'}, {'frequency': 'r', 'id': 435, 'synset': 'falcon.n.01', 'synonyms': ['falcon'], 'def': 'birds of prey having long pointed powerful wings adapted for swift flight', 'name': 'falcon'}, {'frequency': 'f', 'id': 436, 'synset': 'fan.n.01', 'synonyms': ['fan'], 'def': 'a device for creating a current of air by movement of a surface or surfaces', 'name': 'fan'}, {'frequency': 'f', 'id': 437, 'synset': 'faucet.n.01', 'synonyms': ['faucet', 'spigot', 'tap'], 'def': 'a regulator for controlling the flow of a liquid from a reservoir', 'name': 'faucet'}, {'frequency': 'r', 'id': 438, 'synset': 'fedora.n.01', 'synonyms': ['fedora'], 'def': 'a hat made of felt with a creased crown', 'name': 'fedora'}, {'frequency': 'r', 'id': 439, 'synset': 'ferret.n.02', 'synonyms': ['ferret'], 'def': 'domesticated albino variety of the European polecat bred for hunting rats and rabbits', 'name': 'ferret'}, {'frequency': 'c', 'id': 440, 'synset': 'ferris_wheel.n.01', 'synonyms': ['Ferris_wheel'], 'def': 'a large wheel with suspended seats that remain upright as the wheel rotates', 'name': 'Ferris_wheel'}, {'frequency': 'r', 'id': 441, 'synset': 'ferry.n.01', 'synonyms': ['ferry', 'ferryboat'], 'def': 'a boat that transports people or vehicles across a body of water and operates on a regular schedule', 'name': 'ferry'}, {'frequency': 'r', 'id': 442, 'synset': 'fig.n.04', 'synonyms': ['fig_(fruit)'], 'def': 'fleshy sweet pear-shaped yellowish or purple fruit eaten fresh or preserved or dried', 'name': 'fig_(fruit)'}, {'frequency': 'c', 'id': 443, 'synset': 'fighter.n.02', 'synonyms': ['fighter_jet', 'fighter_aircraft', 'attack_aircraft'], 'def': 'a high-speed military or naval airplane designed to destroy enemy targets', 'name': 'fighter_jet'}, {'frequency': 'f', 'id': 444, 'synset': 'figurine.n.01', 'synonyms': ['figurine'], 'def': 'a small carved or molded figure', 'name': 'figurine'}, {'frequency': 'c', 'id': 445, 'synset': 'file.n.03', 'synonyms': ['file_cabinet', 'filing_cabinet'], 'def': 'office furniture consisting of a container for keeping papers in order', 'name': 'file_cabinet'}, {'frequency': 'r', 'id': 446, 'synset': 'file.n.04', 'synonyms': ['file_(tool)'], 'def': 'a steel hand tool with small sharp teeth on some or all of its surfaces; used for smoothing wood or metal', 'name': 'file_(tool)'}, {'frequency': 'f', 'id': 447, 'synset': 'fire_alarm.n.02', 'synonyms': ['fire_alarm', 'smoke_alarm'], 'def': 'an alarm that is tripped off by fire or smoke', 'name': 'fire_alarm'}, {'frequency': 'c', 'id': 448, 'synset': 'fire_engine.n.01', 'synonyms': ['fire_engine', 'fire_truck'], 'def': 'large trucks that carry firefighters and equipment to the site of a fire', 'name': 'fire_engine'}, {'frequency': 'c', 'id': 449, 'synset': 'fire_extinguisher.n.01', 'synonyms': ['fire_extinguisher', 'extinguisher'], 'def': 'a manually operated device for extinguishing small fires', 'name': 'fire_extinguisher'}, {'frequency': 'c', 'id': 450, 'synset': 'fire_hose.n.01', 'synonyms': ['fire_hose'], 'def': 'a large hose that carries water from a fire hydrant to the site of the fire', 'name': 'fire_hose'}, {'frequency': 'f', 'id': 451, 'synset': 'fireplace.n.01', 'synonyms': ['fireplace'], 'def': 'an open recess in a wall at the base of a chimney where a fire can be built', 'name': 'fireplace'}, {'frequency': 'f', 'id': 452, 'synset': 'fireplug.n.01', 'synonyms': ['fireplug', 'fire_hydrant', 'hydrant'], 'def': 'an upright hydrant for drawing water to use in fighting a fire', 'name': 'fireplug'}, {'frequency': 'c', 'id': 453, 'synset': 'fish.n.01', 'synonyms': ['fish'], 'def': 'any of various mostly cold-blooded aquatic vertebrates usually having scales and breathing through gills', 'name': 'fish'}, {'frequency': 'r', 'id': 454, 'synset': 'fish.n.02', 'synonyms': ['fish_(food)'], 'def': 'the flesh of fish used as food', 'name': 'fish_(food)'}, {'frequency': 'r', 'id': 455, 'synset': 'fishbowl.n.02', 'synonyms': ['fishbowl', 'goldfish_bowl'], 'def': 'a transparent bowl in which small fish are kept', 'name': 'fishbowl'}, {'frequency': 'r', 'id': 456, 'synset': 'fishing_boat.n.01', 'synonyms': ['fishing_boat', 'fishing_vessel'], 'def': 'a vessel for fishing', 'name': 'fishing_boat'}, {'frequency': 'c', 'id': 457, 'synset': 'fishing_rod.n.01', 'synonyms': ['fishing_rod', 'fishing_pole'], 'def': 'a rod that is used in fishing to extend the fishing line', 'name': 'fishing_rod'}, {'frequency': 'f', 'id': 458, 'synset': 'flag.n.01', 'synonyms': ['flag'], 'def': 'emblem usually consisting of a rectangular piece of cloth of distinctive design (do not include pole)', 'name': 'flag'}, {'frequency': 'f', 'id': 459, 'synset': 'flagpole.n.02', 'synonyms': ['flagpole', 'flagstaff'], 'def': 'a tall staff or pole on which a flag is raised', 'name': 'flagpole'}, {'frequency': 'c', 'id': 460, 'synset': 'flamingo.n.01', 'synonyms': ['flamingo'], 'def': 'large pink web-footed bird with down-bent bill', 'name': 'flamingo'}, {'frequency': 'c', 'id': 461, 'synset': 'flannel.n.01', 'synonyms': ['flannel'], 'def': 'a soft light woolen fabric; used for clothing', 'name': 'flannel'}, {'frequency': 'r', 'id': 462, 'synset': 'flash.n.10', 'synonyms': ['flash', 'flashbulb'], 'def': 'a lamp for providing momentary light to take a photograph', 'name': 'flash'}, {'frequency': 'c', 'id': 463, 'synset': 'flashlight.n.01', 'synonyms': ['flashlight', 'torch'], 'def': 'a small portable battery-powered electric lamp', 'name': 'flashlight'}, {'frequency': 'r', 'id': 464, 'synset': 'fleece.n.03', 'synonyms': ['fleece'], 'def': 'a soft bulky fabric with deep pile; used chiefly for clothing', 'name': 'fleece'}, {'frequency': 'f', 'id': 465, 'synset': 'flip-flop.n.02', 'synonyms': ['flip-flop_(sandal)'], 'def': 'a backless sandal held to the foot by a thong between two toes', 'name': 'flip-flop_(sandal)'}, {'frequency': 'c', 'id': 466, 'synset': 'flipper.n.01', 'synonyms': ['flipper_(footwear)', 'fin_(footwear)'], 'def': 'a shoe to aid a person in swimming', 'name': 'flipper_(footwear)'}, {'frequency': 'f', 'id': 467, 'synset': 'flower_arrangement.n.01', 'synonyms': ['flower_arrangement', 'floral_arrangement'], 'def': 'a decorative arrangement of flowers', 'name': 'flower_arrangement'}, {'frequency': 'c', 'id': 468, 'synset': 'flute.n.02', 'synonyms': ['flute_glass', 'champagne_flute'], 'def': 'a tall narrow wineglass', 'name': 'flute_glass'}, {'frequency': 'r', 'id': 469, 'synset': 'foal.n.01', 'synonyms': ['foal'], 'def': 'a young horse', 'name': 'foal'}, {'frequency': 'c', 'id': 470, 'synset': 'folding_chair.n.01', 'synonyms': ['folding_chair'], 'def': 'a chair that can be folded flat for storage', 'name': 'folding_chair'}, {'frequency': 'c', 'id': 471, 'synset': 'food_processor.n.01', 'synonyms': ['food_processor'], 'def': 'a kitchen appliance for shredding, blending, chopping, or slicing food', 'name': 'food_processor'}, {'frequency': 'c', 'id': 472, 'synset': 'football.n.02', 'synonyms': ['football_(American)'], 'def': 'the inflated oblong ball used in playing American football', 'name': 'football_(American)'}, {'frequency': 'r', 'id': 473, 'synset': 'football_helmet.n.01', 'synonyms': ['football_helmet'], 'def': 'a padded helmet with a face mask to protect the head of football players', 'name': 'football_helmet'}, {'frequency': 'c', 'id': 474, 'synset': 'footstool.n.01', 'synonyms': ['footstool', 'footrest'], 'def': 'a low seat or a stool to rest the feet of a seated person', 'name': 'footstool'}, {'frequency': 'f', 'id': 475, 'synset': 'fork.n.01', 'synonyms': ['fork'], 'def': 'cutlery used for serving and eating food', 'name': 'fork'}, {'frequency': 'r', 'id': 476, 'synset': 'forklift.n.01', 'synonyms': ['forklift'], 'def': 'an industrial vehicle with a power operated fork in front that can be inserted under loads to lift and move them', 'name': 'forklift'}, {'frequency': 'r', 'id': 477, 'synset': 'freight_car.n.01', 'synonyms': ['freight_car'], 'def': 'a railway car that carries freight', 'name': 'freight_car'}, {'frequency': 'r', 'id': 478, 'synset': 'french_toast.n.01', 'synonyms': ['French_toast'], 'def': 'bread slice dipped in egg and milk and fried', 'name': 'French_toast'}, {'frequency': 'c', 'id': 479, 'synset': 'freshener.n.01', 'synonyms': ['freshener', 'air_freshener'], 'def': 'anything that freshens', 'name': 'freshener'}, {'frequency': 'f', 'id': 480, 'synset': 'frisbee.n.01', 'synonyms': ['frisbee'], 'def': 'a light, plastic disk propelled with a flip of the wrist for recreation or competition', 'name': 'frisbee'}, {'frequency': 'c', 'id': 481, 'synset': 'frog.n.01', 'synonyms': ['frog', 'toad', 'toad_frog'], 'def': 'a tailless stout-bodied amphibians with long hind limbs for leaping', 'name': 'frog'}, {'frequency': 'c', 'id': 482, 'synset': 'fruit_juice.n.01', 'synonyms': ['fruit_juice'], 'def': 'drink produced by squeezing or crushing fruit', 'name': 'fruit_juice'}, {'frequency': 'r', 'id': 483, 'synset': 'fruit_salad.n.01', 'synonyms': ['fruit_salad'], 'def': 'salad composed of fruits', 'name': 'fruit_salad'}, {'frequency': 'c', 'id': 484, 'synset': 'frying_pan.n.01', 'synonyms': ['frying_pan', 'frypan', 'skillet'], 'def': 'a pan used for frying foods', 'name': 'frying_pan'}, {'frequency': 'r', 'id': 485, 'synset': 'fudge.n.01', 'synonyms': ['fudge'], 'def': 'soft creamy candy', 'name': 'fudge'}, {'frequency': 'r', 'id': 486, 'synset': 'funnel.n.02', 'synonyms': ['funnel'], 'def': 'a cone-shaped utensil used to channel a substance into a container with a small mouth', 'name': 'funnel'}, {'frequency': 'c', 'id': 487, 'synset': 'futon.n.01', 'synonyms': ['futon'], 'def': 'a pad that is used for sleeping on the floor or on a raised frame', 'name': 'futon'}, {'frequency': 'r', 'id': 488, 'synset': 'gag.n.02', 'synonyms': ['gag', 'muzzle'], 'def': "restraint put into a person's mouth to prevent speaking or shouting", 'name': 'gag'}, {'frequency': 'r', 'id': 489, 'synset': 'garbage.n.03', 'synonyms': ['garbage'], 'def': 'a receptacle where waste can be discarded', 'name': 'garbage'}, {'frequency': 'c', 'id': 490, 'synset': 'garbage_truck.n.01', 'synonyms': ['garbage_truck'], 'def': 'a truck for collecting domestic refuse', 'name': 'garbage_truck'}, {'frequency': 'c', 'id': 491, 'synset': 'garden_hose.n.01', 'synonyms': ['garden_hose'], 'def': 'a hose used for watering a lawn or garden', 'name': 'garden_hose'}, {'frequency': 'c', 'id': 492, 'synset': 'gargle.n.01', 'synonyms': ['gargle', 'mouthwash'], 'def': 'a medicated solution used for gargling and rinsing the mouth', 'name': 'gargle'}, {'frequency': 'r', 'id': 493, 'synset': 'gargoyle.n.02', 'synonyms': ['gargoyle'], 'def': 'an ornament consisting of a grotesquely carved figure of a person or animal', 'name': 'gargoyle'}, {'frequency': 'c', 'id': 494, 'synset': 'garlic.n.02', 'synonyms': ['garlic', 'ail'], 'def': 'aromatic bulb used as seasoning', 'name': 'garlic'}, {'frequency': 'r', 'id': 495, 'synset': 'gasmask.n.01', 'synonyms': ['gasmask', 'respirator', 'gas_helmet'], 'def': 'a protective face mask with a filter', 'name': 'gasmask'}, {'frequency': 'r', 'id': 496, 'synset': 'gazelle.n.01', 'synonyms': ['gazelle'], 'def': 'small swift graceful antelope of Africa and Asia having lustrous eyes', 'name': 'gazelle'}, {'frequency': 'c', 'id': 497, 'synset': 'gelatin.n.02', 'synonyms': ['gelatin', 'jelly'], 'def': 'an edible jelly made with gelatin and used as a dessert or salad base or a coating for foods', 'name': 'gelatin'}, {'frequency': 'r', 'id': 498, 'synset': 'gem.n.02', 'synonyms': ['gemstone'], 'def': 'a crystalline rock that can be cut and polished for jewelry', 'name': 'gemstone'}, {'frequency': 'c', 'id': 499, 'synset': 'giant_panda.n.01', 'synonyms': ['giant_panda', 'panda', 'panda_bear'], 'def': 'large black-and-white herbivorous mammal of bamboo forests of China and Tibet', 'name': 'giant_panda'}, {'frequency': 'c', 'id': 500, 'synset': 'gift_wrap.n.01', 'synonyms': ['gift_wrap'], 'def': 'attractive wrapping paper suitable for wrapping gifts', 'name': 'gift_wrap'}, {'frequency': 'c', 'id': 501, 'synset': 'ginger.n.03', 'synonyms': ['ginger', 'gingerroot'], 'def': 'the root of the common ginger plant; used fresh as a seasoning', 'name': 'ginger'}, {'frequency': 'f', 'id': 502, 'synset': 'giraffe.n.01', 'synonyms': ['giraffe'], 'def': 'tall animal having a spotted coat and small horns and very long neck and legs', 'name': 'giraffe'}, {'frequency': 'c', 'id': 503, 'synset': 'girdle.n.02', 'synonyms': ['cincture', 'sash', 'waistband', 'waistcloth'], 'def': 'a band of material around the waist that strengthens a skirt or trousers', 'name': 'cincture'}, {'frequency': 'f', 'id': 504, 'synset': 'glass.n.02', 'synonyms': ['glass_(drink_container)', 'drinking_glass'], 'def': 'a container for holding liquids while drinking', 'name': 'glass_(drink_container)'}, {'frequency': 'c', 'id': 505, 'synset': 'globe.n.03', 'synonyms': ['globe'], 'def': 'a sphere on which a map (especially of the earth) is represented', 'name': 'globe'}, {'frequency': 'f', 'id': 506, 'synset': 'glove.n.02', 'synonyms': ['glove'], 'def': 'handwear covering the hand', 'name': 'glove'}, {'frequency': 'c', 'id': 507, 'synset': 'goat.n.01', 'synonyms': ['goat'], 'def': 'a common goat', 'name': 'goat'}, {'frequency': 'f', 'id': 508, 'synset': 'goggles.n.01', 'synonyms': ['goggles'], 'def': 'tight-fitting spectacles worn to protect the eyes', 'name': 'goggles'}, {'frequency': 'r', 'id': 509, 'synset': 'goldfish.n.01', 'synonyms': ['goldfish'], 'def': 'small golden or orange-red freshwater fishes used as pond or aquarium pets', 'name': 'goldfish'}, {'frequency': 'r', 'id': 510, 'synset': 'golf_club.n.02', 'synonyms': ['golf_club', 'golf-club'], 'def': 'golf equipment used by a golfer to hit a golf ball', 'name': 'golf_club'}, {'frequency': 'c', 'id': 511, 'synset': 'golfcart.n.01', 'synonyms': ['golfcart'], 'def': 'a small motor vehicle in which golfers can ride between shots', 'name': 'golfcart'}, {'frequency': 'r', 'id': 512, 'synset': 'gondola.n.02', 'synonyms': ['gondola_(boat)'], 'def': 'long narrow flat-bottomed boat propelled by sculling; traditionally used on canals of Venice', 'name': 'gondola_(boat)'}, {'frequency': 'c', 'id': 513, 'synset': 'goose.n.01', 'synonyms': ['goose'], 'def': 'loud, web-footed long-necked aquatic birds usually larger than ducks', 'name': 'goose'}, {'frequency': 'r', 'id': 514, 'synset': 'gorilla.n.01', 'synonyms': ['gorilla'], 'def': 'largest ape', 'name': 'gorilla'}, {'frequency': 'r', 'id': 515, 'synset': 'gourd.n.02', 'synonyms': ['gourd'], 'def': 'any of numerous inedible fruits with hard rinds', 'name': 'gourd'}, {'frequency': 'r', 'id': 516, 'synset': 'gown.n.04', 'synonyms': ['surgical_gown', 'scrubs_(surgical_clothing)'], 'def': 'protective garment worn by surgeons during operations', 'name': 'surgical_gown'}, {'frequency': 'f', 'id': 517, 'synset': 'grape.n.01', 'synonyms': ['grape'], 'def': 'any of various juicy fruit with green or purple skins; grow in clusters', 'name': 'grape'}, {'frequency': 'r', 'id': 518, 'synset': 'grasshopper.n.01', 'synonyms': ['grasshopper'], 'def': 'plant-eating insect with hind legs adapted for leaping', 'name': 'grasshopper'}, {'frequency': 'c', 'id': 519, 'synset': 'grater.n.01', 'synonyms': ['grater'], 'def': 'utensil with sharp perforations for shredding foods (as vegetables or cheese)', 'name': 'grater'}, {'frequency': 'c', 'id': 520, 'synset': 'gravestone.n.01', 'synonyms': ['gravestone', 'headstone', 'tombstone'], 'def': 'a stone that is used to mark a grave', 'name': 'gravestone'}, {'frequency': 'r', 'id': 521, 'synset': 'gravy_boat.n.01', 'synonyms': ['gravy_boat', 'gravy_holder'], 'def': 'a dish (often boat-shaped) for serving gravy or sauce', 'name': 'gravy_boat'}, {'frequency': 'c', 'id': 522, 'synset': 'green_bean.n.02', 'synonyms': ['green_bean'], 'def': 'a common bean plant cultivated for its slender green edible pods', 'name': 'green_bean'}, {'frequency': 'c', 'id': 523, 'synset': 'green_onion.n.01', 'synonyms': ['green_onion', 'spring_onion', 'scallion'], 'def': 'a young onion before the bulb has enlarged', 'name': 'green_onion'}, {'frequency': 'r', 'id': 524, 'synset': 'griddle.n.01', 'synonyms': ['griddle'], 'def': 'cooking utensil consisting of a flat heated surface on which food is cooked', 'name': 'griddle'}, {'frequency': 'r', 'id': 525, 'synset': 'grillroom.n.01', 'synonyms': ['grillroom', 'grill_(restaurant)'], 'def': 'a restaurant where food is cooked on a grill', 'name': 'grillroom'}, {'frequency': 'r', 'id': 526, 'synset': 'grinder.n.04', 'synonyms': ['grinder_(tool)'], 'def': 'a machine tool that polishes metal', 'name': 'grinder_(tool)'}, {'frequency': 'r', 'id': 527, 'synset': 'grits.n.01', 'synonyms': ['grits', 'hominy_grits'], 'def': 'coarsely ground corn boiled as a breakfast dish', 'name': 'grits'}, {'frequency': 'c', 'id': 528, 'synset': 'grizzly.n.01', 'synonyms': ['grizzly', 'grizzly_bear'], 'def': 'powerful brownish-yellow bear of the uplands of western North America', 'name': 'grizzly'}, {'frequency': 'c', 'id': 529, 'synset': 'grocery_bag.n.01', 'synonyms': ['grocery_bag'], 'def': "a sack for holding customer's groceries", 'name': 'grocery_bag'}, {'frequency': 'r', 'id': 530, 'synset': 'guacamole.n.01', 'synonyms': ['guacamole'], 'def': 'a dip made of mashed avocado mixed with chopped onions and other seasonings', 'name': 'guacamole'}, {'frequency': 'f', 'id': 531, 'synset': 'guitar.n.01', 'synonyms': ['guitar'], 'def': 'a stringed instrument usually having six strings; played by strumming or plucking', 'name': 'guitar'}, {'frequency': 'c', 'id': 532, 'synset': 'gull.n.02', 'synonyms': ['gull', 'seagull'], 'def': 'mostly white aquatic bird having long pointed wings and short legs', 'name': 'gull'}, {'frequency': 'c', 'id': 533, 'synset': 'gun.n.01', 'synonyms': ['gun'], 'def': 'a weapon that discharges a bullet at high velocity from a metal tube', 'name': 'gun'}, {'frequency': 'r', 'id': 534, 'synset': 'hair_spray.n.01', 'synonyms': ['hair_spray'], 'def': 'substance sprayed on the hair to hold it in place', 'name': 'hair_spray'}, {'frequency': 'c', 'id': 535, 'synset': 'hairbrush.n.01', 'synonyms': ['hairbrush'], 'def': "a brush used to groom a person's hair", 'name': 'hairbrush'}, {'frequency': 'c', 'id': 536, 'synset': 'hairnet.n.01', 'synonyms': ['hairnet'], 'def': 'a small net that someone wears over their hair to keep it in place', 'name': 'hairnet'}, {'frequency': 'c', 'id': 537, 'synset': 'hairpin.n.01', 'synonyms': ['hairpin'], 'def': "a double pronged pin used to hold women's hair in place", 'name': 'hairpin'}, {'frequency': 'f', 'id': 538, 'synset': 'ham.n.01', 'synonyms': ['ham', 'jambon', 'gammon'], 'def': 'meat cut from the thigh of a hog (usually smoked)', 'name': 'ham'}, {'frequency': 'c', 'id': 539, 'synset': 'hamburger.n.01', 'synonyms': ['hamburger', 'beefburger', 'burger'], 'def': 'a sandwich consisting of a patty of minced beef served on a bun', 'name': 'hamburger'}, {'frequency': 'c', 'id': 540, 'synset': 'hammer.n.02', 'synonyms': ['hammer'], 'def': 'a hand tool with a heavy head and a handle; used to deliver an impulsive force by striking', 'name': 'hammer'}, {'frequency': 'r', 'id': 541, 'synset': 'hammock.n.02', 'synonyms': ['hammock'], 'def': 'a hanging bed of canvas or rope netting (usually suspended between two trees)', 'name': 'hammock'}, {'frequency': 'r', 'id': 542, 'synset': 'hamper.n.02', 'synonyms': ['hamper'], 'def': 'a basket usually with a cover', 'name': 'hamper'}, {'frequency': 'r', 'id': 543, 'synset': 'hamster.n.01', 'synonyms': ['hamster'], 'def': 'short-tailed burrowing rodent with large cheek pouches', 'name': 'hamster'}, {'frequency': 'c', 'id': 544, 'synset': 'hand_blower.n.01', 'synonyms': ['hair_dryer'], 'def': 'a hand-held electric blower that can blow warm air onto the hair', 'name': 'hair_dryer'}, {'frequency': 'r', 'id': 545, 'synset': 'hand_glass.n.01', 'synonyms': ['hand_glass', 'hand_mirror'], 'def': 'a mirror intended to be held in the hand', 'name': 'hand_glass'}, {'frequency': 'f', 'id': 546, 'synset': 'hand_towel.n.01', 'synonyms': ['hand_towel', 'face_towel'], 'def': 'a small towel used to dry the hands or face', 'name': 'hand_towel'}, {'frequency': 'c', 'id': 547, 'synset': 'handcart.n.01', 'synonyms': ['handcart', 'pushcart', 'hand_truck'], 'def': 'wheeled vehicle that can be pushed by a person', 'name': 'handcart'}, {'frequency': 'r', 'id': 548, 'synset': 'handcuff.n.01', 'synonyms': ['handcuff'], 'def': 'shackle that consists of a metal loop that can be locked around the wrist', 'name': 'handcuff'}, {'frequency': 'c', 'id': 549, 'synset': 'handkerchief.n.01', 'synonyms': ['handkerchief'], 'def': 'a square piece of cloth used for wiping the eyes or nose or as a costume accessory', 'name': 'handkerchief'}, {'frequency': 'f', 'id': 550, 'synset': 'handle.n.01', 'synonyms': ['handle', 'grip', 'handgrip'], 'def': 'the appendage to an object that is designed to be held in order to use or move it', 'name': 'handle'}, {'frequency': 'r', 'id': 551, 'synset': 'handsaw.n.01', 'synonyms': ['handsaw', "carpenter's_saw"], 'def': 'a saw used with one hand for cutting wood', 'name': 'handsaw'}, {'frequency': 'r', 'id': 552, 'synset': 'hardback.n.01', 'synonyms': ['hardback_book', 'hardcover_book'], 'def': 'a book with cardboard or cloth or leather covers', 'name': 'hardback_book'}, {'frequency': 'r', 'id': 553, 'synset': 'harmonium.n.01', 'synonyms': ['harmonium', 'organ_(musical_instrument)', 'reed_organ_(musical_instrument)'], 'def': 'a free-reed instrument in which air is forced through the reeds by bellows', 'name': 'harmonium'}, {'frequency': 'f', 'id': 554, 'synset': 'hat.n.01', 'synonyms': ['hat'], 'def': 'headwear that protects the head from bad weather, sun, or worn for fashion', 'name': 'hat'}, {'frequency': 'r', 'id': 555, 'synset': 'hatbox.n.01', 'synonyms': ['hatbox'], 'def': 'a round piece of luggage for carrying hats', 'name': 'hatbox'}, {'frequency': 'r', 'id': 556, 'synset': 'hatch.n.03', 'synonyms': ['hatch'], 'def': 'a movable barrier covering a hatchway', 'name': 'hatch'}, {'frequency': 'c', 'id': 557, 'synset': 'head_covering.n.01', 'synonyms': ['veil'], 'def': 'a garment that covers the head and face', 'name': 'veil'}, {'frequency': 'f', 'id': 558, 'synset': 'headband.n.01', 'synonyms': ['headband'], 'def': 'a band worn around or over the head', 'name': 'headband'}, {'frequency': 'f', 'id': 559, 'synset': 'headboard.n.01', 'synonyms': ['headboard'], 'def': 'a vertical board or panel forming the head of a bedstead', 'name': 'headboard'}, {'frequency': 'f', 'id': 560, 'synset': 'headlight.n.01', 'synonyms': ['headlight', 'headlamp'], 'def': 'a powerful light with reflector; attached to the front of an automobile or locomotive', 'name': 'headlight'}, {'frequency': 'c', 'id': 561, 'synset': 'headscarf.n.01', 'synonyms': ['headscarf'], 'def': 'a kerchief worn over the head and tied under the chin', 'name': 'headscarf'}, {'frequency': 'r', 'id': 562, 'synset': 'headset.n.01', 'synonyms': ['headset'], 'def': 'receiver consisting of a pair of headphones', 'name': 'headset'}, {'frequency': 'c', 'id': 563, 'synset': 'headstall.n.01', 'synonyms': ['headstall_(for_horses)', 'headpiece_(for_horses)'], 'def': "the band that is the part of a bridle that fits around a horse's head", 'name': 'headstall_(for_horses)'}, {'frequency': 'r', 'id': 564, 'synset': 'hearing_aid.n.02', 'synonyms': ['hearing_aid'], 'def': 'an acoustic device used to direct sound to the ear of a hearing-impaired person', 'name': 'hearing_aid'}, {'frequency': 'c', 'id': 565, 'synset': 'heart.n.02', 'synonyms': ['heart'], 'def': 'a muscular organ; its contractions move the blood through the body', 'name': 'heart'}, {'frequency': 'c', 'id': 566, 'synset': 'heater.n.01', 'synonyms': ['heater', 'warmer'], 'def': 'device that heats water or supplies warmth to a room', 'name': 'heater'}, {'frequency': 'c', 'id': 567, 'synset': 'helicopter.n.01', 'synonyms': ['helicopter'], 'def': 'an aircraft without wings that obtains its lift from the rotation of overhead blades', 'name': 'helicopter'}, {'frequency': 'f', 'id': 568, 'synset': 'helmet.n.02', 'synonyms': ['helmet'], 'def': 'a protective headgear made of hard material to resist blows', 'name': 'helmet'}, {'frequency': 'r', 'id': 569, 'synset': 'heron.n.02', 'synonyms': ['heron'], 'def': 'grey or white wading bird with long neck and long legs and (usually) long bill', 'name': 'heron'}, {'frequency': 'c', 'id': 570, 'synset': 'highchair.n.01', 'synonyms': ['highchair', 'feeding_chair'], 'def': 'a chair for feeding a very young child', 'name': 'highchair'}, {'frequency': 'f', 'id': 571, 'synset': 'hinge.n.01', 'synonyms': ['hinge'], 'def': 'a joint that holds two parts together so that one can swing relative to the other', 'name': 'hinge'}, {'frequency': 'r', 'id': 572, 'synset': 'hippopotamus.n.01', 'synonyms': ['hippopotamus'], 'def': 'massive thick-skinned animal living in or around rivers of tropical Africa', 'name': 'hippopotamus'}, {'frequency': 'r', 'id': 573, 'synset': 'hockey_stick.n.01', 'synonyms': ['hockey_stick'], 'def': 'sports implement consisting of a stick used by hockey players to move the puck', 'name': 'hockey_stick'}, {'frequency': 'c', 'id': 574, 'synset': 'hog.n.03', 'synonyms': ['hog', 'pig'], 'def': 'domestic swine', 'name': 'hog'}, {'frequency': 'f', 'id': 575, 'synset': 'home_plate.n.01', 'synonyms': ['home_plate_(baseball)', 'home_base_(baseball)'], 'def': '(baseball) a rubber slab where the batter stands; it must be touched by a base runner in order to score', 'name': 'home_plate_(baseball)'}, {'frequency': 'c', 'id': 576, 'synset': 'honey.n.01', 'synonyms': ['honey'], 'def': 'a sweet yellow liquid produced by bees', 'name': 'honey'}, {'frequency': 'f', 'id': 577, 'synset': 'hood.n.06', 'synonyms': ['fume_hood', 'exhaust_hood'], 'def': 'metal covering leading to a vent that exhausts smoke or fumes', 'name': 'fume_hood'}, {'frequency': 'f', 'id': 578, 'synset': 'hook.n.05', 'synonyms': ['hook'], 'def': 'a curved or bent implement for suspending or pulling something', 'name': 'hook'}, {'frequency': 'f', 'id': 579, 'synset': 'horse.n.01', 'synonyms': ['horse'], 'def': 'a common horse', 'name': 'horse'}, {'frequency': 'f', 'id': 580, 'synset': 'hose.n.03', 'synonyms': ['hose', 'hosepipe'], 'def': 'a flexible pipe for conveying a liquid or gas', 'name': 'hose'}, {'frequency': 'r', 'id': 581, 'synset': 'hot-air_balloon.n.01', 'synonyms': ['hot-air_balloon'], 'def': 'balloon for travel through the air in a basket suspended below a large bag of heated air', 'name': 'hot-air_balloon'}, {'frequency': 'r', 'id': 582, 'synset': 'hot_plate.n.01', 'synonyms': ['hotplate'], 'def': 'a portable electric appliance for heating or cooking or keeping food warm', 'name': 'hotplate'}, {'frequency': 'c', 'id': 583, 'synset': 'hot_sauce.n.01', 'synonyms': ['hot_sauce'], 'def': 'a pungent peppery sauce', 'name': 'hot_sauce'}, {'frequency': 'r', 'id': 584, 'synset': 'hourglass.n.01', 'synonyms': ['hourglass'], 'def': 'a sandglass timer that runs for sixty minutes', 'name': 'hourglass'}, {'frequency': 'r', 'id': 585, 'synset': 'houseboat.n.01', 'synonyms': ['houseboat'], 'def': 'a barge that is designed and equipped for use as a dwelling', 'name': 'houseboat'}, {'frequency': 'r', 'id': 586, 'synset': 'hummingbird.n.01', 'synonyms': ['hummingbird'], 'def': 'tiny American bird having brilliant iridescent plumage and long slender bills', 'name': 'hummingbird'}, {'frequency': 'r', 'id': 587, 'synset': 'hummus.n.01', 'synonyms': ['hummus', 'humus', 'hommos', 'hoummos', 'humous'], 'def': 'a thick spread made from mashed chickpeas', 'name': 'hummus'}, {'frequency': 'c', 'id': 588, 'synset': 'ice_bear.n.01', 'synonyms': ['polar_bear'], 'def': 'white bear of Arctic regions', 'name': 'polar_bear'}, {'frequency': 'c', 'id': 589, 'synset': 'ice_cream.n.01', 'synonyms': ['icecream'], 'def': 'frozen dessert containing cream and sugar and flavoring', 'name': 'icecream'}, {'frequency': 'r', 'id': 590, 'synset': 'ice_lolly.n.01', 'synonyms': ['popsicle'], 'def': 'ice cream or water ice on a small wooden stick', 'name': 'popsicle'}, {'frequency': 'c', 'id': 591, 'synset': 'ice_maker.n.01', 'synonyms': ['ice_maker'], 'def': 'an appliance included in some electric refrigerators for making ice cubes', 'name': 'ice_maker'}, {'frequency': 'r', 'id': 592, 'synset': 'ice_pack.n.01', 'synonyms': ['ice_pack', 'ice_bag'], 'def': 'a waterproof bag filled with ice: applied to the body (especially the head) to cool or reduce swelling', 'name': 'ice_pack'}, {'frequency': 'r', 'id': 593, 'synset': 'ice_skate.n.01', 'synonyms': ['ice_skate'], 'def': 'skate consisting of a boot with a steel blade fitted to the sole', 'name': 'ice_skate'}, {'frequency': 'r', 'id': 594, 'synset': 'ice_tea.n.01', 'synonyms': ['ice_tea', 'iced_tea'], 'def': 'strong tea served over ice', 'name': 'ice_tea'}, {'frequency': 'c', 'id': 595, 'synset': 'igniter.n.01', 'synonyms': ['igniter', 'ignitor', 'lighter'], 'def': 'a substance or device used to start a fire', 'name': 'igniter'}, {'frequency': 'r', 'id': 596, 'synset': 'incense.n.01', 'synonyms': ['incense'], 'def': 'a substance that produces a fragrant odor when burned', 'name': 'incense'}, {'frequency': 'r', 'id': 597, 'synset': 'inhaler.n.01', 'synonyms': ['inhaler', 'inhalator'], 'def': 'a dispenser that produces a chemical vapor to be inhaled through mouth or nose', 'name': 'inhaler'}, {'frequency': 'c', 'id': 598, 'synset': 'ipod.n.01', 'synonyms': ['iPod'], 'def': 'a pocket-sized device used to play music files', 'name': 'iPod'}, {'frequency': 'c', 'id': 599, 'synset': 'iron.n.04', 'synonyms': ['iron_(for_clothing)', 'smoothing_iron_(for_clothing)'], 'def': 'home appliance consisting of a flat metal base that is heated and used to smooth cloth', 'name': 'iron_(for_clothing)'}, {'frequency': 'r', 'id': 600, 'synset': 'ironing_board.n.01', 'synonyms': ['ironing_board'], 'def': 'narrow padded board on collapsible supports; used for ironing clothes', 'name': 'ironing_board'}, {'frequency': 'f', 'id': 601, 'synset': 'jacket.n.01', 'synonyms': ['jacket'], 'def': 'a waist-length coat', 'name': 'jacket'}, {'frequency': 'r', 'id': 602, 'synset': 'jam.n.01', 'synonyms': ['jam'], 'def': 'preserve of crushed fruit', 'name': 'jam'}, {'frequency': 'f', 'id': 603, 'synset': 'jean.n.01', 'synonyms': ['jean', 'blue_jean', 'denim'], 'def': '(usually plural) close-fitting trousers of heavy denim for manual work or casual wear', 'name': 'jean'}, {'frequency': 'c', 'id': 604, 'synset': 'jeep.n.01', 'synonyms': ['jeep', 'landrover'], 'def': 'a car suitable for traveling over rough terrain', 'name': 'jeep'}, {'frequency': 'r', 'id': 605, 'synset': 'jelly_bean.n.01', 'synonyms': ['jelly_bean', 'jelly_egg'], 'def': 'sugar-glazed jellied candy', 'name': 'jelly_bean'}, {'frequency': 'f', 'id': 606, 'synset': 'jersey.n.03', 'synonyms': ['jersey', 'T-shirt', 'tee_shirt'], 'def': 'a close-fitting pullover shirt', 'name': 'jersey'}, {'frequency': 'c', 'id': 607, 'synset': 'jet.n.01', 'synonyms': ['jet_plane', 'jet-propelled_plane'], 'def': 'an airplane powered by one or more jet engines', 'name': 'jet_plane'}, {'frequency': 'c', 'id': 608, 'synset': 'jewelry.n.01', 'synonyms': ['jewelry', 'jewellery'], 'def': 'an adornment (as a bracelet or ring or necklace) made of precious metals and set with gems (or imitation gems)', 'name': 'jewelry'}, {'frequency': 'r', 'id': 609, 'synset': 'joystick.n.02', 'synonyms': ['joystick'], 'def': 'a control device for computers consisting of a vertical handle that can move freely in two directions', 'name': 'joystick'}, {'frequency': 'r', 'id': 610, 'synset': 'jump_suit.n.01', 'synonyms': ['jumpsuit'], 'def': "one-piece garment fashioned after a parachutist's uniform", 'name': 'jumpsuit'}, {'frequency': 'c', 'id': 611, 'synset': 'kayak.n.01', 'synonyms': ['kayak'], 'def': 'a small canoe consisting of a light frame made watertight with animal skins', 'name': 'kayak'}, {'frequency': 'r', 'id': 612, 'synset': 'keg.n.02', 'synonyms': ['keg'], 'def': 'small cask or barrel', 'name': 'keg'}, {'frequency': 'r', 'id': 613, 'synset': 'kennel.n.01', 'synonyms': ['kennel', 'doghouse'], 'def': 'outbuilding that serves as a shelter for a dog', 'name': 'kennel'}, {'frequency': 'c', 'id': 614, 'synset': 'kettle.n.01', 'synonyms': ['kettle', 'boiler'], 'def': 'a metal pot for stewing or boiling; usually has a lid', 'name': 'kettle'}, {'frequency': 'f', 'id': 615, 'synset': 'key.n.01', 'synonyms': ['key'], 'def': 'metal instrument used to unlock a lock', 'name': 'key'}, {'frequency': 'r', 'id': 616, 'synset': 'keycard.n.01', 'synonyms': ['keycard'], 'def': 'a plastic card used to gain access typically to a door', 'name': 'keycard'}, {'frequency': 'r', 'id': 617, 'synset': 'kilt.n.01', 'synonyms': ['kilt'], 'def': 'a knee-length pleated tartan skirt worn by men as part of the traditional dress in the Highlands of northern Scotland', 'name': 'kilt'}, {'frequency': 'c', 'id': 618, 'synset': 'kimono.n.01', 'synonyms': ['kimono'], 'def': 'a loose robe; imitated from robes originally worn by Japanese', 'name': 'kimono'}, {'frequency': 'f', 'id': 619, 'synset': 'kitchen_sink.n.01', 'synonyms': ['kitchen_sink'], 'def': 'a sink in a kitchen', 'name': 'kitchen_sink'}, {'frequency': 'c', 'id': 620, 'synset': 'kitchen_table.n.01', 'synonyms': ['kitchen_table'], 'def': 'a table in the kitchen', 'name': 'kitchen_table'}, {'frequency': 'f', 'id': 621, 'synset': 'kite.n.03', 'synonyms': ['kite'], 'def': 'plaything consisting of a light frame covered with tissue paper; flown in wind at end of a string', 'name': 'kite'}, {'frequency': 'c', 'id': 622, 'synset': 'kitten.n.01', 'synonyms': ['kitten', 'kitty'], 'def': 'young domestic cat', 'name': 'kitten'}, {'frequency': 'c', 'id': 623, 'synset': 'kiwi.n.03', 'synonyms': ['kiwi_fruit'], 'def': 'fuzzy brown egg-shaped fruit with slightly tart green flesh', 'name': 'kiwi_fruit'}, {'frequency': 'f', 'id': 624, 'synset': 'knee_pad.n.01', 'synonyms': ['knee_pad'], 'def': 'protective garment consisting of a pad worn by football or baseball or hockey players', 'name': 'knee_pad'}, {'frequency': 'f', 'id': 625, 'synset': 'knife.n.01', 'synonyms': ['knife'], 'def': 'tool with a blade and point used as a cutting instrument', 'name': 'knife'}, {'frequency': 'r', 'id': 626, 'synset': 'knight.n.02', 'synonyms': ['knight_(chess_piece)', 'horse_(chess_piece)'], 'def': 'a chess game piece shaped to resemble the head of a horse', 'name': 'knight_(chess_piece)'}, {'frequency': 'r', 'id': 627, 'synset': 'knitting_needle.n.01', 'synonyms': ['knitting_needle'], 'def': 'needle consisting of a slender rod with pointed ends; usually used in pairs', 'name': 'knitting_needle'}, {'frequency': 'f', 'id': 628, 'synset': 'knob.n.02', 'synonyms': ['knob'], 'def': 'a round handle often found on a door', 'name': 'knob'}, {'frequency': 'r', 'id': 629, 'synset': 'knocker.n.05', 'synonyms': ['knocker_(on_a_door)', 'doorknocker'], 'def': 'a device (usually metal and ornamental) attached by a hinge to a door', 'name': 'knocker_(on_a_door)'}, {'frequency': 'r', 'id': 630, 'synset': 'koala.n.01', 'synonyms': ['koala', 'koala_bear'], 'def': 'sluggish tailless Australian marsupial with grey furry ears and coat', 'name': 'koala'}, {'frequency': 'r', 'id': 631, 'synset': 'lab_coat.n.01', 'synonyms': ['lab_coat', 'laboratory_coat'], 'def': 'a light coat worn to protect clothing from substances used while working in a laboratory', 'name': 'lab_coat'}, {'frequency': 'f', 'id': 632, 'synset': 'ladder.n.01', 'synonyms': ['ladder'], 'def': 'steps consisting of two parallel members connected by rungs', 'name': 'ladder'}, {'frequency': 'c', 'id': 633, 'synset': 'ladle.n.01', 'synonyms': ['ladle'], 'def': 'a spoon-shaped vessel with a long handle frequently used to transfer liquids', 'name': 'ladle'}, {'frequency': 'r', 'id': 634, 'synset': 'ladybug.n.01', 'synonyms': ['ladybug', 'ladybeetle', 'ladybird_beetle'], 'def': 'small round bright-colored and spotted beetle, typically red and black', 'name': 'ladybug'}, {'frequency': 'c', 'id': 635, 'synset': 'lamb.n.01', 'synonyms': ['lamb_(animal)'], 'def': 'young sheep', 'name': 'lamb_(animal)'}, {'frequency': 'r', 'id': 636, 'synset': 'lamb_chop.n.01', 'synonyms': ['lamb-chop', 'lambchop'], 'def': 'chop cut from a lamb', 'name': 'lamb-chop'}, {'frequency': 'f', 'id': 637, 'synset': 'lamp.n.02', 'synonyms': ['lamp'], 'def': 'a piece of furniture holding one or more electric light bulbs', 'name': 'lamp'}, {'frequency': 'f', 'id': 638, 'synset': 'lamppost.n.01', 'synonyms': ['lamppost'], 'def': 'a metal post supporting an outdoor lamp (such as a streetlight)', 'name': 'lamppost'}, {'frequency': 'f', 'id': 639, 'synset': 'lampshade.n.01', 'synonyms': ['lampshade'], 'def': 'a protective ornamental shade used to screen a light bulb from direct view', 'name': 'lampshade'}, {'frequency': 'c', 'id': 640, 'synset': 'lantern.n.01', 'synonyms': ['lantern'], 'def': 'light in a transparent protective case', 'name': 'lantern'}, {'frequency': 'f', 'id': 641, 'synset': 'lanyard.n.02', 'synonyms': ['lanyard', 'laniard'], 'def': 'a cord worn around the neck to hold a knife or whistle, etc.', 'name': 'lanyard'}, {'frequency': 'f', 'id': 642, 'synset': 'laptop.n.01', 'synonyms': ['laptop_computer', 'notebook_computer'], 'def': 'a portable computer small enough to use in your lap', 'name': 'laptop_computer'}, {'frequency': 'r', 'id': 643, 'synset': 'lasagna.n.01', 'synonyms': ['lasagna', 'lasagne'], 'def': 'baked dish of layers of lasagna pasta with sauce and cheese and meat or vegetables', 'name': 'lasagna'}, {'frequency': 'c', 'id': 644, 'synset': 'latch.n.02', 'synonyms': ['latch'], 'def': 'a bar that can be lowered or slid into a groove to fasten a door or gate', 'name': 'latch'}, {'frequency': 'r', 'id': 645, 'synset': 'lawn_mower.n.01', 'synonyms': ['lawn_mower'], 'def': 'garden tool for mowing grass on lawns', 'name': 'lawn_mower'}, {'frequency': 'r', 'id': 646, 'synset': 'leather.n.01', 'synonyms': ['leather'], 'def': 'an animal skin made smooth and flexible by removing the hair and then tanning', 'name': 'leather'}, {'frequency': 'c', 'id': 647, 'synset': 'legging.n.01', 'synonyms': ['legging_(clothing)', 'leging_(clothing)', 'leg_covering'], 'def': 'a garment covering the leg (usually extending from the knee to the ankle)', 'name': 'legging_(clothing)'}, {'frequency': 'c', 'id': 648, 'synset': 'lego.n.01', 'synonyms': ['Lego', 'Lego_set'], 'def': "a child's plastic construction set for making models from blocks", 'name': 'Lego'}, {'frequency': 'f', 'id': 649, 'synset': 'lemon.n.01', 'synonyms': ['lemon'], 'def': 'yellow oval fruit with juicy acidic flesh', 'name': 'lemon'}, {'frequency': 'r', 'id': 650, 'synset': 'lemonade.n.01', 'synonyms': ['lemonade'], 'def': 'sweetened beverage of diluted lemon juice', 'name': 'lemonade'}, {'frequency': 'f', 'id': 651, 'synset': 'lettuce.n.02', 'synonyms': ['lettuce'], 'def': 'leafy plant commonly eaten in salad or on sandwiches', 'name': 'lettuce'}, {'frequency': 'f', 'id': 652, 'synset': 'license_plate.n.01', 'synonyms': ['license_plate', 'numberplate'], 'def': "a plate mounted on the front and back of car and bearing the car's registration number", 'name': 'license_plate'}, {'frequency': 'f', 'id': 653, 'synset': 'life_buoy.n.01', 'synonyms': ['life_buoy', 'lifesaver', 'life_belt', 'life_ring'], 'def': 'a ring-shaped life preserver used to prevent drowning (NOT a life-jacket or vest)', 'name': 'life_buoy'}, {'frequency': 'f', 'id': 654, 'synset': 'life_jacket.n.01', 'synonyms': ['life_jacket', 'life_vest'], 'def': 'life preserver consisting of a sleeveless jacket of buoyant or inflatable design', 'name': 'life_jacket'}, {'frequency': 'f', 'id': 655, 'synset': 'light_bulb.n.01', 'synonyms': ['lightbulb'], 'def': 'glass bulb or tube shaped electric device that emits light (DO NOT MARK LAMPS AS A WHOLE)', 'name': 'lightbulb'}, {'frequency': 'r', 'id': 656, 'synset': 'lightning_rod.n.02', 'synonyms': ['lightning_rod', 'lightning_conductor'], 'def': 'a metallic conductor that is attached to a high point and leads to the ground', 'name': 'lightning_rod'}, {'frequency': 'c', 'id': 657, 'synset': 'lime.n.06', 'synonyms': ['lime'], 'def': 'the green acidic fruit of any of various lime trees', 'name': 'lime'}, {'frequency': 'r', 'id': 658, 'synset': 'limousine.n.01', 'synonyms': ['limousine'], 'def': 'long luxurious car; usually driven by a chauffeur', 'name': 'limousine'}, {'frequency': 'r', 'id': 659, 'synset': 'linen.n.02', 'synonyms': ['linen_paper'], 'def': 'a high-quality paper made of linen fibers or with a linen finish', 'name': 'linen_paper'}, {'frequency': 'c', 'id': 660, 'synset': 'lion.n.01', 'synonyms': ['lion'], 'def': 'large gregarious predatory cat of Africa and India', 'name': 'lion'}, {'frequency': 'c', 'id': 661, 'synset': 'lip_balm.n.01', 'synonyms': ['lip_balm'], 'def': 'a balm applied to the lips', 'name': 'lip_balm'}, {'frequency': 'c', 'id': 662, 'synset': 'lipstick.n.01', 'synonyms': ['lipstick', 'lip_rouge'], 'def': 'makeup that is used to color the lips', 'name': 'lipstick'}, {'frequency': 'r', 'id': 663, 'synset': 'liquor.n.01', 'synonyms': ['liquor', 'spirits', 'hard_liquor', 'liqueur', 'cordial'], 'def': 'an alcoholic beverage that is distilled rather than fermented', 'name': 'liquor'}, {'frequency': 'r', 'id': 664, 'synset': 'lizard.n.01', 'synonyms': ['lizard'], 'def': 'a reptile with usually two pairs of legs and a tapering tail', 'name': 'lizard'}, {'frequency': 'r', 'id': 665, 'synset': 'loafer.n.02', 'synonyms': ['Loafer_(type_of_shoe)'], 'def': 'a low leather step-in shoe', 'name': 'Loafer_(type_of_shoe)'}, {'frequency': 'f', 'id': 666, 'synset': 'log.n.01', 'synonyms': ['log'], 'def': 'a segment of the trunk of a tree when stripped of branches', 'name': 'log'}, {'frequency': 'c', 'id': 667, 'synset': 'lollipop.n.02', 'synonyms': ['lollipop'], 'def': 'hard candy on a stick', 'name': 'lollipop'}, {'frequency': 'c', 'id': 668, 'synset': 'lotion.n.01', 'synonyms': ['lotion'], 'def': 'any of various cosmetic preparations that are applied to the skin', 'name': 'lotion'}, {'frequency': 'f', 'id': 669, 'synset': 'loudspeaker.n.01', 'synonyms': ['speaker_(stero_equipment)'], 'def': 'electronic device that produces sound often as part of a stereo system', 'name': 'speaker_(stero_equipment)'}, {'frequency': 'c', 'id': 670, 'synset': 'love_seat.n.01', 'synonyms': ['loveseat'], 'def': 'small sofa that seats two people', 'name': 'loveseat'}, {'frequency': 'r', 'id': 671, 'synset': 'machine_gun.n.01', 'synonyms': ['machine_gun'], 'def': 'a rapidly firing automatic gun', 'name': 'machine_gun'}, {'frequency': 'f', 'id': 672, 'synset': 'magazine.n.02', 'synonyms': ['magazine'], 'def': 'a paperback periodic publication', 'name': 'magazine'}, {'frequency': 'f', 'id': 673, 'synset': 'magnet.n.01', 'synonyms': ['magnet'], 'def': 'a device that attracts iron and produces a magnetic field', 'name': 'magnet'}, {'frequency': 'r', 'id': 674, 'synset': 'mail_slot.n.01', 'synonyms': ['mail_slot'], 'def': 'a slot (usually in a door) through which mail can be delivered', 'name': 'mail_slot'}, {'frequency': 'c', 'id': 675, 'synset': 'mailbox.n.01', 'synonyms': ['mailbox_(at_home)', 'letter_box_(at_home)'], 'def': 'a private box for delivery of mail', 'name': 'mailbox_(at_home)'}, {'frequency': 'r', 'id': 676, 'synset': 'mallet.n.01', 'synonyms': ['mallet'], 'def': 'a sports implement with a long handle and a hammer-like head used to hit a ball', 'name': 'mallet'}, {'frequency': 'r', 'id': 677, 'synset': 'mammoth.n.01', 'synonyms': ['mammoth'], 'def': 'any of numerous extinct elephants widely distributed in the Pleistocene', 'name': 'mammoth'}, {'frequency': 'c', 'id': 678, 'synset': 'mandarin.n.05', 'synonyms': ['mandarin_orange'], 'def': 'a somewhat flat reddish-orange loose skinned citrus of China', 'name': 'mandarin_orange'}, {'frequency': 'c', 'id': 679, 'synset': 'manger.n.01', 'synonyms': ['manger', 'trough'], 'def': 'a container (usually in a barn or stable) from which cattle or horses feed', 'name': 'manger'}, {'frequency': 'f', 'id': 680, 'synset': 'manhole.n.01', 'synonyms': ['manhole'], 'def': 'a hole (usually with a flush cover) through which a person can gain access to an underground structure', 'name': 'manhole'}, {'frequency': 'c', 'id': 681, 'synset': 'map.n.01', 'synonyms': ['map'], 'def': "a diagrammatic representation of the earth's surface (or part of it)", 'name': 'map'}, {'frequency': 'c', 'id': 682, 'synset': 'marker.n.03', 'synonyms': ['marker'], 'def': 'a writing implement for making a mark', 'name': 'marker'}, {'frequency': 'r', 'id': 683, 'synset': 'martini.n.01', 'synonyms': ['martini'], 'def': 'a cocktail made of gin (or vodka) with dry vermouth', 'name': 'martini'}, {'frequency': 'r', 'id': 684, 'synset': 'mascot.n.01', 'synonyms': ['mascot'], 'def': 'a person or animal that is adopted by a team or other group as a symbolic figure', 'name': 'mascot'}, {'frequency': 'c', 'id': 685, 'synset': 'mashed_potato.n.01', 'synonyms': ['mashed_potato'], 'def': 'potato that has been peeled and boiled and then mashed', 'name': 'mashed_potato'}, {'frequency': 'r', 'id': 686, 'synset': 'masher.n.02', 'synonyms': ['masher'], 'def': 'a kitchen utensil used for mashing (e.g. potatoes)', 'name': 'masher'}, {'frequency': 'f', 'id': 687, 'synset': 'mask.n.04', 'synonyms': ['mask', 'facemask'], 'def': 'a protective covering worn over the face', 'name': 'mask'}, {'frequency': 'f', 'id': 688, 'synset': 'mast.n.01', 'synonyms': ['mast'], 'def': 'a vertical spar for supporting sails', 'name': 'mast'}, {'frequency': 'c', 'id': 689, 'synset': 'mat.n.03', 'synonyms': ['mat_(gym_equipment)', 'gym_mat'], 'def': 'sports equipment consisting of a piece of thick padding on the floor for gymnastics', 'name': 'mat_(gym_equipment)'}, {'frequency': 'r', 'id': 690, 'synset': 'matchbox.n.01', 'synonyms': ['matchbox'], 'def': 'a box for holding matches', 'name': 'matchbox'}, {'frequency': 'f', 'id': 691, 'synset': 'mattress.n.01', 'synonyms': ['mattress'], 'def': 'a thick pad filled with resilient material used as a bed or part of a bed', 'name': 'mattress'}, {'frequency': 'c', 'id': 692, 'synset': 'measuring_cup.n.01', 'synonyms': ['measuring_cup'], 'def': 'graduated cup used to measure liquid or granular ingredients', 'name': 'measuring_cup'}, {'frequency': 'c', 'id': 693, 'synset': 'measuring_stick.n.01', 'synonyms': ['measuring_stick', 'ruler_(measuring_stick)', 'measuring_rod'], 'def': 'measuring instrument having a sequence of marks at regular intervals', 'name': 'measuring_stick'}, {'frequency': 'c', 'id': 694, 'synset': 'meatball.n.01', 'synonyms': ['meatball'], 'def': 'ground meat formed into a ball and fried or simmered in broth', 'name': 'meatball'}, {'frequency': 'c', 'id': 695, 'synset': 'medicine.n.02', 'synonyms': ['medicine'], 'def': 'something that treats or prevents or alleviates the symptoms of disease', 'name': 'medicine'}, {'frequency': 'r', 'id': 696, 'synset': 'melon.n.01', 'synonyms': ['melon'], 'def': 'fruit of the gourd family having a hard rind and sweet juicy flesh', 'name': 'melon'}, {'frequency': 'f', 'id': 697, 'synset': 'microphone.n.01', 'synonyms': ['microphone'], 'def': 'device for converting sound waves into electrical energy', 'name': 'microphone'}, {'frequency': 'r', 'id': 698, 'synset': 'microscope.n.01', 'synonyms': ['microscope'], 'def': 'magnifier of the image of small objects', 'name': 'microscope'}, {'frequency': 'f', 'id': 699, 'synset': 'microwave.n.02', 'synonyms': ['microwave_oven'], 'def': 'kitchen appliance that cooks food by passing an electromagnetic wave through it', 'name': 'microwave_oven'}, {'frequency': 'r', 'id': 700, 'synset': 'milestone.n.01', 'synonyms': ['milestone', 'milepost'], 'def': 'stone post at side of a road to show distances', 'name': 'milestone'}, {'frequency': 'c', 'id': 701, 'synset': 'milk.n.01', 'synonyms': ['milk'], 'def': 'a white nutritious liquid secreted by mammals and used as food by human beings', 'name': 'milk'}, {'frequency': 'f', 'id': 702, 'synset': 'minivan.n.01', 'synonyms': ['minivan'], 'def': 'a small box-shaped passenger van', 'name': 'minivan'}, {'frequency': 'r', 'id': 703, 'synset': 'mint.n.05', 'synonyms': ['mint_candy'], 'def': 'a candy that is flavored with a mint oil', 'name': 'mint_candy'}, {'frequency': 'f', 'id': 704, 'synset': 'mirror.n.01', 'synonyms': ['mirror'], 'def': 'polished surface that forms images by reflecting light', 'name': 'mirror'}, {'frequency': 'c', 'id': 705, 'synset': 'mitten.n.01', 'synonyms': ['mitten'], 'def': 'glove that encases the thumb separately and the other four fingers together', 'name': 'mitten'}, {'frequency': 'c', 'id': 706, 'synset': 'mixer.n.04', 'synonyms': ['mixer_(kitchen_tool)', 'stand_mixer'], 'def': 'a kitchen utensil that is used for mixing foods', 'name': 'mixer_(kitchen_tool)'}, {'frequency': 'c', 'id': 707, 'synset': 'money.n.03', 'synonyms': ['money'], 'def': 'the official currency issued by a government or national bank', 'name': 'money'}, {'frequency': 'f', 'id': 708, 'synset': 'monitor.n.04', 'synonyms': ['monitor_(computer_equipment) computer_monitor'], 'def': 'a computer monitor', 'name': 'monitor_(computer_equipment) computer_monitor'}, {'frequency': 'c', 'id': 709, 'synset': 'monkey.n.01', 'synonyms': ['monkey'], 'def': 'any of various long-tailed primates', 'name': 'monkey'}, {'frequency': 'f', 'id': 710, 'synset': 'motor.n.01', 'synonyms': ['motor'], 'def': 'machine that converts other forms of energy into mechanical energy and so imparts motion', 'name': 'motor'}, {'frequency': 'f', 'id': 711, 'synset': 'motor_scooter.n.01', 'synonyms': ['motor_scooter', 'scooter'], 'def': 'a wheeled vehicle with small wheels and a low-powered engine', 'name': 'motor_scooter'}, {'frequency': 'r', 'id': 712, 'synset': 'motor_vehicle.n.01', 'synonyms': ['motor_vehicle', 'automotive_vehicle'], 'def': 'a self-propelled wheeled vehicle that does not run on rails', 'name': 'motor_vehicle'}, {'frequency': 'r', 'id': 713, 'synset': 'motorboat.n.01', 'synonyms': ['motorboat', 'powerboat'], 'def': 'a boat propelled by an internal-combustion engine', 'name': 'motorboat'}, {'frequency': 'f', 'id': 714, 'synset': 'motorcycle.n.01', 'synonyms': ['motorcycle'], 'def': 'a motor vehicle with two wheels and a strong frame', 'name': 'motorcycle'}, {'frequency': 'f', 'id': 715, 'synset': 'mound.n.01', 'synonyms': ['mound_(baseball)', "pitcher's_mound"], 'def': '(baseball) the slight elevation on which the pitcher stands', 'name': 'mound_(baseball)'}, {'frequency': 'r', 'id': 716, 'synset': 'mouse.n.01', 'synonyms': ['mouse_(animal_rodent)'], 'def': 'a small rodent with pointed snouts and small ears on elongated bodies with slender usually hairless tails', 'name': 'mouse_(animal_rodent)'}, {'frequency': 'f', 'id': 717, 'synset': 'mouse.n.04', 'synonyms': ['mouse_(computer_equipment)', 'computer_mouse'], 'def': 'a computer input device that controls an on-screen pointer', 'name': 'mouse_(computer_equipment)'}, {'frequency': 'f', 'id': 718, 'synset': 'mousepad.n.01', 'synonyms': ['mousepad'], 'def': 'a small portable pad that provides an operating surface for a computer mouse', 'name': 'mousepad'}, {'frequency': 'c', 'id': 719, 'synset': 'muffin.n.01', 'synonyms': ['muffin'], 'def': 'a sweet quick bread baked in a cup-shaped pan', 'name': 'muffin'}, {'frequency': 'f', 'id': 720, 'synset': 'mug.n.04', 'synonyms': ['mug'], 'def': 'with handle and usually cylindrical', 'name': 'mug'}, {'frequency': 'f', 'id': 721, 'synset': 'mushroom.n.02', 'synonyms': ['mushroom'], 'def': 'a common mushroom', 'name': 'mushroom'}, {'frequency': 'r', 'id': 722, 'synset': 'music_stool.n.01', 'synonyms': ['music_stool', 'piano_stool'], 'def': 'a stool for piano players; usually adjustable in height', 'name': 'music_stool'}, {'frequency': 'r', 'id': 723, 'synset': 'musical_instrument.n.01', 'synonyms': ['musical_instrument', 'instrument_(musical)'], 'def': 'any of various devices or contrivances that can be used to produce musical tones or sounds', 'name': 'musical_instrument'}, {'frequency': 'r', 'id': 724, 'synset': 'nailfile.n.01', 'synonyms': ['nailfile'], 'def': 'a small flat file for shaping the nails', 'name': 'nailfile'}, {'frequency': 'r', 'id': 725, 'synset': 'nameplate.n.01', 'synonyms': ['nameplate'], 'def': 'a plate bearing a name', 'name': 'nameplate'}, {'frequency': 'f', 'id': 726, 'synset': 'napkin.n.01', 'synonyms': ['napkin', 'table_napkin', 'serviette'], 'def': 'a small piece of table linen or paper that is used to wipe the mouth and to cover the lap in order to protect clothing', 'name': 'napkin'}, {'frequency': 'r', 'id': 727, 'synset': 'neckerchief.n.01', 'synonyms': ['neckerchief'], 'def': 'a kerchief worn around the neck', 'name': 'neckerchief'}, {'frequency': 'f', 'id': 728, 'synset': 'necklace.n.01', 'synonyms': ['necklace'], 'def': 'jewelry consisting of a cord or chain (often bearing gems) worn about the neck as an ornament', 'name': 'necklace'}, {'frequency': 'f', 'id': 729, 'synset': 'necktie.n.01', 'synonyms': ['necktie', 'tie_(necktie)'], 'def': 'neckwear consisting of a long narrow piece of material worn under a collar and tied in knot at the front', 'name': 'necktie'}, {'frequency': 'r', 'id': 730, 'synset': 'needle.n.03', 'synonyms': ['needle'], 'def': 'a sharp pointed implement (usually metal)', 'name': 'needle'}, {'frequency': 'c', 'id': 731, 'synset': 'nest.n.01', 'synonyms': ['nest'], 'def': 'a structure in which animals lay eggs or give birth to their young', 'name': 'nest'}, {'frequency': 'r', 'id': 732, 'synset': 'newsstand.n.01', 'synonyms': ['newsstand'], 'def': 'a stall where newspapers and other periodicals are sold', 'name': 'newsstand'}, {'frequency': 'c', 'id': 733, 'synset': 'nightwear.n.01', 'synonyms': ['nightshirt', 'nightwear', 'sleepwear', 'nightclothes'], 'def': 'garments designed to be worn in bed', 'name': 'nightshirt'}, {'frequency': 'r', 'id': 734, 'synset': 'nosebag.n.01', 'synonyms': ['nosebag_(for_animals)', 'feedbag'], 'def': 'a canvas bag that is used to feed an animal (such as a horse); covers the muzzle and fastens at the top of the head', 'name': 'nosebag_(for_animals)'}, {'frequency': 'r', 'id': 735, 'synset': 'noseband.n.01', 'synonyms': ['noseband_(for_animals)', 'nosepiece_(for_animals)'], 'def': "a strap that is the part of a bridle that goes over the animal's nose", 'name': 'noseband_(for_animals)'}, {'frequency': 'f', 'id': 736, 'synset': 'notebook.n.01', 'synonyms': ['notebook'], 'def': 'a book with blank pages for recording notes or memoranda', 'name': 'notebook'}, {'frequency': 'c', 'id': 737, 'synset': 'notepad.n.01', 'synonyms': ['notepad'], 'def': 'a pad of paper for keeping notes', 'name': 'notepad'}, {'frequency': 'c', 'id': 738, 'synset': 'nut.n.03', 'synonyms': ['nut'], 'def': 'a small metal block (usually square or hexagonal) with internal screw thread to be fitted onto a bolt', 'name': 'nut'}, {'frequency': 'r', 'id': 739, 'synset': 'nutcracker.n.01', 'synonyms': ['nutcracker'], 'def': 'a hand tool used to crack nuts open', 'name': 'nutcracker'}, {'frequency': 'c', 'id': 740, 'synset': 'oar.n.01', 'synonyms': ['oar'], 'def': 'an implement used to propel or steer a boat', 'name': 'oar'}, {'frequency': 'r', 'id': 741, 'synset': 'octopus.n.01', 'synonyms': ['octopus_(food)'], 'def': 'tentacles of octopus prepared as food', 'name': 'octopus_(food)'}, {'frequency': 'r', 'id': 742, 'synset': 'octopus.n.02', 'synonyms': ['octopus_(animal)'], 'def': 'bottom-living cephalopod having a soft oval body with eight long tentacles', 'name': 'octopus_(animal)'}, {'frequency': 'c', 'id': 743, 'synset': 'oil_lamp.n.01', 'synonyms': ['oil_lamp', 'kerosene_lamp', 'kerosine_lamp'], 'def': 'a lamp that burns oil (as kerosine) for light', 'name': 'oil_lamp'}, {'frequency': 'c', 'id': 744, 'synset': 'olive_oil.n.01', 'synonyms': ['olive_oil'], 'def': 'oil from olives', 'name': 'olive_oil'}, {'frequency': 'r', 'id': 745, 'synset': 'omelet.n.01', 'synonyms': ['omelet', 'omelette'], 'def': 'beaten eggs cooked until just set; may be folded around e.g. ham or cheese or jelly', 'name': 'omelet'}, {'frequency': 'f', 'id': 746, 'synset': 'onion.n.01', 'synonyms': ['onion'], 'def': 'the bulb of an onion plant', 'name': 'onion'}, {'frequency': 'f', 'id': 747, 'synset': 'orange.n.01', 'synonyms': ['orange_(fruit)'], 'def': 'orange (FRUIT of an orange tree)', 'name': 'orange_(fruit)'}, {'frequency': 'c', 'id': 748, 'synset': 'orange_juice.n.01', 'synonyms': ['orange_juice'], 'def': 'bottled or freshly squeezed juice of oranges', 'name': 'orange_juice'}, {'frequency': 'r', 'id': 749, 'synset': 'oregano.n.01', 'synonyms': ['oregano', 'marjoram'], 'def': 'aromatic Eurasian perennial herb used in cooking and baking', 'name': 'oregano'}, {'frequency': 'c', 'id': 750, 'synset': 'ostrich.n.02', 'synonyms': ['ostrich'], 'def': 'fast-running African flightless bird with two-toed feet; largest living bird', 'name': 'ostrich'}, {'frequency': 'c', 'id': 751, 'synset': 'ottoman.n.03', 'synonyms': ['ottoman', 'pouf', 'pouffe', 'hassock'], 'def': 'thick cushion used as a seat', 'name': 'ottoman'}, {'frequency': 'c', 'id': 752, 'synset': 'overall.n.01', 'synonyms': ['overalls_(clothing)'], 'def': 'work clothing consisting of denim trousers usually with a bib and shoulder straps', 'name': 'overalls_(clothing)'}, {'frequency': 'c', 'id': 753, 'synset': 'owl.n.01', 'synonyms': ['owl'], 'def': 'nocturnal bird of prey with hawk-like beak and claws and large head with front-facing eyes', 'name': 'owl'}, {'frequency': 'c', 'id': 754, 'synset': 'packet.n.03', 'synonyms': ['packet'], 'def': 'a small package or bundle', 'name': 'packet'}, {'frequency': 'r', 'id': 755, 'synset': 'pad.n.03', 'synonyms': ['inkpad', 'inking_pad', 'stamp_pad'], 'def': 'absorbent material saturated with ink used to transfer ink evenly to a rubber stamp', 'name': 'inkpad'}, {'frequency': 'c', 'id': 756, 'synset': 'pad.n.04', 'synonyms': ['pad'], 'def': 'a flat mass of soft material used for protection, stuffing, or comfort', 'name': 'pad'}, {'frequency': 'c', 'id': 757, 'synset': 'paddle.n.04', 'synonyms': ['paddle', 'boat_paddle'], 'def': 'a short light oar used without an oarlock to propel a canoe or small boat', 'name': 'paddle'}, {'frequency': 'c', 'id': 758, 'synset': 'padlock.n.01', 'synonyms': ['padlock'], 'def': 'a detachable, portable lock', 'name': 'padlock'}, {'frequency': 'r', 'id': 759, 'synset': 'paintbox.n.01', 'synonyms': ['paintbox'], 'def': "a box containing a collection of cubes or tubes of artists' paint", 'name': 'paintbox'}, {'frequency': 'c', 'id': 760, 'synset': 'paintbrush.n.01', 'synonyms': ['paintbrush'], 'def': 'a brush used as an applicator to apply paint', 'name': 'paintbrush'}, {'frequency': 'f', 'id': 761, 'synset': 'painting.n.01', 'synonyms': ['painting'], 'def': 'graphic art consisting of an artistic composition made by applying paints to a surface', 'name': 'painting'}, {'frequency': 'c', 'id': 762, 'synset': 'pajama.n.02', 'synonyms': ['pajamas', 'pyjamas'], 'def': 'loose-fitting nightclothes worn for sleeping or lounging', 'name': 'pajamas'}, {'frequency': 'c', 'id': 763, 'synset': 'palette.n.02', 'synonyms': ['palette', 'pallet'], 'def': 'board that provides a flat surface on which artists mix paints and the range of colors used', 'name': 'palette'}, {'frequency': 'f', 'id': 764, 'synset': 'pan.n.01', 'synonyms': ['pan_(for_cooking)', 'cooking_pan'], 'def': 'cooking utensil consisting of a wide metal vessel', 'name': 'pan_(for_cooking)'}, {'frequency': 'r', 'id': 765, 'synset': 'pan.n.03', 'synonyms': ['pan_(metal_container)'], 'def': 'shallow container made of metal', 'name': 'pan_(metal_container)'}, {'frequency': 'c', 'id': 766, 'synset': 'pancake.n.01', 'synonyms': ['pancake'], 'def': 'a flat cake of thin batter fried on both sides on a griddle', 'name': 'pancake'}, {'frequency': 'r', 'id': 767, 'synset': 'pantyhose.n.01', 'synonyms': ['pantyhose'], 'def': "a woman's tights consisting of underpants and stockings", 'name': 'pantyhose'}, {'frequency': 'r', 'id': 768, 'synset': 'papaya.n.02', 'synonyms': ['papaya'], 'def': 'large oval melon-like tropical fruit with yellowish flesh', 'name': 'papaya'}, {'frequency': 'r', 'id': 769, 'synset': 'paper_clip.n.01', 'synonyms': ['paperclip'], 'def': 'a wire or plastic clip for holding sheets of paper together', 'name': 'paperclip'}, {'frequency': 'f', 'id': 770, 'synset': 'paper_plate.n.01', 'synonyms': ['paper_plate'], 'def': 'a disposable plate made of cardboard', 'name': 'paper_plate'}, {'frequency': 'f', 'id': 771, 'synset': 'paper_towel.n.01', 'synonyms': ['paper_towel'], 'def': 'a disposable towel made of absorbent paper', 'name': 'paper_towel'}, {'frequency': 'r', 'id': 772, 'synset': 'paperback_book.n.01', 'synonyms': ['paperback_book', 'paper-back_book', 'softback_book', 'soft-cover_book'], 'def': 'a book with paper covers', 'name': 'paperback_book'}, {'frequency': 'r', 'id': 773, 'synset': 'paperweight.n.01', 'synonyms': ['paperweight'], 'def': 'a weight used to hold down a stack of papers', 'name': 'paperweight'}, {'frequency': 'c', 'id': 774, 'synset': 'parachute.n.01', 'synonyms': ['parachute'], 'def': 'rescue equipment consisting of a device that fills with air and retards your fall', 'name': 'parachute'}, {'frequency': 'r', 'id': 775, 'synset': 'parakeet.n.01', 'synonyms': ['parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet', 'parroquet'], 'def': 'any of numerous small slender long-tailed parrots', 'name': 'parakeet'}, {'frequency': 'c', 'id': 776, 'synset': 'parasail.n.01', 'synonyms': ['parasail_(sports)'], 'def': 'parachute that will lift a person up into the air when it is towed by a motorboat or a car', 'name': 'parasail_(sports)'}, {'frequency': 'r', 'id': 777, 'synset': 'parchment.n.01', 'synonyms': ['parchment'], 'def': 'a superior paper resembling sheepskin', 'name': 'parchment'}, {'frequency': 'r', 'id': 778, 'synset': 'parka.n.01', 'synonyms': ['parka', 'anorak'], 'def': "a kind of heavy jacket (`windcheater' is a British term)", 'name': 'parka'}, {'frequency': 'f', 'id': 779, 'synset': 'parking_meter.n.01', 'synonyms': ['parking_meter'], 'def': 'a coin-operated timer located next to a parking space', 'name': 'parking_meter'}, {'frequency': 'c', 'id': 780, 'synset': 'parrot.n.01', 'synonyms': ['parrot'], 'def': 'usually brightly colored tropical birds with short hooked beaks and the ability to mimic sounds', 'name': 'parrot'}, {'frequency': 'c', 'id': 781, 'synset': 'passenger_car.n.01', 'synonyms': ['passenger_car_(part_of_a_train)', 'coach_(part_of_a_train)'], 'def': 'a railcar where passengers ride', 'name': 'passenger_car_(part_of_a_train)'}, {'frequency': 'r', 'id': 782, 'synset': 'passenger_ship.n.01', 'synonyms': ['passenger_ship'], 'def': 'a ship built to carry passengers', 'name': 'passenger_ship'}, {'frequency': 'r', 'id': 783, 'synset': 'passport.n.02', 'synonyms': ['passport'], 'def': 'a document issued by a country to a citizen allowing that person to travel abroad and re-enter the home country', 'name': 'passport'}, {'frequency': 'f', 'id': 784, 'synset': 'pastry.n.02', 'synonyms': ['pastry'], 'def': 'any of various baked foods made of dough or batter', 'name': 'pastry'}, {'frequency': 'r', 'id': 785, 'synset': 'patty.n.01', 'synonyms': ['patty_(food)'], 'def': 'small flat mass of chopped food', 'name': 'patty_(food)'}, {'frequency': 'c', 'id': 786, 'synset': 'pea.n.01', 'synonyms': ['pea_(food)'], 'def': 'seed of a pea plant used for food', 'name': 'pea_(food)'}, {'frequency': 'c', 'id': 787, 'synset': 'peach.n.03', 'synonyms': ['peach'], 'def': 'downy juicy fruit with sweet yellowish or whitish flesh', 'name': 'peach'}, {'frequency': 'c', 'id': 788, 'synset': 'peanut_butter.n.01', 'synonyms': ['peanut_butter'], 'def': 'a spread made from ground peanuts', 'name': 'peanut_butter'}, {'frequency': 'c', 'id': 789, 'synset': 'pear.n.01', 'synonyms': ['pear'], 'def': 'sweet juicy gritty-textured fruit available in many varieties', 'name': 'pear'}, {'frequency': 'r', 'id': 790, 'synset': 'peeler.n.03', 'synonyms': ['peeler_(tool_for_fruit_and_vegetables)'], 'def': 'a device for peeling vegetables or fruits', 'name': 'peeler_(tool_for_fruit_and_vegetables)'}, {'frequency': 'r', 'id': 791, 'synset': 'pegboard.n.01', 'synonyms': ['pegboard'], 'def': 'a board perforated with regularly spaced holes into which pegs can be fitted', 'name': 'pegboard'}, {'frequency': 'c', 'id': 792, 'synset': 'pelican.n.01', 'synonyms': ['pelican'], 'def': 'large long-winged warm-water seabird having a large bill with a distensible pouch for fish', 'name': 'pelican'}, {'frequency': 'f', 'id': 793, 'synset': 'pen.n.01', 'synonyms': ['pen'], 'def': 'a writing implement with a point from which ink flows', 'name': 'pen'}, {'frequency': 'c', 'id': 794, 'synset': 'pencil.n.01', 'synonyms': ['pencil'], 'def': 'a thin cylindrical pointed writing implement made of wood and graphite', 'name': 'pencil'}, {'frequency': 'r', 'id': 795, 'synset': 'pencil_box.n.01', 'synonyms': ['pencil_box', 'pencil_case'], 'def': 'a box for holding pencils', 'name': 'pencil_box'}, {'frequency': 'r', 'id': 796, 'synset': 'pencil_sharpener.n.01', 'synonyms': ['pencil_sharpener'], 'def': 'a rotary implement for sharpening the point on pencils', 'name': 'pencil_sharpener'}, {'frequency': 'r', 'id': 797, 'synset': 'pendulum.n.01', 'synonyms': ['pendulum'], 'def': 'an apparatus consisting of an object mounted so that it swings freely under the influence of gravity', 'name': 'pendulum'}, {'frequency': 'c', 'id': 798, 'synset': 'penguin.n.01', 'synonyms': ['penguin'], 'def': 'short-legged flightless birds of cold southern regions having webbed feet and wings modified as flippers', 'name': 'penguin'}, {'frequency': 'r', 'id': 799, 'synset': 'pennant.n.02', 'synonyms': ['pennant'], 'def': 'a flag longer than it is wide (and often tapering)', 'name': 'pennant'}, {'frequency': 'r', 'id': 800, 'synset': 'penny.n.02', 'synonyms': ['penny_(coin)'], 'def': 'a coin worth one-hundredth of the value of the basic unit', 'name': 'penny_(coin)'}, {'frequency': 'c', 'id': 801, 'synset': 'pepper.n.03', 'synonyms': ['pepper', 'peppercorn'], 'def': 'pungent seasoning from the berry of the common pepper plant; whole or ground', 'name': 'pepper'}, {'frequency': 'c', 'id': 802, 'synset': 'pepper_mill.n.01', 'synonyms': ['pepper_mill', 'pepper_grinder'], 'def': 'a mill for grinding pepper', 'name': 'pepper_mill'}, {'frequency': 'c', 'id': 803, 'synset': 'perfume.n.02', 'synonyms': ['perfume'], 'def': 'a toiletry that emits and diffuses a fragrant odor', 'name': 'perfume'}, {'frequency': 'r', 'id': 804, 'synset': 'persimmon.n.02', 'synonyms': ['persimmon'], 'def': 'orange fruit resembling a plum; edible when fully ripe', 'name': 'persimmon'}, {'frequency': 'f', 'id': 805, 'synset': 'person.n.01', 'synonyms': ['baby', 'child', 'boy', 'girl', 'man', 'woman', 'person', 'human'], 'def': 'a human being', 'name': 'baby'}, {'frequency': 'r', 'id': 806, 'synset': 'pet.n.01', 'synonyms': ['pet'], 'def': 'a domesticated animal kept for companionship or amusement', 'name': 'pet'}, {'frequency': 'r', 'id': 807, 'synset': 'petfood.n.01', 'synonyms': ['petfood', 'pet-food'], 'def': 'food prepared for animal pets', 'name': 'petfood'}, {'frequency': 'r', 'id': 808, 'synset': 'pew.n.01', 'synonyms': ['pew_(church_bench)', 'church_bench'], 'def': 'long bench with backs; used in church by the congregation', 'name': 'pew_(church_bench)'}, {'frequency': 'r', 'id': 809, 'synset': 'phonebook.n.01', 'synonyms': ['phonebook', 'telephone_book', 'telephone_directory'], 'def': 'a directory containing an alphabetical list of telephone subscribers and their telephone numbers', 'name': 'phonebook'}, {'frequency': 'c', 'id': 810, 'synset': 'phonograph_record.n.01', 'synonyms': ['phonograph_record', 'phonograph_recording', 'record_(phonograph_recording)'], 'def': 'sound recording consisting of a typically black disk with a continuous groove', 'name': 'phonograph_record'}, {'frequency': 'c', 'id': 811, 'synset': 'piano.n.01', 'synonyms': ['piano'], 'def': 'a keyboard instrument that is played by depressing keys that cause hammers to strike tuned strings and produce sounds', 'name': 'piano'}, {'frequency': 'f', 'id': 812, 'synset': 'pickle.n.01', 'synonyms': ['pickle'], 'def': 'vegetables (especially cucumbers) preserved in brine or vinegar', 'name': 'pickle'}, {'frequency': 'f', 'id': 813, 'synset': 'pickup.n.01', 'synonyms': ['pickup_truck'], 'def': 'a light truck with an open body and low sides and a tailboard', 'name': 'pickup_truck'}, {'frequency': 'c', 'id': 814, 'synset': 'pie.n.01', 'synonyms': ['pie'], 'def': 'dish baked in pastry-lined pan often with a pastry top', 'name': 'pie'}, {'frequency': 'c', 'id': 815, 'synset': 'pigeon.n.01', 'synonyms': ['pigeon'], 'def': 'wild and domesticated birds having a heavy body and short legs', 'name': 'pigeon'}, {'frequency': 'r', 'id': 816, 'synset': 'piggy_bank.n.01', 'synonyms': ['piggy_bank', 'penny_bank'], 'def': "a child's coin bank (often shaped like a pig)", 'name': 'piggy_bank'}, {'frequency': 'f', 'id': 817, 'synset': 'pillow.n.01', 'synonyms': ['pillow'], 'def': 'a cushion to support the head of a sleeping person', 'name': 'pillow'}, {'frequency': 'r', 'id': 818, 'synset': 'pin.n.09', 'synonyms': ['pin_(non_jewelry)'], 'def': 'a small slender (often pointed) piece of wood or metal used to support or fasten or attach things', 'name': 'pin_(non_jewelry)'}, {'frequency': 'f', 'id': 819, 'synset': 'pineapple.n.02', 'synonyms': ['pineapple'], 'def': 'large sweet fleshy tropical fruit with a tuft of stiff leaves', 'name': 'pineapple'}, {'frequency': 'c', 'id': 820, 'synset': 'pinecone.n.01', 'synonyms': ['pinecone'], 'def': 'the seed-producing cone of a pine tree', 'name': 'pinecone'}, {'frequency': 'r', 'id': 821, 'synset': 'ping-pong_ball.n.01', 'synonyms': ['ping-pong_ball'], 'def': 'light hollow ball used in playing table tennis', 'name': 'ping-pong_ball'}, {'frequency': 'r', 'id': 822, 'synset': 'pinwheel.n.03', 'synonyms': ['pinwheel'], 'def': 'a toy consisting of vanes of colored paper or plastic that is pinned to a stick and spins when it is pointed into the wind', 'name': 'pinwheel'}, {'frequency': 'r', 'id': 823, 'synset': 'pipe.n.01', 'synonyms': ['tobacco_pipe'], 'def': 'a tube with a small bowl at one end; used for smoking tobacco', 'name': 'tobacco_pipe'}, {'frequency': 'f', 'id': 824, 'synset': 'pipe.n.02', 'synonyms': ['pipe', 'piping'], 'def': 'a long tube made of metal or plastic that is used to carry water or oil or gas etc.', 'name': 'pipe'}, {'frequency': 'r', 'id': 825, 'synset': 'pistol.n.01', 'synonyms': ['pistol', 'handgun'], 'def': 'a firearm that is held and fired with one hand', 'name': 'pistol'}, {'frequency': 'r', 'id': 826, 'synset': 'pita.n.01', 'synonyms': ['pita_(bread)', 'pocket_bread'], 'def': 'usually small round bread that can open into a pocket for filling', 'name': 'pita_(bread)'}, {'frequency': 'f', 'id': 827, 'synset': 'pitcher.n.02', 'synonyms': ['pitcher_(vessel_for_liquid)', 'ewer'], 'def': 'an open vessel with a handle and a spout for pouring', 'name': 'pitcher_(vessel_for_liquid)'}, {'frequency': 'r', 'id': 828, 'synset': 'pitchfork.n.01', 'synonyms': ['pitchfork'], 'def': 'a long-handled hand tool with sharp widely spaced prongs for lifting and pitching hay', 'name': 'pitchfork'}, {'frequency': 'f', 'id': 829, 'synset': 'pizza.n.01', 'synonyms': ['pizza'], 'def': 'Italian open pie made of thin bread dough spread with a spiced mixture of e.g. tomato sauce and cheese', 'name': 'pizza'}, {'frequency': 'f', 'id': 830, 'synset': 'place_mat.n.01', 'synonyms': ['place_mat'], 'def': 'a mat placed on a table for an individual place setting', 'name': 'place_mat'}, {'frequency': 'f', 'id': 831, 'synset': 'plate.n.04', 'synonyms': ['plate'], 'def': 'dish on which food is served or from which food is eaten', 'name': 'plate'}, {'frequency': 'c', 'id': 832, 'synset': 'platter.n.01', 'synonyms': ['platter'], 'def': 'a large shallow dish used for serving food', 'name': 'platter'}, {'frequency': 'r', 'id': 833, 'synset': 'playing_card.n.01', 'synonyms': ['playing_card'], 'def': 'one of a pack of cards that are used to play card games', 'name': 'playing_card'}, {'frequency': 'r', 'id': 834, 'synset': 'playpen.n.01', 'synonyms': ['playpen'], 'def': 'a portable enclosure in which babies may be left to play', 'name': 'playpen'}, {'frequency': 'c', 'id': 835, 'synset': 'pliers.n.01', 'synonyms': ['pliers', 'plyers'], 'def': 'a gripping hand tool with two hinged arms and (usually) serrated jaws', 'name': 'pliers'}, {'frequency': 'r', 'id': 836, 'synset': 'plow.n.01', 'synonyms': ['plow_(farm_equipment)', 'plough_(farm_equipment)'], 'def': 'a farm tool having one or more heavy blades to break the soil and cut a furrow prior to sowing', 'name': 'plow_(farm_equipment)'}, {'frequency': 'r', 'id': 837, 'synset': 'pocket_watch.n.01', 'synonyms': ['pocket_watch'], 'def': 'a watch that is carried in a small watch pocket', 'name': 'pocket_watch'}, {'frequency': 'c', 'id': 838, 'synset': 'pocketknife.n.01', 'synonyms': ['pocketknife'], 'def': 'a knife with a blade that folds into the handle; suitable for carrying in the pocket', 'name': 'pocketknife'}, {'frequency': 'c', 'id': 839, 'synset': 'poker.n.01', 'synonyms': ['poker_(fire_stirring_tool)', 'stove_poker', 'fire_hook'], 'def': 'fire iron consisting of a metal rod with a handle; used to stir a fire', 'name': 'poker_(fire_stirring_tool)'}, {'frequency': 'f', 'id': 840, 'synset': 'pole.n.01', 'synonyms': ['pole', 'post'], 'def': 'a long (usually round) rod of wood or metal or plastic', 'name': 'pole'}, {'frequency': 'r', 'id': 841, 'synset': 'police_van.n.01', 'synonyms': ['police_van', 'police_wagon', 'paddy_wagon', 'patrol_wagon'], 'def': 'van used by police to transport prisoners', 'name': 'police_van'}, {'frequency': 'f', 'id': 842, 'synset': 'polo_shirt.n.01', 'synonyms': ['polo_shirt', 'sport_shirt'], 'def': 'a shirt with short sleeves designed for comfort and casual wear', 'name': 'polo_shirt'}, {'frequency': 'r', 'id': 843, 'synset': 'poncho.n.01', 'synonyms': ['poncho'], 'def': 'a blanket-like cloak with a hole in the center for the head', 'name': 'poncho'}, {'frequency': 'c', 'id': 844, 'synset': 'pony.n.05', 'synonyms': ['pony'], 'def': 'any of various breeds of small gentle horses usually less than five feet high at the shoulder', 'name': 'pony'}, {'frequency': 'r', 'id': 845, 'synset': 'pool_table.n.01', 'synonyms': ['pool_table', 'billiard_table', 'snooker_table'], 'def': 'game equipment consisting of a heavy table on which pool is played', 'name': 'pool_table'}, {'frequency': 'f', 'id': 846, 'synset': 'pop.n.02', 'synonyms': ['pop_(soda)', 'soda_(pop)', 'tonic', 'soft_drink'], 'def': 'a sweet drink containing carbonated water and flavoring', 'name': 'pop_(soda)'}, {'frequency': 'r', 'id': 847, 'synset': 'portrait.n.02', 'synonyms': ['portrait', 'portrayal'], 'def': 'any likeness of a person, in any medium', 'name': 'portrait'}, {'frequency': 'c', 'id': 848, 'synset': 'postbox.n.01', 'synonyms': ['postbox_(public)', 'mailbox_(public)'], 'def': 'public box for deposit of mail', 'name': 'postbox_(public)'}, {'frequency': 'c', 'id': 849, 'synset': 'postcard.n.01', 'synonyms': ['postcard', 'postal_card', 'mailing-card'], 'def': 'a card for sending messages by post without an envelope', 'name': 'postcard'}, {'frequency': 'f', 'id': 850, 'synset': 'poster.n.01', 'synonyms': ['poster', 'placard'], 'def': 'a sign posted in a public place as an advertisement', 'name': 'poster'}, {'frequency': 'f', 'id': 851, 'synset': 'pot.n.01', 'synonyms': ['pot'], 'def': 'metal or earthenware cooking vessel that is usually round and deep; often has a handle and lid', 'name': 'pot'}, {'frequency': 'f', 'id': 852, 'synset': 'pot.n.04', 'synonyms': ['flowerpot'], 'def': 'a container in which plants are cultivated', 'name': 'flowerpot'}, {'frequency': 'f', 'id': 853, 'synset': 'potato.n.01', 'synonyms': ['potato'], 'def': 'an edible tuber native to South America', 'name': 'potato'}, {'frequency': 'c', 'id': 854, 'synset': 'potholder.n.01', 'synonyms': ['potholder'], 'def': 'an insulated pad for holding hot pots', 'name': 'potholder'}, {'frequency': 'c', 'id': 855, 'synset': 'pottery.n.01', 'synonyms': ['pottery', 'clayware'], 'def': 'ceramic ware made from clay and baked in a kiln', 'name': 'pottery'}, {'frequency': 'c', 'id': 856, 'synset': 'pouch.n.01', 'synonyms': ['pouch'], 'def': 'a small or medium size container for holding or carrying things', 'name': 'pouch'}, {'frequency': 'r', 'id': 857, 'synset': 'power_shovel.n.01', 'synonyms': ['power_shovel', 'excavator', 'digger'], 'def': 'a machine for excavating', 'name': 'power_shovel'}, {'frequency': 'c', 'id': 858, 'synset': 'prawn.n.01', 'synonyms': ['prawn', 'shrimp'], 'def': 'any of various edible decapod crustaceans', 'name': 'prawn'}, {'frequency': 'f', 'id': 859, 'synset': 'printer.n.03', 'synonyms': ['printer', 'printing_machine'], 'def': 'a machine that prints', 'name': 'printer'}, {'frequency': 'c', 'id': 860, 'synset': 'projectile.n.01', 'synonyms': ['projectile_(weapon)', 'missile'], 'def': 'a weapon that is forcibly thrown or projected at a targets', 'name': 'projectile_(weapon)'}, {'frequency': 'c', 'id': 861, 'synset': 'projector.n.02', 'synonyms': ['projector'], 'def': 'an optical instrument that projects an enlarged image onto a screen', 'name': 'projector'}, {'frequency': 'f', 'id': 862, 'synset': 'propeller.n.01', 'synonyms': ['propeller', 'propellor'], 'def': 'a mechanical device that rotates to push against air or water', 'name': 'propeller'}, {'frequency': 'r', 'id': 863, 'synset': 'prune.n.01', 'synonyms': ['prune'], 'def': 'dried plum', 'name': 'prune'}, {'frequency': 'r', 'id': 864, 'synset': 'pudding.n.01', 'synonyms': ['pudding'], 'def': 'any of various soft thick unsweetened baked dishes', 'name': 'pudding'}, {'frequency': 'r', 'id': 865, 'synset': 'puffer.n.02', 'synonyms': ['puffer_(fish)', 'pufferfish', 'blowfish', 'globefish'], 'def': 'fishes whose elongated spiny body can inflate itself with water or air to form a globe', 'name': 'puffer_(fish)'}, {'frequency': 'r', 'id': 866, 'synset': 'puffin.n.01', 'synonyms': ['puffin'], 'def': 'seabirds having short necks and brightly colored compressed bills', 'name': 'puffin'}, {'frequency': 'r', 'id': 867, 'synset': 'pug.n.01', 'synonyms': ['pug-dog'], 'def': 'small compact smooth-coated breed of Asiatic origin having a tightly curled tail and broad flat wrinkled muzzle', 'name': 'pug-dog'}, {'frequency': 'c', 'id': 868, 'synset': 'pumpkin.n.02', 'synonyms': ['pumpkin'], 'def': 'usually large pulpy deep-yellow round fruit of the squash family maturing in late summer or early autumn', 'name': 'pumpkin'}, {'frequency': 'r', 'id': 869, 'synset': 'punch.n.03', 'synonyms': ['puncher'], 'def': 'a tool for making holes or indentations', 'name': 'puncher'}, {'frequency': 'r', 'id': 870, 'synset': 'puppet.n.01', 'synonyms': ['puppet', 'marionette'], 'def': 'a small figure of a person operated from above with strings by a puppeteer', 'name': 'puppet'}, {'frequency': 'r', 'id': 871, 'synset': 'puppy.n.01', 'synonyms': ['puppy'], 'def': 'a young dog', 'name': 'puppy'}, {'frequency': 'r', 'id': 872, 'synset': 'quesadilla.n.01', 'synonyms': ['quesadilla'], 'def': 'a tortilla that is filled with cheese and heated', 'name': 'quesadilla'}, {'frequency': 'r', 'id': 873, 'synset': 'quiche.n.02', 'synonyms': ['quiche'], 'def': 'a tart filled with rich unsweetened custard; often contains other ingredients (as cheese or ham or seafood or vegetables)', 'name': 'quiche'}, {'frequency': 'f', 'id': 874, 'synset': 'quilt.n.01', 'synonyms': ['quilt', 'comforter'], 'def': 'bedding made of two layers of cloth filled with stuffing and stitched together', 'name': 'quilt'}, {'frequency': 'c', 'id': 875, 'synset': 'rabbit.n.01', 'synonyms': ['rabbit'], 'def': 'any of various burrowing animals of the family Leporidae having long ears and short tails', 'name': 'rabbit'}, {'frequency': 'r', 'id': 876, 'synset': 'racer.n.02', 'synonyms': ['race_car', 'racing_car'], 'def': 'a fast car that competes in races', 'name': 'race_car'}, {'frequency': 'c', 'id': 877, 'synset': 'racket.n.04', 'synonyms': ['racket', 'racquet'], 'def': 'a sports implement used to strike a ball in various games', 'name': 'racket'}, {'frequency': 'r', 'id': 878, 'synset': 'radar.n.01', 'synonyms': ['radar'], 'def': 'measuring instrument in which the echo of a pulse of microwave radiation is used to detect and locate distant objects', 'name': 'radar'}, {'frequency': 'c', 'id': 879, 'synset': 'radiator.n.03', 'synonyms': ['radiator'], 'def': 'a mechanism consisting of a metal honeycomb through which hot fluids circulate', 'name': 'radiator'}, {'frequency': 'c', 'id': 880, 'synset': 'radio_receiver.n.01', 'synonyms': ['radio_receiver', 'radio_set', 'radio', 'tuner_(radio)'], 'def': 'an electronic receiver that detects and demodulates and amplifies transmitted radio signals', 'name': 'radio_receiver'}, {'frequency': 'c', 'id': 881, 'synset': 'radish.n.03', 'synonyms': ['radish', 'daikon'], 'def': 'pungent edible root of any of various cultivated radish plants', 'name': 'radish'}, {'frequency': 'c', 'id': 882, 'synset': 'raft.n.01', 'synonyms': ['raft'], 'def': 'a flat float (usually made of logs or planks) that can be used for transport or as a platform for swimmers', 'name': 'raft'}, {'frequency': 'r', 'id': 883, 'synset': 'rag_doll.n.01', 'synonyms': ['rag_doll'], 'def': 'a cloth doll that is stuffed and (usually) painted', 'name': 'rag_doll'}, {'frequency': 'c', 'id': 884, 'synset': 'raincoat.n.01', 'synonyms': ['raincoat', 'waterproof_jacket'], 'def': 'a water-resistant coat', 'name': 'raincoat'}, {'frequency': 'c', 'id': 885, 'synset': 'ram.n.05', 'synonyms': ['ram_(animal)'], 'def': 'uncastrated adult male sheep', 'name': 'ram_(animal)'}, {'frequency': 'c', 'id': 886, 'synset': 'raspberry.n.02', 'synonyms': ['raspberry'], 'def': 'red or black edible aggregate berries usually smaller than the related blackberries', 'name': 'raspberry'}, {'frequency': 'r', 'id': 887, 'synset': 'rat.n.01', 'synonyms': ['rat'], 'def': 'any of various long-tailed rodents similar to but larger than a mouse', 'name': 'rat'}, {'frequency': 'c', 'id': 888, 'synset': 'razorblade.n.01', 'synonyms': ['razorblade'], 'def': 'a blade that has very sharp edge', 'name': 'razorblade'}, {'frequency': 'c', 'id': 889, 'synset': 'reamer.n.01', 'synonyms': ['reamer_(juicer)', 'juicer', 'juice_reamer'], 'def': 'a squeezer with a conical ridged center that is used for squeezing juice from citrus fruit', 'name': 'reamer_(juicer)'}, {'frequency': 'f', 'id': 890, 'synset': 'rearview_mirror.n.01', 'synonyms': ['rearview_mirror'], 'def': 'car mirror that reflects the view out of the rear window', 'name': 'rearview_mirror'}, {'frequency': 'c', 'id': 891, 'synset': 'receipt.n.02', 'synonyms': ['receipt'], 'def': 'an acknowledgment (usually tangible) that payment has been made', 'name': 'receipt'}, {'frequency': 'c', 'id': 892, 'synset': 'recliner.n.01', 'synonyms': ['recliner', 'reclining_chair', 'lounger_(chair)'], 'def': 'an armchair whose back can be lowered and foot can be raised to allow the sitter to recline in it', 'name': 'recliner'}, {'frequency': 'r', 'id': 893, 'synset': 'record_player.n.01', 'synonyms': ['record_player', 'phonograph_(record_player)', 'turntable'], 'def': 'machine in which rotating records cause a stylus to vibrate and the vibrations are amplified acoustically or electronically', 'name': 'record_player'}, {'frequency': 'r', 'id': 894, 'synset': 'red_cabbage.n.02', 'synonyms': ['red_cabbage'], 'def': 'compact head of purplish-red leaves', 'name': 'red_cabbage'}, {'frequency': 'f', 'id': 895, 'synset': 'reflector.n.01', 'synonyms': ['reflector'], 'def': 'device that reflects light, radiation, etc.', 'name': 'reflector'}, {'frequency': 'f', 'id': 896, 'synset': 'remote_control.n.01', 'synonyms': ['remote_control'], 'def': 'a device that can be used to control a machine or apparatus from a distance', 'name': 'remote_control'}, {'frequency': 'c', 'id': 897, 'synset': 'rhinoceros.n.01', 'synonyms': ['rhinoceros'], 'def': 'massive powerful herbivorous odd-toed ungulate of southeast Asia and Africa having very thick skin and one or two horns on the snout', 'name': 'rhinoceros'}, {'frequency': 'r', 'id': 898, 'synset': 'rib.n.03', 'synonyms': ['rib_(food)'], 'def': 'cut of meat including one or more ribs', 'name': 'rib_(food)'}, {'frequency': 'r', 'id': 899, 'synset': 'rifle.n.01', 'synonyms': ['rifle'], 'def': 'a shoulder firearm with a long barrel', 'name': 'rifle'}, {'frequency': 'f', 'id': 900, 'synset': 'ring.n.08', 'synonyms': ['ring'], 'def': 'jewelry consisting of a circlet of precious metal (often set with jewels) worn on the finger', 'name': 'ring'}, {'frequency': 'r', 'id': 901, 'synset': 'river_boat.n.01', 'synonyms': ['river_boat'], 'def': 'a boat used on rivers or to ply a river', 'name': 'river_boat'}, {'frequency': 'r', 'id': 902, 'synset': 'road_map.n.02', 'synonyms': ['road_map'], 'def': '(NOT A ROAD) a MAP showing roads (for automobile travel)', 'name': 'road_map'}, {'frequency': 'c', 'id': 903, 'synset': 'robe.n.01', 'synonyms': ['robe'], 'def': 'any loose flowing garment', 'name': 'robe'}, {'frequency': 'c', 'id': 904, 'synset': 'rocking_chair.n.01', 'synonyms': ['rocking_chair'], 'def': 'a chair mounted on rockers', 'name': 'rocking_chair'}, {'frequency': 'r', 'id': 905, 'synset': 'roller_skate.n.01', 'synonyms': ['roller_skate'], 'def': 'a shoe with pairs of rollers (small hard wheels) fixed to the sole', 'name': 'roller_skate'}, {'frequency': 'r', 'id': 906, 'synset': 'rollerblade.n.01', 'synonyms': ['Rollerblade'], 'def': 'an in-line variant of a roller skate', 'name': 'Rollerblade'}, {'frequency': 'c', 'id': 907, 'synset': 'rolling_pin.n.01', 'synonyms': ['rolling_pin'], 'def': 'utensil consisting of a cylinder (usually of wood) with a handle at each end; used to roll out dough', 'name': 'rolling_pin'}, {'frequency': 'r', 'id': 908, 'synset': 'root_beer.n.01', 'synonyms': ['root_beer'], 'def': 'carbonated drink containing extracts of roots and herbs', 'name': 'root_beer'}, {'frequency': 'c', 'id': 909, 'synset': 'router.n.02', 'synonyms': ['router_(computer_equipment)'], 'def': 'a device that forwards data packets between computer networks', 'name': 'router_(computer_equipment)'}, {'frequency': 'f', 'id': 910, 'synset': 'rubber_band.n.01', 'synonyms': ['rubber_band', 'elastic_band'], 'def': 'a narrow band of elastic rubber used to hold things (such as papers) together', 'name': 'rubber_band'}, {'frequency': 'c', 'id': 911, 'synset': 'runner.n.08', 'synonyms': ['runner_(carpet)'], 'def': 'a long narrow carpet', 'name': 'runner_(carpet)'}, {'frequency': 'f', 'id': 912, 'synset': 'sack.n.01', 'synonyms': ['plastic_bag', 'paper_bag'], 'def': "a bag made of paper or plastic for holding customer's purchases", 'name': 'plastic_bag'}, {'frequency': 'f', 'id': 913, 'synset': 'saddle.n.01', 'synonyms': ['saddle_(on_an_animal)'], 'def': 'a seat for the rider of a horse or camel', 'name': 'saddle_(on_an_animal)'}, {'frequency': 'f', 'id': 914, 'synset': 'saddle_blanket.n.01', 'synonyms': ['saddle_blanket', 'saddlecloth', 'horse_blanket'], 'def': 'stable gear consisting of a blanket placed under the saddle', 'name': 'saddle_blanket'}, {'frequency': 'c', 'id': 915, 'synset': 'saddlebag.n.01', 'synonyms': ['saddlebag'], 'def': 'a large bag (or pair of bags) hung over a saddle', 'name': 'saddlebag'}, {'frequency': 'r', 'id': 916, 'synset': 'safety_pin.n.01', 'synonyms': ['safety_pin'], 'def': 'a pin in the form of a clasp; has a guard so the point of the pin will not stick the user', 'name': 'safety_pin'}, {'frequency': 'c', 'id': 917, 'synset': 'sail.n.01', 'synonyms': ['sail'], 'def': 'a large piece of fabric by means of which wind is used to propel a sailing vessel', 'name': 'sail'}, {'frequency': 'c', 'id': 918, 'synset': 'salad.n.01', 'synonyms': ['salad'], 'def': 'food mixtures either arranged on a plate or tossed and served with a moist dressing; usually consisting of or including greens', 'name': 'salad'}, {'frequency': 'r', 'id': 919, 'synset': 'salad_plate.n.01', 'synonyms': ['salad_plate', 'salad_bowl'], 'def': 'a plate or bowl for individual servings of salad', 'name': 'salad_plate'}, {'frequency': 'r', 'id': 920, 'synset': 'salami.n.01', 'synonyms': ['salami'], 'def': 'highly seasoned fatty sausage of pork and beef usually dried', 'name': 'salami'}, {'frequency': 'r', 'id': 921, 'synset': 'salmon.n.01', 'synonyms': ['salmon_(fish)'], 'def': 'any of various large food and game fishes of northern waters', 'name': 'salmon_(fish)'}, {'frequency': 'r', 'id': 922, 'synset': 'salmon.n.03', 'synonyms': ['salmon_(food)'], 'def': 'flesh of any of various marine or freshwater fish of the family Salmonidae', 'name': 'salmon_(food)'}, {'frequency': 'r', 'id': 923, 'synset': 'salsa.n.01', 'synonyms': ['salsa'], 'def': 'spicy sauce of tomatoes and onions and chili peppers to accompany Mexican foods', 'name': 'salsa'}, {'frequency': 'f', 'id': 924, 'synset': 'saltshaker.n.01', 'synonyms': ['saltshaker'], 'def': 'a shaker with a perforated top for sprinkling salt', 'name': 'saltshaker'}, {'frequency': 'f', 'id': 925, 'synset': 'sandal.n.01', 'synonyms': ['sandal_(type_of_shoe)'], 'def': 'a shoe consisting of a sole fastened by straps to the foot', 'name': 'sandal_(type_of_shoe)'}, {'frequency': 'f', 'id': 926, 'synset': 'sandwich.n.01', 'synonyms': ['sandwich'], 'def': 'two (or more) slices of bread with a filling between them', 'name': 'sandwich'}, {'frequency': 'r', 'id': 927, 'synset': 'satchel.n.01', 'synonyms': ['satchel'], 'def': 'luggage consisting of a small case with a flat bottom and (usually) a shoulder strap', 'name': 'satchel'}, {'frequency': 'r', 'id': 928, 'synset': 'saucepan.n.01', 'synonyms': ['saucepan'], 'def': 'a deep pan with a handle; used for stewing or boiling', 'name': 'saucepan'}, {'frequency': 'f', 'id': 929, 'synset': 'saucer.n.02', 'synonyms': ['saucer'], 'def': 'a small shallow dish for holding a cup at the table', 'name': 'saucer'}, {'frequency': 'f', 'id': 930, 'synset': 'sausage.n.01', 'synonyms': ['sausage'], 'def': 'highly seasoned minced meat stuffed in casings', 'name': 'sausage'}, {'frequency': 'r', 'id': 931, 'synset': 'sawhorse.n.01', 'synonyms': ['sawhorse', 'sawbuck'], 'def': 'a framework for holding wood that is being sawed', 'name': 'sawhorse'}, {'frequency': 'r', 'id': 932, 'synset': 'sax.n.02', 'synonyms': ['saxophone'], 'def': "a wind instrument with a `J'-shaped form typically made of brass", 'name': 'saxophone'}, {'frequency': 'f', 'id': 933, 'synset': 'scale.n.07', 'synonyms': ['scale_(measuring_instrument)'], 'def': 'a measuring instrument for weighing; shows amount of mass', 'name': 'scale_(measuring_instrument)'}, {'frequency': 'r', 'id': 934, 'synset': 'scarecrow.n.01', 'synonyms': ['scarecrow', 'strawman'], 'def': 'an effigy in the shape of a man to frighten birds away from seeds', 'name': 'scarecrow'}, {'frequency': 'f', 'id': 935, 'synset': 'scarf.n.01', 'synonyms': ['scarf'], 'def': 'a garment worn around the head or neck or shoulders for warmth or decoration', 'name': 'scarf'}, {'frequency': 'c', 'id': 936, 'synset': 'school_bus.n.01', 'synonyms': ['school_bus'], 'def': 'a bus used to transport children to or from school', 'name': 'school_bus'}, {'frequency': 'f', 'id': 937, 'synset': 'scissors.n.01', 'synonyms': ['scissors'], 'def': 'a tool having two crossed pivoting blades with looped handles', 'name': 'scissors'}, {'frequency': 'c', 'id': 938, 'synset': 'scoreboard.n.01', 'synonyms': ['scoreboard'], 'def': 'a large board for displaying the score of a contest (and some other information)', 'name': 'scoreboard'}, {'frequency': 'c', 'id': 939, 'synset': 'scrambled_eggs.n.01', 'synonyms': ['scrambled_eggs'], 'def': 'eggs beaten and cooked to a soft firm consistency while stirring', 'name': 'scrambled_eggs'}, {'frequency': 'r', 'id': 940, 'synset': 'scraper.n.01', 'synonyms': ['scraper'], 'def': 'any of various hand tools for scraping', 'name': 'scraper'}, {'frequency': 'r', 'id': 941, 'synset': 'scratcher.n.03', 'synonyms': ['scratcher'], 'def': 'a device used for scratching', 'name': 'scratcher'}, {'frequency': 'c', 'id': 942, 'synset': 'screwdriver.n.01', 'synonyms': ['screwdriver'], 'def': 'a hand tool for driving screws; has a tip that fits into the head of a screw', 'name': 'screwdriver'}, {'frequency': 'c', 'id': 943, 'synset': 'scrub_brush.n.01', 'synonyms': ['scrubbing_brush'], 'def': 'a brush with short stiff bristles for heavy cleaning', 'name': 'scrubbing_brush'}, {'frequency': 'c', 'id': 944, 'synset': 'sculpture.n.01', 'synonyms': ['sculpture'], 'def': 'a three-dimensional work of art', 'name': 'sculpture'}, {'frequency': 'r', 'id': 945, 'synset': 'seabird.n.01', 'synonyms': ['seabird', 'seafowl'], 'def': 'a bird that frequents coastal waters and the open ocean: gulls; pelicans; gannets; cormorants; albatrosses; petrels; etc.', 'name': 'seabird'}, {'frequency': 'r', 'id': 946, 'synset': 'seahorse.n.02', 'synonyms': ['seahorse'], 'def': 'small fish with horse-like heads bent sharply downward and curled tails', 'name': 'seahorse'}, {'frequency': 'r', 'id': 947, 'synset': 'seaplane.n.01', 'synonyms': ['seaplane', 'hydroplane'], 'def': 'an airplane that can land on or take off from water', 'name': 'seaplane'}, {'frequency': 'c', 'id': 948, 'synset': 'seashell.n.01', 'synonyms': ['seashell'], 'def': 'the shell of a marine organism', 'name': 'seashell'}, {'frequency': 'r', 'id': 949, 'synset': 'seedling.n.01', 'synonyms': ['seedling'], 'def': 'young plant or tree grown from a seed', 'name': 'seedling'}, {'frequency': 'c', 'id': 950, 'synset': 'serving_dish.n.01', 'synonyms': ['serving_dish'], 'def': 'a dish used for serving food', 'name': 'serving_dish'}, {'frequency': 'r', 'id': 951, 'synset': 'sewing_machine.n.01', 'synonyms': ['sewing_machine'], 'def': 'a textile machine used as a home appliance for sewing', 'name': 'sewing_machine'}, {'frequency': 'r', 'id': 952, 'synset': 'shaker.n.03', 'synonyms': ['shaker'], 'def': 'a container in which something can be shaken', 'name': 'shaker'}, {'frequency': 'c', 'id': 953, 'synset': 'shampoo.n.01', 'synonyms': ['shampoo'], 'def': 'cleansing agent consisting of soaps or detergents used for washing the hair', 'name': 'shampoo'}, {'frequency': 'r', 'id': 954, 'synset': 'shark.n.01', 'synonyms': ['shark'], 'def': 'typically large carnivorous fishes with sharpe teeth', 'name': 'shark'}, {'frequency': 'r', 'id': 955, 'synset': 'sharpener.n.01', 'synonyms': ['sharpener'], 'def': 'any implement that is used to make something (an edge or a point) sharper', 'name': 'sharpener'}, {'frequency': 'r', 'id': 956, 'synset': 'sharpie.n.03', 'synonyms': ['Sharpie'], 'def': 'a pen with indelible ink that will write on any surface', 'name': 'Sharpie'}, {'frequency': 'r', 'id': 957, 'synset': 'shaver.n.03', 'synonyms': ['shaver_(electric)', 'electric_shaver', 'electric_razor'], 'def': 'a razor powered by an electric motor', 'name': 'shaver_(electric)'}, {'frequency': 'c', 'id': 958, 'synset': 'shaving_cream.n.01', 'synonyms': ['shaving_cream', 'shaving_soap'], 'def': 'toiletry consisting that forms a rich lather for softening the beard before shaving', 'name': 'shaving_cream'}, {'frequency': 'r', 'id': 959, 'synset': 'shawl.n.01', 'synonyms': ['shawl'], 'def': 'cloak consisting of an oblong piece of cloth used to cover the head and shoulders', 'name': 'shawl'}, {'frequency': 'r', 'id': 960, 'synset': 'shears.n.01', 'synonyms': ['shears'], 'def': 'large scissors with strong blades', 'name': 'shears'}, {'frequency': 'f', 'id': 961, 'synset': 'sheep.n.01', 'synonyms': ['sheep'], 'def': 'woolly usually horned ruminant mammal related to the goat', 'name': 'sheep'}, {'frequency': 'r', 'id': 962, 'synset': 'shepherd_dog.n.01', 'synonyms': ['shepherd_dog', 'sheepdog'], 'def': 'any of various usually long-haired breeds of dog reared to herd and guard sheep', 'name': 'shepherd_dog'}, {'frequency': 'r', 'id': 963, 'synset': 'sherbert.n.01', 'synonyms': ['sherbert', 'sherbet'], 'def': 'a frozen dessert made primarily of fruit juice and sugar', 'name': 'sherbert'}, {'frequency': 'r', 'id': 964, 'synset': 'shield.n.02', 'synonyms': ['shield'], 'def': 'armor carried on the arm to intercept blows', 'name': 'shield'}, {'frequency': 'f', 'id': 965, 'synset': 'shirt.n.01', 'synonyms': ['shirt'], 'def': 'a garment worn on the upper half of the body', 'name': 'shirt'}, {'frequency': 'f', 'id': 966, 'synset': 'shoe.n.01', 'synonyms': ['shoe', 'sneaker_(type_of_shoe)', 'tennis_shoe'], 'def': 'common footwear covering the foot', 'name': 'shoe'}, {'frequency': 'c', 'id': 967, 'synset': 'shopping_bag.n.01', 'synonyms': ['shopping_bag'], 'def': 'a bag made of plastic or strong paper (often with handles); used to transport goods after shopping', 'name': 'shopping_bag'}, {'frequency': 'c', 'id': 968, 'synset': 'shopping_cart.n.01', 'synonyms': ['shopping_cart'], 'def': 'a handcart that holds groceries or other goods while shopping', 'name': 'shopping_cart'}, {'frequency': 'f', 'id': 969, 'synset': 'short_pants.n.01', 'synonyms': ['short_pants', 'shorts_(clothing)', 'trunks_(clothing)'], 'def': 'trousers that end at or above the knee', 'name': 'short_pants'}, {'frequency': 'r', 'id': 970, 'synset': 'shot_glass.n.01', 'synonyms': ['shot_glass'], 'def': 'a small glass adequate to hold a single swallow of whiskey', 'name': 'shot_glass'}, {'frequency': 'c', 'id': 971, 'synset': 'shoulder_bag.n.01', 'synonyms': ['shoulder_bag'], 'def': 'a large handbag that can be carried by a strap looped over the shoulder', 'name': 'shoulder_bag'}, {'frequency': 'c', 'id': 972, 'synset': 'shovel.n.01', 'synonyms': ['shovel'], 'def': 'a hand tool for lifting loose material such as snow, dirt, etc.', 'name': 'shovel'}, {'frequency': 'f', 'id': 973, 'synset': 'shower.n.01', 'synonyms': ['shower_head'], 'def': 'a plumbing fixture that sprays water over you', 'name': 'shower_head'}, {'frequency': 'f', 'id': 974, 'synset': 'shower_curtain.n.01', 'synonyms': ['shower_curtain'], 'def': 'a curtain that keeps water from splashing out of the shower area', 'name': 'shower_curtain'}, {'frequency': 'r', 'id': 975, 'synset': 'shredder.n.01', 'synonyms': ['shredder_(for_paper)'], 'def': 'a device that shreds documents', 'name': 'shredder_(for_paper)'}, {'frequency': 'r', 'id': 976, 'synset': 'sieve.n.01', 'synonyms': ['sieve', 'screen_(sieve)'], 'def': 'a strainer for separating lumps from powdered material or grading particles', 'name': 'sieve'}, {'frequency': 'f', 'id': 977, 'synset': 'signboard.n.01', 'synonyms': ['signboard'], 'def': 'structure displaying a board on which advertisements can be posted', 'name': 'signboard'}, {'frequency': 'c', 'id': 978, 'synset': 'silo.n.01', 'synonyms': ['silo'], 'def': 'a cylindrical tower used for storing goods', 'name': 'silo'}, {'frequency': 'f', 'id': 979, 'synset': 'sink.n.01', 'synonyms': ['sink'], 'def': 'plumbing fixture consisting of a water basin fixed to a wall or floor and having a drainpipe', 'name': 'sink'}, {'frequency': 'f', 'id': 980, 'synset': 'skateboard.n.01', 'synonyms': ['skateboard'], 'def': 'a board with wheels that is ridden in a standing or crouching position and propelled by foot', 'name': 'skateboard'}, {'frequency': 'c', 'id': 981, 'synset': 'skewer.n.01', 'synonyms': ['skewer'], 'def': 'a long pin for holding meat in position while it is being roasted', 'name': 'skewer'}, {'frequency': 'f', 'id': 982, 'synset': 'ski.n.01', 'synonyms': ['ski'], 'def': 'sports equipment for skiing on snow', 'name': 'ski'}, {'frequency': 'f', 'id': 983, 'synset': 'ski_boot.n.01', 'synonyms': ['ski_boot'], 'def': 'a stiff boot that is fastened to a ski with a ski binding', 'name': 'ski_boot'}, {'frequency': 'f', 'id': 984, 'synset': 'ski_parka.n.01', 'synonyms': ['ski_parka', 'ski_jacket'], 'def': 'a parka to be worn while skiing', 'name': 'ski_parka'}, {'frequency': 'f', 'id': 985, 'synset': 'ski_pole.n.01', 'synonyms': ['ski_pole'], 'def': 'a pole with metal points used as an aid in skiing', 'name': 'ski_pole'}, {'frequency': 'f', 'id': 986, 'synset': 'skirt.n.02', 'synonyms': ['skirt'], 'def': 'a garment hanging from the waist; worn mainly by girls and women', 'name': 'skirt'}, {'frequency': 'c', 'id': 987, 'synset': 'sled.n.01', 'synonyms': ['sled', 'sledge', 'sleigh'], 'def': 'a vehicle or flat object for transportation over snow by sliding or pulled by dogs, etc.', 'name': 'sled'}, {'frequency': 'c', 'id': 988, 'synset': 'sleeping_bag.n.01', 'synonyms': ['sleeping_bag'], 'def': 'large padded bag designed to be slept in outdoors', 'name': 'sleeping_bag'}, {'frequency': 'r', 'id': 989, 'synset': 'sling.n.05', 'synonyms': ['sling_(bandage)', 'triangular_bandage'], 'def': 'bandage to support an injured forearm; slung over the shoulder or neck', 'name': 'sling_(bandage)'}, {'frequency': 'c', 'id': 990, 'synset': 'slipper.n.01', 'synonyms': ['slipper_(footwear)', 'carpet_slipper_(footwear)'], 'def': 'low footwear that can be slipped on and off easily; usually worn indoors', 'name': 'slipper_(footwear)'}, {'frequency': 'r', 'id': 991, 'synset': 'smoothie.n.02', 'synonyms': ['smoothie'], 'def': 'a thick smooth drink consisting of fresh fruit pureed with ice cream or yoghurt or milk', 'name': 'smoothie'}, {'frequency': 'r', 'id': 992, 'synset': 'snake.n.01', 'synonyms': ['snake', 'serpent'], 'def': 'limbless scaly elongate reptile; some are venomous', 'name': 'snake'}, {'frequency': 'f', 'id': 993, 'synset': 'snowboard.n.01', 'synonyms': ['snowboard'], 'def': 'a board that resembles a broad ski or a small surfboard; used in a standing position to slide down snow-covered slopes', 'name': 'snowboard'}, {'frequency': 'c', 'id': 994, 'synset': 'snowman.n.01', 'synonyms': ['snowman'], 'def': 'a figure of a person made of packed snow', 'name': 'snowman'}, {'frequency': 'c', 'id': 995, 'synset': 'snowmobile.n.01', 'synonyms': ['snowmobile'], 'def': 'tracked vehicle for travel on snow having skis in front', 'name': 'snowmobile'}, {'frequency': 'f', 'id': 996, 'synset': 'soap.n.01', 'synonyms': ['soap'], 'def': 'a cleansing agent made from the salts of vegetable or animal fats', 'name': 'soap'}, {'frequency': 'f', 'id': 997, 'synset': 'soccer_ball.n.01', 'synonyms': ['soccer_ball'], 'def': "an inflated ball used in playing soccer (called `football' outside of the United States)", 'name': 'soccer_ball'}, {'frequency': 'f', 'id': 998, 'synset': 'sock.n.01', 'synonyms': ['sock'], 'def': 'cloth covering for the foot; worn inside the shoe; reaches to between the ankle and the knee', 'name': 'sock'}, {'frequency': 'r', 'id': 999, 'synset': 'soda_fountain.n.02', 'synonyms': ['soda_fountain'], 'def': 'an apparatus for dispensing soda water', 'name': 'soda_fountain'}, {'frequency': 'r', 'id': 1000, 'synset': 'soda_water.n.01', 'synonyms': ['carbonated_water', 'club_soda', 'seltzer', 'sparkling_water'], 'def': 'effervescent beverage artificially charged with carbon dioxide', 'name': 'carbonated_water'}, {'frequency': 'f', 'id': 1001, 'synset': 'sofa.n.01', 'synonyms': ['sofa', 'couch', 'lounge'], 'def': 'an upholstered seat for more than one person', 'name': 'sofa'}, {'frequency': 'r', 'id': 1002, 'synset': 'softball.n.01', 'synonyms': ['softball'], 'def': 'ball used in playing softball', 'name': 'softball'}, {'frequency': 'c', 'id': 1003, 'synset': 'solar_array.n.01', 'synonyms': ['solar_array', 'solar_battery', 'solar_panel'], 'def': 'electrical device consisting of a large array of connected solar cells', 'name': 'solar_array'}, {'frequency': 'r', 'id': 1004, 'synset': 'sombrero.n.02', 'synonyms': ['sombrero'], 'def': 'a straw hat with a tall crown and broad brim; worn in American southwest and in Mexico', 'name': 'sombrero'}, {'frequency': 'c', 'id': 1005, 'synset': 'soup.n.01', 'synonyms': ['soup'], 'def': 'liquid food especially of meat or fish or vegetable stock often containing pieces of solid food', 'name': 'soup'}, {'frequency': 'r', 'id': 1006, 'synset': 'soup_bowl.n.01', 'synonyms': ['soup_bowl'], 'def': 'a bowl for serving soup', 'name': 'soup_bowl'}, {'frequency': 'c', 'id': 1007, 'synset': 'soupspoon.n.01', 'synonyms': ['soupspoon'], 'def': 'a spoon with a rounded bowl for eating soup', 'name': 'soupspoon'}, {'frequency': 'c', 'id': 1008, 'synset': 'sour_cream.n.01', 'synonyms': ['sour_cream', 'soured_cream'], 'def': 'soured light cream', 'name': 'sour_cream'}, {'frequency': 'r', 'id': 1009, 'synset': 'soya_milk.n.01', 'synonyms': ['soya_milk', 'soybean_milk', 'soymilk'], 'def': 'a milk substitute containing soybean flour and water; used in some infant formulas and in making tofu', 'name': 'soya_milk'}, {'frequency': 'r', 'id': 1010, 'synset': 'space_shuttle.n.01', 'synonyms': ['space_shuttle'], 'def': "a reusable spacecraft with wings for a controlled descent through the Earth's atmosphere", 'name': 'space_shuttle'}, {'frequency': 'r', 'id': 1011, 'synset': 'sparkler.n.02', 'synonyms': ['sparkler_(fireworks)'], 'def': 'a firework that burns slowly and throws out a shower of sparks', 'name': 'sparkler_(fireworks)'}, {'frequency': 'f', 'id': 1012, 'synset': 'spatula.n.02', 'synonyms': ['spatula'], 'def': 'a hand tool with a thin flexible blade used to mix or spread soft substances', 'name': 'spatula'}, {'frequency': 'r', 'id': 1013, 'synset': 'spear.n.01', 'synonyms': ['spear', 'lance'], 'def': 'a long pointed rod used as a tool or weapon', 'name': 'spear'}, {'frequency': 'f', 'id': 1014, 'synset': 'spectacles.n.01', 'synonyms': ['spectacles', 'specs', 'eyeglasses', 'glasses'], 'def': 'optical instrument consisting of a frame that holds a pair of lenses for correcting defective vision', 'name': 'spectacles'}, {'frequency': 'c', 'id': 1015, 'synset': 'spice_rack.n.01', 'synonyms': ['spice_rack'], 'def': 'a rack for displaying containers filled with spices', 'name': 'spice_rack'}, {'frequency': 'r', 'id': 1016, 'synset': 'spider.n.01', 'synonyms': ['spider'], 'def': 'predatory arachnid with eight legs, two poison fangs, two feelers, and usually two silk-spinning organs at the back end of the body', 'name': 'spider'}, {'frequency': 'c', 'id': 1017, 'synset': 'sponge.n.01', 'synonyms': ['sponge'], 'def': 'a porous mass usable to absorb water typically used for cleaning', 'name': 'sponge'}, {'frequency': 'f', 'id': 1018, 'synset': 'spoon.n.01', 'synonyms': ['spoon'], 'def': 'a piece of cutlery with a shallow bowl-shaped container and a handle', 'name': 'spoon'}, {'frequency': 'c', 'id': 1019, 'synset': 'sportswear.n.01', 'synonyms': ['sportswear', 'athletic_wear', 'activewear'], 'def': 'attire worn for sport or for casual wear', 'name': 'sportswear'}, {'frequency': 'c', 'id': 1020, 'synset': 'spotlight.n.02', 'synonyms': ['spotlight'], 'def': 'a lamp that produces a strong beam of light to illuminate a restricted area; used to focus attention of a stage performer', 'name': 'spotlight'}, {'frequency': 'r', 'id': 1021, 'synset': 'squirrel.n.01', 'synonyms': ['squirrel'], 'def': 'a kind of arboreal rodent having a long bushy tail', 'name': 'squirrel'}, {'frequency': 'c', 'id': 1022, 'synset': 'stapler.n.01', 'synonyms': ['stapler_(stapling_machine)'], 'def': 'a machine that inserts staples into sheets of paper in order to fasten them together', 'name': 'stapler_(stapling_machine)'}, {'frequency': 'r', 'id': 1023, 'synset': 'starfish.n.01', 'synonyms': ['starfish', 'sea_star'], 'def': 'echinoderms characterized by five arms extending from a central disk', 'name': 'starfish'}, {'frequency': 'f', 'id': 1024, 'synset': 'statue.n.01', 'synonyms': ['statue_(sculpture)'], 'def': 'a sculpture representing a human or animal', 'name': 'statue_(sculpture)'}, {'frequency': 'c', 'id': 1025, 'synset': 'steak.n.01', 'synonyms': ['steak_(food)'], 'def': 'a slice of meat cut from the fleshy part of an animal or large fish', 'name': 'steak_(food)'}, {'frequency': 'r', 'id': 1026, 'synset': 'steak_knife.n.01', 'synonyms': ['steak_knife'], 'def': 'a sharp table knife used in eating steak', 'name': 'steak_knife'}, {'frequency': 'r', 'id': 1027, 'synset': 'steamer.n.02', 'synonyms': ['steamer_(kitchen_appliance)'], 'def': 'a cooking utensil that can be used to cook food by steaming it', 'name': 'steamer_(kitchen_appliance)'}, {'frequency': 'f', 'id': 1028, 'synset': 'steering_wheel.n.01', 'synonyms': ['steering_wheel'], 'def': 'a handwheel that is used for steering', 'name': 'steering_wheel'}, {'frequency': 'r', 'id': 1029, 'synset': 'stencil.n.01', 'synonyms': ['stencil'], 'def': 'a sheet of material (metal, plastic, etc.) that has been perforated with a pattern; ink or paint can pass through the perforations to create the printed pattern on the surface below', 'name': 'stencil'}, {'frequency': 'r', 'id': 1030, 'synset': 'step_ladder.n.01', 'synonyms': ['stepladder'], 'def': 'a folding portable ladder hinged at the top', 'name': 'stepladder'}, {'frequency': 'c', 'id': 1031, 'synset': 'step_stool.n.01', 'synonyms': ['step_stool'], 'def': 'a stool that has one or two steps that fold under the seat', 'name': 'step_stool'}, {'frequency': 'c', 'id': 1032, 'synset': 'stereo.n.01', 'synonyms': ['stereo_(sound_system)'], 'def': 'electronic device for playing audio', 'name': 'stereo_(sound_system)'}, {'frequency': 'r', 'id': 1033, 'synset': 'stew.n.02', 'synonyms': ['stew'], 'def': 'food prepared by stewing especially meat or fish with vegetables', 'name': 'stew'}, {'frequency': 'r', 'id': 1034, 'synset': 'stirrer.n.02', 'synonyms': ['stirrer'], 'def': 'an implement used for stirring', 'name': 'stirrer'}, {'frequency': 'f', 'id': 1035, 'synset': 'stirrup.n.01', 'synonyms': ['stirrup'], 'def': "support consisting of metal loops into which rider's feet go", 'name': 'stirrup'}, {'frequency': 'c', 'id': 1036, 'synset': 'stocking.n.01', 'synonyms': ['stockings_(leg_wear)'], 'def': 'close-fitting hosiery to cover the foot and leg; come in matched pairs', 'name': 'stockings_(leg_wear)'}, {'frequency': 'f', 'id': 1037, 'synset': 'stool.n.01', 'synonyms': ['stool'], 'def': 'a simple seat without a back or arms', 'name': 'stool'}, {'frequency': 'f', 'id': 1038, 'synset': 'stop_sign.n.01', 'synonyms': ['stop_sign'], 'def': 'a traffic sign to notify drivers that they must come to a complete stop', 'name': 'stop_sign'}, {'frequency': 'f', 'id': 1039, 'synset': 'stoplight.n.01', 'synonyms': ['brake_light'], 'def': 'a red light on the rear of a motor vehicle that signals when the brakes are applied', 'name': 'brake_light'}, {'frequency': 'f', 'id': 1040, 'synset': 'stove.n.01', 'synonyms': ['stove', 'kitchen_stove', 'range_(kitchen_appliance)', 'kitchen_range', 'cooking_stove'], 'def': 'a kitchen appliance used for cooking food', 'name': 'stove'}, {'frequency': 'c', 'id': 1041, 'synset': 'strainer.n.01', 'synonyms': ['strainer'], 'def': 'a filter to retain larger pieces while smaller pieces and liquids pass through', 'name': 'strainer'}, {'frequency': 'f', 'id': 1042, 'synset': 'strap.n.01', 'synonyms': ['strap'], 'def': 'an elongated strip of material for binding things together or holding', 'name': 'strap'}, {'frequency': 'f', 'id': 1043, 'synset': 'straw.n.04', 'synonyms': ['straw_(for_drinking)', 'drinking_straw'], 'def': 'a thin paper or plastic tube used to suck liquids into the mouth', 'name': 'straw_(for_drinking)'}, {'frequency': 'f', 'id': 1044, 'synset': 'strawberry.n.01', 'synonyms': ['strawberry'], 'def': 'sweet fleshy red fruit', 'name': 'strawberry'}, {'frequency': 'f', 'id': 1045, 'synset': 'street_sign.n.01', 'synonyms': ['street_sign'], 'def': 'a sign visible from the street', 'name': 'street_sign'}, {'frequency': 'f', 'id': 1046, 'synset': 'streetlight.n.01', 'synonyms': ['streetlight', 'street_lamp'], 'def': 'a lamp supported on a lamppost; for illuminating a street', 'name': 'streetlight'}, {'frequency': 'r', 'id': 1047, 'synset': 'string_cheese.n.01', 'synonyms': ['string_cheese'], 'def': 'cheese formed in long strings twisted together', 'name': 'string_cheese'}, {'frequency': 'r', 'id': 1048, 'synset': 'stylus.n.02', 'synonyms': ['stylus'], 'def': 'a pointed tool for writing or drawing or engraving', 'name': 'stylus'}, {'frequency': 'r', 'id': 1049, 'synset': 'subwoofer.n.01', 'synonyms': ['subwoofer'], 'def': 'a loudspeaker that is designed to reproduce very low bass frequencies', 'name': 'subwoofer'}, {'frequency': 'r', 'id': 1050, 'synset': 'sugar_bowl.n.01', 'synonyms': ['sugar_bowl'], 'def': 'a dish in which sugar is served', 'name': 'sugar_bowl'}, {'frequency': 'r', 'id': 1051, 'synset': 'sugarcane.n.01', 'synonyms': ['sugarcane_(plant)'], 'def': 'juicy canes whose sap is a source of molasses and commercial sugar; fresh canes are sometimes chewed for the juice', 'name': 'sugarcane_(plant)'}, {'frequency': 'c', 'id': 1052, 'synset': 'suit.n.01', 'synonyms': ['suit_(clothing)'], 'def': 'a set of garments (usually including a jacket and trousers or skirt) for outerwear all of the same fabric and color', 'name': 'suit_(clothing)'}, {'frequency': 'c', 'id': 1053, 'synset': 'sunflower.n.01', 'synonyms': ['sunflower'], 'def': 'any plant of the genus Helianthus having large flower heads with dark disk florets and showy yellow rays', 'name': 'sunflower'}, {'frequency': 'f', 'id': 1054, 'synset': 'sunglasses.n.01', 'synonyms': ['sunglasses'], 'def': 'spectacles that are darkened or polarized to protect the eyes from the glare of the sun', 'name': 'sunglasses'}, {'frequency': 'c', 'id': 1055, 'synset': 'sunhat.n.01', 'synonyms': ['sunhat'], 'def': 'a hat with a broad brim that protects the face from direct exposure to the sun', 'name': 'sunhat'}, {'frequency': 'r', 'id': 1056, 'synset': 'sunscreen.n.01', 'synonyms': ['sunscreen', 'sunblock'], 'def': 'a cream spread on the skin; contains a chemical to filter out ultraviolet light and so protect from sunburn', 'name': 'sunscreen'}, {'frequency': 'f', 'id': 1057, 'synset': 'surfboard.n.01', 'synonyms': ['surfboard'], 'def': 'a narrow buoyant board for riding surf', 'name': 'surfboard'}, {'frequency': 'c', 'id': 1058, 'synset': 'sushi.n.01', 'synonyms': ['sushi'], 'def': 'rice (with raw fish) wrapped in seaweed', 'name': 'sushi'}, {'frequency': 'c', 'id': 1059, 'synset': 'swab.n.02', 'synonyms': ['mop'], 'def': 'cleaning implement consisting of absorbent material fastened to a handle; for cleaning floors', 'name': 'mop'}, {'frequency': 'c', 'id': 1060, 'synset': 'sweat_pants.n.01', 'synonyms': ['sweat_pants'], 'def': 'loose-fitting trousers with elastic cuffs; worn by athletes', 'name': 'sweat_pants'}, {'frequency': 'c', 'id': 1061, 'synset': 'sweatband.n.02', 'synonyms': ['sweatband'], 'def': 'a band of material tied around the forehead or wrist to absorb sweat', 'name': 'sweatband'}, {'frequency': 'f', 'id': 1062, 'synset': 'sweater.n.01', 'synonyms': ['sweater'], 'def': 'a crocheted or knitted garment covering the upper part of the body', 'name': 'sweater'}, {'frequency': 'f', 'id': 1063, 'synset': 'sweatshirt.n.01', 'synonyms': ['sweatshirt'], 'def': 'cotton knit pullover with long sleeves worn during athletic activity', 'name': 'sweatshirt'}, {'frequency': 'c', 'id': 1064, 'synset': 'sweet_potato.n.02', 'synonyms': ['sweet_potato'], 'def': 'the edible tuberous root of the sweet potato vine', 'name': 'sweet_potato'}, {'frequency': 'f', 'id': 1065, 'synset': 'swimsuit.n.01', 'synonyms': ['swimsuit', 'swimwear', 'bathing_suit', 'swimming_costume', 'bathing_costume', 'swimming_trunks', 'bathing_trunks'], 'def': 'garment worn for swimming', 'name': 'swimsuit'}, {'frequency': 'c', 'id': 1066, 'synset': 'sword.n.01', 'synonyms': ['sword'], 'def': 'a cutting or thrusting weapon that has a long metal blade', 'name': 'sword'}, {'frequency': 'r', 'id': 1067, 'synset': 'syringe.n.01', 'synonyms': ['syringe'], 'def': 'a medical instrument used to inject or withdraw fluids', 'name': 'syringe'}, {'frequency': 'r', 'id': 1068, 'synset': 'tabasco.n.02', 'synonyms': ['Tabasco_sauce'], 'def': 'very spicy sauce (trade name Tabasco) made from fully-aged red peppers', 'name': 'Tabasco_sauce'}, {'frequency': 'r', 'id': 1069, 'synset': 'table-tennis_table.n.01', 'synonyms': ['table-tennis_table', 'ping-pong_table'], 'def': 'a table used for playing table tennis', 'name': 'table-tennis_table'}, {'frequency': 'f', 'id': 1070, 'synset': 'table.n.02', 'synonyms': ['table'], 'def': 'a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs', 'name': 'table'}, {'frequency': 'c', 'id': 1071, 'synset': 'table_lamp.n.01', 'synonyms': ['table_lamp'], 'def': 'a lamp that sits on a table', 'name': 'table_lamp'}, {'frequency': 'f', 'id': 1072, 'synset': 'tablecloth.n.01', 'synonyms': ['tablecloth'], 'def': 'a covering spread over a dining table', 'name': 'tablecloth'}, {'frequency': 'r', 'id': 1073, 'synset': 'tachometer.n.01', 'synonyms': ['tachometer'], 'def': 'measuring instrument for indicating speed of rotation', 'name': 'tachometer'}, {'frequency': 'r', 'id': 1074, 'synset': 'taco.n.02', 'synonyms': ['taco'], 'def': 'a small tortilla cupped around a filling', 'name': 'taco'}, {'frequency': 'f', 'id': 1075, 'synset': 'tag.n.02', 'synonyms': ['tag'], 'def': 'a label associated with something for the purpose of identification or information', 'name': 'tag'}, {'frequency': 'f', 'id': 1076, 'synset': 'taillight.n.01', 'synonyms': ['taillight', 'rear_light'], 'def': 'lamp (usually red) mounted at the rear of a motor vehicle', 'name': 'taillight'}, {'frequency': 'r', 'id': 1077, 'synset': 'tambourine.n.01', 'synonyms': ['tambourine'], 'def': 'a shallow drum with a single drumhead and with metallic disks in the sides', 'name': 'tambourine'}, {'frequency': 'r', 'id': 1078, 'synset': 'tank.n.01', 'synonyms': ['army_tank', 'armored_combat_vehicle', 'armoured_combat_vehicle'], 'def': 'an enclosed armored military vehicle; has a cannon and moves on caterpillar treads', 'name': 'army_tank'}, {'frequency': 'c', 'id': 1079, 'synset': 'tank.n.02', 'synonyms': ['tank_(storage_vessel)', 'storage_tank'], 'def': 'a large (usually metallic) vessel for holding gases or liquids', 'name': 'tank_(storage_vessel)'}, {'frequency': 'f', 'id': 1080, 'synset': 'tank_top.n.01', 'synonyms': ['tank_top_(clothing)'], 'def': 'a tight-fitting sleeveless shirt with wide shoulder straps and low neck and no front opening', 'name': 'tank_top_(clothing)'}, {'frequency': 'c', 'id': 1081, 'synset': 'tape.n.01', 'synonyms': ['tape_(sticky_cloth_or_paper)'], 'def': 'a long thin piece of cloth or paper as used for binding or fastening', 'name': 'tape_(sticky_cloth_or_paper)'}, {'frequency': 'c', 'id': 1082, 'synset': 'tape.n.04', 'synonyms': ['tape_measure', 'measuring_tape'], 'def': 'measuring instrument consisting of a narrow strip (cloth or metal) marked in inches or centimeters and used for measuring lengths', 'name': 'tape_measure'}, {'frequency': 'c', 'id': 1083, 'synset': 'tapestry.n.02', 'synonyms': ['tapestry'], 'def': 'a heavy textile with a woven design; used for curtains and upholstery', 'name': 'tapestry'}, {'frequency': 'f', 'id': 1084, 'synset': 'tarpaulin.n.01', 'synonyms': ['tarp'], 'def': 'waterproofed canvas', 'name': 'tarp'}, {'frequency': 'c', 'id': 1085, 'synset': 'tartan.n.01', 'synonyms': ['tartan', 'plaid'], 'def': 'a cloth having a crisscross design', 'name': 'tartan'}, {'frequency': 'c', 'id': 1086, 'synset': 'tassel.n.01', 'synonyms': ['tassel'], 'def': 'adornment consisting of a bunch of cords fastened at one end', 'name': 'tassel'}, {'frequency': 'r', 'id': 1087, 'synset': 'tea_bag.n.01', 'synonyms': ['tea_bag'], 'def': 'a measured amount of tea in a bag for an individual serving of tea', 'name': 'tea_bag'}, {'frequency': 'c', 'id': 1088, 'synset': 'teacup.n.02', 'synonyms': ['teacup'], 'def': 'a cup from which tea is drunk', 'name': 'teacup'}, {'frequency': 'c', 'id': 1089, 'synset': 'teakettle.n.01', 'synonyms': ['teakettle'], 'def': 'kettle for boiling water to make tea', 'name': 'teakettle'}, {'frequency': 'c', 'id': 1090, 'synset': 'teapot.n.01', 'synonyms': ['teapot'], 'def': 'pot for brewing tea; usually has a spout and handle', 'name': 'teapot'}, {'frequency': 'f', 'id': 1091, 'synset': 'teddy.n.01', 'synonyms': ['teddy_bear'], 'def': "plaything consisting of a child's toy bear (usually plush and stuffed with soft materials)", 'name': 'teddy_bear'}, {'frequency': 'f', 'id': 1092, 'synset': 'telephone.n.01', 'synonyms': ['telephone', 'phone', 'telephone_set'], 'def': 'electronic device for communicating by voice over long distances', 'name': 'telephone'}, {'frequency': 'c', 'id': 1093, 'synset': 'telephone_booth.n.01', 'synonyms': ['telephone_booth', 'phone_booth', 'call_box', 'telephone_box', 'telephone_kiosk'], 'def': 'booth for using a telephone', 'name': 'telephone_booth'}, {'frequency': 'f', 'id': 1094, 'synset': 'telephone_pole.n.01', 'synonyms': ['telephone_pole', 'telegraph_pole', 'telegraph_post'], 'def': 'tall pole supporting telephone wires', 'name': 'telephone_pole'}, {'frequency': 'r', 'id': 1095, 'synset': 'telephoto_lens.n.01', 'synonyms': ['telephoto_lens', 'zoom_lens'], 'def': 'a camera lens that magnifies the image', 'name': 'telephoto_lens'}, {'frequency': 'c', 'id': 1096, 'synset': 'television_camera.n.01', 'synonyms': ['television_camera', 'tv_camera'], 'def': 'television equipment for capturing and recording video', 'name': 'television_camera'}, {'frequency': 'f', 'id': 1097, 'synset': 'television_receiver.n.01', 'synonyms': ['television_set', 'tv', 'tv_set'], 'def': 'an electronic device that receives television signals and displays them on a screen', 'name': 'television_set'}, {'frequency': 'f', 'id': 1098, 'synset': 'tennis_ball.n.01', 'synonyms': ['tennis_ball'], 'def': 'ball about the size of a fist used in playing tennis', 'name': 'tennis_ball'}, {'frequency': 'f', 'id': 1099, 'synset': 'tennis_racket.n.01', 'synonyms': ['tennis_racket'], 'def': 'a racket used to play tennis', 'name': 'tennis_racket'}, {'frequency': 'r', 'id': 1100, 'synset': 'tequila.n.01', 'synonyms': ['tequila'], 'def': 'Mexican liquor made from fermented juices of an agave plant', 'name': 'tequila'}, {'frequency': 'c', 'id': 1101, 'synset': 'thermometer.n.01', 'synonyms': ['thermometer'], 'def': 'measuring instrument for measuring temperature', 'name': 'thermometer'}, {'frequency': 'c', 'id': 1102, 'synset': 'thermos.n.01', 'synonyms': ['thermos_bottle'], 'def': 'vacuum flask that preserves temperature of hot or cold drinks', 'name': 'thermos_bottle'}, {'frequency': 'c', 'id': 1103, 'synset': 'thermostat.n.01', 'synonyms': ['thermostat'], 'def': 'a regulator for automatically regulating temperature by starting or stopping the supply of heat', 'name': 'thermostat'}, {'frequency': 'r', 'id': 1104, 'synset': 'thimble.n.02', 'synonyms': ['thimble'], 'def': 'a small metal cap to protect the finger while sewing; can be used as a small container', 'name': 'thimble'}, {'frequency': 'c', 'id': 1105, 'synset': 'thread.n.01', 'synonyms': ['thread', 'yarn'], 'def': 'a fine cord of twisted fibers (of cotton or silk or wool or nylon etc.) used in sewing and weaving', 'name': 'thread'}, {'frequency': 'c', 'id': 1106, 'synset': 'thumbtack.n.01', 'synonyms': ['thumbtack', 'drawing_pin', 'pushpin'], 'def': 'a tack for attaching papers to a bulletin board or drawing board', 'name': 'thumbtack'}, {'frequency': 'c', 'id': 1107, 'synset': 'tiara.n.01', 'synonyms': ['tiara'], 'def': 'a jeweled headdress worn by women on formal occasions', 'name': 'tiara'}, {'frequency': 'c', 'id': 1108, 'synset': 'tiger.n.02', 'synonyms': ['tiger'], 'def': 'large feline of forests in most of Asia having a tawny coat with black stripes', 'name': 'tiger'}, {'frequency': 'c', 'id': 1109, 'synset': 'tights.n.01', 'synonyms': ['tights_(clothing)', 'leotards'], 'def': 'skintight knit hose covering the body from the waist to the feet worn by acrobats and dancers and as stockings by women and girls', 'name': 'tights_(clothing)'}, {'frequency': 'c', 'id': 1110, 'synset': 'timer.n.01', 'synonyms': ['timer', 'stopwatch'], 'def': 'a timepiece that measures a time interval and signals its end', 'name': 'timer'}, {'frequency': 'f', 'id': 1111, 'synset': 'tinfoil.n.01', 'synonyms': ['tinfoil'], 'def': 'foil made of tin or an alloy of tin and lead', 'name': 'tinfoil'}, {'frequency': 'r', 'id': 1112, 'synset': 'tinsel.n.01', 'synonyms': ['tinsel'], 'def': 'a showy decoration that is basically valueless', 'name': 'tinsel'}, {'frequency': 'f', 'id': 1113, 'synset': 'tissue.n.02', 'synonyms': ['tissue_paper'], 'def': 'a soft thin (usually translucent) paper', 'name': 'tissue_paper'}, {'frequency': 'c', 'id': 1114, 'synset': 'toast.n.01', 'synonyms': ['toast_(food)'], 'def': 'slice of bread that has been toasted', 'name': 'toast_(food)'}, {'frequency': 'f', 'id': 1115, 'synset': 'toaster.n.02', 'synonyms': ['toaster'], 'def': 'a kitchen appliance (usually electric) for toasting bread', 'name': 'toaster'}, {'frequency': 'c', 'id': 1116, 'synset': 'toaster_oven.n.01', 'synonyms': ['toaster_oven'], 'def': 'kitchen appliance consisting of a small electric oven for toasting or warming food', 'name': 'toaster_oven'}, {'frequency': 'f', 'id': 1117, 'synset': 'toilet.n.02', 'synonyms': ['toilet'], 'def': 'a plumbing fixture for defecation and urination', 'name': 'toilet'}, {'frequency': 'f', 'id': 1118, 'synset': 'toilet_tissue.n.01', 'synonyms': ['toilet_tissue', 'toilet_paper', 'bathroom_tissue'], 'def': 'a soft thin absorbent paper for use in toilets', 'name': 'toilet_tissue'}, {'frequency': 'f', 'id': 1119, 'synset': 'tomato.n.01', 'synonyms': ['tomato'], 'def': 'mildly acid red or yellow pulpy fruit eaten as a vegetable', 'name': 'tomato'}, {'frequency': 'c', 'id': 1120, 'synset': 'tongs.n.01', 'synonyms': ['tongs'], 'def': 'any of various devices for taking hold of objects; usually have two hinged legs with handles above and pointed hooks below', 'name': 'tongs'}, {'frequency': 'c', 'id': 1121, 'synset': 'toolbox.n.01', 'synonyms': ['toolbox'], 'def': 'a box or chest or cabinet for holding hand tools', 'name': 'toolbox'}, {'frequency': 'f', 'id': 1122, 'synset': 'toothbrush.n.01', 'synonyms': ['toothbrush'], 'def': 'small brush; has long handle; used to clean teeth', 'name': 'toothbrush'}, {'frequency': 'f', 'id': 1123, 'synset': 'toothpaste.n.01', 'synonyms': ['toothpaste'], 'def': 'a dentifrice in the form of a paste', 'name': 'toothpaste'}, {'frequency': 'c', 'id': 1124, 'synset': 'toothpick.n.01', 'synonyms': ['toothpick'], 'def': 'pick consisting of a small strip of wood or plastic; used to pick food from between the teeth', 'name': 'toothpick'}, {'frequency': 'c', 'id': 1125, 'synset': 'top.n.09', 'synonyms': ['cover'], 'def': 'covering for a hole (especially a hole in the top of a container)', 'name': 'cover'}, {'frequency': 'c', 'id': 1126, 'synset': 'tortilla.n.01', 'synonyms': ['tortilla'], 'def': 'thin unleavened pancake made from cornmeal or wheat flour', 'name': 'tortilla'}, {'frequency': 'c', 'id': 1127, 'synset': 'tow_truck.n.01', 'synonyms': ['tow_truck'], 'def': 'a truck equipped to hoist and pull wrecked cars (or to remove cars from no-parking zones)', 'name': 'tow_truck'}, {'frequency': 'f', 'id': 1128, 'synset': 'towel.n.01', 'synonyms': ['towel'], 'def': 'a rectangular piece of absorbent cloth (or paper) for drying or wiping', 'name': 'towel'}, {'frequency': 'f', 'id': 1129, 'synset': 'towel_rack.n.01', 'synonyms': ['towel_rack', 'towel_rail', 'towel_bar'], 'def': 'a rack consisting of one or more bars on which towels can be hung', 'name': 'towel_rack'}, {'frequency': 'f', 'id': 1130, 'synset': 'toy.n.03', 'synonyms': ['toy'], 'def': 'a device regarded as providing amusement', 'name': 'toy'}, {'frequency': 'c', 'id': 1131, 'synset': 'tractor.n.01', 'synonyms': ['tractor_(farm_equipment)'], 'def': 'a wheeled vehicle with large wheels; used in farming and other applications', 'name': 'tractor_(farm_equipment)'}, {'frequency': 'f', 'id': 1132, 'synset': 'traffic_light.n.01', 'synonyms': ['traffic_light'], 'def': 'a device to control vehicle traffic often consisting of three or more lights', 'name': 'traffic_light'}, {'frequency': 'r', 'id': 1133, 'synset': 'trail_bike.n.01', 'synonyms': ['dirt_bike'], 'def': 'a lightweight motorcycle equipped with rugged tires and suspension for off-road use', 'name': 'dirt_bike'}, {'frequency': 'c', 'id': 1134, 'synset': 'trailer_truck.n.01', 'synonyms': ['trailer_truck', 'tractor_trailer', 'trucking_rig', 'articulated_lorry', 'semi_truck'], 'def': 'a truck consisting of a tractor and trailer together', 'name': 'trailer_truck'}, {'frequency': 'f', 'id': 1135, 'synset': 'train.n.01', 'synonyms': ['train_(railroad_vehicle)', 'railroad_train'], 'def': 'public or private transport provided by a line of railway cars coupled together and drawn by a locomotive', 'name': 'train_(railroad_vehicle)'}, {'frequency': 'r', 'id': 1136, 'synset': 'trampoline.n.01', 'synonyms': ['trampoline'], 'def': 'gymnastic apparatus consisting of a strong canvas sheet attached with springs to a metal frame', 'name': 'trampoline'}, {'frequency': 'f', 'id': 1137, 'synset': 'tray.n.01', 'synonyms': ['tray'], 'def': 'an open receptacle for holding or displaying or serving articles or food', 'name': 'tray'}, {'frequency': 'r', 'id': 1138, 'synset': 'tree_house.n.01', 'synonyms': ['tree_house'], 'def': '(NOT A TREE) a PLAYHOUSE built in the branches of a tree', 'name': 'tree_house'}, {'frequency': 'r', 'id': 1139, 'synset': 'trench_coat.n.01', 'synonyms': ['trench_coat'], 'def': 'a military style raincoat; belted with deep pockets', 'name': 'trench_coat'}, {'frequency': 'r', 'id': 1140, 'synset': 'triangle.n.05', 'synonyms': ['triangle_(musical_instrument)'], 'def': 'a percussion instrument consisting of a metal bar bent in the shape of an open triangle', 'name': 'triangle_(musical_instrument)'}, {'frequency': 'r', 'id': 1141, 'synset': 'tricycle.n.01', 'synonyms': ['tricycle'], 'def': 'a vehicle with three wheels that is moved by foot pedals', 'name': 'tricycle'}, {'frequency': 'c', 'id': 1142, 'synset': 'tripod.n.01', 'synonyms': ['tripod'], 'def': 'a three-legged rack used for support', 'name': 'tripod'}, {'frequency': 'f', 'id': 1143, 'synset': 'trouser.n.01', 'synonyms': ['trousers', 'pants_(clothing)'], 'def': 'a garment extending from the waist to the knee or ankle, covering each leg separately', 'name': 'trousers'}, {'frequency': 'f', 'id': 1144, 'synset': 'truck.n.01', 'synonyms': ['truck'], 'def': 'an automotive vehicle suitable for hauling', 'name': 'truck'}, {'frequency': 'r', 'id': 1145, 'synset': 'truffle.n.03', 'synonyms': ['truffle_(chocolate)', 'chocolate_truffle'], 'def': 'creamy chocolate candy', 'name': 'truffle_(chocolate)'}, {'frequency': 'c', 'id': 1146, 'synset': 'trunk.n.02', 'synonyms': ['trunk'], 'def': 'luggage consisting of a large strong case used when traveling or for storage', 'name': 'trunk'}, {'frequency': 'r', 'id': 1147, 'synset': 'tub.n.02', 'synonyms': ['vat'], 'def': 'a large open vessel for holding or storing liquids', 'name': 'vat'}, {'frequency': 'c', 'id': 1148, 'synset': 'turban.n.01', 'synonyms': ['turban'], 'def': 'a traditional headdress consisting of a long scarf wrapped around the head', 'name': 'turban'}, {'frequency': 'r', 'id': 1149, 'synset': 'turkey.n.01', 'synonyms': ['turkey_(bird)'], 'def': 'large gallinaceous bird with fan-shaped tail; widely domesticated for food', 'name': 'turkey_(bird)'}, {'frequency': 'c', 'id': 1150, 'synset': 'turkey.n.04', 'synonyms': ['turkey_(food)'], 'def': 'flesh of large domesticated fowl usually roasted', 'name': 'turkey_(food)'}, {'frequency': 'r', 'id': 1151, 'synset': 'turnip.n.01', 'synonyms': ['turnip'], 'def': 'widely cultivated plant having a large fleshy edible white or yellow root', 'name': 'turnip'}, {'frequency': 'c', 'id': 1152, 'synset': 'turtle.n.02', 'synonyms': ['turtle'], 'def': 'any of various aquatic and land reptiles having a bony shell and flipper-like limbs for swimming', 'name': 'turtle'}, {'frequency': 'r', 'id': 1153, 'synset': 'turtleneck.n.01', 'synonyms': ['turtleneck_(clothing)', 'polo-neck'], 'def': 'a sweater or jersey with a high close-fitting collar', 'name': 'turtleneck_(clothing)'}, {'frequency': 'r', 'id': 1154, 'synset': 'typewriter.n.01', 'synonyms': ['typewriter'], 'def': 'hand-operated character printer for printing written messages one character at a time', 'name': 'typewriter'}, {'frequency': 'f', 'id': 1155, 'synset': 'umbrella.n.01', 'synonyms': ['umbrella'], 'def': 'a lightweight handheld collapsible canopy', 'name': 'umbrella'}, {'frequency': 'c', 'id': 1156, 'synset': 'underwear.n.01', 'synonyms': ['underwear', 'underclothes', 'underclothing', 'underpants'], 'def': 'undergarment worn next to the skin and under the outer garments', 'name': 'underwear'}, {'frequency': 'r', 'id': 1157, 'synset': 'unicycle.n.01', 'synonyms': ['unicycle'], 'def': 'a vehicle with a single wheel that is driven by pedals', 'name': 'unicycle'}, {'frequency': 'c', 'id': 1158, 'synset': 'urinal.n.01', 'synonyms': ['urinal'], 'def': 'a plumbing fixture (usually attached to the wall) used by men to urinate', 'name': 'urinal'}, {'frequency': 'r', 'id': 1159, 'synset': 'urn.n.01', 'synonyms': ['urn'], 'def': 'a large vase that usually has a pedestal or feet', 'name': 'urn'}, {'frequency': 'c', 'id': 1160, 'synset': 'vacuum.n.04', 'synonyms': ['vacuum_cleaner'], 'def': 'an electrical home appliance that cleans by suction', 'name': 'vacuum_cleaner'}, {'frequency': 'c', 'id': 1161, 'synset': 'valve.n.03', 'synonyms': ['valve'], 'def': 'control consisting of a mechanical device for controlling the flow of a fluid', 'name': 'valve'}, {'frequency': 'f', 'id': 1162, 'synset': 'vase.n.01', 'synonyms': ['vase'], 'def': 'an open jar of glass or porcelain used as an ornament or to hold flowers', 'name': 'vase'}, {'frequency': 'c', 'id': 1163, 'synset': 'vending_machine.n.01', 'synonyms': ['vending_machine'], 'def': 'a slot machine for selling goods', 'name': 'vending_machine'}, {'frequency': 'f', 'id': 1164, 'synset': 'vent.n.01', 'synonyms': ['vent', 'blowhole', 'air_vent'], 'def': 'a hole for the escape of gas or air', 'name': 'vent'}, {'frequency': 'c', 'id': 1165, 'synset': 'videotape.n.01', 'synonyms': ['videotape'], 'def': 'a video recording made on magnetic tape', 'name': 'videotape'}, {'frequency': 'r', 'id': 1166, 'synset': 'vinegar.n.01', 'synonyms': ['vinegar'], 'def': 'sour-tasting liquid produced usually by oxidation of the alcohol in wine or cider and used as a condiment or food preservative', 'name': 'vinegar'}, {'frequency': 'r', 'id': 1167, 'synset': 'violin.n.01', 'synonyms': ['violin', 'fiddle'], 'def': 'bowed stringed instrument that is the highest member of the violin family', 'name': 'violin'}, {'frequency': 'r', 'id': 1168, 'synset': 'vodka.n.01', 'synonyms': ['vodka'], 'def': 'unaged colorless liquor originating in Russia', 'name': 'vodka'}, {'frequency': 'r', 'id': 1169, 'synset': 'volleyball.n.02', 'synonyms': ['volleyball'], 'def': 'an inflated ball used in playing volleyball', 'name': 'volleyball'}, {'frequency': 'r', 'id': 1170, 'synset': 'vulture.n.01', 'synonyms': ['vulture'], 'def': 'any of various large birds of prey having naked heads and weak claws and feeding chiefly on carrion', 'name': 'vulture'}, {'frequency': 'c', 'id': 1171, 'synset': 'waffle.n.01', 'synonyms': ['waffle'], 'def': 'pancake batter baked in a waffle iron', 'name': 'waffle'}, {'frequency': 'r', 'id': 1172, 'synset': 'waffle_iron.n.01', 'synonyms': ['waffle_iron'], 'def': 'a kitchen appliance for baking waffles', 'name': 'waffle_iron'}, {'frequency': 'c', 'id': 1173, 'synset': 'wagon.n.01', 'synonyms': ['wagon'], 'def': 'any of various kinds of wheeled vehicles drawn by an animal or a tractor', 'name': 'wagon'}, {'frequency': 'c', 'id': 1174, 'synset': 'wagon_wheel.n.01', 'synonyms': ['wagon_wheel'], 'def': 'a wheel of a wagon', 'name': 'wagon_wheel'}, {'frequency': 'c', 'id': 1175, 'synset': 'walking_stick.n.01', 'synonyms': ['walking_stick'], 'def': 'a stick carried in the hand for support in walking', 'name': 'walking_stick'}, {'frequency': 'c', 'id': 1176, 'synset': 'wall_clock.n.01', 'synonyms': ['wall_clock'], 'def': 'a clock mounted on a wall', 'name': 'wall_clock'}, {'frequency': 'f', 'id': 1177, 'synset': 'wall_socket.n.01', 'synonyms': ['wall_socket', 'wall_plug', 'electric_outlet', 'electrical_outlet', 'outlet', 'electric_receptacle'], 'def': 'receptacle providing a place in a wiring system where current can be taken to run electrical devices', 'name': 'wall_socket'}, {'frequency': 'c', 'id': 1178, 'synset': 'wallet.n.01', 'synonyms': ['wallet', 'billfold'], 'def': 'a pocket-size case for holding papers and paper money', 'name': 'wallet'}, {'frequency': 'r', 'id': 1179, 'synset': 'walrus.n.01', 'synonyms': ['walrus'], 'def': 'either of two large northern marine mammals having ivory tusks and tough hide over thick blubber', 'name': 'walrus'}, {'frequency': 'r', 'id': 1180, 'synset': 'wardrobe.n.01', 'synonyms': ['wardrobe'], 'def': 'a tall piece of furniture that provides storage space for clothes; has a door and rails or hooks for hanging clothes', 'name': 'wardrobe'}, {'frequency': 'r', 'id': 1181, 'synset': 'wasabi.n.02', 'synonyms': ['wasabi'], 'def': 'the thick green root of the wasabi plant that the Japanese use in cooking and that tastes like strong horseradish', 'name': 'wasabi'}, {'frequency': 'c', 'id': 1182, 'synset': 'washer.n.03', 'synonyms': ['automatic_washer', 'washing_machine'], 'def': 'a home appliance for washing clothes and linens automatically', 'name': 'automatic_washer'}, {'frequency': 'f', 'id': 1183, 'synset': 'watch.n.01', 'synonyms': ['watch', 'wristwatch'], 'def': 'a small, portable timepiece', 'name': 'watch'}, {'frequency': 'f', 'id': 1184, 'synset': 'water_bottle.n.01', 'synonyms': ['water_bottle'], 'def': 'a bottle for holding water', 'name': 'water_bottle'}, {'frequency': 'c', 'id': 1185, 'synset': 'water_cooler.n.01', 'synonyms': ['water_cooler'], 'def': 'a device for cooling and dispensing drinking water', 'name': 'water_cooler'}, {'frequency': 'c', 'id': 1186, 'synset': 'water_faucet.n.01', 'synonyms': ['water_faucet', 'water_tap', 'tap_(water_faucet)'], 'def': 'a faucet for drawing water from a pipe or cask', 'name': 'water_faucet'}, {'frequency': 'r', 'id': 1187, 'synset': 'water_filter.n.01', 'synonyms': ['water_filter'], 'def': 'a filter to remove impurities from the water supply', 'name': 'water_filter'}, {'frequency': 'r', 'id': 1188, 'synset': 'water_heater.n.01', 'synonyms': ['water_heater', 'hot-water_heater'], 'def': 'a heater and storage tank to supply heated water', 'name': 'water_heater'}, {'frequency': 'r', 'id': 1189, 'synset': 'water_jug.n.01', 'synonyms': ['water_jug'], 'def': 'a jug that holds water', 'name': 'water_jug'}, {'frequency': 'r', 'id': 1190, 'synset': 'water_pistol.n.01', 'synonyms': ['water_gun', 'squirt_gun'], 'def': 'plaything consisting of a toy pistol that squirts water', 'name': 'water_gun'}, {'frequency': 'c', 'id': 1191, 'synset': 'water_scooter.n.01', 'synonyms': ['water_scooter', 'sea_scooter', 'jet_ski'], 'def': 'a motorboat resembling a motor scooter (NOT A SURFBOARD OR WATER SKI)', 'name': 'water_scooter'}, {'frequency': 'c', 'id': 1192, 'synset': 'water_ski.n.01', 'synonyms': ['water_ski'], 'def': 'broad ski for skimming over water towed by a speedboat (DO NOT MARK WATER)', 'name': 'water_ski'}, {'frequency': 'c', 'id': 1193, 'synset': 'water_tower.n.01', 'synonyms': ['water_tower'], 'def': 'a large reservoir for water', 'name': 'water_tower'}, {'frequency': 'c', 'id': 1194, 'synset': 'watering_can.n.01', 'synonyms': ['watering_can'], 'def': 'a container with a handle and a spout with a perforated nozzle; used to sprinkle water over plants', 'name': 'watering_can'}, {'frequency': 'c', 'id': 1195, 'synset': 'watermelon.n.02', 'synonyms': ['watermelon'], 'def': 'large oblong or roundish melon with a hard green rind and sweet watery red or occasionally yellowish pulp', 'name': 'watermelon'}, {'frequency': 'f', 'id': 1196, 'synset': 'weathervane.n.01', 'synonyms': ['weathervane', 'vane_(weathervane)', 'wind_vane'], 'def': 'mechanical device attached to an elevated structure; rotates freely to show the direction of the wind', 'name': 'weathervane'}, {'frequency': 'c', 'id': 1197, 'synset': 'webcam.n.01', 'synonyms': ['webcam'], 'def': 'a digital camera designed to take digital photographs and transmit them over the internet', 'name': 'webcam'}, {'frequency': 'c', 'id': 1198, 'synset': 'wedding_cake.n.01', 'synonyms': ['wedding_cake', 'bridecake'], 'def': 'a rich cake with two or more tiers and covered with frosting and decorations; served at a wedding reception', 'name': 'wedding_cake'}, {'frequency': 'c', 'id': 1199, 'synset': 'wedding_ring.n.01', 'synonyms': ['wedding_ring', 'wedding_band'], 'def': 'a ring given to the bride and/or groom at the wedding', 'name': 'wedding_ring'}, {'frequency': 'f', 'id': 1200, 'synset': 'wet_suit.n.01', 'synonyms': ['wet_suit'], 'def': 'a close-fitting garment made of a permeable material; worn in cold water to retain body heat', 'name': 'wet_suit'}, {'frequency': 'f', 'id': 1201, 'synset': 'wheel.n.01', 'synonyms': ['wheel'], 'def': 'a circular frame with spokes (or a solid disc) that can rotate on a shaft or axle', 'name': 'wheel'}, {'frequency': 'c', 'id': 1202, 'synset': 'wheelchair.n.01', 'synonyms': ['wheelchair'], 'def': 'a movable chair mounted on large wheels', 'name': 'wheelchair'}, {'frequency': 'c', 'id': 1203, 'synset': 'whipped_cream.n.01', 'synonyms': ['whipped_cream'], 'def': 'cream that has been beaten until light and fluffy', 'name': 'whipped_cream'}, {'frequency': 'r', 'id': 1204, 'synset': 'whiskey.n.01', 'synonyms': ['whiskey'], 'def': 'a liquor made from fermented mash of grain', 'name': 'whiskey'}, {'frequency': 'r', 'id': 1205, 'synset': 'whistle.n.03', 'synonyms': ['whistle'], 'def': 'a small wind instrument that produces a whistling sound by blowing into it', 'name': 'whistle'}, {'frequency': 'r', 'id': 1206, 'synset': 'wick.n.02', 'synonyms': ['wick'], 'def': 'a loosely woven cord in a candle or oil lamp that is lit on fire', 'name': 'wick'}, {'frequency': 'c', 'id': 1207, 'synset': 'wig.n.01', 'synonyms': ['wig'], 'def': 'hairpiece covering the head and made of real or synthetic hair', 'name': 'wig'}, {'frequency': 'c', 'id': 1208, 'synset': 'wind_chime.n.01', 'synonyms': ['wind_chime'], 'def': 'a decorative arrangement of pieces of metal or glass or pottery that hang together loosely so the wind can cause them to tinkle', 'name': 'wind_chime'}, {'frequency': 'c', 'id': 1209, 'synset': 'windmill.n.01', 'synonyms': ['windmill'], 'def': 'a mill that is powered by the wind', 'name': 'windmill'}, {'frequency': 'c', 'id': 1210, 'synset': 'window_box.n.01', 'synonyms': ['window_box_(for_plants)'], 'def': 'a container for growing plants on a windowsill', 'name': 'window_box_(for_plants)'}, {'frequency': 'f', 'id': 1211, 'synset': 'windshield_wiper.n.01', 'synonyms': ['windshield_wiper', 'windscreen_wiper', 'wiper_(for_windshield/screen)'], 'def': 'a mechanical device that cleans the windshield', 'name': 'windshield_wiper'}, {'frequency': 'c', 'id': 1212, 'synset': 'windsock.n.01', 'synonyms': ['windsock', 'air_sock', 'air-sleeve', 'wind_sleeve', 'wind_cone'], 'def': 'a truncated cloth cone mounted on a mast/pole; shows wind direction', 'name': 'windsock'}, {'frequency': 'f', 'id': 1213, 'synset': 'wine_bottle.n.01', 'synonyms': ['wine_bottle'], 'def': 'a bottle for holding wine', 'name': 'wine_bottle'}, {'frequency': 'r', 'id': 1214, 'synset': 'wine_bucket.n.01', 'synonyms': ['wine_bucket', 'wine_cooler'], 'def': 'a bucket of ice used to chill a bottle of wine', 'name': 'wine_bucket'}, {'frequency': 'f', 'id': 1215, 'synset': 'wineglass.n.01', 'synonyms': ['wineglass'], 'def': 'a glass that has a stem and in which wine is served', 'name': 'wineglass'}, {'frequency': 'r', 'id': 1216, 'synset': 'wing_chair.n.01', 'synonyms': ['wing_chair'], 'def': 'easy chair having wings on each side of a high back', 'name': 'wing_chair'}, {'frequency': 'c', 'id': 1217, 'synset': 'winker.n.02', 'synonyms': ['blinder_(for_horses)'], 'def': 'blinds that prevent a horse from seeing something on either side', 'name': 'blinder_(for_horses)'}, {'frequency': 'c', 'id': 1218, 'synset': 'wok.n.01', 'synonyms': ['wok'], 'def': 'pan with a convex bottom; used for frying in Chinese cooking', 'name': 'wok'}, {'frequency': 'r', 'id': 1219, 'synset': 'wolf.n.01', 'synonyms': ['wolf'], 'def': 'a wild carnivorous mammal of the dog family, living and hunting in packs', 'name': 'wolf'}, {'frequency': 'c', 'id': 1220, 'synset': 'wooden_spoon.n.02', 'synonyms': ['wooden_spoon'], 'def': 'a spoon made of wood', 'name': 'wooden_spoon'}, {'frequency': 'c', 'id': 1221, 'synset': 'wreath.n.01', 'synonyms': ['wreath'], 'def': 'an arrangement of flowers, leaves, or stems fastened in a ring', 'name': 'wreath'}, {'frequency': 'c', 'id': 1222, 'synset': 'wrench.n.03', 'synonyms': ['wrench', 'spanner'], 'def': 'a hand tool that is used to hold or twist a nut or bolt', 'name': 'wrench'}, {'frequency': 'c', 'id': 1223, 'synset': 'wristband.n.01', 'synonyms': ['wristband'], 'def': 'band consisting of a part of a sleeve that covers the wrist', 'name': 'wristband'}, {'frequency': 'f', 'id': 1224, 'synset': 'wristlet.n.01', 'synonyms': ['wristlet', 'wrist_band'], 'def': 'a band or bracelet worn around the wrist', 'name': 'wristlet'}, {'frequency': 'r', 'id': 1225, 'synset': 'yacht.n.01', 'synonyms': ['yacht'], 'def': 'an expensive vessel propelled by sail or power and used for cruising or racing', 'name': 'yacht'}, {'frequency': 'r', 'id': 1226, 'synset': 'yak.n.02', 'synonyms': ['yak'], 'def': 'large long-haired wild ox of Tibet often domesticated', 'name': 'yak'}, {'frequency': 'c', 'id': 1227, 'synset': 'yogurt.n.01', 'synonyms': ['yogurt', 'yoghurt', 'yoghourt'], 'def': 'a custard-like food made from curdled milk', 'name': 'yogurt'}, {'frequency': 'r', 'id': 1228, 'synset': 'yoke.n.07', 'synonyms': ['yoke_(animal_equipment)'], 'def': 'gear joining two animals at the neck; NOT egg yolk', 'name': 'yoke_(animal_equipment)'}, {'frequency': 'f', 'id': 1229, 'synset': 'zebra.n.01', 'synonyms': ['zebra'], 'def': 'any of several fleet black-and-white striped African equines', 'name': 'zebra'}, {'frequency': 'c', 'id': 1230, 'synset': 'zucchini.n.02', 'synonyms': ['zucchini', 'courgette'], 'def': 'small cucumber-shaped vegetable marrow; typically dark green', 'name': 'zucchini'}] # noqa +# fmt: on diff --git a/detectron2/data/datasets/pascal_voc.py b/detectron2/data/datasets/pascal_voc.py new file mode 100644 index 0000000000..8053af6653 --- /dev/null +++ b/detectron2/data/datasets/pascal_voc.py @@ -0,0 +1,78 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from fvcore.common.file_io import PathManager +import os +import numpy as np +import xml.etree.ElementTree as ET + +from detectron2.structures import BoxMode +from detectron2.data import DatasetCatalog, MetadataCatalog + + +__all__ = ["register_pascal_voc"] + + +# fmt: off +CLASS_NAMES = [ + "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", + "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", + "pottedplant", "sheep", "sofa", "train", "tvmonitor", +] +# fmt: on + + +def load_voc_instances(dirname: str, split: str): + """ + Load Pascal VOC detection annotations to Detectron2 format. + + Args: + dirname: Contain "Annotations", "ImageSets", "JPEGImages" + split (str): one of "train", "test", "val", "trainval" + """ + with PathManager.open(os.path.join(dirname, "ImageSets", "Main", split + ".txt")) as f: + fileids = np.loadtxt(f, dtype=np.str) + + dicts = [] + for fileid in fileids: + anno_file = os.path.join(dirname, "Annotations", fileid + ".xml") + jpeg_file = os.path.join(dirname, "JPEGImages", fileid + ".jpg") + + tree = ET.parse(anno_file) + + r = { + "file_name": jpeg_file, + "image_id": fileid, + "height": int(tree.findall("./size/height")[0].text), + "width": int(tree.findall("./size/width")[0].text), + } + instances = [] + + for obj in tree.findall("object"): + cls = obj.find("name").text + # We include "difficult" samples in training. + # Based on limited experiments, they don't hurt accuracy. + # difficult = int(obj.find("difficult").text) + # if difficult == 1: + # continue + bbox = obj.find("bndbox") + bbox = [float(bbox.find(x).text) for x in ["xmin", "ymin", "xmax", "ymax"]] + # Original annotations are integers in the range [1, W or H] + # Assuming they mean 1-based pixel indices (inclusive), + # a box with annotation (xmin=1, xmax=W) covers the whole image. + # In coordinate space this is represented by (xmin=0, xmax=W) + bbox[0] -= 1.0 + bbox[1] -= 1.0 + instances.append( + {"category_id": CLASS_NAMES.index(cls), "bbox": bbox, "bbox_mode": BoxMode.XYXY_ABS} + ) + r["annotations"] = instances + dicts.append(r) + return dicts + + +def register_pascal_voc(name, dirname, split, year): + DatasetCatalog.register(name, lambda: load_voc_instances(dirname, split)) + MetadataCatalog.get(name).set( + thing_classes=CLASS_NAMES, dirname=dirname, year=year, split=split + ) diff --git a/detectron2/data/datasets/register_coco.py b/detectron2/data/datasets/register_coco.py new file mode 100644 index 0000000000..4ac0fa3a03 --- /dev/null +++ b/detectron2/data/datasets/register_coco.py @@ -0,0 +1,124 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy + +from detectron2.data import DatasetCatalog, MetadataCatalog +from .coco import load_coco_json, load_sem_seg + +""" +This file contains functions to register a COCO-format dataset to the DatasetCatalog. +""" + +__all__ = ["register_coco_instances", "register_coco_panoptic_separated"] + + +def register_coco_instances(name, metadata, json_file, image_root): + """ + Register a dataset in COCO's json annotation format for + instance detection, instance segmentation and keypoint detection. + (i.e., Type 1 and 2 in http://cocodataset.org/#format-data. + `instances*.json` and `person_keypoints*.json` in the dataset). + + This is an example of how to register a new dataset. + You can do something similar to this function, to register new datasets. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str): directory which contains all the images. + """ + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata + ) + + +def register_coco_panoptic_separated( + name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json +): + """ + Register a COCO panoptic segmentation dataset named `name`. + The annotations in this registered dataset will contain both instance annotations and + semantic annotations, each with its own contiguous ids. Hence it's called "separated". + + It follows the setting used by the PanopticFPN paper: + + 1. The instance annotations directly come from polygons in the COCO + instances annotation task, rather than from the masks in the COCO panoptic annotations. + + The two format have small differences: + Polygons in the instance annotations may have overlaps. + The mask annotations are produced by labeling the overlapped polygons + with depth ordering. + + 2. The semantic annotations are converted from panoptic annotations, where + all "things" are assigned a semantic id of 0. + All semantic categories will therefore have ids in contiguous + range [1, #stuff_categories]. + + This function will also register a pure semantic segmentation dataset + named `name + '_stuffonly'`. + + Args: + name (str): the name that identifies a dataset, + e.g. "coco_2017_train_panoptic" + metadata (str): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images + panoptic_json (str): path to the json panoptic annotation file + sem_seg_root (str): directory which contains all the ground truth segmentation annotations. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + "_separated" + DatasetCatalog.register( + panoptic_name, + lambda: merge_to_panoptic( + load_coco_json(instances_json, image_root, panoptic_name), + load_sem_seg(sem_seg_root, image_root), + ), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + sem_seg_root=sem_seg_root, + json_file=instances_json, # TODO rename + evaluator_type="coco_panoptic_seg", + **metadata + ) + + semantic_name = name + "_stuffonly" + DatasetCatalog.register(semantic_name, lambda: load_sem_seg(sem_seg_root, image_root)) + MetadataCatalog.get(semantic_name).set( + sem_seg_root=sem_seg_root, image_root=image_root, evaluator_type="sem_seg", **metadata + ) + + +def merge_to_panoptic(detection_dicts, sem_seg_dicts): + """ + Create dataset dicts for panoptic segmentation, by + merging two dicts using "file_name" field to match their entries. + + Args: + detection_dicts (list[dict]): lists of dicts for object detection or instance segmentation. + sem_seg_dicts (list[dict]): lists of dicts for semantic segmentation. + + Returns: + list[dict] (one per input image): Each dict contains all (key, value) pairs from dicts in + both detection_dicts and sem_seg_dicts that correspond to the same image. + The function assumes that the same key in different dicts has the same value. + """ + results = [] + sem_seg_file_to_entry = {x["file_name"]: x for x in sem_seg_dicts} + assert len(sem_seg_file_to_entry) > 0 + + for det_dict in detection_dicts: + dic = copy.copy(det_dict) + dic.update(sem_seg_file_to_entry[dic["file_name"]]) + results.append(dic) + return results diff --git a/detectron2/data/detection_utils.py b/detectron2/data/detection_utils.py new file mode 100644 index 0000000000..2e5e70f2d5 --- /dev/null +++ b/detectron2/data/detection_utils.py @@ -0,0 +1,397 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +""" +Common data processing utilities that are used in a +typical object detection data pipeline. +""" +import logging +import numpy as np +import torch +from fvcore.common.file_io import PathManager +from PIL import Image + +from detectron2.structures import ( + BitMasks, + Boxes, + BoxMode, + Instances, + Keypoints, + PolygonMasks, + RotatedBoxes, +) + +from . import transforms as T +from .catalog import MetadataCatalog + + +class SizeMismatchError(ValueError): + """ + When loaded image has difference width/height compared with annoation. + """ + + +def read_image(file_name, format=None): + """ + Read an image into the given format. + + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + format (dict): one of the supported image modes in PIL, or "BGR" + + Returns: + image (np.ndarray): an HWC image + """ + with PathManager.open(file_name, "rb") as f: + image = Image.open(f) + + if format is not None: + # PIL only supports RGB, so convert to RGB and flip channels over below + conversion_format = format + if format == "BGR": + conversion_format = "RGB" + image = image.convert(conversion_format) + image = np.asarray(image) + if format == "BGR": + # flip channels if needed + image = image[:, :, ::-1] + # PIL squeezes out the channel dimension for "L", so make it HWC + if format == "L": + image = np.expand_dims(image, -1) + return image + + +def check_image_size(dataset_dict, image): + """ + Raise an error if the image does not match the size specified in the dict. + """ + if "width" in dataset_dict or "height" in dataset_dict: + image_wh = (image.shape[1], image.shape[0]) + expected_wh = (dataset_dict["width"], dataset_dict["height"]) + if not image_wh == expected_wh: + raise SizeMismatchError( + "mismatch (W,H), got {}, expect {}".format(image_wh, expected_wh) + ) + + +def transform_proposals(dataset_dict, image_shape, transforms, min_box_side_len, proposal_topk): + """ + Apply transformations to the proposals in dataset_dict, if any. + + Args: + dataset_dict (dict): a dict read from the dataset, possibly + contains fields "proposal_boxes", "proposal_objectness_logits", "proposal_bbox_mode" + image_shape (tuple): height, width + transforms (TransformList): + min_box_side_len (int): keep proposals with at least this size + proposal_topk (int): only keep top-K scoring proposals + + The input dict is modified in-place, with abovementioned keys removed. A new + key "proposals" will be added. Its value is an `Instances` + object which contains the transformed proposals in its field + "proposal_boxes" and "objectness_logits". + """ + if "proposal_boxes" in dataset_dict: + # Tranform proposal boxes + boxes = transforms.apply_box( + BoxMode.convert( + dataset_dict.pop("proposal_boxes"), + dataset_dict.pop("proposal_bbox_mode"), + BoxMode.XYXY_ABS, + ) + ) + boxes = Boxes(boxes) + objectness_logits = torch.as_tensor( + dataset_dict.pop("proposal_objectness_logits").astype("float32") + ) + + boxes.clip(image_shape) + keep = boxes.nonempty(threshold=min_box_side_len) + boxes = boxes[keep] + objectness_logits = objectness_logits[keep] + + proposals = Instances(image_shape) + proposals.proposal_boxes = boxes[:proposal_topk] + proposals.objectness_logits = objectness_logits[:proposal_topk] + dataset_dict["proposals"] = proposals + + +def transform_instance_annotations( + annotation, transforms, image_size, *, keypoint_hflip_indices=None +): + """ + Apply transforms to box, segmentation and keypoints of annotations of a single instance. + + It will use `transforms.apply_box` for the box, and + `transforms.apply_coords` for segmentation polygons & keypoints. + If you need anything more specially designed for each data structure, + you'll need to implement your own version of this function or the transforms. + + Args: + annotation (dict): dict of instance annotations for a single instance. + transforms (TransformList): + image_size (tuple): the height, width of the transformed image + keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. + + Returns: + dict: + the same input dict with fields "bbox", "segmentation", "keypoints" + transformed according to `transforms`. + The "bbox_mode" field will be set to XYXY_ABS. + """ + bbox = BoxMode.convert(annotation["bbox"], annotation["bbox_mode"], BoxMode.XYXY_ABS) + # Note that bbox is 1d (per-instance bounding box) + annotation["bbox"] = transforms.apply_box([bbox])[0] + annotation["bbox_mode"] = BoxMode.XYXY_ABS + + if "segmentation" in annotation: + # each instance contains 1 or more polygons + polygons = [np.asarray(p).reshape(-1, 2) for p in annotation["segmentation"]] + annotation["segmentation"] = [p.reshape(-1) for p in transforms.apply_polygons(polygons)] + + if "keypoints" in annotation: + keypoints = transform_keypoint_annotations( + annotation["keypoints"], transforms, image_size, keypoint_hflip_indices + ) + annotation["keypoints"] = keypoints + + return annotation + + +def transform_keypoint_annotations(keypoints, transforms, image_size, keypoint_hflip_indices=None): + """ + Transform keypoint annotations of an image. + + Args: + keypoints (list[float]): Nx3 float in Detectron2 Dataset format. + transforms (TransformList): + image_size (tuple): the height, width of the transformed image + keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. + """ + # (N*3,) -> (N, 3) + keypoints = np.asarray(keypoints, dtype="float64").reshape(-1, 3) + keypoints[:, :2] = transforms.apply_coords(keypoints[:, :2]) + + # This assumes that HorizFlipTransform is the only one that does flip + do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1 + + # Alternative way: check if probe points was horizontally flipped. + # probe = np.asarray([[0.0, 0.0], [image_width, 0.0]]) + # probe_aug = transforms.apply_coords(probe.copy()) + # do_hflip = np.sign(probe[1][0] - probe[0][0]) != np.sign(probe_aug[1][0] - probe_aug[0][0]) # noqa + + # If flipped, swap each keypoint with its opposite-handed equivalent + if do_hflip: + assert keypoint_hflip_indices is not None + keypoints = keypoints[keypoint_hflip_indices, :] + + # Maintain COCO convention that if visibility == 0, then x, y = 0 + # TODO may need to reset visibility for cropped keypoints, + # but it does not matter for our existing algorithms + keypoints[keypoints[:, 2] == 0] = 0 + return keypoints + + +def annotations_to_instances(annos, image_size, mask_format="polygon"): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + It will contain fields "gt_boxes", "gt_classes", + "gt_masks", "gt_keypoints", if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = [BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos] + target = Instances(image_size) + boxes = target.gt_boxes = Boxes(boxes) + boxes.clip(image_size) + + classes = [obj["category_id"] for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + if len(annos) and "segmentation" in annos[0]: + polygons = [obj["segmentation"] for obj in annos] + if mask_format == "polygon": + masks = PolygonMasks(polygons) + else: + assert mask_format == "bitmask", mask_format + masks = BitMasks.from_polygon_masks(polygons, *image_size) + target.gt_masks = masks + + if len(annos) and "keypoints" in annos[0]: + kpts = [obj.get("keypoints", []) for obj in annos] + target.gt_keypoints = Keypoints(kpts) + + return target + + +def annotations_to_instances_rotated(annos, image_size): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + Compared to `annotations_to_instances`, this function is for rotated boxes only + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + Containing fields "gt_boxes", "gt_classes", + if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = [obj["bbox"] for obj in annos] + target = Instances(image_size) + boxes = target.gt_boxes = RotatedBoxes(boxes) + boxes.clip(image_size) + + classes = [obj["category_id"] for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + return target + + +def filter_empty_instances(instances, by_box=True, by_mask=True): + """ + Filter out empty instances in an `Instances` object. + + Args: + instances (Instances): + by_box (bool): whether to filter out instances with empty boxes + by_mask (bool): whether to filter out instances with empty masks + + Returns: + Instances: the filtered instances. + """ + assert by_box or by_mask + r = [] + if by_box: + r.append(instances.gt_boxes.nonempty()) + if instances.has("gt_masks") and by_mask: + r.append(instances.gt_masks.nonempty()) + + # TODO: can also filter visible keypoints + + if not r: + return instances + m = r[0] + for x in r[1:]: + m = m & x + return instances[m] + + +def create_keypoint_hflip_indices(dataset_names): + """ + Args: + dataset_names (list[str]): list of dataset names + Returns: + ndarray[int]: a vector of size=#keypoints, storing the + horizontally-flipped keypoint indices. + """ + + check_metadata_consistency("keypoint_names", dataset_names) + check_metadata_consistency("keypoint_flip_map", dataset_names) + + meta = MetadataCatalog.get(dataset_names[0]) + names = meta.keypoint_names + # TODO flip -> hflip + flip_map = dict(meta.keypoint_flip_map) + flip_map.update({v: k for k, v in flip_map.items()}) + flipped_names = [i if i not in flip_map else flip_map[i] for i in names] + flip_indices = [names.index(i) for i in flipped_names] + return np.asarray(flip_indices) + + +def gen_crop_transform_with_instance(crop_size, image_size, instance): + """ + Generate a CropTransform so that the cropping region contains + the center of the given instance. + + Args: + crop_size (tuple): h, w in pixels + image_size (tuple): h, w + instance (dict): an annotation dict of one instance, in Detectron2's + dataset format. + """ + crop_size = np.asarray(crop_size, dtype=np.int32) + bbox = BoxMode.convert(instance["bbox"], instance["bbox_mode"], BoxMode.XYXY_ABS) + center_yx = (bbox[1] + bbox[3]) * 0.5, (bbox[0] + bbox[2]) * 0.5 + + min_yx = np.maximum(np.floor(center_yx).astype(np.int32) - crop_size, 0) + max_yx = np.maximum(np.asarray(image_size, dtype=np.int32) - crop_size, 0) + max_yx = np.minimum(max_yx, np.ceil(center_yx).astype(np.int32)) + + y0 = np.random.randint(min_yx[0], max_yx[0] + 1) + x0 = np.random.randint(min_yx[1], max_yx[1] + 1) + return T.CropTransform(x0, y0, crop_size[1], crop_size[0]) + + +def check_metadata_consistency(key, dataset_names): + """ + Check that the datasets have consistent metadata. + + Args: + key (str): a metadata key + dataset_names (list[str]): a list of dataset names + + Raises: + AttributeError: if the key does not exist in the metadata + ValueError: if the given datasets do not have the same metadata values defined by key + """ + if len(dataset_names) == 0: + return + logger = logging.getLogger(__name__) + entries_per_dataset = [getattr(MetadataCatalog.get(d), key) for d in dataset_names] + for idx, entry in enumerate(entries_per_dataset): + if entry != entries_per_dataset[0]: + logger.error( + "Metadata '{}' for dataset '{}' is '{}'".format(key, dataset_names[idx], str(entry)) + ) + logger.error( + "Metadata '{}' for dataset '{}' is '{}'".format( + key, dataset_names[0], str(entries_per_dataset[0]) + ) + ) + raise ValueError("Datasets have different metadata '{}'!".format(key)) + + +def build_transform_gen(cfg, is_train): + """ + Create a list of :class:`TransformGen` from config. + Now it includes resizing and flipping. + + Returns: + list[TransformGen] + """ + if is_train: + min_size = cfg.INPUT.MIN_SIZE_TRAIN + max_size = cfg.INPUT.MAX_SIZE_TRAIN + sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING + else: + min_size = cfg.INPUT.MIN_SIZE_TEST + max_size = cfg.INPUT.MAX_SIZE_TEST + sample_style = "choice" + if sample_style == "range": + assert len(min_size) == 2, "more than 2 ({}) min_size(s) are provided for ranges".format( + len(min_size) + ) + + logger = logging.getLogger(__name__) + tfm_gens = [] + if not min_size == 0: # set to zero to disable resize + tfm_gens.append(T.ResizeShortestEdge(min_size, max_size, sample_style)) + if is_train: + tfm_gens.append(T.RandomFlip()) + logger.info("TransformGens used in training: " + str(tfm_gens)) + return tfm_gens diff --git a/detectron2/data/samplers/__init__.py b/detectron2/data/samplers/__init__.py new file mode 100644 index 0000000000..9cfa8a6525 --- /dev/null +++ b/detectron2/data/samplers/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .distributed_sampler import InferenceSampler, RepeatFactorTrainingSampler, TrainingSampler +from .grouped_batch_sampler import GroupedBatchSampler + +__all__ = [ + "GroupedBatchSampler", + "TrainingSampler", + "InferenceSampler", + "RepeatFactorTrainingSampler", +] diff --git a/detectron2/data/samplers/distributed_sampler.py b/detectron2/data/samplers/distributed_sampler.py new file mode 100644 index 0000000000..43b7ea2858 --- /dev/null +++ b/detectron2/data/samplers/distributed_sampler.py @@ -0,0 +1,199 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import itertools +import math +from collections import defaultdict +from typing import Optional +import torch +from torch.utils.data.sampler import Sampler + +from detectron2.utils import comm + + +class TrainingSampler(Sampler): + """ + In training, we only care about the "infinite stream" of training data. + So this sampler produces an infinite stream of indices and + all workers cooperate to correctly shuffle the indices and sample different indices. + + The samplers in each worker effectively produces `indices[worker_id::num_workers]` + where `indices` is an infinite stream of indices consisting of + `shuffle(range(size)) + suhffle(range(size)) + ...` (if shuffle is True) + or `range(size) + range(size) + ...` (if shuffle is False) + """ + + def __init__(self, size: int, shuffle: bool = True, seed: Optional[int] = None): + """ + Args: + size (int): the total number of data of the underlying dataset to sample from + shuffle (bool): whether to shuffle the indices or not + seed (int): the initial seed of the shuffle. Must be the same + across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + """ + self._size = size + assert size > 0 + self._shuffle = shuffle + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + def __iter__(self): + start = self._rank + yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + if self._shuffle: + yield from torch.randperm(self._size, generator=g) + else: + yield from torch.arange(self._size) + + +class RepeatFactorTrainingSampler(Sampler): + """ + Similar to TrainingSampler, but suitable for training on class imbalanced datasets + like LVIS. In each epoch, an image may appear multiple times based on its "repeat + factor". The repeat factor for an image is a function of the frequency the rarest + category labeled in that image. The "frequency of category c" in [0, 1] is defined + as the fraction of images in the training set (without repeats) in which category c + appears. + + See https://arxiv.org/abs/1908.03195 (>= v2) Appendix B.2. + """ + + def __init__(self, dataset_dicts, repeat_thresh, shuffle=True, seed=None): + """ + Args: + dataset_dicts (list[dict]): annotations in Detectron2 dataset format. + repeat_thresh (float): frequency threshold below which data is repeated. + shuffle (bool): whether to shuffle the indices or not + seed (int): the initial seed of the shuffle. Must be the same + across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + """ + self._shuffle = shuffle + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + # Get fractional repeat factors and split into whole number (_int_part) + # and fractional (_frac_part) parts. + rep_factors = self._get_repeat_factors(dataset_dicts, repeat_thresh) + self._int_part = torch.trunc(rep_factors) + self._frac_part = rep_factors - self._int_part + + def _get_repeat_factors(self, dataset_dicts, repeat_thresh): + """ + Compute (fractional) per-image repeat factors. + + Args: + See __init__. + + Returns: + torch.Tensor: the i-th element is the repeat factor for the dataset image + at index i. + """ + # 1. For each category c, compute the fraction of images that contain it: f(c) + category_freq = defaultdict(int) + for dataset_dict in dataset_dicts: # For each image (without repeats) + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + for cat_id in cat_ids: + category_freq[cat_id] += 1 + num_images = len(dataset_dicts) + for k, v in category_freq.items(): + category_freq[k] = v / num_images + + # 2. For each category c, compute the category-level repeat factor: + # r(c) = max(1, sqrt(t / f(c))) + category_rep = { + cat_id: max(1.0, math.sqrt(repeat_thresh / cat_freq)) + for cat_id, cat_freq in category_freq.items() + } + + # 3. For each image I, compute the image-level repeat factor: + # r(I) = max_{c in I} r(c) + rep_factors = [] + for dataset_dict in dataset_dicts: + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + rep_factor = max({category_rep[cat_id] for cat_id in cat_ids}) + rep_factors.append(rep_factor) + + return torch.tensor(rep_factors, dtype=torch.float32) + + def _get_epoch_indices(self, generator): + """ + Create a list of dataset indices (with repeats) to use for one epoch. + + Args: + generator (torch.Generator): pseudo random number generator used for + stochastic rounding. + + Returns: + torch.Tensor: list of dataset indicies to use in one epoch. Each index + is repeated based on its calculated repeat factor. + """ + # Since repeat factors are fractional, we use stochastic rounding so + # that the target repeat factor is achieved in expectation over the + # course of training + rands = torch.rand(len(self._frac_part), generator=generator) + rep_factors = self._int_part + (rands < self._frac_part).float() + # Construct a list of indices in which we repeat images as specified + indices = [] + for dataset_index, rep_factor in enumerate(rep_factors): + indices.extend([dataset_index] * int(rep_factor.item())) + return torch.tensor(indices, dtype=torch.int64) + + def __iter__(self): + start = self._rank + yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + # Sample indices with repeats determined by stochastic rounding; each + # "epoch" may have a slightly different size due to the rounding. + indices = self._get_epoch_indices(g) + if self._shuffle: + randperm = torch.randperm(len(indices), generator=g) + yield from indices[randperm] + else: + yield from indices + + +class InferenceSampler(Sampler): + """ + Produce indices for inference. + Inference needs to run on the __exact__ set of samples, + therefore when the total number of samples is not divisible by the number of workers, + this sampler produces different number of samples on different workers. + """ + + def __init__(self, size: int): + """ + Args: + size (int): the total number of data of the underlying dataset to sample from + """ + self._size = size + assert size > 0 + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + shard_size = (self._size - 1) // self._world_size + 1 + begin = shard_size * self._rank + end = min(shard_size * (self._rank + 1), self._size) + self._local_indices = range(begin, end) + + def __iter__(self): + yield from self._local_indices + + def __len__(self): + return len(self._local_indices) diff --git a/detectron2/data/samplers/grouped_batch_sampler.py b/detectron2/data/samplers/grouped_batch_sampler.py new file mode 100644 index 0000000000..5f16172567 --- /dev/null +++ b/detectron2/data/samplers/grouped_batch_sampler.py @@ -0,0 +1,46 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import torch +from torch.utils.data.sampler import BatchSampler, Sampler + + +class GroupedBatchSampler(BatchSampler): + """ + Wraps another sampler to yield a mini-batch of indices. + It enforces that the batch only contain elements from the same group. + It also tries to provide mini-batches which follows an ordering which is + as close as possible to the ordering from the original sampler. + + Arguments: + sampler (Sampler): Base sampler. + group_ids (list[int]): If the sampler produces indices in range [0, N), + `group_ids` must be a list of `N` ints which contains the group id of each sample. + The group ids must be a continuous set of integers starting from + 0, i.e. they must be in the range [0, num_groups). + batch_size (int): Size of mini-batch. + """ + + def __init__(self, sampler, group_ids, batch_size): + if not isinstance(sampler, Sampler): + raise ValueError( + "sampler should be an instance of " + "torch.utils.data.Sampler, but got sampler={}".format(sampler) + ) + self.sampler = sampler + self.group_ids = torch.as_tensor(group_ids) + assert self.group_ids.dim() == 1 + self.batch_size = batch_size + self.groups = torch.unique(self.group_ids).sort(0)[0] + # group ids must range in [0, #group) + assert self.groups[0].item() == 0 and self.groups[-1].item() == len(self.groups) - 1 + + # buffer the indices of each group until batch size is reached + self.buffer_per_group = [[] for k in self.groups] + + def __iter__(self): + for idx in self.sampler: + group_id = self.group_ids[idx] + group_buffer = self.buffer_per_group[group_id] + group_buffer.append(idx) + if len(group_buffer) == self.batch_size: + yield group_buffer[:] # yield a copy of the list + del group_buffer[:] diff --git a/detectron2/data/transforms/__init__.py b/detectron2/data/transforms/__init__.py new file mode 100644 index 0000000000..f7638bb580 --- /dev/null +++ b/detectron2/data/transforms/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .transform import * +from fvcore.transforms.transform import * +from .transform_gen import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/detectron2/data/transforms/transform.py b/detectron2/data/transforms/transform.py new file mode 100644 index 0000000000..524c673e58 --- /dev/null +++ b/detectron2/data/transforms/transform.py @@ -0,0 +1,183 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# File: transform.py + +import numpy as np +from fvcore.transforms.transform import HFlipTransform, NoOpTransform, Transform +from PIL import Image + +__all__ = ["ExtentTransform", "ResizeTransform"] + + +class ExtentTransform(Transform): + """ + Extracts a subregion from the source image and scales it to the output size. + + The fill color is used to map pixels from the source rect that fall outside + the source image. + + See: https://pillow.readthedocs.io/en/latest/PIL.html#PIL.ImageTransform.ExtentTransform + """ + + def __init__(self, src_rect, output_size, interp=Image.LINEAR, fill=0): + """ + Args: + src_rect (x0, y0, x1, y1): src coordinates + output_size (h, w): dst image size + interp: PIL interpolation methods + fill: Fill color used when src_rect extends outside image + """ + super().__init__() + self._set_attributes(locals()) + + def apply_image(self, img, interp=None): + h, w = self.output_size + ret = Image.fromarray(img).transform( + size=(w, h), + method=Image.EXTENT, + data=self.src_rect, + resample=interp if interp else self.interp, + fill=self.fill, + ) + return np.asarray(ret) + + def apply_coords(self, coords): + # Transform image center from source coordinates into output coordinates + # and then map the new origin to the corner of the output image. + h, w = self.output_size + x0, y0, x1, y1 = self.src_rect + new_coords = coords.astype(np.float32) + new_coords[:, 0] -= 0.5 * (x0 + x1) + new_coords[:, 1] -= 0.5 * (y0 + y1) + new_coords[:, 0] *= w / (x1 - x0) + new_coords[:, 1] *= h / (y1 - y0) + new_coords[:, 0] += 0.5 * w + new_coords[:, 1] += 0.5 * h + return new_coords + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=Image.NEAREST) + return segmentation + + +class ResizeTransform(Transform): + """ + Resize the image to a target size. + """ + + def __init__(self, h, w, new_h, new_w, interp): + """ + Args: + h, w (int): original image size + new_h, new_w (int): new image size + interp: PIL interpolation methods + """ + # TODO decide on PIL vs opencv + super().__init__() + self._set_attributes(locals()) + + def apply_image(self, img, interp=None): + assert img.shape[:2] == (self.h, self.w) + pil_image = Image.fromarray(img) + interp_method = interp if interp is not None else self.interp + pil_image = pil_image.resize((self.new_w, self.new_h), interp_method) + ret = np.asarray(pil_image) + return ret + + def apply_coords(self, coords): + coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w) + coords[:, 1] = coords[:, 1] * (self.new_h * 1.0 / self.h) + return coords + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=Image.NEAREST) + return segmentation + + +def HFlip_rotated_box(transform, rotated_boxes): + """ + Apply the horizontal flip transform on an rotated boxes. + + Args: + rotated_boxes (ndarray): Nx5 floating point array of + (x_center, y_center, width, height, angle_degrees) format + in absolute coordinates. + """ + # Transform x_center + rotated_boxes[:, 0] = transform.width - rotated_boxes[:, 0] + # Transform angle + rotated_boxes[:, 4] = -rotated_boxes[:, 4] + return rotated_boxes + + +def Resize_rotated_box(transform, rotated_boxes): + # Note: when scale_factor_x != scale_factor_y, + # the rotated box does not preserve the rectangular shape when the angle + # is not a multiple of 90 degrees under resize transformation. + # Instead, the shape is a parallelogram (that has skew) + # Here we make an approximation by fitting a rotated rectangle to the + # parallelogram that shares the same midpoints on the left and right edge + scale_factor_x = transform.new_w * 1.0 / transform.w + scale_factor_y = transform.new_h * 1.0 / transform.h + rotated_boxes[:, 0] *= scale_factor_x + rotated_boxes[:, 1] *= scale_factor_y + theta = rotated_boxes[:, 4] * np.pi / 180.0 + c = np.cos(theta) + s = np.sin(theta) + + # In image space, y is top->down and x is left->right + # Consider the local coordintate system for the rotated box, + # where the box center is located at (0, 0), and the four vertices ABCD are + # A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2) + # the midpoint of the left edge AD of the rotated box E is: + # E = (A+D)/2 = (-w / 2, 0) + # the midpoint of the top edge AB of the rotated box F is: + # F(0, -h / 2) + # To get the old coordinates in the global system, apply the rotation transformation + # (Note: the right-handed coordinate system for image space is yOx): + # (old_x, old_y) = (s * y + c * x, c * y - s * x) + # E(old) = (s * 0 + c * (-w/2), c * 0 - s * (-w/2)) = (-c * w / 2, s * w / 2) + # F(old) = (s * (-h / 2) + c * 0, c * (-h / 2) - s * 0) = (-s * h / 2, -c * h / 2) + # After applying the scaling factor (sfx, sfy): + # E(new) = (-sfx * c * w / 2, sfy * s * w / 2) + # F(new) = (-sfx * s * h / 2, -sfy * c * h / 2) + # The new width after scaling tranformation becomes: + + # w(new) = |E(new) - O| * 2 + # = sqrt[(sfx * c * w / 2)^2 + (sfy * s * w / 2)^2] * 2 + # = sqrt[(sfx * c)^2 + (sfy * s)^2] * w + # i.e., scale_factor_w = sqrt[(sfx * c)^2 + (sfy * s)^2] + # + # For example, + # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_w == scale_factor_x; + # when |angle| = 90, c = 0, |s| = 1, scale_factor_w == scale_factor_y + rotated_boxes[:, 2] *= np.sqrt(np.square(scale_factor_x * c) + np.square(scale_factor_y * s)) + + # h(new) = |F(new) - O| * 2 + # = sqrt[(sfx * s * h / 2)^2 + (sfy * c * h / 2)^2] * 2 + # = sqrt[(sfx * s)^2 + (sfy * c)^2] * h + # i.e., scale_factor_h = sqrt[(sfx * s)^2 + (sfy * c)^2] + # + # For example, + # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_h == scale_factor_y; + # when |angle| = 90, c = 0, |s| = 1, scale_factor_h == scale_factor_x + rotated_boxes[:, 3] *= np.sqrt(np.square(scale_factor_x * s) + np.square(scale_factor_y * c)) + + # The angle is the rotation angle from y-axis in image space to the height + # vector (top->down in the box's local coordinate system) of the box in CCW. + # + # angle(new) = angle_yOx(O - F(new)) + # = angle_yOx( (sfx * s * h / 2, sfy * c * h / 2) ) + # = atan2(sfx * s * h / 2, sfy * c * h / 2) + # = atan2(sfx * s, sfy * c) + # + # For example, + # when sfx == sfy, angle(new) == atan2(s, c) == angle(old) + rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s, scale_factor_y * c) * 180 / np.pi + + return rotated_boxes + + +HFlipTransform.register_type("rotated_box", HFlip_rotated_box) +NoOpTransform.register_type("rotated_box", lambda t, x: x) +ResizeTransform.register_type("rotated_box", Resize_rotated_box) diff --git a/detectron2/data/transforms/transform_gen.py b/detectron2/data/transforms/transform_gen.py new file mode 100644 index 0000000000..5db34945e7 --- /dev/null +++ b/detectron2/data/transforms/transform_gen.py @@ -0,0 +1,447 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# File: transformer.py + +import inspect +import numpy as np +import pprint +import sys +from abc import ABCMeta, abstractmethod +from fvcore.transforms.transform import ( + BlendTransform, + CropTransform, + HFlipTransform, + NoOpTransform, + Transform, + TransformList, +) +from PIL import Image + +from .transform import ExtentTransform, ResizeTransform + +__all__ = [ + "RandomBrightness", + "RandomContrast", + "RandomCrop", + "RandomExtent", + "RandomFlip", + "RandomSaturation", + "RandomLighting", + "Resize", + "ResizeShortestEdge", + "TransformGen", + "apply_transform_gens", +] + + +def check_dtype(img): + assert isinstance(img, np.ndarray), "[TransformGen] Needs an numpy array, but got a {}!".format( + type(img) + ) + assert not isinstance(img.dtype, np.integer) or ( + img.dtype == np.uint8 + ), "[TransformGen] Got image of type {}, use uint8 or floating points instead!".format( + img.dtype + ) + assert img.ndim in [2, 3], img.ndim + + +class TransformGen(metaclass=ABCMeta): + """ + TransformGen takes an image of type uint8 in range [0, 255], or + floating point in range [0, 1] or [0, 255] as input. + + It creates a :class:`Transform` based on the given image, sometimes with randomness. + The transform can then be used to transform images + or other data (boxes, points, annotations, etc.) associated with it. + + The assumption made in this class + is that the image itself is sufficient to instantiate a transform. + When this assumption is not true, you need to create the transforms by your own. + + A list of `TransformGen` can be applied with :func:`apply_transform_gens`. + """ + + def _init(self, params=None): + if params: + for k, v in params.items(): + if k != "self" and not k.startswith("_"): + setattr(self, k, v) + + @abstractmethod + def get_transform(self, img): + pass + + def _rand_range(self, low=1.0, high=None, size=None): + """ + Uniform float random number between low and high. + """ + if high is None: + low, high = 0, low + if size is None: + size = [] + return np.random.uniform(low, high, size) + + def __repr__(self): + """ + Produce something like: + "MyTransformGen(field1={self.field1}, field2={self.field2})" + """ + try: + argspec = inspect.getargspec(self.__init__) + assert argspec.varargs is None, "The default __repr__ doesn't work for varargs!" + assert argspec.keywords is None, "The default __repr__ doesn't work for kwargs!" + fields = argspec.args[1:] + index_field_has_default = len(fields) - ( + 0 if argspec.defaults is None else len(argspec.defaults) + ) + + classname = type(self).__name__ + argstr = [] + for idx, f in enumerate(fields): + assert hasattr(self, f), ( + "Attribute {} not found! " + "Default __repr__ only works if attributes match the constructor.".format(f) + ) + attr = getattr(self, f) + if idx >= index_field_has_default: + if attr is argspec.defaults[idx - index_field_has_default]: + continue + argstr.append("{}={}".format(f, pprint.pformat(attr))) + return "{}({})".format(classname, ", ".join(argstr)) + except AssertionError: + return super().__repr__() + + __str__ = __repr__ + + +class RandomFlip(TransformGen): + """ + Flip the image horizontally with the given probability. + + TODO Vertical flip to be implemented. + """ + + def __init__(self, prob=0.5): + """ + Args: + prob (float): probability of flip. + """ + horiz, vert = True, False + # TODO implement vertical flip when we need it + super().__init__() + + if horiz and vert: + raise ValueError("Cannot do both horiz and vert. Please use two Flip instead.") + if not horiz and not vert: + raise ValueError("At least one of horiz or vert has to be True!") + self._init(locals()) + + def get_transform(self, img): + _, w = img.shape[:2] + do = self._rand_range() < self.prob + if do: + return HFlipTransform(w) + else: + return NoOpTransform() + + +class Resize(TransformGen): + """ Resize image to a target size""" + + def __init__(self, shape, interp=Image.BILINEAR): + """ + Args: + shape: (h, w) tuple or a int + interp: PIL interpolation method + """ + if isinstance(shape, int): + shape = (shape, shape) + shape = tuple(shape) + self._init(locals()) + + def get_transform(self, img): + return ResizeTransform( + img.shape[0], img.shape[1], self.shape[0], self.shape[1], self.interp + ) + + +class ResizeShortestEdge(TransformGen): + """ + Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge. + If `max_size` is reached, then downscale so that the longer edge does not exceed max_size. + """ + + def __init__( + self, short_edge_length, max_size=sys.maxsize, sample_style="range", interp=Image.BILINEAR + ): + """ + Args: + short_edge_length (list[int]): If ``sample_style=="range"``, + a [min, max] interval from which to sample the shortest edge length. + If ``sample_style=="choice"``, a list of shortest edge lengths to sample from. + max_size (int): maximum allowed longest edge length. + sample_style (str): either "range" or "choice". + """ + super().__init__() + assert sample_style in ["range", "choice"], sample_style + + self.is_range = sample_style == "range" + if isinstance(short_edge_length, int): + short_edge_length = (short_edge_length, short_edge_length) + self._init(locals()) + + def get_transform(self, img): + h, w = img.shape[:2] + + if self.is_range: + size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1) + else: + size = np.random.choice(self.short_edge_length) + + scale = size * 1.0 / min(h, w) + if h < w: + newh, neww = size, scale * w + else: + newh, neww = scale * h, size + if max(newh, neww) > self.max_size: + scale = self.max_size * 1.0 / max(newh, neww) + newh = newh * scale + neww = neww * scale + neww = int(neww + 0.5) + newh = int(newh + 0.5) + return ResizeTransform(h, w, newh, neww, self.interp) + + +class RandomCrop(TransformGen): + """ + Randomly crop a subimage out of an image. + """ + + def __init__(self, crop_type: str, crop_size): + """ + Args: + crop_type (str): one of "relative_range", "relative", "absolute". + See `config/defaults.py` for explanation. + crop_size (tuple[float]): the relative ratio or absolute pixels of + height and width + """ + super().__init__() + assert crop_type in ["relative_range", "relative", "absolute"] + self._init(locals()) + + def get_transform(self, img): + h, w = img.shape[:2] + croph, cropw = self.get_crop_size((h, w)) + assert h >= croph and w >= cropw, "Shape computation in {} has bugs.".format(self) + h0 = np.random.randint(h - croph + 1) + w0 = np.random.randint(w - cropw + 1) + return CropTransform(w0, h0, cropw, croph) + + def get_crop_size(self, image_size): + """ + Args: + image_size (tuple): height, width + + Returns: + crop_size (tuple): height, width in absolute pixels + """ + h, w = image_size + if self.crop_type == "relative": + ch, cw = self.crop_size + return int(h * ch + 0.5), int(w * cw + 0.5) + elif self.crop_type == "relative_range": + crop_size = np.asarray(self.crop_size, dtype=np.float32) + ch, cw = crop_size + np.random.rand(2) * (1 - crop_size) + return int(h * ch + 0.5), int(w * cw + 0.5) + elif self.crop_type == "absolute": + return self.crop_size + else: + NotImplementedError("Unknown crop type {}".format(self.crop_type)) + + +class RandomExtent(TransformGen): + """ + Outputs an image by cropping a random "subrect" of the source image. + + The subrect can be parameterized to include pixels outside the source image, + in which case they will be set to zeros (i.e. black). The size of the output + image will vary with the size of the random subrect. + """ + + def __init__(self, scale_range, shift_range): + """ + Args: + output_size (h, w): Dimensions of output image + scale_range (l, h): Range of input-to-output size scaling factor + shift_range (x, y): Range of shifts of the cropped subrect. The rect + is shifted by [w / 2 * Uniform(-x, x), h / 2 * Uniform(-y, y)], + where (w, h) is the (width, height) of the input image. Set each + component to zero to crop at the image's center. + """ + super().__init__() + self._init(locals()) + + def get_transform(self, img): + img_h, img_w = img.shape[:2] + + # Initialize src_rect to fit the input image. + src_rect = np.array([-0.5 * img_w, -0.5 * img_h, 0.5 * img_w, 0.5 * img_h]) + + # Apply a random scaling to the src_rect. + src_rect *= np.random.uniform(self.scale_range[0], self.scale_range[1]) + + # Apply a random shift to the coordinates origin. + src_rect[0::2] += self.shift_range[0] * img_w * (np.random.rand() - 0.5) + src_rect[1::2] += self.shift_range[1] * img_h * (np.random.rand() - 0.5) + + # Map src_rect coordinates into image coordinates (center at corner). + src_rect[0::2] += 0.5 * img_w + src_rect[1::2] += 0.5 * img_h + + return ExtentTransform( + src_rect=(src_rect[0], src_rect[1], src_rect[2], src_rect[3]), + output_size=(int(src_rect[3] - src_rect[1]), int(src_rect[2] - src_rect[0])), + ) + + +class RandomContrast(TransformGen): + """ + Randomly transforms image contrast. + + Contrast intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce contrast + - intensity = 1 will preserve the input image + - intensity > 1 will increase contrast + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation + intensity_max (float): Maximum augmentation + """ + super().__init__() + self._init(locals()) + + def get_transform(self, img): + w = np.random.uniform(self.intensity_min, self.intensity_max) + return BlendTransform(src_image=img.mean(), src_weight=1 - w, dst_weight=w) + + +class RandomBrightness(TransformGen): + """ + Randomly transforms image brightness. + + Brightness intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce brightness + - intensity = 1 will preserve the input image + - intensity > 1 will increase brightness + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation + intensity_max (float): Maximum augmentation + """ + super().__init__() + self._init(locals()) + + def get_transform(self, img): + w = np.random.uniform(self.intensity_min, self.intensity_max) + return BlendTransform(src_image=0, src_weight=1 - w, dst_weight=w) + + +class RandomSaturation(TransformGen): + """ + Randomly transforms image saturation. + + Saturation intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce saturation (make the image more grayscale) + - intensity = 1 will preserve the input image + - intensity > 1 will increase saturation + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation (1 preserves input). + intensity_max (float): Maximum augmentation (1 preserves input). + """ + super().__init__() + self._init(locals()) + + def get_transform(self, img): + assert img.shape[-1] == 3, "Saturation only works on RGB images" + w = np.random.uniform(self.intensity_min, self.intensity_max) + grayscale = img.dot([0.299, 0.587, 0.114])[:, :, np.newaxis] + return BlendTransform(src_image=grayscale, src_weight=1 - w, dst_weight=w) + + +class RandomLighting(TransformGen): + """ + Randomly transforms image color using fixed PCA over ImageNet. + + The degree of color jittering is randomly sampled via a normal distribution, + with standard deviation given by the scale parameter. + """ + + def __init__(self, scale): + """ + Args: + scale (float): Standard deviation of principal component weighting. + """ + super().__init__() + self._init(locals()) + self.eigen_vecs = np.array( + [[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], [-0.5836, -0.6948, 0.4203]] + ) + self.eigen_vals = np.array([0.2175, 0.0188, 0.0045]) + + def get_transform(self, img): + assert img.shape[-1] == 3, "Saturation only works on RGB images" + weights = np.random.normal(scale=self.scale, size=3) + return BlendTransform( + src_image=self.eigen_vecs.dot(weights * self.eigen_vals), src_weight=1.0, dst_weight=1.0 + ) + + +def apply_transform_gens(transform_gens, img): + """ + Apply a list of :class:`TransformGen` on the input image, and + returns the transformed image and a list of transforms. + + We cannot simply create and return all transforms without + applying it to the image, because a subsequent transform may + need the output of the previous one. + + Args: + transform_gens (list): list of :class:`TransformGen` instance to + be applied. + img (ndarray): uint8 or floating point images with 1 or 3 channels. + + Returns: + ndarray: the transformed image + TransformList: contain the transforms that's used. + """ + for g in transform_gens: + assert isinstance(g, TransformGen), g + + check_dtype(img) + + tfms = [] + for g in transform_gens: + tfm = g.get_transform(img) + assert isinstance( + tfm, Transform + ), "TransformGen {} must return an instance of Transform! Got {} instead".format(g, tfm) + img = tfm.apply_image(img) + tfms.append(tfm) + return img, TransformList(tfms) diff --git a/detectron2/engine/__init__.py b/detectron2/engine/__init__.py new file mode 100644 index 0000000000..6a4538da3e --- /dev/null +++ b/detectron2/engine/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + +from .launch import * +from .train_loop import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] + + +# prefer to let hooks and defaults live in separate namespaces (therefore not in __all__) +# but still make them available here +from .hooks import * +from .defaults import * diff --git a/detectron2/engine/defaults.py b/detectron2/engine/defaults.py new file mode 100644 index 0000000000..c1f231309a --- /dev/null +++ b/detectron2/engine/defaults.py @@ -0,0 +1,425 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +""" +This file contains components with some default boilerplate logic user may need +in training / testing. They will not work for everyeone, but many users may find them useful. + +The behavior of functions/classes in this file is subject to change, +since they are meant to represent the "common default behavior" people need in their projects. +""" + +import argparse +import logging +import os +from collections import OrderedDict +import torch +from fvcore.common.file_io import PathManager +from fvcore.nn.precise_bn import get_bn_modules +from torch.nn.parallel import DistributedDataParallel + +import detectron2.data.transforms as T +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.data import ( + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, +) +from detectron2.evaluation import ( + DatasetEvaluator, + inference_on_dataset, + print_csv_format, + verify_results, +) +from detectron2.modeling import build_model +from detectron2.solver import build_lr_scheduler, build_optimizer +from detectron2.utils import comm +from detectron2.utils.collect_env import collect_env_info +from detectron2.utils.env import seed_all_rng +from detectron2.utils.events import CommonMetricPrinter, JSONWriter, TensorboardXWriter +from detectron2.utils.logger import setup_logger + +from . import hooks +from .train_loop import SimpleTrainer + +__all__ = ["default_argument_parser", "default_setup", "DefaultPredictor", "DefaultTrainer"] + + +def default_argument_parser(): + """ + Create a parser with some common arguments used by detectron2 users. + + Returns: + argparse.ArgumentParser: + """ + parser = argparse.ArgumentParser(description="Detectron2 Training") + parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") + parser.add_argument( + "--resume", + action="store_true", + help="whether to attempt to resume from the checkpoint directory", + ) + parser.add_argument("--eval-only", action="store_true", help="perform evaluation only") + parser.add_argument("--num-gpus", type=int, default=1, help="number of gpus *per machine*") + parser.add_argument("--num-machines", type=int, default=1) + parser.add_argument( + "--machine-rank", type=int, default=0, help="the rank of this machine (unique per machine)" + ) + + # PyTorch still may leave orphan processes in multi-gpu training. + # Therefore we use a deterministic way to obtain port, + # so that users are aware of orphan processes by seeing the port occupied. + port = 2 ** 15 + 2 ** 14 + hash(os.getuid()) % 2 ** 14 + parser.add_argument("--dist-url", default="tcp://127.0.0.1:{}".format(port)) + parser.add_argument( + "opts", + help="Modify config options using the command-line", + default=None, + nargs=argparse.REMAINDER, + ) + return parser + + +def default_setup(cfg, args): + """ + Perform some basic common setups at the beginning of a job, including: + + 1. Set up the detectron2 logger + 2. Log basic information about environment, cmdline arguments, and config + 3. Backup the config to the output directory + + Args: + cfg (CfgNode): the full config to be used + args (argparse.NameSpace): the command line arguments to be logged + """ + output_dir = cfg.OUTPUT_DIR + if comm.is_main_process() and output_dir: + PathManager.mkdirs(output_dir) + + rank = comm.get_rank() + setup_logger(output_dir, distributed_rank=rank, name="fvcore") + logger = setup_logger(output_dir, distributed_rank=rank) + + logger.info("Rank of current process: {}. World size: {}".format(rank, comm.get_world_size())) + logger.info("Environment info:\n" + collect_env_info()) + + logger.info("Command line arguments: " + str(args)) + if hasattr(args, "config_file"): + logger.info( + "Contents of args.config_file={}:\n{}".format( + args.config_file, PathManager.open(args.config_file, "r").read() + ) + ) + + logger.info("Running with full config:\n{}".format(cfg)) + if comm.is_main_process() and output_dir: + # Note: some of our scripts may expect the existence of + # config.yaml in output directory + path = os.path.join(output_dir, "config.yaml") + with PathManager.open(path, "w") as f: + f.write(cfg.dump()) + logger.info("Full config saved to {}".format(os.path.abspath(path))) + + # make sure each worker has a different, yet deterministic seed if specified + seed_all_rng(None if cfg.SEED < 0 else cfg.SEED + rank) + + # cudnn benchmark has large overhead. It shouldn't be used considering the small size of + # typical validation set. + if not (hasattr(args, "eval_only") and args.eval_only): + torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK + + +class DefaultPredictor: + """ + Create a simple end-to-end predictor with the given config. + The predictor takes an BGR image and produce a dict of predictions. + + Attributes: + metadata (Metadata): the metadata of the underlying dataset, obtained from + cfg.DATASETS.TEST. + """ + + def __init__(self, cfg): + self.cfg = cfg.clone() # cfg can be modified by model + self.model = build_model(self.cfg) + self.model.eval() + self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0]) + + checkpointer = DetectionCheckpointer(self.model) + checkpointer.load(cfg.MODEL.WEIGHTS) + + self.transform_gen = T.ResizeShortestEdge( + [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST + ) + + self.input_format = cfg.INPUT.FORMAT + assert self.input_format in ["RGB", "BGR"], self.input_format + + @torch.no_grad() + def __call__(self, original_image): + """ + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + + Returns: + predictions (dict): the output of the model + """ + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.transform_gen.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + inputs = {"image": image, "height": height, "width": width} + predictions = self.model([inputs])[0] + return predictions + + +class DefaultTrainer(SimpleTrainer): + """ + A trainer with default training logic. Compared to `SimpleTrainer`, it + contains the following logic in addition: + + 1. Create model, optimizer, scheduler, dataloader from the given config. + 2. Load a checkpoint or `cfg.MODEL.WEIGHTS`, if exists. + 3. Register a few common hooks. + + It is created to simplify the **standard model training workflow** and reduce code boilerplate + for users who only need the standard training workflow, with standard features. + It means this class makes *many assumptions* about your training logic that + may easily become invalid in a new research. In fact, any assumptions beyond those made in the + :class:`SimpleTrainer` are too much for research. + + The code of this class has been annotated about restrictive assumptions it mades. + When they do not work for you, you're encouraged to write your own training logic. + + Also note that the behavior of this class, like other functions/classes in + this file, is not stable, since it is meant to represent the "common default behavior". + It is only guaranteed to work well with the standard models and training workflow in detectron2. + To obtain more stable behavior, write your own training logic with other public APIs. + + Attributes: + scheduler: + checkpointer (DetectionCheckpointer): + cfg (CfgNode): + """ + + def __init__(self, cfg): + """ + Args: + cfg (CfgNode): + """ + # Assume these objects must be constructed in this order. + model = self.build_model(cfg) + optimizer = self.build_optimizer(cfg, model) + data_loader = self.build_train_loader(cfg) + + # For training, wrap with DDP. But don't need this for inference. + if comm.get_world_size() > 1: + model = DistributedDataParallel( + model, device_ids=[comm.get_local_rank()], broadcast_buffers=False + ) + super().__init__(model, data_loader, optimizer) + + self.scheduler = self.build_lr_scheduler(cfg, optimizer) + # Assume no other objects need to be checkpointed. + # We can later make it checkpoint the stateful hooks + self.checkpointer = DetectionCheckpointer( + # Assume you want to save checkpoits together with logs/statistics + model, + cfg.OUTPUT_DIR, + optimizer=optimizer, + scheduler=self.scheduler, + ) + self.start_iter = 0 + self.max_iter = cfg.SOLVER.MAX_ITER + self.cfg = cfg + + self.register_hooks(self.build_hooks()) + + def resume_or_load(self, resume=True): + """ + If `resume==True`, and last checkpoint exists, resume from it. + + Otherwise, load a model specified by the config. + + Args: + resume (bool): whether to do resume or not + """ + # The checkpoint stores the training iteration that just finished, thus we start + # at the next iteration (or iter zero if there's no checkpoint). + self.start_iter = ( + self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume).get( + "iteration", -1 + ) + + 1 + ) + + def build_hooks(self): + """ + Build a list of default hooks. + + Returns: + list[HookBase]: + """ + cfg = self.cfg.clone() + cfg.defrost() + cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN + + ret = [ + hooks.IterationTimer(), + hooks.LRScheduler(self.optimizer, self.scheduler), + hooks.PreciseBN( + # Run at the same freq as (but before) evaluation. + cfg.TEST.EVAL_PERIOD, + self.model, + # Build a new data loader to not affect training + self.build_train_loader(cfg), + cfg.TEST.PRECISE_BN.NUM_ITER, + ) + if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model) + else None, + ] + + # Do PreciseBN before checkpointer, because it updates the model and need to + # be saved by checkpointer. + # This is not always the best: if checkpointing has a different frequency, + # some checkpoints may have more precise statistics than others. + if comm.is_main_process(): + ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD)) + + def test_and_save_results(): + self._last_eval_results = self.test(self.cfg, self.model) + return self._last_eval_results + + # Do evaluation after checkpointer, because then if it fails, + # we can use the saved checkpoint to debug. + ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results)) + + if comm.is_main_process(): + # run writers in the end, so that evaluation metrics are written + ret.append(hooks.PeriodicWriter(self.build_writers())) + return ret + + def build_writers(self): + """ + Build a list of default writers, that write metrics to the screen, + a json file, and a tensorboard event file respectively. + + Returns: + list[Writer]: a list of objects that have a ``.write`` method. + """ + # Assume the default print/log frequency. + return [ + # It may not always print what you want to see, since it prints "common" metrics only. + CommonMetricPrinter(self.max_iter), + JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")), + TensorboardXWriter(self.cfg.OUTPUT_DIR), + ] + + def train(self): + """ + Run training. + + Returns: + OrderedDict of results, if evaluation is enabled. Otherwise None. + """ + super().train(self.start_iter, self.max_iter) + if hasattr(self, "_last_eval_results") and comm.is_main_process(): + verify_results(self.cfg, self._last_eval_results) + return self._last_eval_results + + @classmethod + def build_model(cls, cfg): + """ + Returns: + torch.nn.Module: + """ + model = build_model(cfg) + logger = logging.getLogger(__name__) + logger.info("Model:\n{}".format(model)) + return model + + @classmethod + def build_optimizer(cls, cfg, model): + """ + Returns: + torch.optim.Optimizer: + """ + return build_optimizer(cfg, model) + + @classmethod + def build_lr_scheduler(cls, cfg, optimizer): + return build_lr_scheduler(cfg, optimizer) + + @classmethod + def build_train_loader(cls, cfg): + """ + Returns: + iterable + """ + return build_detection_train_loader(cfg) + + @classmethod + def build_test_loader(cls, cfg, dataset_name): + """ + Returns: + iterable + """ + return build_detection_test_loader(cfg, dataset_name) + + @classmethod + def build_evaluator(cls, cfg, dataset_name): + """ + Returns: + DatasetEvaluator + """ + raise NotImplementedError + + @classmethod + def test(cls, cfg, model, evaluators=None): + """ + Args: + cfg (CfgNode): + model (nn.Module): + evaluators (list[DatasetEvaluator] or None): if None, will call + :meth:`build_evaluator`. Otherwise, must have the same length as + `cfg.DATASETS.TEST`. + + Returns: + dict: a dict of result metrics + """ + logger = logging.getLogger(__name__) + if isinstance(evaluators, DatasetEvaluator): + evaluators = [evaluators] + if evaluators is not None: + assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format( + len(cfg.DATASETS.TEST), len(evaluators) + ) + + results = OrderedDict() + for idx, dataset_name in enumerate(cfg.DATASETS.TEST): + data_loader = cls.build_test_loader(cfg, dataset_name) + # When evaluators are passed in as arguments, + # implicitly assume that evaluators can be created before data_loader. + evaluator = ( + evaluators[idx] + if evaluators is not None + else cls.build_evaluator(cfg, dataset_name) + ) + results_i = inference_on_dataset(model, data_loader, evaluator) + results[dataset_name] = results_i + if comm.is_main_process(): + assert isinstance( + results_i, dict + ), "Evaluator must return a dict on the main process. Got {} instead.".format( + results_i + ) + logger.info("Evaluation results for {} in csv format:".format(dataset_name)) + print_csv_format(results_i) + + if len(results) == 1: + results = list(results.values())[0] + return results diff --git a/detectron2/engine/hooks.py b/detectron2/engine/hooks.py new file mode 100644 index 0000000000..c73779764f --- /dev/null +++ b/detectron2/engine/hooks.py @@ -0,0 +1,404 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import datetime +import logging +import os +import tempfile +import time +from collections import Counter +import torch +from fvcore.common.checkpoint import PeriodicCheckpointer as _PeriodicCheckpointer +from fvcore.common.file_io import PathManager +from fvcore.common.timer import Timer +from fvcore.nn.precise_bn import get_bn_modules, update_bn_stats + +import detectron2.utils.comm as comm +from detectron2.evaluation.testing import flatten_results_dict +from detectron2.utils.events import EventStorage + +from .train_loop import HookBase + +__all__ = [ + "CallbackHook", + "IterationTimer", + "PeriodicWriter", + "PeriodicCheckpointer", + "LRScheduler", + "AutogradProfiler", + "EvalHook", + "PreciseBN", +] + + +""" +Implement some common hooks. +""" + + +class CallbackHook(HookBase): + """ + Create a hook using callback functions provided by the user. + """ + + def __init__(self, *, before_train=None, after_train=None, before_step=None, after_step=None): + """ + Each argument is a function that takes one argument: the trainer. + """ + self._before_train = before_train + self._before_step = before_step + self._after_step = after_step + self._after_train = after_train + + def before_train(self): + if self._before_train: + self._before_train(self.trainer) + + def after_train(self): + if self._after_train: + self._after_train(self.trainer) + # The functions may be closures that hold reference to the trainer + # Therefore, delete them to avoid circular reference. + del self._before_train, self._after_train + del self._before_step, self._after_step + + def before_step(self): + if self._before_step: + self._before_step(self.trainer) + + def after_step(self): + if self._after_step: + self._after_step(self.trainer) + + +class IterationTimer(HookBase): + """ + Track the time spent for each iteration (each run_step call in the trainer). + Print a summary in the end of training. + + This hook uses the time between the call to its :meth:`before_step` + and :meth:`after_step` methods. + Under the convention that :meth:`before_step` of all hooks should only + take negligible amount of time, the :class:`IterationTimer` hook should be + placed at the beginning of the list of hooks to obtain accurate timing. + """ + + def __init__(self, warmup_iter=3): + """ + Args: + warmup_iter (int): the number of iterations at the beginning to exclude + from timing. + """ + self._warmup_iter = warmup_iter + self._step_timer = Timer() + + def before_train(self): + self._start_time = time.perf_counter() + self._total_timer = Timer() + self._total_timer.pause() + + def after_train(self): + logger = logging.getLogger(__name__) + total_time = time.perf_counter() - self._start_time + total_time_minus_hooks = self._total_timer.seconds() + hook_time = total_time - total_time_minus_hooks + + num_iter = self.trainer.iter + 1 - self.trainer.start_iter - self._warmup_iter + + if num_iter > 0 and total_time_minus_hooks > 0: + # Speed is meaningful only after warmup + # NOTE this format is parsed by grep in some scripts + logger.info( + "Overall training speed: {} iterations in {} ({:.4f} s / it)".format( + num_iter, + str(datetime.timedelta(seconds=int(total_time_minus_hooks))), + total_time_minus_hooks / num_iter, + ) + ) + + logger.info( + "Total training time: {} ({} on hooks)".format( + str(datetime.timedelta(seconds=int(total_time))), + str(datetime.timedelta(seconds=int(hook_time))), + ) + ) + + def before_step(self): + self._step_timer.reset() + self._total_timer.resume() + + def after_step(self): + # +1 because we're in after_step + iter_done = self.trainer.iter - self.trainer.start_iter + 1 + if iter_done >= self._warmup_iter: + sec = self._step_timer.seconds() + self.trainer.storage.put_scalars(time=sec) + else: + self._start_time = time.perf_counter() + self._total_timer.reset() + + self._total_timer.pause() + + +class PeriodicWriter(HookBase): + """ + Write events to EventStorage periodically. + + It is executed every ``period`` iterations and after the last iteration. + """ + + def __init__(self, writers, period=20): + """ + Args: + writers (list): a list of objects with a "write" method. + period (int): + """ + self._writers = writers + self._period = period + + def after_step(self): + if (self.trainer.iter + 1) % self._period == 0 or ( + self.trainer.iter == self.trainer.max_iter - 1 + ): + for writer in self._writers: + writer.write() + + +class PeriodicCheckpointer(_PeriodicCheckpointer, HookBase): + """ + Same as :class:`detectron2.checkpoint.PeriodicCheckpointer`, but as a hook. + + Note that when used as a hook, + it is unable to save additional data other than what's defined + by the given `checkpointer`. + + It is executed every ``period`` iterations and after the last iteration. + """ + + def before_train(self): + self.max_iter = self.trainer.max_iter + + def after_step(self): + # No way to use **kwargs + self.step(self.trainer.iter) + + +class LRScheduler(HookBase): + """ + A hook which executes a torch builtin LR scheduler and summarizes the LR. + It is executed after every iteration. + """ + + def __init__(self, optimizer, scheduler): + """ + Args: + optimizer (torch.optim.Optimizer): + scheduler (torch.optim._LRScheduler) + """ + self._optimizer = optimizer + self._scheduler = scheduler + + # NOTE: some heuristics on what LR to summarize + # summarize the param group with most parameters + largest_group = max(len(g["params"]) for g in optimizer.param_groups) + + if largest_group == 1: + # If all groups have one parameter, + # then find the most common initial LR, and use it for summary + lr_count = Counter([g["lr"] for g in optimizer.param_groups]) + lr = lr_count.most_common()[0][0] + for i, g in enumerate(optimizer.param_groups): + if g["lr"] == lr: + self._best_param_group_id = i + break + else: + for i, g in enumerate(optimizer.param_groups): + if len(g["params"]) == largest_group: + self._best_param_group_id = i + break + + def after_step(self): + lr = self._optimizer.param_groups[self._best_param_group_id]["lr"] + self.trainer.storage.put_scalar("lr", lr, smoothing_hint=False) + self._scheduler.step() + + +class AutogradProfiler(HookBase): + """ + A hook which runs `torch.autograd.profiler.profile`. + + Note: + When used together with NCCL on older version of GPUs, + autograd profiler may cause deadlock because it unnecessarily allocates + memory on every device it sees. The memory management calls, if + interleaved with NCCL calls, lead to deadlock on GPUs that do not + support `cudaLaunchCooperativeKernelMultiDevice`. + """ + + def __init__(self, enable_predicate, output_dir, *, use_cuda=True): + """ + Args: + enable_predicate (callable[trainer -> bool]): a function which takes a trainer, + and returns whether to enable the profiler. + It will be called once every step, and can be used to select which steps to profile. + output_dir (str): the output directory to dump tracing files. + use_cuda (bool): same as in `torch.autograd.profiler.profile`. + """ + self._enable_predicate = enable_predicate + self._use_cuda = use_cuda + self._output_dir = output_dir + + def before_step(self): + if self._enable_predicate(self.trainer): + self._profiler = torch.autograd.profiler.profile(use_cuda=self._use_cuda) + self._profiler.__enter__() + else: + self._profiler = None + + def after_step(self): + if self._profiler is None: + return + self._profiler.__exit__(None, None, None) + out_file = os.path.join( + self._output_dir, "profiler-trace-iter{}.json".format(self.trainer.iter) + ) + if "://" not in out_file: + self._profiler.export_chrome_trace(out_file) + else: + # Support non-posix filesystems + with tempfile.TemporaryDirectory(prefix="detectron2_profiler") as d: + tmp_file = os.path.join(d, "tmp.json") + self._profiler.export_chrome_trace(tmp_file) + with open(tmp_file) as f: + content = f.read() + with PathManager.open(out_file, "w") as f: + f.write(content) + + +class EvalHook(HookBase): + """ + Run an evaluation function periodically, and at the end of training. + + It is executed every ``eval_period`` iterations and after the last iteration. + """ + + def __init__(self, eval_period, eval_function): + """ + Args: + eval_period (int): the period to run `eval_function`. + eval_function (callable): a function which takes no arguments, and + returns a nested dict of evaluation metrics. + + Note: + This hook must be enabled in all or none workers. + If you would like only certain workers to perform evaluation, + give other workers a no-op function (`eval_function=lambda: None`). + """ + self._period = eval_period + self._func = eval_function + + def after_step(self): + next_iter = self.trainer.iter + 1 + is_final = next_iter == self.trainer.max_iter + if is_final or (self._period > 0 and next_iter % self._period == 0): + results = self._func() + + if results: + assert isinstance( + results, dict + ), "Eval function must return a dict. Got {} instead.".format(results) + + flattened_results = flatten_results_dict(results) + for k, v in flattened_results.items(): + try: + v = float(v) + except Exception: + raise ValueError( + "[EvalHook] eval_function should return a nested dict of float. " + "Got '{}: {}' instead.".format(k, v) + ) + self.trainer.storage.put_scalars(**flattened_results, smoothing_hint=False) + + # Evaluation may take different time among workers. + # A barrier make them start the next iteration together. + comm.synchronize() + + def after_train(self): + # func is likely a closure that holds reference to the trainer + # therefore we clean it to avoid circular reference in the end + del self._func + + +class PreciseBN(HookBase): + """ + The standard implementation of BatchNorm uses EMA in inference, which is + sometimes suboptimal. + This class computes the true average of statistics rather than the moving average, + and put true averages to every BN layer in the given model. + + It is executed every ``period`` iterations and after the last iteration. + """ + + def __init__(self, period, model, data_loader, num_iter): + """ + Args: + period (int): the period this hook is run, or 0 to not run during training. + The hook will always run in the end of training. + model (nn.Module): a module whose all BN layers in training mode will be + updated by precise BN. + Note that user is responsible for ensuring the BN layers to be + updated are in training mode when this hook is triggered. + data_loader (iterable): it will produce data to be run by `model(data)`. + num_iter (int): number of iterations used to compute the precise + statistics. + """ + self._logger = logging.getLogger(__name__) + if len(get_bn_modules(model)) == 0: + self._logger.info( + "PreciseBN is disabled because model does not contain BN layers in training mode." + ) + self._disabled = True + return + + self._model = model + self._data_loader = data_loader + self._num_iter = num_iter + self._period = period + self._disabled = False + + self._data_iter = None + + def after_step(self): + next_iter = self.trainer.iter + 1 + is_final = next_iter == self.trainer.max_iter + if is_final or (self._period > 0 and next_iter % self._period == 0): + self.update_stats() + + def update_stats(self): + """ + Update the model with precise statistics. Users can manually call this method. + """ + if self._disabled: + return + + if self._data_iter is None: + self._data_iter = iter(self._data_loader) + + num_iter = 0 + + def data_loader(): + nonlocal num_iter + num_iter += 1 + if num_iter % 100 == 0: + self._logger.info( + "Running precise-BN ... {}/{} iterations.".format(num_iter, self._num_iter) + ) + # This way we can reuse the same iterator + yield next(self._data_iter) + + with EventStorage(): # capture events in a new storage to discard them + self._logger.info( + "Running precise-BN for {} iterations... ".format(self._num_iter) + + "Note that this could produce different statistics every time." + ) + update_bn_stats(self._model, data_loader(), self._num_iter) diff --git a/detectron2/engine/launch.py b/detectron2/engine/launch.py new file mode 100644 index 0000000000..cf1cadacbb --- /dev/null +++ b/detectron2/engine/launch.py @@ -0,0 +1,83 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +from detectron2.utils import comm + +__all__ = ["launch"] + + +def _find_free_port(): + import socket + + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + # Binding to port 0 will cause the OS to find an available port for us + sock.bind(("", 0)) + port = sock.getsockname()[1] + sock.close() + # NOTE: there is still a chance the port could be taken by other processes. + return port + + +def launch(main_func, num_gpus_per_machine, num_machines=1, machine_rank=0, dist_url=None, args=()): + """ + Args: + main_func: a function that will be called by `main_func(*args)` + num_machines (int): the total number of machines + machine_rank (int): the rank of this machine (one per machine) + dist_url (str): url to connect to for distributed training, including protocal + e.g. "tcp://127.0.0.1:8686". + Can be set to auto to automatically select a free port on localhost + args (tuple): arguments passed to main_func + """ + world_size = num_machines * num_gpus_per_machine + if world_size > 1: + # https://github.com/pytorch/pytorch/pull/14391 + # TODO prctl in spawned processes + + if dist_url == "auto": + assert num_machines == 1, "dist_url=auto cannot work with distributed training." + port = _find_free_port() + dist_url = f"tcp://127.0.0.1:{port}" + + mp.spawn( + _distributed_worker, + nprocs=num_gpus_per_machine, + args=(main_func, world_size, num_gpus_per_machine, machine_rank, dist_url, args), + daemon=False, + ) + else: + main_func(*args) + + +def _distributed_worker( + local_rank, main_func, world_size, num_gpus_per_machine, machine_rank, dist_url, args +): + global_rank = machine_rank * num_gpus_per_machine + local_rank + try: + dist.init_process_group( + backend="NCCL", init_method=dist_url, world_size=world_size, rank=global_rank + ) + except Exception as e: + logger = logging.getLogger(__name__) + logger.error("Process group URL: {}".format(dist_url)) + raise e + # synchronize is needed here to prevent a possible timeout after calling init_process_group + # See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172 + comm.synchronize() + + assert num_gpus_per_machine <= torch.cuda.device_count() + torch.cuda.set_device(local_rank) + + # Setup the local process group (which contains ranks within the same machine) + assert comm._LOCAL_PROCESS_GROUP is None + num_machines = world_size // num_gpus_per_machine + for i in range(num_machines): + ranks_on_i = list(range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine)) + pg = dist.new_group(ranks_on_i) + if i == machine_rank: + comm._LOCAL_PROCESS_GROUP = pg + + main_func(*args) diff --git a/detectron2/engine/train_loop.py b/detectron2/engine/train_loop.py new file mode 100644 index 0000000000..b510526dd6 --- /dev/null +++ b/detectron2/engine/train_loop.py @@ -0,0 +1,255 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import logging +import numpy as np +import time +import weakref +import torch + +import detectron2.utils.comm as comm +from detectron2.utils.events import EventStorage + +__all__ = ["HookBase", "TrainerBase", "SimpleTrainer"] + + +class HookBase: + """ + Base class for hooks that can be registered with :class:`TrainerBase`. + + Each hook can implement 4 methods. The way they are called is demonstrated + in the following snippet: + + .. code-block:: python + + hook.before_train() + for iter in range(start_iter, max_iter): + hook.before_step() + trainer.run_step() + hook.after_step() + hook.after_train() + + Notes: + 1. In the hook method, users can access `self.trainer` to access more + properties about the context (e.g., current iteration). + + 2. A hook that does something in :meth:`before_step` can often be + implemented equivalently in :meth:`after_step`. + If the hook takes non-trivial time, it is strongly recommended to + implement the hook in :meth:`after_step` instead of :meth:`before_step`. + The convention is that :meth:`before_step` should only take negligible time. + + Following this convention will allow hooks that do care about the difference + between :meth:`before_step` and :meth:`after_step` (e.g., timer) to + function properly. + + Attributes: + trainer: A weak reference to the trainer object. Set by the trainer when the hook is + registered. + """ + + def before_train(self): + """ + Called before the first iteration. + """ + pass + + def after_train(self): + """ + Called after the last iteration. + """ + pass + + def before_step(self): + """ + Called before each iteration. + """ + pass + + def after_step(self): + """ + Called after each iteration. + """ + pass + + +class TrainerBase: + """ + Base class for iterative trainer with hooks. + + The only assumption we made here is: the training runs in a loop. + A subclass can implement what the loop is. + We made no assumptions about the existence of dataloader, optimizer, model, etc. + + Attributes: + iter(int): the current iteration. + + start_iter(int): The iteration to start with. + By convention the minimum possible value is 0. + + max_iter(int): The iteration to end training. + + storage(EventStorage): An EventStorage that's opened during the course of training. + """ + + def __init__(self): + self._hooks = [] + + def register_hooks(self, hooks): + """ + Register hooks to the trainer. The hooks are executed in the order + they are registered. + + Args: + hooks (list[Optional[HookBase]]): list of hooks + """ + hooks = [h for h in hooks if h is not None] + for h in hooks: + assert isinstance(h, HookBase) + # To avoid circular reference, hooks and trainer cannot own each other. + # This noramlly does not matter, but will cause memory leak if the + # involved objects contain __del__: + # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/ + h.trainer = weakref.proxy(self) + self._hooks.extend(hooks) + + def train(self, start_iter: int, max_iter: int): + """ + Args: + start_iter, max_iter (int): See docs above + """ + logger = logging.getLogger(__name__) + logger.info("Starting training from iteration {}".format(start_iter)) + + self.iter = self.start_iter = start_iter + self.max_iter = max_iter + + with EventStorage(start_iter) as self.storage: + try: + self.before_train() + for self.iter in range(start_iter, max_iter): + self.before_step() + self.run_step() + self.after_step() + finally: + self.after_train() + + def before_train(self): + for h in self._hooks: + h.before_train() + + def after_train(self): + for h in self._hooks: + h.after_train() + + def before_step(self): + for h in self._hooks: + h.before_step() + + def after_step(self): + for h in self._hooks: + h.after_step() + # this guarantees, that in each hook's after_step, storage.iter == trainer.iter + self.storage.step() + + def run_step(self): + raise NotImplementedError + + +class SimpleTrainer(TrainerBase): + """ + A simple trainer for the most common type of task: + single-cost single-optimizer single-data-source iterative optimization. + It assumes that every step, you: + + 1. Compute the loss with a data from the data_loader. + 2. Compute the gradients with the above loss. + 3. Update the model with the optimizer. + + If you want to do anything fancier than this, + either subclass TrainerBase and implement your own `run_step`, + or write your own training loop. + """ + + def __init__(self, model, data_loader, optimizer): + """ + Args: + model: a torch Module. Takes a data from data_loader and returns a + dict of losses. + data_loader: an iterable. Contains data to be used to call model. + optimizer: a torch optimizer. + """ + super().__init__() + + """ + We set the model to training mode in the trainer. + However it's valid to train a model that's in eval mode. + If you want your model (or a submodule of it) to behave + like evaluation during training, you can overwrite its train() method. + """ + model.train() + + self.model = model + self.data_loader = data_loader + self._data_loader_iter = iter(data_loader) + self.optimizer = optimizer + + def run_step(self): + """ + Implement the standard training logic described above. + """ + assert self.model.training, "[SimpleTrainer] model was changed to eval mode!" + start = time.perf_counter() + """ + If your want to do something with the data, you can wrap the dataloader. + """ + data = next(self._data_loader_iter) + data_time = time.perf_counter() - start + + """ + If your want to do something with the losses, you can wrap the model. + """ + loss_dict = self.model(data) + losses = sum(loss for loss in loss_dict.values()) + if not torch.isfinite(losses).all(): + raise FloatingPointError( + "Loss became infinite or NaN at iteration={}!\nloss_dict = {}".format( + self.iter, loss_dict + ) + ) + + # gather metrics among all workers for logging + metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()} + metrics_dict["data_time"] = data_time + # This assumes we do DDP-style training, which is currently the only + # supported method in detectron2. + all_metrics_dict = comm.gather(metrics_dict) + self._write_metrics(all_metrics_dict) + + self.optimizer.zero_grad() + losses.backward() + + """ + If you need gradient clipping/scaling or other processing, you can + wrap the optimizer with your custom `step()` method. + """ + self.optimizer.step() + + def _write_metrics(self, all_metrics_dict): + """ + Args: + all_metrics_dict (list[dict]): list of metrics dict from all workers + """ + if comm.is_main_process(): + # data_time among workers can have high variance. The actual latency + # caused by data_time is the maximum among workers. + data_time = np.max([x.pop("data_time") for x in all_metrics_dict]) + + metrics_dict = { + k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys() + } + total_losses_reduced = sum(loss for loss in metrics_dict.values()) + + self.storage.put_scalars(data_time=data_time, total_loss=total_losses_reduced) + if len(metrics_dict) > 1: + self.storage.put_scalars(**metrics_dict) diff --git a/detectron2/evaluation/__init__.py b/detectron2/evaluation/__init__.py new file mode 100644 index 0000000000..975d8f7dcf --- /dev/null +++ b/detectron2/evaluation/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .cityscapes_evaluation import CityscapesEvaluator +from .coco_evaluation import COCOEvaluator +from .evaluator import DatasetEvaluator, DatasetEvaluators, inference_context, inference_on_dataset +from .lvis_evaluation import LVISEvaluator +from .panoptic_evaluation import COCOPanopticEvaluator +from .pascal_voc_evaluation import PascalVOCDetectionEvaluator +from .sem_seg_evaluation import SemSegEvaluator +from .testing import print_csv_format, verify_results + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/detectron2/evaluation/cityscapes_evaluation.py b/detectron2/evaluation/cityscapes_evaluation.py new file mode 100644 index 0000000000..8ba4169b32 --- /dev/null +++ b/detectron2/evaluation/cityscapes_evaluation.py @@ -0,0 +1,114 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import glob +import logging +import os +import tempfile +from collections import OrderedDict +import torch +from PIL import Image + +from detectron2.data import MetadataCatalog +from detectron2.utils import comm + +from .evaluator import DatasetEvaluator + + +class CityscapesEvaluator(DatasetEvaluator): + """ + Evaluate instance segmentation results using cityscapes API. + + Note: + * It does not work in multi-machine distributed training. + * It contains a synchronization, therefore has to be used on all ranks. + """ + + def __init__(self, dataset_name): + """ + Args: + dataset_name (str): the name of the dataset. + It must have the following metadata associated with it: + "thing_classes", "gt_dir". + """ + self._metadata = MetadataCatalog.get(dataset_name) + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + def reset(self): + self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_") + self._temp_dir = self._working_dir.name + # All workers will write to the same results directory + # TODO this does not work in distributed training + self._temp_dir = comm.all_gather(self._temp_dir)[0] + if self._temp_dir != self._working_dir.name: + self._working_dir.cleanup() + self._logger.info( + "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir) + ) + + def process(self, inputs, outputs): + from cityscapesscripts.helpers.labels import name2label + + for input, output in zip(inputs, outputs): + file_name = input["file_name"] + basename = os.path.splitext(os.path.basename(file_name))[0] + pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt") + + output = output["instances"].to(self._cpu_device) + num_instances = len(output) + with open(pred_txt, "w") as fout: + for i in range(num_instances): + pred_class = output.pred_classes[i] + classes = self._metadata.thing_classes[pred_class] + class_id = name2label[classes].id + score = output.scores[i] + mask = output.pred_masks[i].numpy().astype("uint8") + png_filename = os.path.join( + self._temp_dir, basename + "_{}_{}.png".format(i, classes) + ) + + Image.fromarray(mask * 255).save(png_filename) + fout.write("{} {} {}\n".format(os.path.basename(png_filename), class_id, score)) + + def evaluate(self): + """ + Returns: + dict: has a key "segm", whose value is a dict of "AP" and "AP50". + """ + comm.synchronize() + if comm.get_rank() > 0: + return + os.environ["CITYSCAPES_DATASET"] = os.path.abspath( + os.path.join(self._metadata.gt_dir, "..", "..") + ) + # Load the Cityscapes eval script *after* setting the required env var, + # since the script reads CITYSCAPES_DATASET into global variables at load time. + import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval + + self._logger.info("Evaluating results under {} ...".format(self._temp_dir)) + + # set some global states in cityscapes evaluation API, before evaluting + cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir) + cityscapes_eval.args.predictionWalk = None + cityscapes_eval.args.JSONOutput = False + cityscapes_eval.args.colorized = False + cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json") + + # These lines are adopted from + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa + groundTruthImgList = glob.glob(cityscapes_eval.args.groundTruthSearch) + assert len( + groundTruthImgList + ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format( + cityscapes_eval.args.groundTruthSearch + ) + predictionImgList = [] + for gt in groundTruthImgList: + predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args)) + results = cityscapes_eval.evaluateImgLists( + predictionImgList, groundTruthImgList, cityscapes_eval.args + )["averages"] + + ret = OrderedDict() + ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100} + self._working_dir.cleanup() + return ret diff --git a/detectron2/evaluation/coco_evaluation.py b/detectron2/evaluation/coco_evaluation.py new file mode 100644 index 0000000000..d4bc2a2e58 --- /dev/null +++ b/detectron2/evaluation/coco_evaluation.py @@ -0,0 +1,465 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +import pickle +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from fvcore.common.file_io import PathManager +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.data import MetadataCatalog +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.logger import create_small_table + +from .evaluator import DatasetEvaluator + + +class COCOEvaluator(DatasetEvaluator): + """ + Evaluate object proposal, instance detection/segmentation, keypoint detection + outputs using COCO's metrics and APIs. + """ + + def __init__(self, dataset_name, cfg, distributed, output_dir=None): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have the following corresponding metadata: + "json_file": the path to the COCO format annotation + cfg (CfgNode): config instance + distributed (True): if True, will collect results from all ranks for evaluation. + Otherwise, will evaluate the results in the current process. + output_dir (str): optional, an output directory to dump results. + """ + self._tasks = self._tasks_from_config(cfg) + self._distributed = distributed + self._output_dir = output_dir + + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + self._metadata = MetadataCatalog.get(dataset_name) + json_file = PathManager.get_local_path(self._metadata.json_file) + with contextlib.redirect_stdout(io.StringIO()): + self._coco_api = COCO(json_file) + + self._kpt_oks_sigmas = cfg.TEST.KEYPOINT_OKS_SIGMAS + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = len(self._coco_api.getAnnIds()) > 0 + + def reset(self): + self._predictions = [] + self._coco_results = [] + + def _tasks_from_config(self, cfg): + """ + Returns: + tuple[str]: tasks that can be evaluated under the given configuration. + """ + tasks = ("bbox",) + if cfg.MODEL.MASK_ON: + tasks = tasks + ("segm",) + if cfg.MODEL.KEYPOINT_ON: + tasks = tasks + ("keypoints",) + return tasks + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + # TODO this is ugly + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + + if instances.has("pred_masks"): + # use RLE to encode the masks, because they are too large and takes memory + # since this evaluator stores outputs of the entire dataset + # Our model may predict bool array, but cocoapi expects uint8 + rles = [ + mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + for mask in instances.pred_masks + ] + for rle in rles: + # "counts" is an array encoded by mask_util as a byte-stream. Python3's + # json writer which always produces strings cannot serialize a bytestream + # unless you decode it. Thankfully, utf-8 works out (which is also what + # the pycocotools/_mask.pyx does). + rle["counts"] = rle["counts"].decode("utf-8") + instances.pred_masks_rle = rles + instances.remove("pred_masks") + + prediction["instances"] = instances_to_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + self._predictions.append(prediction) + + def evaluate(self): + if self._distributed: + comm.synchronize() + self._predictions = comm.gather(self._predictions, dst=0) + self._predictions = list(itertools.chain(*self._predictions)) + + if not comm.is_main_process(): + return {} + + if len(self._predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(self._predictions, f) + + self._results = OrderedDict() + if "proposals" in self._predictions[0]: + self._eval_box_proposals() + if "instances" in self._predictions[0]: + self._eval_predictions(set(self._tasks)) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _eval_predictions(self, tasks): + """ + Evaluate self._predictions on the given tasks. + Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + self._coco_results = list(itertools.chain(*[x["instances"] for x in self._predictions])) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + reverse_id_mapping = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + for result in self._coco_results: + result["category_id"] = reverse_id_mapping[result["category_id"]] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(self._coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating predictions ...") + for task in sorted(tasks): + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, self._coco_results, task, kpt_oks_sigmas=self._kpt_oks_sigmas + ) + if len(self._coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _eval_box_proposals(self): + """ + Evaluate the box proposals in self._predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in self._predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals( + self._predictions, self._coco_api, area=area, limit=limit + ) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + def _derive_coco_results(self, coco_eval, iou_type, class_names=None): + """ + Derive the desired score numbers from summerized COCOeval. + + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], + }[iou_type] + + if coco_eval is None: + self._logger.warn("No predictions from the model! Set scores to -1") + return {metric: -1 for metric in metrics} + + # the standard metrics + results = {metric: float(coco_eval.stats[idx] * 100) for idx, metric in enumerate(metrics)} + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format(iou_type) + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def instances_to_json(instances, img_id): + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.pred_boxes.tensor.numpy() + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + boxes = boxes.tolist() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + + has_mask = instances.has("pred_masks_rle") + if has_mask: + rles = instances.pred_masks_rle + + has_keypoints = instances.has("pred_keypoints") + if has_keypoints: + keypoints = instances.pred_keypoints + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "score": scores[k], + } + if has_mask: + result["segmentation"] = rles[k] + if has_keypoints: + # In COCO annotations, + # keypoints coordinates are pixel indices. + # However our predictions are floating point coordinates. + # Therefore we subtract 0.5 to be consistent with the annotation format. + # This is the inverse of data loading logic in `datasets/coco.py`. + keypoints[k][:, :2] -= 0.5 + result["keypoints"] = keypoints[k].flatten().tolist() + results.append(result) + return results + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official COCO API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0 ** 2, 1e5 ** 2], # all + [0 ** 2, 32 ** 2], # small + [32 ** 2, 96 ** 2], # medium + [96 ** 2, 1e5 ** 2], # large + [96 ** 2, 128 ** 2], # 96-128 + [128 ** 2, 256 ** 2], # 128-256 + [256 ** 2, 512 ** 2], # 256-512 + [512 ** 2, 1e5 ** 2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"]) + anno = coco_api.loadAnns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + for obj in anno + if obj["iscrowd"] == 0 + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = torch.cat(gt_overlaps, dim=0) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_coco(coco_gt, coco_results, iou_type, kpt_oks_sigmas=None): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + if iou_type == "segm": + coco_results = copy.deepcopy(coco_results) + # When evaluating mask AP, if the results contain bbox, cocoapi will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in coco_results: + c.pop("bbox", None) + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = COCOeval(coco_gt, coco_dt, iou_type) + # Use the COCO default keypoint OKS sigmas unless overrides are specified + if kpt_oks_sigmas: + coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas) + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval diff --git a/detectron2/evaluation/evaluator.py b/detectron2/evaluation/evaluator.py new file mode 100644 index 0000000000..465615dbbc --- /dev/null +++ b/detectron2/evaluation/evaluator.py @@ -0,0 +1,160 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import datetime +import logging +import time +from collections import OrderedDict +from contextlib import contextmanager +import torch + +from detectron2.utils.comm import is_main_process + + +class DatasetEvaluator: + """ + Base class for a dataset evaluator. + + The function :func:`inference_on_dataset` runs the model over + all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs. + + This class will accumulate information of the inputs/outputs (by :meth:`process`), + and produce evaluation results in the end (by :meth:`evaluate`). + """ + + def reset(self): + """ + Preparation for a new round of evaluation. + Should be called before starting a round of evaluation. + """ + pass + + def process(self, input, output): + """ + Process an input/output pair. + + Args: + input: the input that's used to call the model. + output: the return value of `model(output)` + """ + pass + + def evaluate(self): + """ + Evaluate/summarize the performance, after processing all input/output pairs. + + Returns: + dict: + A new evaluator class can return a dict of arbitrary format + as long as the user can process the results. + In our train_net.py, we expect the following format: + + * key: the name of the task (e.g., bbox) + * value: a dict of {metric name: score}, e.g.: {"AP50": 80} + """ + pass + + +class DatasetEvaluators(DatasetEvaluator): + def __init__(self, evaluators): + assert len(evaluators) + super().__init__() + self._evaluators = evaluators + + def reset(self): + for evaluator in self._evaluators: + evaluator.reset() + + def process(self, input, output): + for evaluator in self._evaluators: + evaluator.process(input, output) + + def evaluate(self): + results = OrderedDict() + for evaluator in self._evaluators: + result = evaluator.evaluate() + if is_main_process(): + for k, v in result.items(): + assert ( + k not in results + ), "Different evalutors produce results with the same key {}".format(k) + results[k] = v + return results + + +def inference_on_dataset(model, data_loader, evaluator): + """ + Run model (in eval mode) on the data_loader and evaluate the metrics with evaluator. + + Args: + model (nn.Module): a module which accepts an object from + `data_loader` and returns some outputs. It will be temporarily set to `eval` mode. + + If you wish to evaluate a model in `training` mode instead, you can + wrap the given model and override its behavior of `.eval()` and `.train()`. + data_loader: an iterable object with a length. + The elements it generates will be the inputs to the model. + evaluator (DatasetEvaluator): the evaluator to run + + Returns: + The return value of `evaluator.evalute()` + """ + num_devices = torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1 + logger = logging.getLogger(__name__) + logger.info("Start inference on {} images".format(len(data_loader))) + + total = len(data_loader) # inference data loader must have a fixed length + evaluator.reset() + + logging_interval = 50 + num_warmup = min(5, logging_interval - 1, total - 1) + start_time = time.time() + with inference_context(model), torch.no_grad(): + for idx, inputs in enumerate(data_loader): + if idx == num_warmup: + start_time = time.time() + + outputs = model(inputs) + evaluator.process(inputs, outputs) + + if (idx + 1) % logging_interval == 0: + duration = time.time() - start_time + seconds_per_img = duration / (idx + 1 - num_warmup) + eta = datetime.timedelta( + seconds=int(seconds_per_img * (total - num_warmup) - duration) + ) + logger.info( + "Inference done {}/{}. {:.4f} s / img. ETA={}".format( + idx + 1, total, seconds_per_img, str(eta) + ) + ) + + # Measure the time only for this worker (before the synchronization barrier) + total_time = int(time.time() - start_time) + total_time_str = str(datetime.timedelta(seconds=total_time)) + # NOTE this format is parsed by grep + logger.info( + "Total inference time: {} ({:.6f} s / img per device, on {} devices)".format( + total_time_str, total_time / (total - num_warmup), num_devices + ) + ) + + results = evaluator.evaluate() + # An evaluator may return None when not in main process. + # Replace it by an empty dict instead to make it easier for downstream code to handle + if results is None: + results = {} + return results + + +@contextmanager +def inference_context(model): + """ + A context where the model is temporarily changed to eval mode, + and restored to previous mode afterwards. + + Args: + model: a torch Module + """ + training_mode = model.training + model.eval() + yield + model.train(training_mode) diff --git a/detectron2/evaluation/lvis_evaluation.py b/detectron2/evaluation/lvis_evaluation.py new file mode 100644 index 0000000000..1befa28ba7 --- /dev/null +++ b/detectron2/evaluation/lvis_evaluation.py @@ -0,0 +1,360 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy +import itertools +import json +import logging +import numpy as np +import os +import pickle +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from fvcore.common.file_io import PathManager + +import detectron2.utils.comm as comm +from detectron2.data import MetadataCatalog +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.logger import create_small_table + +from .coco_evaluation import instances_to_json +from .evaluator import DatasetEvaluator + + +class LVISEvaluator(DatasetEvaluator): + """ + Evaluate object proposal and instance detection/segmentation outputs using + LVIS's metrics and evaluation API. + """ + + def __init__(self, dataset_name, cfg, distributed, output_dir=None): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have the following corresponding metadata: + "json_file": the path to the LVIS format annotation + cfg (CfgNode): config instance + distributed (True): if True, will collect results from all ranks for evaluation. + Otherwise, will evaluate the results in the current process. + output_dir (str): optional, an output directory to dump results. + """ + from lvis import LVIS + + self._tasks = self._tasks_from_config(cfg) + self._distributed = distributed + self._output_dir = output_dir + + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + self._metadata = MetadataCatalog.get(dataset_name) + json_file = PathManager.get_local_path(self._metadata.json_file) + self._lvis_api = LVIS(json_file) + # Test set json files do not contain annotations (evaluation must be + # performed using the LVIS evaluation server). + self._do_evaluation = len(self._lvis_api.get_ann_ids()) > 0 + + def reset(self): + self._predictions = [] + self._lvis_results = [] + + def _tasks_from_config(self, cfg): + """ + Returns: + tuple[str]: tasks that can be evaluated under the given configuration. + """ + tasks = ("bbox",) + if cfg.MODEL.MASK_ON: + tasks = tasks + ("segm",) + return tasks + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a LVIS model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a LVIS model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + # TODO this is ugly + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + + if instances.has("pred_masks"): + # use RLE to encode the masks, because they are too large and takes memory + # since this evaluator stores outputs of the entire dataset + rles = [ + mask_util.encode(np.array(mask[:, :, None], order="F"))[0] + for mask in instances.pred_masks + ] + for rle in rles: + # "counts" is an array encoded by mask_util as a byte-stream. Python3's + # json writer which always produces strings cannot serialize a bytestream + # unless you decode it. Thankfully, utf-8 works out (which is also what + # the pycocotools/_mask.pyx does). + rle["counts"] = rle["counts"].decode("utf-8") + instances.pred_masks_rle = rles + instances.remove("pred_masks") + + prediction["instances"] = instances_to_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + self._predictions.append(prediction) + + def evaluate(self): + if self._distributed: + comm.synchronize() + self._predictions = comm.gather(self._predictions, dst=0) + self._predictions = list(itertools.chain(*self._predictions)) + + if not comm.is_main_process(): + return + + if len(self._predictions) == 0: + self._logger.warning("[LVISEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(self._predictions, f) + + self._results = OrderedDict() + if "proposals" in self._predictions[0]: + self._eval_box_proposals() + if "instances" in self._predictions[0]: + self._eval_predictions(set(self._tasks)) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _eval_predictions(self, tasks): + """ + Evaluate self._predictions on the given tasks. + Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results in the LVIS format ...") + self._lvis_results = list(itertools.chain(*[x["instances"] for x in self._predictions])) + + # unmap the category ids for LVIS (from 0-indexed to 1-indexed) + for result in self._lvis_results: + result["category_id"] += 1 + + if self._output_dir: + file_path = os.path.join(self._output_dir, "lvis_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(self._lvis_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating predictions ...") + for task in sorted(tasks): + res = _evaluate_predictions_on_lvis( + self._lvis_api, + self._lvis_results, + task, + class_names=self._metadata.get("thing_classes"), + ) + self._results[task] = res + + def _eval_box_proposals(self): + """ + Evaluate the box proposals in self._predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in self._predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals( + self._predictions, self._lvis_api, area=area, limit=limit + ) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, lvis_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official LVIS API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0 ** 2, 1e5 ** 2], # all + [0 ** 2, 32 ** 2], # small + [32 ** 2, 96 ** 2], # medium + [96 ** 2, 1e5 ** 2], # large + [96 ** 2, 128 ** 2], # 96-128 + [128 ** 2, 256 ** 2], # 128-256 + [256 ** 2, 512 ** 2], # 256-512 + [512 ** 2, 1e5 ** 2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = lvis_api.get_ann_ids(img_ids=[prediction_dict["image_id"]]) + anno = lvis_api.load_anns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) for obj in anno + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = torch.cat(gt_overlaps, dim=0) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_lvis(lvis_gt, lvis_results, iou_type, class_names=None): + """ + Args: + iou_type (str): + kpt_oks_sigmas (list[float]): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + }[iou_type] + + logger = logging.getLogger(__name__) + + if len(lvis_results) == 0: # TODO: check if needed + logger.warn("No predictions from the model! Set scores to -1") + return {metric: -1 for metric in metrics} + + if iou_type == "segm": + lvis_results = copy.deepcopy(lvis_results) + # When evaluating mask AP, if the results contain bbox, LVIS API will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in lvis_results: + c.pop("bbox", None) + + from lvis import LVISEval, LVISResults + + lvis_results = LVISResults(lvis_gt, lvis_results) + lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type) + lvis_eval.run() + lvis_eval.print_results() + + # Pull the standard metrics from the LVIS results + results = lvis_eval.get_results() + results = {metric: float(results[metric] * 100) for metric in metrics} + logger.info("Evaluation results for {}: \n".format(iou_type) + create_small_table(results)) + return results diff --git a/detectron2/evaluation/panoptic_evaluation.py b/detectron2/evaluation/panoptic_evaluation.py new file mode 100644 index 0000000000..f4054261d2 --- /dev/null +++ b/detectron2/evaluation/panoptic_evaluation.py @@ -0,0 +1,171 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import contextlib +import io +import itertools +import json +import logging +import os +import tempfile +from collections import OrderedDict +from fvcore.common.file_io import PathManager +from PIL import Image +from tabulate import tabulate + +from detectron2.data import MetadataCatalog +from detectron2.utils import comm + +from .evaluator import DatasetEvaluator + +logger = logging.getLogger(__name__) + + +class COCOPanopticEvaluator(DatasetEvaluator): + """ + Evaluate Panoptic Quality metrics on COCO using PanopticAPI. + It saves panoptic segmentation prediction in `output_dir` + + It contains a synchronize call and has to be called from all workers. + """ + + def __init__(self, dataset_name, output_dir): + """ + Args: + dataset_name (str): name of the dataset + output_dir (str): output directory to save results for evaluation + """ + self._metadata = MetadataCatalog.get(dataset_name) + self._thing_contiguous_id_to_dataset_id = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + self._stuff_contiguous_id_to_dataset_id = { + v: k for k, v in self._metadata.stuff_dataset_id_to_contiguous_id.items() + } + + self._predictions_json = os.path.join(output_dir, "predictions.json") + self._predictions_dir = os.path.join(output_dir, "predictions") + + def reset(self): + self._predictions = [] + + def _convert_category_id(self, segment_info): + isthing = segment_info.pop("isthing", None) + if isthing is None: + # the model produces panoptic category id directly. No more conversion needed + return segment_info + if isthing is True: + segment_info["category_id"] = self._thing_contiguous_id_to_dataset_id[ + segment_info["category_id"] + ] + else: + segment_info["category_id"] = self._stuff_contiguous_id_to_dataset_id[ + segment_info["category_id"] + ] + return segment_info + + def process(self, inputs, outputs): + from panopticapi.utils import id2rgb + + for input, output in zip(inputs, outputs): + panoptic_img, segments_info = output["panoptic_seg"] + panoptic_img = panoptic_img.cpu().numpy() + + file_name = os.path.basename(input["file_name"]) + file_name_png = os.path.splitext(file_name)[0] + ".png" + with io.BytesIO() as out: + Image.fromarray(id2rgb(panoptic_img)).save(out, format="PNG") + segments_info = [self._convert_category_id(x) for x in segments_info] + self._predictions.append( + { + "image_id": input["image_id"], + "file_name": file_name_png, + "png_string": out.getvalue(), + "segments_info": segments_info, + } + ) + + def evaluate(self): + comm.synchronize() + + self._predictions = comm.gather(self._predictions) + self._predictions = list(itertools.chain(*self._predictions)) + if not comm.is_main_process(): + return + + gt_json = PathManager.get_local_path(self._metadata.panoptic_json) + gt_folder = self._metadata.panoptic_root + + with tempfile.TemporaryDirectory(prefix="panoptic_eval") as pred_dir: + if "://" not in self._predictions_dir: + pred_dir = self._predictions_dir + os.makedirs(pred_dir, exist_ok=True) + + logger.info("Writing all panoptic predictions to {} ...".format(pred_dir)) + for p in self._predictions: + with open(os.path.join(pred_dir, p["file_name"]), "wb") as f: + f.write(p.pop("png_string")) + + with open(gt_json, "r") as f: + json_data = json.load(f) + json_data["annotations"] = self._predictions + with PathManager.open(self._predictions_json, "w") as f: + f.write(json.dumps(json_data)) + + from panopticapi.evaluation import pq_compute + + with contextlib.redirect_stdout(io.StringIO()): + pq_res = pq_compute( + gt_json, + PathManager.get_local_path(self._predictions_json), + gt_folder=gt_folder, + pred_folder=pred_dir, + ) + + res = {} + res["PQ"] = 100 * pq_res["All"]["pq"] + res["SQ"] = 100 * pq_res["All"]["sq"] + res["RQ"] = 100 * pq_res["All"]["rq"] + res["PQ_th"] = 100 * pq_res["Things"]["pq"] + res["SQ_th"] = 100 * pq_res["Things"]["sq"] + res["RQ_th"] = 100 * pq_res["Things"]["rq"] + res["PQ_st"] = 100 * pq_res["Stuff"]["pq"] + res["SQ_st"] = 100 * pq_res["Stuff"]["sq"] + res["RQ_st"] = 100 * pq_res["Stuff"]["rq"] + + results = OrderedDict({"panoptic_seg": res}) + _print_panoptic_results(pq_res) + + return results + + +def _print_panoptic_results(pq_res): + headers = ["", "PQ", "SQ", "RQ", "#categories"] + data = [] + for name in ["All", "Things", "Stuff"]: + row = [name] + [pq_res[name][k] * 100 for k in ["pq", "sq", "rq"]] + [pq_res[name]["n"]] + data.append(row) + table = tabulate( + data, headers=headers, tablefmt="pipe", floatfmt=".3f", stralign="center", numalign="center" + ) + logger.info("Panoptic Evaluation Results:\n" + table) + + +if __name__ == "__main__": + from detectron2.utils.logger import setup_logger + + logger = setup_logger() + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--gt-json") + parser.add_argument("--gt-dir") + parser.add_argument("--pred-json") + parser.add_argument("--pred-dir") + args = parser.parse_args() + + from panopticapi.evaluation import pq_compute + + with contextlib.redirect_stdout(io.StringIO()): + pq_res = pq_compute( + args.gt_json, args.pred_json, gt_folder=args.gt_dir, pred_folder=args.pred_dir + ) + _print_panoptic_results(pq_res) diff --git a/detectron2/evaluation/pascal_voc_evaluation.py b/detectron2/evaluation/pascal_voc_evaluation.py new file mode 100644 index 0000000000..d555447bf6 --- /dev/null +++ b/detectron2/evaluation/pascal_voc_evaluation.py @@ -0,0 +1,292 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import logging +import numpy as np +import os +import tempfile +import xml.etree.ElementTree as ET +from collections import OrderedDict, defaultdict +from functools import lru_cache +import torch + +from detectron2.data import MetadataCatalog +from detectron2.utils import comm + +from .evaluator import DatasetEvaluator + + +class PascalVOCDetectionEvaluator(DatasetEvaluator): + """ + Evaluate Pascal VOC AP. + It contains a synchronization, therefore has to be called from all ranks. + + Note that this is a rewrite of the official Matlab API. + The results should be similar, but not identical to the one produced by + the official API. + """ + + def __init__(self, dataset_name): + """ + Args: + dataste_name (str): name of the dataset, e.g., "voc_2007_test" + """ + self._dataset_name = dataset_name + meta = MetadataCatalog.get(dataset_name) + self._anno_file_template = os.path.join(meta.dirname, "Annotations", "{}.xml") + self._image_set_path = os.path.join(meta.dirname, "ImageSets", "Main", meta.split + ".txt") + self._class_names = meta.thing_classes + assert meta.year in [2007, 2012], meta.year + self._is_2007 = meta.year == 2007 + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + def reset(self): + self._predictions = defaultdict(list) # class name -> list of prediction strings + + def process(self, inputs, outputs): + for input, output in zip(inputs, outputs): + image_id = input["image_id"] + instances = output["instances"].to(self._cpu_device) + boxes = instances.pred_boxes.tensor.numpy() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + for box, score, cls in zip(boxes, scores, classes): + xmin, ymin, xmax, ymax = box + # The inverse of data loading logic in `datasets/pascal_voc.py` + xmin += 1 + ymin += 1 + self._predictions[cls].append( + f"{image_id} {score:.3f} {xmin:.1f} {ymin:.1f} {xmax:.1f} {ymax:.1f}" + ) + + def evaluate(self): + """ + Returns: + dict: has a key "segm", whose value is a dict of "AP", "AP50", and "AP75". + """ + all_predictions = comm.gather(self._predictions, dst=0) + if not comm.is_main_process(): + return + predictions = defaultdict(list) + for predictions_per_rank in all_predictions: + for clsid, lines in predictions_per_rank.items(): + predictions[clsid].extend(lines) + del all_predictions + + self._logger.info( + "Evaluating {} using {} metric. " + "Note that results do not use the official Matlab API.".format( + self._dataset_name, 2007 if self._is_2007 else 2012 + ) + ) + + with tempfile.TemporaryDirectory(prefix="pascal_voc_eval_") as dirname: + res_file_template = os.path.join(dirname, "{}.txt") + + aps = defaultdict(list) # iou -> ap per class + for cls_id, cls_name in enumerate(self._class_names): + lines = predictions.get(cls_id, [""]) + + with open(res_file_template.format(cls_name), "w") as f: + f.write("\n".join(lines)) + + for thresh in range(50, 100, 5): + rec, prec, ap = voc_eval( + res_file_template, + self._anno_file_template, + self._image_set_path, + cls_name, + ovthresh=thresh / 100.0, + use_07_metric=self._is_2007, + ) + aps[thresh].append(ap * 100) + + ret = OrderedDict() + mAP = {iou: np.mean(x) for iou, x in aps.items()} + ret["bbox"] = {"AP": np.mean(list(mAP.values())), "AP50": mAP[50], "AP75": mAP[75]} + return ret + + +############################################################################## +# +# Below code is modified from +# https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py +# -------------------------------------------------------- +# Fast/er R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Bharath Hariharan +# -------------------------------------------------------- + +"""Python implementation of the PASCAL VOC devkit's AP evaluation code.""" + + +@lru_cache(maxsize=None) +def parse_rec(filename): + """Parse a PASCAL VOC xml file.""" + tree = ET.parse(filename) + objects = [] + for obj in tree.findall("object"): + obj_struct = {} + obj_struct["name"] = obj.find("name").text + obj_struct["pose"] = obj.find("pose").text + obj_struct["truncated"] = int(obj.find("truncated").text) + obj_struct["difficult"] = int(obj.find("difficult").text) + bbox = obj.find("bndbox") + obj_struct["bbox"] = [ + int(bbox.find("xmin").text), + int(bbox.find("ymin").text), + int(bbox.find("xmax").text), + int(bbox.find("ymax").text), + ] + objects.append(obj_struct) + + return objects + + +def voc_ap(rec, prec, use_07_metric=False): + """Compute VOC AP given precision and recall. If use_07_metric is true, uses + the VOC 07 11-point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0.0 + for t in np.arange(0.0, 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11.0 + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.0], rec, [1.0])) + mpre = np.concatenate(([0.0], prec, [0.0])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def voc_eval(detpath, annopath, imagesetfile, classname, ovthresh=0.5, use_07_metric=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + + Top level function that does the PASCAL VOC evaluation. + + detpath: Path to detections + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + + # first load gt + # read list of images + with open(imagesetfile, "r") as f: + lines = f.readlines() + imagenames = [x.strip() for x in lines] + + # load annots + recs = {} + for imagename in imagenames: + recs[imagename] = parse_rec(annopath.format(imagename)) + + # extract gt objects for this class + class_recs = {} + npos = 0 + for imagename in imagenames: + R = [obj for obj in recs[imagename] if obj["name"] == classname] + bbox = np.array([x["bbox"] for x in R]) + difficult = np.array([x["difficult"] for x in R]).astype(np.bool) + # difficult = np.array([False for x in R]).astype(np.bool) # treat all "difficult" as GT + det = [False] * len(R) + npos = npos + sum(~difficult) + class_recs[imagename] = {"bbox": bbox, "difficult": difficult, "det": det} + + # read dets + detfile = detpath.format(classname) + with open(detfile, "r") as f: + lines = f.readlines() + + splitlines = [x.strip().split(" ") for x in lines] + image_ids = [x[0] for x in splitlines] + confidence = np.array([float(x[1]) for x in splitlines]) + BB = np.array([[float(z) for z in x[2:]] for x in splitlines]).reshape(-1, 4) + + # sort by confidence + sorted_ind = np.argsort(-confidence) + BB = BB[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + R = class_recs[image_ids[d]] + bb = BB[d, :].astype(float) + ovmax = -np.inf + BBGT = R["bbox"].astype(float) + + if BBGT.size > 0: + # compute overlaps + # intersection + ixmin = np.maximum(BBGT[:, 0], bb[0]) + iymin = np.maximum(BBGT[:, 1], bb[1]) + ixmax = np.minimum(BBGT[:, 2], bb[2]) + iymax = np.minimum(BBGT[:, 3], bb[3]) + iw = np.maximum(ixmax - ixmin + 1.0, 0.0) + ih = np.maximum(iymax - iymin + 1.0, 0.0) + inters = iw * ih + + # union + uni = ( + (bb[2] - bb[0] + 1.0) * (bb[3] - bb[1] + 1.0) + + (BBGT[:, 2] - BBGT[:, 0] + 1.0) * (BBGT[:, 3] - BBGT[:, 1] + 1.0) + - inters + ) + + overlaps = inters / uni + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + if ovmax > ovthresh: + if not R["difficult"][jmax]: + if not R["det"][jmax]: + tp[d] = 1.0 + R["det"][jmax] = 1 + else: + fp[d] = 1.0 + else: + fp[d] = 1.0 + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap diff --git a/detectron2/evaluation/sem_seg_evaluation.py b/detectron2/evaluation/sem_seg_evaluation.py new file mode 100644 index 0000000000..7de27e0a8f --- /dev/null +++ b/detectron2/evaluation/sem_seg_evaluation.py @@ -0,0 +1,162 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import itertools +import json +import logging +import numpy as np +import os +from collections import OrderedDict +import PIL.Image as Image +import pycocotools.mask as mask_util +import torch +from fvcore.common.file_io import PathManager + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.comm import all_gather, is_main_process, synchronize + +from .evaluator import DatasetEvaluator + + +class SemSegEvaluator(DatasetEvaluator): + """ + Evaluate semantic segmentation + """ + + def __init__(self, dataset_name, distributed, num_classes, ignore_label=255, output_dir=None): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + distributed (True): if True, will collect results from all ranks for evaluation. + Otherwise, will evaluate the results in the current process. + num_classes (int): number of classes + ignore_label (int): value in semantic segmentation ground truth. Predictions for the + corresponding pixels should be ignored. + output_dir (str): an output directory to dump results. + """ + self._dataset_name = dataset_name + self._distributed = distributed + self._output_dir = output_dir + self._num_classes = num_classes + self._ignore_label = ignore_label + self._N = num_classes + 1 + + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + self.image_id_to_gt_file = { + dataset_record["image_id"]: dataset_record["sem_seg_file_name"] + for dataset_record in DatasetCatalog.get(dataset_name) + } + + meta = MetadataCatalog.get(dataset_name) + # Dict that maps contiguous training ids to COCO category ids + try: + c2d = meta.stuff_dataset_id_to_contiguous_id + self._contiguous_id_to_dataset_id = {v: k for k, v in c2d.items()} + except AttributeError: + self._contiguous_id_to_dataset_id = None + + def reset(self): + self._conf_matrix = np.zeros((self._N, self._N), dtype=np.int64) + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a model. + It is a list of dicts. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a model. It is either list of semantic segmentation predictions + (Tensor [H, W]) or list of dicts with key "sem_seg" that contains semantic + segmentation prediction in the same format. + """ + for input, output in zip(inputs, outputs): + output = output["sem_seg"].argmax(dim=0).to(self._cpu_device) + pred = np.array(output, dtype=np.int) + with PathManager.open(self.image_id_to_gt_file[input["image_id"]], "rb") as f: + gt = np.array(Image.open(f), dtype=np.int) + + gt[gt == self._ignore_label] = self._num_classes + + self._conf_matrix += np.bincount( + self._N * pred.reshape(-1) + gt.reshape(-1), minlength=self._N ** 2 + ).reshape(self._N, self._N) + + self._predictions.extend(self.encode_json_sem_seg(pred, input["image_id"])) + + def evaluate(self): + """ + Evaluates standard semantic segmentation metrics (http://cocodataset.org/#stuff-eval): + Mean intersection-over-union averaged across classes (mIoU) + Frequency Weighted IoU (fwIoU) + Mean pixel accuracy averaged across classes (mACC) + Pixel Accuracy (pACC) + """ + if self._distributed: + synchronize() + conf_matrix_list = all_gather(self._conf_matrix) + self._predictions = all_gather(self._predictions) + self._predictions = list(itertools.chain(*self._predictions)) + if not is_main_process(): + return + + self._conf_matrix = np.zeros_like(self._conf_matrix) + for conf_matrix in conf_matrix_list: + self._conf_matrix += conf_matrix + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "sem_seg_predictions.json") + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(self._predictions)) + + acc = np.zeros(self._num_classes, dtype=np.float) + iou = np.zeros(self._num_classes, dtype=np.float) + tp = self._conf_matrix.diagonal()[:-1].astype(np.float) + pos_gt = np.sum(self._conf_matrix[:-1, :-1], axis=0).astype(np.float) + class_weights = pos_gt / np.sum(pos_gt) + pos_pred = np.sum(self._conf_matrix[:-1, :-1], axis=1).astype(np.float) + acc_valid = pos_gt > 0 + acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid] + iou_valid = (pos_gt + pos_pred) > 0 + union = pos_gt + pos_pred - tp + iou[acc_valid] = tp[acc_valid] / union[acc_valid] + macc = np.sum(acc) / np.sum(acc_valid) + miou = np.sum(iou) / np.sum(iou_valid) + fiou = np.sum(iou * class_weights) + pacc = np.sum(tp) / np.sum(pos_gt) + + res = {} + res["mIoU"] = 100 * miou + res["fwIoU"] = 100 * fiou + res["mACC"] = 100 * macc + res["pACC"] = 100 * pacc + + if self._output_dir: + file_path = os.path.join(self._output_dir, "sem_seg_evaluation.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(res, f) + results = OrderedDict({"sem_seg": res}) + self._logger.info(results) + return results + + def encode_json_sem_seg(self, sem_seg, image_id): + """ + Convert semenatic segmentation to COCO stuff format with segments encoded as RLEs. + See http://cocodataset.org/#format-results + """ + json_list = [] + for label in np.unique(sem_seg): + if self._contiguous_id_to_dataset_id is not None: + assert ( + label in self._contiguous_id_to_dataset_id + ), "Label {} is not in the metadata info for {}".format(label, self._dataset_name) + dataset_id = self._contiguous_id_to_dataset_id[label] + else: + dataset_id = int(label) + mask = (sem_seg == label).astype(np.uint8) + mask_rle = mask_util.encode(np.array(mask[:, :, None], order="F"))[0] + mask_rle["counts"] = mask_rle["counts"].decode("utf-8") + json_list.append( + {"image_id": image_id, "category_id": dataset_id, "segmentation": mask_rle} + ) + return json_list diff --git a/detectron2/evaluation/testing.py b/detectron2/evaluation/testing.py new file mode 100644 index 0000000000..ae4310f9f4 --- /dev/null +++ b/detectron2/evaluation/testing.py @@ -0,0 +1,77 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import numpy as np +import pprint +import sys +from collections import Mapping, OrderedDict + + +def print_csv_format(results): + """ + Print main metrics in a format similar to Detectron, + so that they are easy to copypaste into a spreadsheet. + + Args: + results (OrderedDict[dict]): task_name -> {metric -> score} + """ + assert isinstance(results, OrderedDict), results # unordered results cannot be properly printed + logger = logging.getLogger(__name__) + for task, res in results.items(): + # Don't print "AP-category" metrics since they are usually not tracked. + important_res = [(k, v) for k, v in res.items() if "-" not in k] + logger.info("copypaste: Task: {}".format(task)) + logger.info("copypaste: " + ",".join([k[0] for k in important_res])) + logger.info("copypaste: " + ",".join(["{0:.4f}".format(k[1]) for k in important_res])) + + +def verify_results(cfg, results): + """ + Args: + results (OrderedDict[dict]): task_name -> {metric -> score} + + Returns: + bool: whether the verification succeeds or not + """ + expected_results = cfg.TEST.EXPECTED_RESULTS + if not len(expected_results): + return True + + ok = True + for task, metric, expected, tolerance in expected_results: + actual = results[task][metric] + if not np.isfinite(actual): + ok = False + diff = abs(actual - expected) + if diff > tolerance: + ok = False + + logger = logging.getLogger(__name__) + if not ok: + logger.error("Result verification failed!") + logger.error("Expected Results: " + str(expected_results)) + logger.error("Actual Results: " + pprint.pformat(results)) + + sys.exit(1) + else: + logger.info("Results verification passed.") + return ok + + +def flatten_results_dict(results): + """ + Expand a hierarchical dict of scalars into a flat dict of scalars. + If results[k1][k2][k3] = v, the returned dict will have the entry + {"k1/k2/k3": v}. + + Args: + results (dict): + """ + r = {} + for k, v in results.items(): + if isinstance(v, Mapping): + v = flatten_results_dict(v) + for kk, vv in v.items(): + r[k + "/" + kk] = vv + else: + r[k] = v + return r diff --git a/detectron2/layers/__init__.py b/detectron2/layers/__init__.py new file mode 100644 index 0000000000..d31d393ca5 --- /dev/null +++ b/detectron2/layers/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .batch_norm import FrozenBatchNorm2d, get_norm, NaiveSyncBatchNorm +from .deform_conv import DeformConv, ModulatedDeformConv +from .mask_ops import paste_masks_in_image +from .nms import batched_nms, batched_nms_rotated, nms, nms_rotated +from .roi_align import ROIAlign, roi_align +from .roi_align_rotated import ROIAlignRotated, roi_align_rotated +from .shape_spec import ShapeSpec +from .wrappers import BatchNorm2d, Conv2d, ConvTranspose2d, cat, interpolate + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/detectron2/layers/batch_norm.py b/detectron2/layers/batch_norm.py new file mode 100644 index 0000000000..a3f384f92a --- /dev/null +++ b/detectron2/layers/batch_norm.py @@ -0,0 +1,180 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import torch +import torch.distributed as dist +from torch import nn +from torch.autograd.function import Function + +from detectron2.utils import comm + +from .wrappers import BatchNorm2d + + +class FrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + It contains non-trainable buffers called + "weight" and "bias", "running_mean", "running_var", + initialized to perform identity transformation. + + The pre-trained backbone models from Caffe2 only contain "weight" and "bias", + which are computed from the original four parameters of BN. + The affine transform `x * weight + bias` will perform the equivalent + computation of `(x - running_mean) / sqrt(running_var) * weight + bias`. + When loading a backbone model from Caffe2, "running_mean" and "running_var" + will be left unchanged as identity transformation. + + Other pre-trained backbone models may contain all 4 parameters. + + The forward is implemented by `F.batch_norm(..., training=False)`. + """ + + _version = 3 + + def __init__(self, num_features, eps=1e-5): + super().__init__() + self.num_features = num_features + self.eps = eps + self.register_buffer("weight", torch.ones(num_features)) + self.register_buffer("bias", torch.zeros(num_features)) + self.register_buffer("running_mean", torch.zeros(num_features)) + self.register_buffer("running_var", torch.ones(num_features) - eps) + + def forward(self, x): + scale = self.weight * (self.running_var + self.eps).rsqrt() + bias = self.bias - self.running_mean * scale + scale = scale.reshape(1, -1, 1, 1) + bias = bias.reshape(1, -1, 1, 1) + return x * scale + bias + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + # No running_mean/var in early versions + # This will silent the warnings + if prefix + "running_mean" not in state_dict: + state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean) + if prefix + "running_var" not in state_dict: + state_dict[prefix + "running_var"] = torch.ones_like(self.running_var) + + if version is not None and version < 3: + logger = logging.getLogger(__name__) + logger.info("FrozenBatchNorm {} is upgraded to version 3.".format(prefix.rstrip("."))) + # In version < 3, running_var are used without +eps. + state_dict[prefix + "running_var"] -= self.eps + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def __repr__(self): + return "FrozenBatchNorm2d(num_features={}, eps={})".format(self.num_features, self.eps) + + @classmethod + def convert_frozen_batchnorm(cls, module): + """ + Convert BatchNorm/SyncBatchNorm in module into FrozenBatchNorm. + + Args: + module (torch.nn.Module): + + Returns: + If module is BatchNorm/SyncBatchNorm, returns a new module. + Otherwise, in-place convert module and return it. + + Similar to convert_sync_batchnorm in + https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py + """ + bn_module = nn.modules.batchnorm + bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm) + res = module + if isinstance(module, bn_module): + res = cls(module.num_features) + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + module.eps + else: + for name, child in module.named_children(): + new_child = cls.convert_frozen_batchnorm(child) + if new_child is not child: + res.add_module(name, new_child) + return res + + +def get_norm(norm, out_channels): + """ + Args: + norm (str or callable): + + Returns: + nn.Module or None: the normalization layer + """ + if isinstance(norm, str): + if len(norm) == 0: + return None + norm = { + "BN": BatchNorm2d, + "SyncBN": NaiveSyncBatchNorm, + "FrozenBN": FrozenBatchNorm2d, + "GN": lambda channels: nn.GroupNorm(32, channels), + "nnSyncBN": nn.SyncBatchNorm, # keep for debugging + }[norm] + return norm(out_channels) + + +class AllReduce(Function): + @staticmethod + def forward(ctx, input): + input_list = [torch.zeros_like(input) for k in range(dist.get_world_size())] + # Use allgather instead of allreduce since I don't trust in-place operations .. + dist.all_gather(input_list, input, async_op=False) + inputs = torch.stack(input_list, dim=0) + return torch.sum(inputs, dim=0) + + @staticmethod + def backward(ctx, grad_output): + dist.all_reduce(grad_output, async_op=False) + return grad_output + + +class NaiveSyncBatchNorm(BatchNorm2d): + """ + `torch.nn.SyncBatchNorm` has known unknown bugs. + + It produces significantly worse AP (and sometimes goes NaN) + when the batch size on each worker is quite different + (e.g., when scale augmentation is used, or when it is applied to mask head). + + Use this implementation before it is fixed. + It is slower than `torch.nn.SyncBatchNorm`. + """ + + def forward(self, input): + if comm.get_world_size() == 1 or not self.training: + return super().forward(input) + + assert input.shape[0] > 0, "SyncBatchNorm does not support empty inputs" + C = input.shape[1] + mean = torch.mean(input, dim=[0, 2, 3]) + meansqr = torch.mean(input * input, dim=[0, 2, 3]) + + vec = torch.cat([mean, meansqr], dim=0) + vec = AllReduce.apply(vec) * (1.0 / dist.get_world_size()) + + mean, meansqr = torch.split(vec, C) + var = meansqr - mean * mean + self.running_mean += self.momentum * (mean.detach() - self.running_mean) + self.running_var += self.momentum * (var.detach() - self.running_var) + + invstd = torch.rsqrt(var + self.eps) + scale = self.weight * invstd + bias = self.bias - mean * scale + scale = scale.reshape(1, -1, 1, 1) + bias = bias.reshape(1, -1, 1, 1) + return input * scale + bias diff --git a/detectron2/layers/csrc/README.md b/detectron2/layers/csrc/README.md new file mode 100644 index 0000000000..778ed3da0b --- /dev/null +++ b/detectron2/layers/csrc/README.md @@ -0,0 +1,7 @@ + + +To add a new Op: + +1. Create a new directory +2. Implement new ops there +3. Delcare its Python interface in `vision.cpp`. diff --git a/detectron2/layers/csrc/ROIAlign/ROIAlign.h b/detectron2/layers/csrc/ROIAlign/ROIAlign.h new file mode 100644 index 0000000000..01da8e0517 --- /dev/null +++ b/detectron2/layers/csrc/ROIAlign/ROIAlign.h @@ -0,0 +1,126 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#pragma once +#include + +at::Tensor ROIAlign_forward_cpu( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + bool aligned); + +at::Tensor ROIAlign_backward_cpu( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio, + bool aligned); + +#ifdef WITH_CUDA +at::Tensor ROIAlign_forward_cuda( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + bool aligned); + +at::Tensor ROIAlign_backward_cuda( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio, + bool aligned); +#endif + +// Interface for Python +inline at::Tensor ROIAlign_forward( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + bool aligned) { + if (input.type().is_cuda()) { +#ifdef WITH_CUDA + return ROIAlign_forward_cuda( + input, + rois, + spatial_scale, + pooled_height, + pooled_width, + sampling_ratio, + aligned); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + return ROIAlign_forward_cpu( + input, + rois, + spatial_scale, + pooled_height, + pooled_width, + sampling_ratio, + aligned); +} + +inline at::Tensor ROIAlign_backward( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio, + bool aligned) { + if (grad.type().is_cuda()) { +#ifdef WITH_CUDA + return ROIAlign_backward_cuda( + grad, + rois, + spatial_scale, + pooled_height, + pooled_width, + batch_size, + channels, + height, + width, + sampling_ratio, + aligned); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + return ROIAlign_backward_cpu( + grad, + rois, + spatial_scale, + pooled_height, + pooled_width, + batch_size, + channels, + height, + width, + sampling_ratio, + aligned); +} diff --git a/detectron2/layers/csrc/ROIAlign/ROIAlign_cpu.cpp b/detectron2/layers/csrc/ROIAlign/ROIAlign_cpu.cpp new file mode 100644 index 0000000000..0a6490a56a --- /dev/null +++ b/detectron2/layers/csrc/ROIAlign/ROIAlign_cpu.cpp @@ -0,0 +1,495 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include "ROIAlign.h" + +// implementation taken from Caffe2 +template +struct PreCalc { + int pos1; + int pos2; + int pos3; + int pos4; + T w1; + T w2; + T w3; + T w4; +}; + +template +void pre_calc_for_bilinear_interpolate( + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int iy_upper, + const int ix_upper, + T roi_start_h, + T roi_start_w, + T bin_size_h, + T bin_size_w, + int roi_bin_grid_h, + int roi_bin_grid_w, + std::vector>& pre_calc) { + int pre_calc_index = 0; + for (int ph = 0; ph < pooled_height; ph++) { + for (int pw = 0; pw < pooled_width; pw++) { + for (int iy = 0; iy < iy_upper; iy++) { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < ix_upper; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + T x = xx; + T y = yy; + // deal with: inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + PreCalc pc; + pc.pos1 = 0; + pc.pos2 = 0; + pc.pos3 = 0; + pc.pos4 = 0; + pc.w1 = 0; + pc.w2 = 0; + pc.w3 = 0; + pc.w4 = 0; + pre_calc[pre_calc_index] = pc; + pre_calc_index += 1; + continue; + } + + if (y <= 0) { + y = 0; + } + if (x <= 0) { + x = 0; + } + + int y_low = (int)y; + int x_low = (int)x; + int y_high; + int x_high; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + // save weights and indeces + PreCalc pc; + pc.pos1 = y_low * width + x_low; + pc.pos2 = y_low * width + x_high; + pc.pos3 = y_high * width + x_low; + pc.pos4 = y_high * width + x_high; + pc.w1 = w1; + pc.w2 = w2; + pc.w3 = w3; + pc.w4 = w4; + pre_calc[pre_calc_index] = pc; + + pre_calc_index += 1; + } + } + } + } +} + +template +void ROIAlignForward( + const int nthreads, + const T* input, + const T& spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + const T* rois, + T* output, + bool aligned) { + int n_rois = nthreads / channels / pooled_width / pooled_height; + // (n, c, ph, pw) is an element in the pooled output + // can be parallelized using omp + // #pragma omp parallel for num_threads(32) + for (int n = 0; n < n_rois; n++) { + int index_n = n * channels * pooled_width * pooled_height; + + const T* offset_rois = rois + n * 5; + int roi_batch_ind = offset_rois[0]; + + // Do not use rounding; this implementation detail is critical + T offset = aligned ? (T)0.5 : (T)0.0; + T roi_start_w = offset_rois[1] * spatial_scale - offset; + T roi_start_h = offset_rois[2] * spatial_scale - offset; + T roi_end_w = offset_rois[3] * spatial_scale - offset; + T roi_end_h = offset_rois[4] * spatial_scale - offset; + + T roi_width = roi_end_w - roi_start_w; + T roi_height = roi_end_h - roi_start_h; + if (aligned) { + AT_ASSERTM( + roi_width >= 0 && roi_height >= 0, + "ROIs in ROIAlign cannot have non-negative size!"); + } else { // for backward-compatibility only + roi_width = std::max(roi_width, (T)1.); + roi_height = std::max(roi_height, (T)1.); + } + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // We do average (integral) pooling inside a bin + // When the grid is empty, output zeros. + const T count = std::max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4 + + // we want to precalculate indices and weights shared by all channels, + // this is the key point of optimization + std::vector> pre_calc( + roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height); + pre_calc_for_bilinear_interpolate( + height, + width, + pooled_height, + pooled_width, + roi_bin_grid_h, + roi_bin_grid_w, + roi_start_h, + roi_start_w, + bin_size_h, + bin_size_w, + roi_bin_grid_h, + roi_bin_grid_w, + pre_calc); + + for (int c = 0; c < channels; c++) { + int index_n_c = index_n + c * pooled_width * pooled_height; + const T* offset_input = + input + (roi_batch_ind * channels + c) * height * width; + int pre_calc_index = 0; + + for (int ph = 0; ph < pooled_height; ph++) { + for (int pw = 0; pw < pooled_width; pw++) { + int index = index_n_c + ph * pooled_width + pw; + + T output_val = 0.; + for (int iy = 0; iy < roi_bin_grid_h; iy++) { + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + PreCalc pc = pre_calc[pre_calc_index]; + output_val += pc.w1 * offset_input[pc.pos1] + + pc.w2 * offset_input[pc.pos2] + + pc.w3 * offset_input[pc.pos3] + pc.w4 * offset_input[pc.pos4]; + + pre_calc_index += 1; + } + } + output_val /= count; + + output[index] = output_val; + } // for pw + } // for ph + } // for c + } // for n +} + +template +void bilinear_interpolate_gradient( + const int height, + const int width, + T y, + T x, + T& w1, + T& w2, + T& w3, + T& w4, + int& x_low, + int& x_high, + int& y_low, + int& y_high, + const int index /* index for debug only*/) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + w1 = w2 = w3 = w4 = 0.; + x_low = x_high = y_low = y_high = -1; + return; + } + + if (y <= 0) + y = 0; + if (x <= 0) + x = 0; + + y_low = (int)y; + x_low = (int)x; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + + // reference in forward + // T v1 = input[y_low * width + x_low]; + // T v2 = input[y_low * width + x_high]; + // T v3 = input[y_high * width + x_low]; + // T v4 = input[y_high * width + x_high]; + // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + return; +} + +template +inline void add(T* address, const T& val) { + *address += val; +} + +template +void ROIAlignBackward( + const int nthreads, + const T* grad_output, + const T& spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + T* grad_input, + const T* rois, + const int n_stride, + const int c_stride, + const int h_stride, + const int w_stride, + bool aligned) { + for (int index = 0; index < nthreads; index++) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* offset_rois = rois + n * 5; + int roi_batch_ind = offset_rois[0]; + + // Do not use rounding; this implementation detail is critical + T offset = aligned ? (T)0.5 : (T)0.0; + T roi_start_w = offset_rois[1] * spatial_scale - offset; + T roi_start_h = offset_rois[2] * spatial_scale - offset; + T roi_end_w = offset_rois[3] * spatial_scale - offset; + T roi_end_h = offset_rois[4] * spatial_scale - offset; + + T roi_width = roi_end_w - roi_start_w; + T roi_height = roi_end_h - roi_start_h; + if (aligned) { + AT_ASSERTM( + roi_width >= 0 && roi_height >= 0, + "ROIs in ROIAlign do not have non-negative size!"); + } else { // for backward-compatibility only + roi_width = std::max(roi_width, (T)1.); + roi_height = std::max(roi_height, (T)1.); + } + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + T* offset_grad_input = + grad_input + ((roi_batch_ind * channels + c) * height * width); + + int output_offset = n * n_stride + c * c_stride; + const T* offset_grad_output = grad_output + output_offset; + const T grad_output_this_bin = + offset_grad_output[ph * h_stride + pw * w_stride]; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + for (int iy = 0; iy < roi_bin_grid_h; iy++) { + const T y = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T x = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + T w1, w2, w3, w4; + int x_low, x_high, y_low, y_high; + + bilinear_interpolate_gradient( + height, + width, + y, + x, + w1, + w2, + w3, + w4, + x_low, + x_high, + y_low, + y_high, + index); + + T g1 = grad_output_this_bin * w1 / count; + T g2 = grad_output_this_bin * w2 / count; + T g3 = grad_output_this_bin * w3 / count; + T g4 = grad_output_this_bin * w4 / count; + + if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { + // atomic add is not needed for now since it is single threaded + add(offset_grad_input + y_low * width + x_low, static_cast(g1)); + add(offset_grad_input + y_low * width + x_high, static_cast(g2)); + add(offset_grad_input + y_high * width + x_low, static_cast(g3)); + add(offset_grad_input + y_high * width + x_high, static_cast(g4)); + } // if + } // ix + } // iy + } // for +} // ROIAlignBackward + +at::Tensor ROIAlign_forward_cpu( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + bool aligned) { + AT_ASSERTM(input.device().is_cpu(), "input must be a CPU tensor"); + AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor"); + + at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlign_forward_cpu"; + at::checkAllSameType(c, {input_t, rois_t}); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + at::Tensor output = at::zeros( + {num_rois, channels, pooled_height, pooled_width}, input.options()); + + auto output_size = num_rois * pooled_height * pooled_width * channels; + + if (output.numel() == 0) + return output; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), "ROIAlign_forward", [&] { + ROIAlignForward( + output_size, + input.contiguous().data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + rois.contiguous().data_ptr(), + output.data_ptr(), + aligned); + }); + return output; +} + +at::Tensor ROIAlign_backward_cpu( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio, + bool aligned) { + AT_ASSERTM(grad.device().is_cpu(), "grad must be a CPU tensor"); + AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor"); + + at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlign_backward_cpu"; + at::checkAllSameType(c, {grad_t, rois_t}); + + at::Tensor grad_input = + at::zeros({batch_size, channels, height, width}, grad.options()); + + // handle possibly empty gradients + if (grad.numel() == 0) { + return grad_input; + } + + // get stride values to ensure indexing into gradients is correct. + int n_stride = grad.stride(0); + int c_stride = grad.stride(1); + int h_stride = grad.stride(2); + int w_stride = grad.stride(3); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(grad.type(), "ROIAlign_forward", [&] { + ROIAlignBackward( + grad.numel(), + grad.contiguous().data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + grad_input.data_ptr(), + rois.contiguous().data_ptr(), + n_stride, + c_stride, + h_stride, + w_stride, + aligned); + }); + return grad_input; +} diff --git a/detectron2/layers/csrc/ROIAlign/ROIAlign_cuda.cu b/detectron2/layers/csrc/ROIAlign/ROIAlign_cuda.cu new file mode 100644 index 0000000000..44c29667f1 --- /dev/null +++ b/detectron2/layers/csrc/ROIAlign/ROIAlign_cuda.cu @@ -0,0 +1,418 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include +#include +#include + +// TODO make it in a common file +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ + i += blockDim.x * gridDim.x) + +template +__device__ T bilinear_interpolate( + const T* bottom_data, + const int height, + const int width, + T y, + T x, + const int index /* index for debug only*/) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + return 0; + } + + if (y <= 0) + y = 0; + if (x <= 0) + x = 0; + + int y_low = (int)y; + int x_low = (int)x; + int y_high; + int x_high; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + // do bilinear interpolation + T v1 = bottom_data[y_low * width + x_low]; + T v2 = bottom_data[y_low * width + x_high]; + T v3 = bottom_data[y_high * width + x_low]; + T v4 = bottom_data[y_high * width + x_high]; + T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + return val; +} + +template +__global__ void RoIAlignForward( + const int nthreads, + const T* bottom_data, + const T spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + const T* bottom_rois, + T* top_data, + bool aligned) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + + // Do not use rounding; this implementation detail is critical + T offset = aligned ? (T)0.5 : (T)0.0; + T roi_start_w = offset_bottom_rois[1] * spatial_scale - offset; + T roi_start_h = offset_bottom_rois[2] * spatial_scale - offset; + T roi_end_w = offset_bottom_rois[3] * spatial_scale - offset; + T roi_end_h = offset_bottom_rois[4] * spatial_scale - offset; + + T roi_width = roi_end_w - roi_start_w; + T roi_height = roi_end_h - roi_start_h; + if (!aligned) { // for backward-compatibility only + roi_width = max(roi_width, (T)1.); + roi_height = max(roi_height, (T)1.); + } + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + const T* offset_bottom_data = + bottom_data + (roi_batch_ind * channels + c) * height * width; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // We do average (integral) pooling inside a bin + // When the grid is empty, output zeros. + const T count = max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4 + + T output_val = 0.; + for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1 + { + const T y = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T x = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + T val = bilinear_interpolate( + offset_bottom_data, height, width, y, x, index); + output_val += val; + } + } + output_val /= count; + + top_data[index] = output_val; + } +} + +template +__device__ void bilinear_interpolate_gradient( + const int height, + const int width, + T y, + T x, + T& w1, + T& w2, + T& w3, + T& w4, + int& x_low, + int& x_high, + int& y_low, + int& y_high, + const int index /* index for debug only*/) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + w1 = w2 = w3 = w4 = 0.; + x_low = x_high = y_low = y_high = -1; + return; + } + + if (y <= 0) + y = 0; + if (x <= 0) + x = 0; + + y_low = (int)y; + x_low = (int)x; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + + // reference in forward + // T v1 = bottom_data[y_low * width + x_low]; + // T v2 = bottom_data[y_low * width + x_high]; + // T v3 = bottom_data[y_high * width + x_low]; + // T v4 = bottom_data[y_high * width + x_high]; + // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + return; +} + +template +__global__ void RoIAlignBackwardFeature( + const int nthreads, + const T* top_diff, + const int num_rois, + const T spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + T* bottom_diff, + const T* bottom_rois, + bool aligned) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + + // Do not use rounding; this implementation detail is critical + T offset = aligned ? (T)0.5 : (T)0.0; + T roi_start_w = offset_bottom_rois[1] * spatial_scale - offset; + T roi_start_h = offset_bottom_rois[2] * spatial_scale - offset; + T roi_end_w = offset_bottom_rois[3] * spatial_scale - offset; + T roi_end_h = offset_bottom_rois[4] * spatial_scale - offset; + + T roi_width = roi_end_w - roi_start_w; + T roi_height = roi_end_h - roi_start_h; + if (!aligned) { // for backward-compatibility only + roi_width = max(roi_width, (T)1.); + roi_height = max(roi_height, (T)1.); + } + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + T* offset_bottom_diff = + bottom_diff + (roi_batch_ind * channels + c) * height * width; + + int top_offset = (n * channels + c) * pooled_height * pooled_width; + const T* offset_top_diff = top_diff + top_offset; + const T top_diff_this_bin = offset_top_diff[ph * pooled_width + pw]; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1 + { + const T y = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T x = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + T w1, w2, w3, w4; + int x_low, x_high, y_low, y_high; + + bilinear_interpolate_gradient( + height, + width, + y, + x, + w1, + w2, + w3, + w4, + x_low, + x_high, + y_low, + y_high, + index); + + T g1 = top_diff_this_bin * w1 / count; + T g2 = top_diff_this_bin * w2 / count; + T g3 = top_diff_this_bin * w3 / count; + T g4 = top_diff_this_bin * w4 / count; + + if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { + atomicAdd( + offset_bottom_diff + y_low * width + x_low, static_cast(g1)); + atomicAdd( + offset_bottom_diff + y_low * width + x_high, static_cast(g2)); + atomicAdd( + offset_bottom_diff + y_high * width + x_low, static_cast(g3)); + atomicAdd( + offset_bottom_diff + y_high * width + x_high, static_cast(g4)); + } // if + } // ix + } // iy + } // CUDA_1D_KERNEL_LOOP +} // RoIAlignBackward + +at::Tensor ROIAlign_forward_cuda( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + bool aligned) { + AT_ASSERTM(input.device().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlign_forward_cuda"; + at::checkAllSameGPU(c, {input_t, rois_t}); + at::checkAllSameType(c, {input_t, rois_t}); + at::cuda::CUDAGuard device_guard(input.device()); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + auto output = at::empty( + {num_rois, channels, pooled_height, pooled_width}, input.options()); + auto output_size = num_rois * pooled_height * pooled_width * channels; + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min(at::cuda::ATenCeilDiv(output_size, 512L), 4096L)); + dim3 block(512); + + if (output.numel() == 0) { + AT_CUDA_CHECK(cudaGetLastError()); + return output; + } + + AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "ROIAlign_forward", [&] { + RoIAlignForward<<>>( + output_size, + input.contiguous().data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + rois.contiguous().data_ptr(), + output.data_ptr(), + aligned); + }); + cudaDeviceSynchronize(); + AT_CUDA_CHECK(cudaGetLastError()); + return output; +} + +// TODO remove the dependency on input and use instead its sizes -> save memory +at::Tensor ROIAlign_backward_cuda( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio, + bool aligned) { + AT_ASSERTM(grad.device().is_cuda(), "grad must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + + at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2}; + at::CheckedFrom c = "ROIAlign_backward_cuda"; + at::checkAllSameGPU(c, {grad_t, rois_t}); + at::checkAllSameType(c, {grad_t, rois_t}); + at::cuda::CUDAGuard device_guard(grad.device()); + + auto num_rois = rois.size(0); + auto grad_input = + at::zeros({batch_size, channels, height, width}, grad.options()); + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min(at::cuda::ATenCeilDiv(grad.numel(), 512L), 4096L)); + dim3 block(512); + + // handle possibly empty gradients + if (grad.numel() == 0) { + AT_CUDA_CHECK(cudaGetLastError()); + return grad_input; + } + + AT_DISPATCH_FLOATING_TYPES(grad.scalar_type(), "ROIAlign_backward", [&] { + RoIAlignBackwardFeature<<>>( + grad.numel(), + grad.contiguous().data_ptr(), + num_rois, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + grad_input.data_ptr(), + rois.contiguous().data_ptr(), + aligned); + }); + AT_CUDA_CHECK(cudaGetLastError()); + return grad_input; +} diff --git a/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h b/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h new file mode 100644 index 0000000000..15601d8abe --- /dev/null +++ b/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h @@ -0,0 +1,115 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#pragma once +#include + +namespace detectron2 { + +at::Tensor ROIAlignRotated_forward_cpu( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio); + +at::Tensor ROIAlignRotated_backward_cpu( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio); + +#ifdef WITH_CUDA +at::Tensor ROIAlignRotated_forward_cuda( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio); + +at::Tensor ROIAlignRotated_backward_cuda( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio); +#endif + +// Interface for Python +inline at::Tensor ROIAlignRotated_forward( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio) { + if (input.type().is_cuda()) { +#ifdef WITH_CUDA + return ROIAlignRotated_forward_cuda( + input, + rois, + spatial_scale, + pooled_height, + pooled_width, + sampling_ratio); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + return ROIAlignRotated_forward_cpu( + input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio); +} + +inline at::Tensor ROIAlignRotated_backward( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio) { + if (grad.type().is_cuda()) { +#ifdef WITH_CUDA + return ROIAlignRotated_backward_cuda( + grad, + rois, + spatial_scale, + pooled_height, + pooled_width, + batch_size, + channels, + height, + width, + sampling_ratio); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + return ROIAlignRotated_backward_cpu( + grad, + rois, + spatial_scale, + pooled_height, + pooled_width, + batch_size, + channels, + height, + width, + sampling_ratio); +} + +} // namespace detectron2 diff --git a/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp b/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp new file mode 100644 index 0000000000..ec71c530eb --- /dev/null +++ b/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp @@ -0,0 +1,519 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include "ROIAlignRotated.h" + +// Note: this implementation originates from the Caffe2 ROIAlignRotated Op +// and PyTorch ROIAlign (non-rotated) Op implementations. +// The key difference between this implementation and those ones is +// we don't do "legacy offset" in this version, as there aren't many previous +// works, if any, using the "legacy" ROIAlignRotated Op. +// This would make the interface a bit cleaner. + +namespace detectron2 { + +namespace { +template +struct PreCalc { + int pos1; + int pos2; + int pos3; + int pos4; + T w1; + T w2; + T w3; + T w4; +}; + +template +void pre_calc_for_bilinear_interpolate( + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int iy_upper, + const int ix_upper, + T roi_start_h, + T roi_start_w, + T bin_size_h, + T bin_size_w, + int roi_bin_grid_h, + int roi_bin_grid_w, + T roi_center_h, + T roi_center_w, + T cos_theta, + T sin_theta, + std::vector>& pre_calc) { + int pre_calc_index = 0; + for (int ph = 0; ph < pooled_height; ph++) { + for (int pw = 0; pw < pooled_width; pw++) { + for (int iy = 0; iy < iy_upper; iy++) { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < ix_upper; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + // In image space, (y, x) is the order for Right Handed System, + // and this is essentially multiplying the point by a rotation matrix + // to rotate it counterclockwise through angle theta. + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + // deal with: inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + PreCalc pc; + pc.pos1 = 0; + pc.pos2 = 0; + pc.pos3 = 0; + pc.pos4 = 0; + pc.w1 = 0; + pc.w2 = 0; + pc.w3 = 0; + pc.w4 = 0; + pre_calc[pre_calc_index] = pc; + pre_calc_index += 1; + continue; + } + + if (y < 0) { + y = 0; + } + if (x < 0) { + x = 0; + } + + int y_low = (int)y; + int x_low = (int)x; + int y_high; + int x_high; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + // save weights and indeces + PreCalc pc; + pc.pos1 = y_low * width + x_low; + pc.pos2 = y_low * width + x_high; + pc.pos3 = y_high * width + x_low; + pc.pos4 = y_high * width + x_high; + pc.w1 = w1; + pc.w2 = w2; + pc.w3 = w3; + pc.w4 = w4; + pre_calc[pre_calc_index] = pc; + + pre_calc_index += 1; + } + } + } + } +} + +template +void bilinear_interpolate_gradient( + const int height, + const int width, + T y, + T x, + T& w1, + T& w2, + T& w3, + T& w4, + int& x_low, + int& x_high, + int& y_low, + int& y_high) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + w1 = w2 = w3 = w4 = 0.; + x_low = x_high = y_low = y_high = -1; + return; + } + + if (y < 0) { + y = 0; + } + + if (x < 0) { + x = 0; + } + + y_low = (int)y; + x_low = (int)x; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + + // reference in forward + // T v1 = input[y_low * width + x_low]; + // T v2 = input[y_low * width + x_high]; + // T v3 = input[y_high * width + x_low]; + // T v4 = input[y_high * width + x_high]; + // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + return; +} + +template +inline void add(T* address, const T& val) { + *address += val; +} + +} // namespace + +template +void ROIAlignRotatedForward( + const int nthreads, + const T* input, + const T& spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + const T* rois, + T* output) { + int n_rois = nthreads / channels / pooled_width / pooled_height; + // (n, c, ph, pw) is an element in the pooled output + // can be parallelized using omp + // #pragma omp parallel for num_threads(32) + for (int n = 0; n < n_rois; n++) { + int index_n = n * channels * pooled_width * pooled_height; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + AT_ASSERTM( + roi_width >= 0 && roi_height >= 0, + "ROIs in ROIAlignRotated do not have non-negative size!"); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // We do average (integral) pooling inside a bin + const T count = std::max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4 + + // we want to precalculate indices and weights shared by all chanels, + // this is the key point of optimization + std::vector> pre_calc( + roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + pre_calc_for_bilinear_interpolate( + height, + width, + pooled_height, + pooled_width, + roi_bin_grid_h, + roi_bin_grid_w, + roi_start_h, + roi_start_w, + bin_size_h, + bin_size_w, + roi_bin_grid_h, + roi_bin_grid_w, + roi_center_h, + roi_center_w, + cos_theta, + sin_theta, + pre_calc); + + for (int c = 0; c < channels; c++) { + int index_n_c = index_n + c * pooled_width * pooled_height; + const T* offset_input = + input + (roi_batch_ind * channels + c) * height * width; + int pre_calc_index = 0; + + for (int ph = 0; ph < pooled_height; ph++) { + for (int pw = 0; pw < pooled_width; pw++) { + int index = index_n_c + ph * pooled_width + pw; + + T output_val = 0.; + for (int iy = 0; iy < roi_bin_grid_h; iy++) { + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + PreCalc pc = pre_calc[pre_calc_index]; + output_val += pc.w1 * offset_input[pc.pos1] + + pc.w2 * offset_input[pc.pos2] + + pc.w3 * offset_input[pc.pos3] + pc.w4 * offset_input[pc.pos4]; + + pre_calc_index += 1; + } + } + output_val /= count; + + output[index] = output_val; + } // for pw + } // for ph + } // for c + } // for n +} + +template +void ROIAlignRotatedBackward( + const int nthreads, + const T* grad_output, + const T& spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + T* grad_input, + const T* rois, + const int n_stride, + const int c_stride, + const int h_stride, + const int w_stride) { + for (int index = 0; index < nthreads; index++) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + AT_ASSERTM( + roi_width >= 0 && roi_height >= 0, + "ROIs in ROIAlignRotated do not have non-negative size!"); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + T* offset_grad_input = + grad_input + ((roi_batch_ind * channels + c) * height * width); + + int output_offset = n * n_stride + c * c_stride; + const T* offset_grad_output = grad_output + output_offset; + const T grad_output_this_bin = + offset_grad_output[ph * h_stride + pw * w_stride]; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + for (int iy = 0; iy < roi_bin_grid_h; iy++) { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + + T w1, w2, w3, w4; + int x_low, x_high, y_low, y_high; + + bilinear_interpolate_gradient( + height, width, y, x, w1, w2, w3, w4, x_low, x_high, y_low, y_high); + + T g1 = grad_output_this_bin * w1 / count; + T g2 = grad_output_this_bin * w2 / count; + T g3 = grad_output_this_bin * w3 / count; + T g4 = grad_output_this_bin * w4 / count; + + if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { + // atomic add is not needed for now since it is single threaded + add(offset_grad_input + y_low * width + x_low, static_cast(g1)); + add(offset_grad_input + y_low * width + x_high, static_cast(g2)); + add(offset_grad_input + y_high * width + x_low, static_cast(g3)); + add(offset_grad_input + y_high * width + x_high, static_cast(g4)); + } // if + } // ix + } // iy + } // for +} // ROIAlignRotatedBackward + +at::Tensor ROIAlignRotated_forward_cpu( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio) { + AT_ASSERTM(input.device().is_cpu(), "input must be a CPU tensor"); + AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor"); + + at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlign_forward_cpu"; + at::checkAllSameType(c, {input_t, rois_t}); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + at::Tensor output = at::zeros( + {num_rois, channels, pooled_height, pooled_width}, input.options()); + + auto output_size = num_rois * pooled_height * pooled_width * channels; + + if (output.numel() == 0) { + return output; + } + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + input.type(), "ROIAlignRotated_forward", [&] { + ROIAlignRotatedForward( + output_size, + input.contiguous().data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + rois.contiguous().data_ptr(), + output.data_ptr()); + }); + return output; +} + +at::Tensor ROIAlignRotated_backward_cpu( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio) { + AT_ASSERTM(grad.device().is_cpu(), "grad must be a CPU tensor"); + AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor"); + + at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlignRotated_backward_cpu"; + at::checkAllSameType(c, {grad_t, rois_t}); + + at::Tensor grad_input = + at::zeros({batch_size, channels, height, width}, grad.options()); + + // handle possibly empty gradients + if (grad.numel() == 0) { + return grad_input; + } + + // get stride values to ensure indexing into gradients is correct. + int n_stride = grad.stride(0); + int c_stride = grad.stride(1); + int h_stride = grad.stride(2); + int w_stride = grad.stride(3); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + grad.type(), "ROIAlignRotated_forward", [&] { + ROIAlignRotatedBackward( + grad.numel(), + grad.contiguous().data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + grad_input.data_ptr(), + rois.contiguous().data_ptr(), + n_stride, + c_stride, + h_stride, + w_stride); + }); + return grad_input; +} + +} // namespace detectron2 diff --git a/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu b/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu new file mode 100644 index 0000000000..e27c8670d4 --- /dev/null +++ b/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu @@ -0,0 +1,435 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include +#include +#include + +// TODO make it in a common file +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ + i += blockDim.x * gridDim.x) + +// Note: this implementation originates from the Caffe2 ROIAlignRotated Op +// and PyTorch ROIAlign (non-rotated) Op implementations. +// The key difference between this implementation and those ones is +// we don't do "legacy offset" in this version, as there aren't many previous +// works, if any, using the "legacy" ROIAlignRotated Op. +// This would make the interface a bit cleaner. + +namespace detectron2 { + +namespace { + +template +__device__ T bilinear_interpolate( + const T* input, + const int height, + const int width, + T y, + T x) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + return 0; + } + + if (y < 0) { + y = 0; + } + + if (x < 0) { + x = 0; + } + + int y_low = (int)y; + int x_low = (int)x; + int y_high; + int x_high; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + // do bilinear interpolation + T v1 = input[y_low * width + x_low]; + T v2 = input[y_low * width + x_high]; + T v3 = input[y_high * width + x_low]; + T v4 = input[y_high * width + x_high]; + T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + return val; +} + +template +__device__ void bilinear_interpolate_gradient( + const int height, + const int width, + T y, + T x, + T& w1, + T& w2, + T& w3, + T& w4, + int& x_low, + int& x_high, + int& y_low, + int& y_high) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + w1 = w2 = w3 = w4 = 0.; + x_low = x_high = y_low = y_high = -1; + return; + } + + if (y < 0) { + y = 0; + } + + if (x < 0) { + x = 0; + } + + y_low = (int)y; + x_low = (int)x; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + + // reference in forward + // T v1 = input[y_low * width + x_low]; + // T v2 = input[y_low * width + x_high]; + // T v3 = input[y_high * width + x_low]; + // T v4 = input[y_high * width + x_high]; + // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + return; +} + +} // namespace + +template +__global__ void RoIAlignRotatedForward( + const int nthreads, + const T* input, + const T spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + const T* rois, + T* top_data) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + const T* offset_input = + input + (roi_batch_ind * channels + c) * height * width; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + // We do average (inte gral) pooling inside a bin + const T count = max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4 + + T output_val = 0.; + for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1 + { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + + T val = bilinear_interpolate(offset_input, height, width, y, x); + output_val += val; + } + } + output_val /= count; + + top_data[index] = output_val; + } +} + +template +__global__ void RoIAlignRotatedBackwardFeature( + const int nthreads, + const T* top_diff, + const int num_rois, + const T spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + T* bottom_diff, + const T* rois) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + T* offset_bottom_diff = + bottom_diff + (roi_batch_ind * channels + c) * height * width; + + int top_offset = (n * channels + c) * pooled_height * pooled_width; + const T* offset_top_diff = top_diff + top_offset; + const T top_diff_this_bin = offset_top_diff[ph * pooled_width + pw]; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1 + { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + + T w1, w2, w3, w4; + int x_low, x_high, y_low, y_high; + + bilinear_interpolate_gradient( + height, width, y, x, w1, w2, w3, w4, x_low, x_high, y_low, y_high); + + T g1 = top_diff_this_bin * w1 / count; + T g2 = top_diff_this_bin * w2 / count; + T g3 = top_diff_this_bin * w3 / count; + T g4 = top_diff_this_bin * w4 / count; + + if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { + atomicAdd( + offset_bottom_diff + y_low * width + x_low, static_cast(g1)); + atomicAdd( + offset_bottom_diff + y_low * width + x_high, static_cast(g2)); + atomicAdd( + offset_bottom_diff + y_high * width + x_low, static_cast(g3)); + atomicAdd( + offset_bottom_diff + y_high * width + x_high, static_cast(g4)); + } // if + } // ix + } // iy + } // CUDA_1D_KERNEL_LOOP +} // RoIAlignRotatedBackward + +at::Tensor ROIAlignRotated_forward_cuda( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio) { + AT_ASSERTM(input.device().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlignRotated_forward_cuda"; + at::checkAllSameGPU(c, {input_t, rois_t}); + at::checkAllSameType(c, {input_t, rois_t}); + at::cuda::CUDAGuard device_guard(input.device()); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + auto output = at::empty( + {num_rois, channels, pooled_height, pooled_width}, input.options()); + auto output_size = num_rois * pooled_height * pooled_width * channels; + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min(at::cuda::ATenCeilDiv(output_size, 512L), 4096L)); + dim3 block(512); + + if (output.numel() == 0) { + AT_CUDA_CHECK(cudaGetLastError()); + return output; + } + + AT_DISPATCH_FLOATING_TYPES( + input.scalar_type(), "ROIAlignRotated_forward", [&] { + RoIAlignRotatedForward<<>>( + output_size, + input.contiguous().data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + rois.contiguous().data_ptr(), + output.data_ptr()); + }); + cudaDeviceSynchronize(); + AT_CUDA_CHECK(cudaGetLastError()); + return output; +} + +// TODO remove the dependency on input and use instead its sizes -> save memory +at::Tensor ROIAlignRotated_backward_cuda( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio) { + AT_ASSERTM(grad.device().is_cuda(), "grad must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + + at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2}; + at::CheckedFrom c = "ROIAlign_backward_cuda"; + at::checkAllSameGPU(c, {grad_t, rois_t}); + at::checkAllSameType(c, {grad_t, rois_t}); + at::cuda::CUDAGuard device_guard(grad.device()); + + auto num_rois = rois.size(0); + auto grad_input = + at::zeros({batch_size, channels, height, width}, grad.options()); + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min(at::cuda::ATenCeilDiv(grad.numel(), 512L), 4096L)); + dim3 block(512); + + // handle possibly empty gradients + if (grad.numel() == 0) { + AT_CUDA_CHECK(cudaGetLastError()); + return grad_input; + } + + AT_DISPATCH_FLOATING_TYPES( + grad.scalar_type(), "ROIAlignRotated_backward", [&] { + RoIAlignRotatedBackwardFeature<<>>( + grad.numel(), + grad.contiguous().data_ptr(), + num_rois, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + grad_input.data_ptr(), + rois.contiguous().data_ptr()); + }); + AT_CUDA_CHECK(cudaGetLastError()); + return grad_input; +} + +} // namespace detectron2 diff --git a/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated.h b/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated.h new file mode 100644 index 0000000000..937f212a6e --- /dev/null +++ b/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated.h @@ -0,0 +1,35 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#pragma once +#include + +namespace detectron2 { + +at::Tensor box_iou_rotated_cpu( + const at::Tensor& boxes1, + const at::Tensor& boxes2); + +#ifdef WITH_CUDA +at::Tensor box_iou_rotated_cuda( + const at::Tensor& boxes1, + const at::Tensor& boxes2); +#endif + +// Interface for Python +// inline is needed to prevent multiple function definitions when this header is +// included by different cpps +inline at::Tensor box_iou_rotated( + const at::Tensor& boxes1, + const at::Tensor& boxes2) { + assert(boxes1.device().is_cuda() == boxes2.device().is_cuda()); + if (boxes1.device().is_cuda()) { +#ifdef WITH_CUDA + return box_iou_rotated_cuda(boxes1, boxes2); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + + return box_iou_rotated_cpu(boxes1, boxes2); +} + +} // namespace detectron2 diff --git a/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp b/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp new file mode 100644 index 0000000000..a6aaa810c5 --- /dev/null +++ b/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp @@ -0,0 +1,46 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include "box_iou_rotated.h" +#include "box_iou_rotated_utils.h" + +namespace detectron2 { + +template +void box_iou_rotated_cpu_kernel( + const at::Tensor& boxes1, + const at::Tensor& boxes2, + at::Tensor& ious) { + auto widths1 = boxes1.select(1, 2).contiguous(); + auto heights1 = boxes1.select(1, 3).contiguous(); + auto widths2 = boxes2.select(1, 2).contiguous(); + auto heights2 = boxes2.select(1, 3).contiguous(); + + at::Tensor areas1 = widths1 * heights1; + at::Tensor areas2 = widths2 * heights2; + + auto num_boxes1 = boxes1.size(0); + auto num_boxes2 = boxes2.size(0); + + for (int i = 0; i < num_boxes1; i++) { + for (int j = 0; j < num_boxes2; j++) { + ious[i * num_boxes2 + j] = single_box_iou_rotated( + boxes1[i].data_ptr(), boxes2[j].data_ptr()); + } + } +} + +at::Tensor box_iou_rotated_cpu( + const at::Tensor& boxes1, + const at::Tensor& boxes2) { + auto num_boxes1 = boxes1.size(0); + auto num_boxes2 = boxes2.size(0); + at::Tensor ious = + at::empty({num_boxes1 * num_boxes2}, boxes1.options().dtype(at::kFloat)); + + box_iou_rotated_cpu_kernel(boxes1, boxes2, ious); + + // reshape from 1d array to 2d array + auto shape = std::vector{num_boxes1, num_boxes2}; + return ious.reshape(shape); +} + +} // namespace detectron2 diff --git a/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu b/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu new file mode 100644 index 0000000000..79448e33b4 --- /dev/null +++ b/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu @@ -0,0 +1,103 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include +#include +#include +#include "box_iou_rotated_utils.h" + +namespace detectron2 { + +// 2D block with 32 * 16 = 512 threads per block +const int BLOCK_DIM_X = 32; +const int BLOCK_DIM_Y = 16; + +template +__global__ void box_iou_rotated_cuda_kernel( + const int n_boxes1, + const int n_boxes2, + const T* dev_boxes1, + const T* dev_boxes2, + T* dev_ious) { + const int row_start = blockIdx.x * blockDim.x; + const int col_start = blockIdx.y * blockDim.y; + + const int row_size = min(n_boxes1 - row_start, blockDim.x); + const int col_size = min(n_boxes2 - col_start, blockDim.y); + + __shared__ float block_boxes1[BLOCK_DIM_X * 5]; + __shared__ float block_boxes2[BLOCK_DIM_Y * 5]; + + // It's safe to copy using threadIdx.x since BLOCK_DIM_X >= BLOCK_DIM_Y + if (threadIdx.x < row_size && threadIdx.y == 0) { + block_boxes1[threadIdx.x * 5 + 0] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 0]; + block_boxes1[threadIdx.x * 5 + 1] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 1]; + block_boxes1[threadIdx.x * 5 + 2] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 2]; + block_boxes1[threadIdx.x * 5 + 3] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 3]; + block_boxes1[threadIdx.x * 5 + 4] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 4]; + } + + if (threadIdx.x < col_size && threadIdx.y == 0) { + block_boxes2[threadIdx.x * 5 + 0] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 0]; + block_boxes2[threadIdx.x * 5 + 1] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 1]; + block_boxes2[threadIdx.x * 5 + 2] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 2]; + block_boxes2[threadIdx.x * 5 + 3] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 3]; + block_boxes2[threadIdx.x * 5 + 4] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size && threadIdx.y < col_size) { + int offset = (row_start + threadIdx.x) * n_boxes2 + col_start + threadIdx.y; + dev_ious[offset] = single_box_iou_rotated( + block_boxes1 + threadIdx.x * 5, block_boxes2 + threadIdx.y * 5); + } +} + +at::Tensor box_iou_rotated_cuda( + const at::Tensor& boxes1, + const at::Tensor& boxes2) { + using scalar_t = float; + AT_ASSERTM(boxes1.type().is_cuda(), "boxes1 must be a CUDA tensor"); + AT_ASSERTM(boxes2.type().is_cuda(), "boxes2 must be a CUDA tensor"); + at::cuda::CUDAGuard device_guard(boxes1.device()); + + int num_boxes1 = boxes1.size(0); + int num_boxes2 = boxes2.size(0); + + at::Tensor ious = + at::empty({num_boxes1 * num_boxes2}, boxes1.options().dtype(at::kFloat)); + + if (num_boxes1 > 0 && num_boxes2 > 0) { + const int blocks_x = at::cuda::ATenCeilDiv(num_boxes1, BLOCK_DIM_X); + const int blocks_y = at::cuda::ATenCeilDiv(num_boxes2, BLOCK_DIM_Y); + + dim3 blocks(blocks_x, blocks_y); + dim3 threads(BLOCK_DIM_X, BLOCK_DIM_Y); + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + box_iou_rotated_cuda_kernel<<>>( + num_boxes1, + num_boxes2, + boxes1.data_ptr(), + boxes2.data_ptr(), + (scalar_t*)ious.data_ptr()); + + AT_CUDA_CHECK(cudaGetLastError()); + } + + // reshape from 1d array to 2d array + auto shape = std::vector{num_boxes1, num_boxes2}; + return ious.reshape(shape); +} + +} // namespace detectron2 diff --git a/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h b/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h new file mode 100644 index 0000000000..6448f97ff9 --- /dev/null +++ b/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h @@ -0,0 +1,342 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#pragma once + +#include +#include + +#ifdef __CUDACC__ +// Designates functions callable from the host (CPU) and the device (GPU) +#define HOST_DEVICE __host__ __device__ +#define HOST_DEVICE_INLINE HOST_DEVICE __forceinline__ +#else +#include +#define HOST_DEVICE +#define HOST_DEVICE_INLINE HOST_DEVICE inline +#endif + +namespace detectron2 { + +namespace { + +template +struct RotatedBox { + T x_ctr, y_ctr, w, h, a; +}; + +template +struct Point { + T x, y; + HOST_DEVICE_INLINE Point(const T& px = 0, const T& py = 0) : x(px), y(py) {} + HOST_DEVICE_INLINE Point operator+(const Point& p) const { + return Point(x + p.x, y + p.y); + } + HOST_DEVICE_INLINE Point& operator+=(const Point& p) { + x += p.x; + y += p.y; + return *this; + } + HOST_DEVICE_INLINE Point operator-(const Point& p) const { + return Point(x - p.x, y - p.y); + } + HOST_DEVICE_INLINE Point operator*(const T coeff) const { + return Point(x * coeff, y * coeff); + } +}; + +template +HOST_DEVICE_INLINE T dot_2d(const Point& A, const Point& B) { + return A.x * B.x + A.y * B.y; +} + +template +HOST_DEVICE_INLINE T cross_2d(const Point& A, const Point& B) { + return A.x * B.y - B.x * A.y; +} + +template +HOST_DEVICE_INLINE void get_rotated_vertices( + const RotatedBox& box, + Point (&pts)[4]) { + // M_PI / 180. == 0.01745329251 + double theta = box.a * 0.01745329251; + T cosTheta2 = (T)cos(theta) * 0.5f; + T sinTheta2 = (T)sin(theta) * 0.5f; + + // y: top --> down; x: left --> right + pts[0].x = box.x_ctr - sinTheta2 * box.h - cosTheta2 * box.w; + pts[0].y = box.y_ctr + cosTheta2 * box.h - sinTheta2 * box.w; + pts[1].x = box.x_ctr + sinTheta2 * box.h - cosTheta2 * box.w; + pts[1].y = box.y_ctr - cosTheta2 * box.h - sinTheta2 * box.w; + pts[2].x = 2 * box.x_ctr - pts[0].x; + pts[2].y = 2 * box.y_ctr - pts[0].y; + pts[3].x = 2 * box.x_ctr - pts[1].x; + pts[3].y = 2 * box.y_ctr - pts[1].y; +} + +template +HOST_DEVICE_INLINE int get_intersection_points( + const Point (&pts1)[4], + const Point (&pts2)[4], + Point (&intersections)[24]) { + // Line vector + // A line from p1 to p2 is: p1 + (p2-p1)*t, t=[0,1] + Point vec1[4], vec2[4]; + for (int i = 0; i < 4; i++) { + vec1[i] = pts1[(i + 1) % 4] - pts1[i]; + vec2[i] = pts2[(i + 1) % 4] - pts2[i]; + } + + // Line test - test all line combos for intersection + int num = 0; // number of intersections + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + // Solve for 2x2 Ax=b + T det = cross_2d(vec2[j], vec1[i]); + + // This takes care of parallel lines + if (fabs(det) <= 1e-14) { + continue; + } + + auto vec12 = pts2[j] - pts1[i]; + + T t1 = cross_2d(vec2[j], vec12) / det; + T t2 = cross_2d(vec1[i], vec12) / det; + + if (t1 >= 0.0f && t1 <= 1.0f && t2 >= 0.0f && t2 <= 1.0f) { + intersections[num++] = pts1[i] + vec1[i] * t1; + } + } + } + + // Check for vertices of rect1 inside rect2 + { + const auto& AB = vec2[0]; + const auto& DA = vec2[3]; + auto ABdotAB = dot_2d(AB, AB); + auto ADdotAD = dot_2d(DA, DA); + for (int i = 0; i < 4; i++) { + // assume ABCD is the rectangle, and P is the point to be judged + // P is inside ABCD iff. P's projection on AB lies within AB + // and P's projection on AD lies within AD + + auto AP = pts1[i] - pts2[0]; + + auto APdotAB = dot_2d(AP, AB); + auto APdotAD = -dot_2d(AP, DA); + + if ((APdotAB >= 0) && (APdotAD >= 0) && (APdotAB <= ABdotAB) && + (APdotAD <= ADdotAD)) { + intersections[num++] = pts1[i]; + } + } + } + + // Reverse the check - check for vertices of rect2 inside rect1 + { + const auto& AB = vec1[0]; + const auto& DA = vec1[3]; + auto ABdotAB = dot_2d(AB, AB); + auto ADdotAD = dot_2d(DA, DA); + for (int i = 0; i < 4; i++) { + auto AP = pts2[i] - pts1[0]; + + auto APdotAB = dot_2d(AP, AB); + auto APdotAD = -dot_2d(AP, DA); + + if ((APdotAB >= 0) && (APdotAD >= 0) && (APdotAB <= ABdotAB) && + (APdotAD <= ADdotAD)) { + intersections[num++] = pts2[i]; + } + } + } + + return num; +} + +template +HOST_DEVICE_INLINE int convex_hull_graham( + const Point (&p)[24], + const int& num_in, + Point (&q)[24], + bool shift_to_zero = false) { + assert(num_in >= 2); + + // Step 1: + // Find point with minimum y + // if more than 1 points have the same minimum y, + // pick the one with the minimum x. + int t = 0; + for (int i = 1; i < num_in; i++) { + if (p[i].y < p[t].y || (p[i].y == p[t].y && p[i].x < p[t].x)) { + t = i; + } + } + auto& start = p[t]; // starting point + + // Step 2: + // Subtract starting point from every points (for sorting in the next step) + for (int i = 0; i < num_in; i++) { + q[i] = p[i] - start; + } + + // Swap the starting point to position 0 + auto tmp = q[0]; + q[0] = q[t]; + q[t] = tmp; + + // Step 3: + // Sort point 1 ~ num_in according to their relative cross-product values + // (essentially sorting according to angles) + // If the angles are the same, sort according to their distance to origin + T dist[24]; + for (int i = 0; i < num_in; i++) { + dist[i] = dot_2d(q[i], q[i]); + } + +#ifdef __CUDACC__ + // CUDA version + // In the future, we can potentially use thrust + // for sorting here to improve speed (though not guaranteed) + for (int i = 1; i < num_in - 1; i++) { + for (int j = i + 1; j < num_in; j++) { + T crossProduct = cross_2d(q[i], q[j]); + if ((crossProduct < -1e-6) || + (fabs(crossProduct) < 1e-6 && dist[i] > dist[j])) { + auto q_tmp = q[i]; + q[i] = q[j]; + q[j] = q_tmp; + auto dist_tmp = dist[i]; + dist[i] = dist[j]; + dist[j] = dist_tmp; + } + } + } +#else + // CPU version + std::sort( + q + 1, q + num_in, [](const Point& A, const Point& B) -> bool { + T temp = cross_2d(A, B); + if (fabs(temp) < 1e-6) { + return dot_2d(A, A) < dot_2d(B, B); + } else { + return temp > 0; + } + }); +#endif + + // Step 4: + // Make sure there are at least 2 points (that don't overlap with each other) + // in the stack + int k; // index of the non-overlapped second point + for (k = 1; k < num_in; k++) { + if (dist[k] > 1e-8) { + break; + } + } + if (k == num_in) { + // We reach the end, which means the convex hull is just one point + q[0] = p[t]; + return 1; + } + q[1] = q[k]; + int m = 2; // 2 points in the stack + // Step 5: + // Finally we can start the scanning process. + // When a non-convex relationship between the 3 points is found + // (either concave shape or duplicated points), + // we pop the previous point from the stack + // until the 3-point relationship is convex again, or + // until the stack only contains two points + for (int i = k + 1; i < num_in; i++) { + while (m > 1 && cross_2d(q[i] - q[m - 2], q[m - 1] - q[m - 2]) >= 0) { + m--; + } + q[m++] = q[i]; + } + + // Step 6 (Optional): + // In general sense we need the original coordinates, so we + // need to shift the points back (reverting Step 2) + // But if we're only interested in getting the area/perimeter of the shape + // We can simply return. + if (!shift_to_zero) { + for (int i = 0; i < m; i++) { + q[i] += start; + } + } + + return m; +} + +template +HOST_DEVICE_INLINE T polygon_area(const Point (&q)[24], const int& m) { + if (m <= 2) { + return 0; + } + + T area = 0; + for (int i = 1; i < m - 1; i++) { + area += fabs(cross_2d(q[i] - q[0], q[i + 1] - q[0])); + } + + return area / 2.0; +} + +template +HOST_DEVICE_INLINE T rotated_boxes_intersection( + const RotatedBox& box1, + const RotatedBox& box2) { + // There are up to 4 x 4 + 4 + 4 = 24 intersections (including dups) returned + // from rotated_rect_intersection_pts + Point intersectPts[24], orderedPts[24]; + + Point pts1[4]; + Point pts2[4]; + get_rotated_vertices(box1, pts1); + get_rotated_vertices(box2, pts2); + + int num = get_intersection_points(pts1, pts2, intersectPts); + + if (num <= 2) { + return 0.0; + } + + // Convex Hull to order the intersection points in clockwise order and find + // the countour area. + int num_convex = convex_hull_graham(intersectPts, num, orderedPts, true); + return polygon_area(orderedPts, num_convex); +} + +template +HOST_DEVICE_INLINE T +single_box_iou_rotated(T const* const box1_raw, T const* const box2_raw) { + // shift center to the middle point to achieve higher precision in result + RotatedBox box1, box2; + auto center_shift_x = (box1_raw[0] + box2_raw[0]) / 2.0; + auto center_shift_y = (box1_raw[1] + box2_raw[1]) / 2.0; + box1.x_ctr = box1_raw[0] - center_shift_x; + box1.y_ctr = box1_raw[1] - center_shift_y; + box1.w = box1_raw[2]; + box1.h = box1_raw[3]; + box1.a = box1_raw[4]; + box2.x_ctr = box2_raw[0] - center_shift_x; + box2.y_ctr = box2_raw[1] - center_shift_y; + box2.w = box2_raw[2]; + box2.h = box2_raw[3]; + box2.a = box2_raw[4]; + + const T area1 = box1.w * box1.h; + const T area2 = box2.w * box2.h; + if (area1 < 1e-14 || area2 < 1e-14) { + return 0.f; + } + + const T intersection = rotated_boxes_intersection(box1, box2); + const T iou = intersection / (area1 + area2 - intersection); + return iou; +} + +} // namespace + +} // namespace detectron2 diff --git a/detectron2/layers/csrc/deformable/deform_conv.h b/detectron2/layers/csrc/deformable/deform_conv.h new file mode 100644 index 0000000000..43928b7805 --- /dev/null +++ b/detectron2/layers/csrc/deformable/deform_conv.h @@ -0,0 +1,373 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#pragma once +#include + +#ifdef WITH_CUDA +int deform_conv_forward_cuda( + at::Tensor input, + at::Tensor weight, + at::Tensor offset, + at::Tensor output, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step); + +int deform_conv_backward_input_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradInput, + at::Tensor gradOffset, + at::Tensor weight, + at::Tensor columns, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step); + +int deform_conv_backward_parameters_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + float scale, + int im2col_step); + +void modulated_deform_conv_cuda_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor output, + at::Tensor columns, + int kernel_h, + int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int group, + const int deformable_group, + const bool with_bias); + +void modulated_deform_conv_cuda_backward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor columns, + at::Tensor grad_input, + at::Tensor grad_weight, + at::Tensor grad_bias, + at::Tensor grad_offset, + at::Tensor grad_mask, + at::Tensor grad_output, + int kernel_h, + int kernel_w, + int stride_h, + int stride_w, + int pad_h, + int pad_w, + int dilation_h, + int dilation_w, + int group, + int deformable_group, + const bool with_bias); + +#endif + +inline int deform_conv_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor offset, + at::Tensor output, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + if (input.type().is_cuda()) { +#ifdef WITH_CUDA + AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!"); + AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!"); + return deform_conv_forward_cuda( + input, + weight, + offset, + output, + columns, + ones, + kW, + kH, + dW, + dH, + padW, + padH, + dilationW, + dilationH, + group, + deformable_group, + im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +inline int deform_conv_backward_input( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradInput, + at::Tensor gradOffset, + at::Tensor weight, + at::Tensor columns, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + if (gradOutput.type().is_cuda()) { +#ifdef WITH_CUDA + AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!"); + AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!"); + AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!"); + return deform_conv_backward_input_cuda( + input, + offset, + gradOutput, + gradInput, + gradOffset, + weight, + columns, + kW, + kH, + dW, + dH, + padW, + padH, + dilationW, + dilationH, + group, + deformable_group, + im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +inline int deform_conv_backward_filter( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + float scale, + int im2col_step) { + if (gradOutput.type().is_cuda()) { +#ifdef WITH_CUDA + AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!"); + AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!"); + return deform_conv_backward_parameters_cuda( + input, + offset, + gradOutput, + gradWeight, + columns, + ones, + kW, + kH, + dW, + dH, + padW, + padH, + dilationW, + dilationH, + group, + deformable_group, + scale, + im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +inline void modulated_deform_conv_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor output, + at::Tensor columns, + int kernel_h, + int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int group, + const int deformable_group, + const bool with_bias) { + if (input.type().is_cuda()) { +#ifdef WITH_CUDA + AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!"); + AT_CHECK(bias.type().is_cuda(), "bias tensor is not on GPU!"); + AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!"); + return modulated_deform_conv_cuda_forward( + input, + weight, + bias, + ones, + offset, + mask, + output, + columns, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + group, + deformable_group, + with_bias); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +inline void modulated_deform_conv_backward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor columns, + at::Tensor grad_input, + at::Tensor grad_weight, + at::Tensor grad_bias, + at::Tensor grad_offset, + at::Tensor grad_mask, + at::Tensor grad_output, + int kernel_h, + int kernel_w, + int stride_h, + int stride_w, + int pad_h, + int pad_w, + int dilation_h, + int dilation_w, + int group, + int deformable_group, + const bool with_bias) { + if (grad_output.type().is_cuda()) { +#ifdef WITH_CUDA + AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!"); + AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!"); + AT_CHECK(bias.type().is_cuda(), "bias tensor is not on GPU!"); + AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!"); + return modulated_deform_conv_cuda_backward( + input, + weight, + bias, + ones, + offset, + mask, + columns, + grad_input, + grad_weight, + grad_bias, + grad_offset, + grad_mask, + grad_output, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + group, + deformable_group, + with_bias); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} diff --git a/detectron2/layers/csrc/deformable/deform_conv_cuda.cu b/detectron2/layers/csrc/deformable/deform_conv_cuda.cu new file mode 100644 index 0000000000..0f4e637c1a --- /dev/null +++ b/detectron2/layers/csrc/deformable/deform_conv_cuda.cu @@ -0,0 +1,1126 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +// modified from +// https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda.cpp +// Original license: Apache 2.0 + +// modify from +// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c +// Original license: Apache 2.0 + +#include + +#include "deform_conv.h" + +#include +#include + +void deformable_im2col( + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor data_col); + +void deformable_col2im( + const at::Tensor data_col, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_im); + +void deformable_col2im_coord( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_offset); + +void modulated_deformable_im2col_cuda( + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor data_col); + +void modulated_deformable_col2im_cuda( + const at::Tensor data_col, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_im); + +void modulated_deformable_col2im_coord_cuda( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_offset, + at::Tensor grad_mask); + +void shape_check( + at::Tensor input, + at::Tensor offset, + at::Tensor* gradOutput, + at::Tensor weight, + int kH, + int kW, + int dH, + int dW, + int padH, + int padW, + int dilationH, + int dilationW, + int group, + int deformable_group) { + AT_CHECK( + weight.ndimension() == 4, + "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, " + "but got: %s", + weight.ndimension()); + + AT_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + AT_CHECK( + kW > 0 && kH > 0, + "kernel size should be greater than zero, but got kH: %d kW: %d", + kH, + kW); + + AT_CHECK( + (weight.size(2) == kH && weight.size(3) == kW), + "kernel size should be consistent with weight, ", + "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", + kH, + kW, + weight.size(2), + weight.size(3)); + + AT_CHECK( + dW > 0 && dH > 0, + "stride should be greater than zero, but got dH: %d dW: %d", + dH, + dW); + + AT_CHECK( + dilationW > 0 && dilationH > 0, + "dilation should be greater than 0, but got dilationH: %d dilationW: %d", + dilationH, + dilationW); + + int ndim = input.ndimension(); + int dimf = 0; + int dimh = 1; + int dimw = 2; + + if (ndim == 4) { + dimf++; + dimh++; + dimw++; + } + + AT_CHECK( + ndim == 3 || ndim == 4, + "3D or 4D input tensor expected but got: %s", + ndim); + + long nInputPlane = weight.size(1) * group; + long inputHeight = input.size(dimh); + long inputWidth = input.size(dimw); + long nOutputPlane = weight.size(0); + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + + AT_CHECK( + nInputPlane % deformable_group == 0, + "input channels must divide deformable group size"); + + if (outputWidth < 1 || outputHeight < 1) + AT_ERROR( + "Given input size: (%ld x %ld x %ld). " + "Calculated output size: (%ld x %ld x %ld). Output size is too small", + nInputPlane, + inputHeight, + inputWidth, + nOutputPlane, + outputHeight, + outputWidth); + + AT_CHECK( + input.size(1) == nInputPlane, + "invalid number of input planes, expected: %d, but got: %d", + nInputPlane, + input.size(1)); + + AT_CHECK( + (inputHeight >= kH && inputWidth >= kW), + "input image is smaller than kernel"); + + AT_CHECK( + (offset.size(2) == outputHeight && offset.size(3) == outputWidth), + "invalid spatial size of offset, expected height: %d width: %d, but " + "got height: %d width: %d", + outputHeight, + outputWidth, + offset.size(2), + offset.size(3)); + + AT_CHECK( + (offset.size(1) == deformable_group * 2 * kH * kW), + "invalid number of channels of offset"); + + if (gradOutput != NULL) { + AT_CHECK( + gradOutput->size(dimf) == nOutputPlane, + "invalid number of gradOutput planes, expected: %d, but got: %d", + nOutputPlane, + gradOutput->size(dimf)); + + AT_CHECK( + (gradOutput->size(dimh) == outputHeight && + gradOutput->size(dimw) == outputWidth), + "invalid size of gradOutput, expected height: %d width: %d , but " + "got height: %d width: %d", + outputHeight, + outputWidth, + gradOutput->size(dimh), + gradOutput->size(dimw)); + } +} + +int deform_conv_forward_cuda( + at::Tensor input, + at::Tensor weight, + at::Tensor offset, + at::Tensor output, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + // todo: resize columns to include im2col: done + // todo: add im2col_step as input + // todo: add new output buffer and transpose it to output (or directly + // transpose output) todo: possibly change data indexing because of + // parallel_imgs + + shape_check( + input, + offset, + NULL, + weight, + kH, + kW, + dH, + dW, + padH, + padW, + dilationH, + dilationW, + group, + deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + weight = weight.contiguous(); + + int batch = 1; + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input.unsqueeze_(0); + offset.unsqueeze_(0); + } + + // todo: assert batchsize dividable by im2col_step + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = weight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + AT_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); + + output = output.view({batchSize / im2col_step, + im2col_step, + nOutputPlane, + outputHeight, + outputWidth}); + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < outputHeight * outputWidth) { + ones = at::ones({outputHeight, outputWidth}, input.options()); + } + + input = input.view({batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + offset = offset.view({batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + + at::Tensor output_buffer = at::zeros( + {batchSize / im2col_step, + nOutputPlane, + im2col_step * outputHeight, + outputWidth}, + output.options()); + + output_buffer = output_buffer.view({output_buffer.size(0), + group, + output_buffer.size(1) / group, + output_buffer.size(2), + output_buffer.size(3)}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + deformable_im2col( + input[elt], + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + columns); + + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view({group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + + for (int g = 0; g < group; g++) { + output_buffer[elt][g] = output_buffer[elt][g] + .flatten(1) + .addmm_(weight[g].flatten(1), columns[g]) + .view_as(output_buffer[elt][g]); + } + } + + output_buffer = + output_buffer.view({output_buffer.size(0), + output_buffer.size(1) * output_buffer.size(2), + output_buffer.size(3), + output_buffer.size(4)}); + + output_buffer = output_buffer.view({batchSize / im2col_step, + nOutputPlane, + im2col_step, + outputHeight, + outputWidth}); + output_buffer.transpose_(1, 2); + output.copy_(output_buffer); + output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + output = output.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); + } + + return 1; +} + +int deform_conv_backward_input_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradInput, + at::Tensor gradOffset, + at::Tensor weight, + at::Tensor columns, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + shape_check( + input, + offset, + &gradOutput, + weight, + kH, + kW, + dH, + dW, + padH, + padW, + dilationH, + dilationW, + group, + deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + gradOutput = gradOutput.contiguous(); + weight = weight.contiguous(); + + int batch = 1; + + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input = input.view({1, input.size(0), input.size(1), input.size(2)}); + offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)}); + gradOutput = gradOutput.view( + {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); + } + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = weight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + AT_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset"); + gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + // change order of grad output + gradOutput = gradOutput.view({batchSize / im2col_step, + im2col_step, + nOutputPlane, + outputHeight, + outputWidth}); + gradOutput.transpose_(1, 2); + + gradInput = gradInput.view({batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + input = input.view({batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + gradOffset = gradOffset.view({batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + offset = offset.view({batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + // divide into groups + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view({group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + gradOutput = gradOutput.view({gradOutput.size(0), + group, + gradOutput.size(1) / group, + gradOutput.size(2), + gradOutput.size(3), + gradOutput.size(4)}); + + for (int g = 0; g < group; g++) { + columns[g] = columns[g].addmm_( + weight[g].flatten(1).transpose(0, 1), + gradOutput[elt][g].flatten(1), + 0.0f, + 1.0f); + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + gradOutput = gradOutput.view({gradOutput.size(0), + gradOutput.size(1) * gradOutput.size(2), + gradOutput.size(3), + gradOutput.size(4), + gradOutput.size(5)}); + + deformable_col2im_coord( + columns, + input[elt], + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + gradOffset[elt]); + + deformable_col2im( + columns, + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + gradInput[elt]); + } + + gradOutput.transpose_(1, 2); + gradOutput = + gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + gradOffset = gradOffset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); + offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); + gradOffset = + gradOffset.view({offset.size(1), offset.size(2), offset.size(3)}); + } + + return 1; +} + +int deform_conv_backward_parameters_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + float scale, + int im2col_step) { + // todo: transpose and reshape outGrad + // todo: reshape columns + // todo: add im2col_step as input + + shape_check( + input, + offset, + &gradOutput, + gradWeight, + kH, + kW, + dH, + dW, + padH, + padW, + dilationH, + dilationW, + group, + deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + gradOutput = gradOutput.contiguous(); + + int batch = 1; + + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input = input.view( + at::IntList({1, input.size(0), input.size(1), input.size(2)})); + gradOutput = gradOutput.view( + {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); + } + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = gradWeight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + AT_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); + + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + gradOutput = gradOutput.view({batchSize / im2col_step, + im2col_step, + nOutputPlane, + outputHeight, + outputWidth}); + gradOutput.transpose_(1, 2); + + at::Tensor gradOutputBuffer = at::zeros_like(gradOutput); + gradOutputBuffer = gradOutputBuffer.view({batchSize / im2col_step, + nOutputPlane, + im2col_step, + outputHeight, + outputWidth}); + gradOutputBuffer.copy_(gradOutput); + gradOutputBuffer = gradOutputBuffer.view({batchSize / im2col_step, + nOutputPlane, + im2col_step * outputHeight, + outputWidth}); + + gradOutput.transpose_(1, 2); + gradOutput = + gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + input = input.view({batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + offset = offset.view({batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + deformable_im2col( + input[elt], + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + columns); + + // divide into group + gradOutputBuffer = gradOutputBuffer.view({gradOutputBuffer.size(0), + group, + gradOutputBuffer.size(1) / group, + gradOutputBuffer.size(2), + gradOutputBuffer.size(3)}); + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + gradWeight = gradWeight.view({group, + gradWeight.size(0) / group, + gradWeight.size(1), + gradWeight.size(2), + gradWeight.size(3)}); + + for (int g = 0; g < group; g++) { + gradWeight[g] = gradWeight[g] + .flatten(1) + .addmm_( + gradOutputBuffer[elt][g].flatten(1), + columns[g].transpose(1, 0), + 1.0, + scale) + .view_as(gradWeight[g]); + } + gradOutputBuffer = gradOutputBuffer.view( + {gradOutputBuffer.size(0), + gradOutputBuffer.size(1) * gradOutputBuffer.size(2), + gradOutputBuffer.size(3), + gradOutputBuffer.size(4)}); + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1), + gradWeight.size(2), + gradWeight.size(3), + gradWeight.size(4)}); + } + + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + } + + return 1; +} + +void modulated_deform_conv_cuda_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor output, + at::Tensor columns, + int kernel_h, + int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int group, + const int deformable_group, + const bool with_bias) { + AT_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + AT_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) + AT_ERROR( + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", + kernel_h_, + kernel_w, + kernel_h_, + kernel_w_); + if (channels != channels_kernel * group) + AT_ERROR( + "Input shape and kernel channels wont match: (%d vs %d).", + channels, + channels_kernel * group); + + const int height_out = + (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = + (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < height_out * width_out) { + // Resize plane and fill with ones... + ones = at::ones({height_out, width_out}, input.options()); + } + + // resize output + output = output.view({batch, channels_out, height_out, width_out}).zero_(); + // resize temporary columns + columns = at::zeros( + {channels * kernel_h * kernel_w, 1 * height_out * width_out}, + input.options()); + + output = output.view({output.size(0), + group, + output.size(1) / group, + output.size(2), + output.size(3)}); + + for (int b = 0; b < batch; b++) { + modulated_deformable_im2col_cuda( + input[b], + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + columns); + + // divide into group + weight = weight.view({group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + + for (int g = 0; g < group; g++) { + output[b][g] = output[b][g] + .flatten(1) + .addmm_(weight[g].flatten(1), columns[g]) + .view_as(output[b][g]); + } + + weight = weight.view({weight.size(0) * weight.size(1), + weight.size(2), + weight.size(3), + weight.size(4)}); + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + } + + output = output.view({output.size(0), + output.size(1) * output.size(2), + output.size(3), + output.size(4)}); + + if (with_bias) { + output += bias.view({1, bias.size(0), 1, 1}); + } +} + +void modulated_deform_conv_cuda_backward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor columns, + at::Tensor grad_input, + at::Tensor grad_weight, + at::Tensor grad_bias, + at::Tensor grad_offset, + at::Tensor grad_mask, + at::Tensor grad_output, + int kernel_h, + int kernel_w, + int stride_h, + int stride_w, + int pad_h, + int pad_w, + int dilation_h, + int dilation_w, + int group, + int deformable_group, + const bool with_bias) { + AT_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + AT_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) + AT_ERROR( + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", + kernel_h_, + kernel_w, + kernel_h_, + kernel_w_); + if (channels != channels_kernel * group) + AT_ERROR( + "Input shape and kernel channels wont match: (%d vs %d).", + channels, + channels_kernel * group); + + const int height_out = + (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = + (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < height_out * width_out) { + // Resize plane and fill with ones... + ones = at::ones({height_out, width_out}, input.options()); + } + + grad_input = grad_input.view({batch, channels, height, width}); + columns = at::zeros( + {channels * kernel_h * kernel_w, height_out * width_out}, + input.options()); + + grad_output = grad_output.view({grad_output.size(0), + group, + grad_output.size(1) / group, + grad_output.size(2), + grad_output.size(3)}); + + for (int b = 0; b < batch; b++) { + // divide int group + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view({group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + + for (int g = 0; g < group; g++) { + columns[g].addmm_( + weight[g].flatten(1).transpose(0, 1), + grad_output[b][g].flatten(1), + 0.0f, + 1.0f); + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + weight = weight.view({weight.size(0) * weight.size(1), + weight.size(2), + weight.size(3), + weight.size(4)}); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cuda( + columns, + input[b], + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + grad_offset[b], + grad_mask[b]); + // gradient w.r.t. input data + modulated_deformable_col2im_cuda( + columns, + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + grad_input[b]); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and + // group + modulated_deformable_im2col_cuda( + input[b], + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + columns); + + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + grad_weight = grad_weight.view({group, + grad_weight.size(0) / group, + grad_weight.size(1), + grad_weight.size(2), + grad_weight.size(3)}); + if (with_bias) + grad_bias = grad_bias.view({group, grad_bias.size(0) / group}); + + for (int g = 0; g < group; g++) { + grad_weight[g] = + grad_weight[g] + .flatten(1) + .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1)) + .view_as(grad_weight[g]); + if (with_bias) { + grad_bias[g] = + grad_bias[g] + .view({-1, 1}) + .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1})) + .view(-1); + } + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1), + grad_weight.size(2), + grad_weight.size(3), + grad_weight.size(4)}); + if (with_bias) + grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)}); + } + grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1), + grad_output.size(2), + grad_output.size(3), + grad_output.size(4)}); +} diff --git a/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.cu b/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.cu new file mode 100644 index 0000000000..ff436bee6a --- /dev/null +++ b/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.cu @@ -0,0 +1,1268 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +// modified from +// https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu +// Original license: Apache 2.0 +// clang-format off + +// modify from +// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu + +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer ***************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + *AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + *IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE + *FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + *DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + *SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + *CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + *OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + *OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ********************* + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.cuh + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1703.06211 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng + */ + +#include +#include +#include +#include +#include +#include + +using namespace at; + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +const int kMaxGridNum = 65535; + +inline int GET_BLOCKS(const int N) { + return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS); +} + +template +__device__ scalar_t deformable_im2col_bilinear( + const scalar_t* bottom_data, + const int data_width, + const int height, + const int width, + scalar_t h, + scalar_t w) { + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + scalar_t lh = h - h_low; + scalar_t lw = w - w_low; + scalar_t hh = 1 - lh, hw = 1 - lw; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ scalar_t get_gradient_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int h, + const int w, + const int height, + const int width) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ scalar_t get_coordinate_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int height, + const int width, + const scalar_t* im_data, + const int data_width, + const int bp_dir) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + + if (bp_dir == 0) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } else if (bp_dir == 1) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void deformable_im2col_gpu_kernel( + const int n, + const scalar_t* data_im, + const scalar_t* data_offset, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int num_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* data_col) { + CUDA_KERNEL_LOOP(index, n) { + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + const int b_col = (index / width_col / height_col) % batch_size; + const int c_im = (index / width_col / height_col) / batch_size; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + scalar_t* data_col_ptr = data_col + + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + // const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * + // height + h_in) * width + w_in; + const scalar_t* data_im_ptr = + data_im + (b_col * num_channels + c_im) * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) { + for (int j = 0; j < kernel_w; ++j) { + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + + w_col; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + scalar_t val = static_cast(0); + const scalar_t h_im = h_in + i * dilation_h + offset_h; + const scalar_t w_im = w_in + j * dilation_w + offset_w; + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { + // const scalar_t map_h = i * dilation_h + offset_h; + // const scalar_t map_w = j * dilation_w + offset_w; + // const int cur_height = height - h_in; + // const int cur_width = width - w_in; + // val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, + // cur_width, map_h, map_w); + val = deformable_im2col_bilinear( + data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val; + data_col_ptr += batch_size * height_col * width_col; + } + } + } +} + +void deformable_im2col( + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor data_col) { + // num_axes should be smaller than block size + // todo: check parallel_imgs is correctly passed in + int height_col = + (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = + (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = channels * height_col * width_col * parallel_imgs; + int channel_per_deformable_group = channels / deformable_group; + + at::cuda::CUDAGuard device_guard(data_im.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_im.type(), "deformable_im2col_gpu", ([&] { + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + scalar_t* data_col_ = data_col.data_ptr(); + + deformable_im2col_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_im_, + data_offset_, + height, + width, + ksize_h, + ksize_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + parallel_imgs, + channels, + deformable_group, + height_col, + width_col, + data_col_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in deformable_im2col: %s\n", cudaGetErrorString(err)); + } +} + +template +__global__ void deformable_col2im_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_offset, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_im) { + CUDA_KERNEL_LOOP(index, n) { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = + (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = + index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; + const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const scalar_t cur_top_grad = data_col[index]; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) { + for (int dx = -2; dx <= 2; dx++) { + if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && + cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) { + int cur_bottom_grad_pos = + ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + scalar_t weight = get_gradient_weight( + cur_inv_h_data, + cur_inv_w_data, + cur_h + dy, + cur_w + dx, + height, + width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +void deformable_col2im( + const at::Tensor data_col, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_im) { + // todo: make sure parallel_imgs is passed in correctly + int height_col = + (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = + (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = + channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; + int channel_per_deformable_group = channels / deformable_group; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.type(), "deformable_col2im_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + scalar_t* grad_im_ = grad_im.data_ptr(); + + deformable_col2im_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_offset_, + channels, + height, + width, + ksize_h, + ksize_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + parallel_imgs, + deformable_group, + height_col, + width_col, + grad_im_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in deformable_col2im: %s\n", cudaGetErrorString(err)); + } +} + +template +__global__ void deformable_col2im_coord_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_im, + const scalar_t* data_offset, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int offset_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_offset) { + CUDA_KERNEL_LOOP(index, n) { + scalar_t val = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const scalar_t* data_col_ptr = data_col + + deformable_group_index * channel_per_deformable_group * batch_size * + width_col * height_col; + const scalar_t* data_im_ptr = data_im + + (b * deformable_group + deformable_group_index) * + channel_per_deformable_group / kernel_h / kernel_w * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; + col_c += col_step) { + const int col_pos = + (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = + (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = + (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = + (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + + w_out); + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + scalar_t inv_h = h_in + i * dilation_h + offset_h; + scalar_t inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) { + inv_h = inv_w = -2; + } + const scalar_t weight = get_coordinate_weight( + inv_h, + inv_w, + height, + width, + data_im_ptr + cnt * height * width, + width, + bp_dir); + val += weight * data_col_ptr[col_pos]; + cnt += 1; + } + + grad_offset[index] = val; + } +} + +void deformable_col2im_coord( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_offset) { + int height_col = + (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = + (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * + deformable_group * parallel_imgs; + int channel_per_deformable_group = + channels * ksize_h * ksize_w / deformable_group; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.type(), "deformable_col2im_coord_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + scalar_t* grad_offset_ = grad_offset.data_ptr(); + + deformable_col2im_coord_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_im_, + data_offset_, + channels, + height, + width, + ksize_h, + ksize_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + parallel_imgs, + 2 * ksize_h * ksize_w * deformable_group, + deformable_group, + height_col, + width_col, + grad_offset_); + })); +} + +template +__device__ scalar_t dmcn_im2col_bilinear( + const scalar_t* bottom_data, + const int data_width, + const int height, + const int width, + scalar_t h, + scalar_t w) { + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + scalar_t lh = h - h_low; + scalar_t lw = w - w_low; + scalar_t hh = 1 - lh, hw = 1 - lw; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ scalar_t dmcn_get_gradient_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int h, + const int w, + const int height, + const int width) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ scalar_t dmcn_get_coordinate_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int height, + const int width, + const scalar_t* im_data, + const int data_width, + const int bp_dir) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + + if (bp_dir == 0) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } else if (bp_dir == 1) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void modulated_deformable_im2col_gpu_kernel( + const int n, + const scalar_t* data_im, + const scalar_t* data_offset, + const scalar_t* data_mask, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int num_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* data_col) { + CUDA_KERNEL_LOOP(index, n) { + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + const int b_col = (index / width_col / height_col) % batch_size; + const int c_im = (index / width_col / height_col) / batch_size; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + + scalar_t* data_col_ptr = data_col + + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + // const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * + // height + h_in) * width + w_in; + const scalar_t* data_im_ptr = + data_im + (b_col * num_channels + c_im) * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + + const scalar_t* data_mask_ptr = data_mask + + (b_col * deformable_group + deformable_group_index) * kernel_h * + kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) { + for (int j = 0; j < kernel_w; ++j) { + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + + w_col; + const int data_mask_hw_ptr = + ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + scalar_t val = static_cast(0); + const scalar_t h_im = h_in + i * dilation_h + offset_h; + const scalar_t w_im = w_in + j * dilation_w + offset_w; + // if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { + // const float map_h = i * dilation_h + offset_h; + // const float map_w = j * dilation_w + offset_w; + // const int cur_height = height - h_in; + // const int cur_width = width - w_in; + // val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, + // cur_width, map_h, map_w); + val = dmcn_im2col_bilinear( + data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + data_col_ptr += batch_size * height_col * width_col; + // data_col_ptr += height_col * width_col; + } + } + } +} + +template +__global__ void modulated_deformable_col2im_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_offset, + const scalar_t* data_mask, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_im) { + CUDA_KERNEL_LOOP(index, n) { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = + (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = + index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + const scalar_t* data_mask_ptr = data_mask + + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * + height_col * width_col; + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = + ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; + const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const scalar_t cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) { + for (int dx = -2; dx <= 2; dx++) { + if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && + cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) { + int cur_bottom_grad_pos = + ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + scalar_t weight = dmcn_get_gradient_weight( + cur_inv_h_data, + cur_inv_w_data, + cur_h + dy, + cur_w + dx, + height, + width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +template +__global__ void modulated_deformable_col2im_coord_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_im, + const scalar_t* data_offset, + const scalar_t* data_mask, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int offset_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_offset, + scalar_t* grad_mask) { + CUDA_KERNEL_LOOP(index, n) { + scalar_t val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const scalar_t* data_col_ptr = data_col + + deformable_group_index * channel_per_deformable_group * batch_size * + width_col * height_col; + const scalar_t* data_im_ptr = data_im + + (b * deformable_group + deformable_group_index) * + channel_per_deformable_group / kernel_h / kernel_w * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + const scalar_t* data_mask_ptr = data_mask + + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * + height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; + col_c += col_step) { + const int col_pos = + (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = + (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = + (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = + (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + + w_out); + const int data_mask_hw_ptr = + (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + scalar_t inv_h = h_in + i * dilation_h + offset_h; + scalar_t inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) { + inv_h = inv_w = -2; + } else { + mval += data_col_ptr[col_pos] * + dmcn_im2col_bilinear( + data_im_ptr + cnt * height * width, + width, + height, + width, + inv_h, + inv_w); + } + const scalar_t weight = dmcn_get_coordinate_weight( + inv_h, + inv_w, + height, + width, + data_im_ptr + cnt * height * width, + width, + bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + + // deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * + // height_col + h) * width_col + w], mask_req, mval); + grad_mask + [(((b * deformable_group + deformable_group_index) * kernel_h * + kernel_w + + offset_c / 2) * + height_col + + h) * + width_col + + w] = mval; + } +} + +void modulated_deformable_im2col_cuda( + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor data_col) { + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + + at::cuda::CUDAGuard device_guard(data_im.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_im.type(), "modulated_deformable_im2col_gpu", ([&] { + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + const scalar_t* data_mask_ = data_mask.data_ptr(); + scalar_t* data_col_ = data_col.data_ptr(); + + modulated_deformable_im2col_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_im_, + data_offset_, + data_mask_, + height_im, + width_im, + kernel_h, + kenerl_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + batch_size, + channels, + deformable_group, + height_col, + width_col, + data_col_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf( + "error in modulated_deformable_im2col_cuda: %s\n", + cudaGetErrorString(err)); + } +} + +void modulated_deformable_col2im_cuda( + const at::Tensor data_col, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_im) { + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = + channels * kernel_h * kernel_w * batch_size * height_col * width_col; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.type(), "modulated_deformable_col2im_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + const scalar_t* data_mask_ = data_mask.data_ptr(); + scalar_t* grad_im_ = grad_im.data_ptr(); + + modulated_deformable_col2im_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_offset_, + data_mask_, + channels, + height_im, + width_im, + kernel_h, + kernel_w, + pad_h, + pad_h, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + batch_size, + deformable_group, + height_col, + width_col, + grad_im_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf( + "error in modulated_deformable_col2im_cuda: %s\n", + cudaGetErrorString(err)); + } +} + +void modulated_deformable_col2im_coord_cuda( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_offset, + at::Tensor grad_mask) { + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * + kernel_w * deformable_group; + const int channel_per_deformable_group = + channels * kernel_h * kernel_w / deformable_group; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.type(), "modulated_deformable_col2im_coord_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + const scalar_t* data_mask_ = data_mask.data_ptr(); + scalar_t* grad_offset_ = grad_offset.data_ptr(); + scalar_t* grad_mask_ = grad_mask.data_ptr(); + + modulated_deformable_col2im_coord_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_im_, + data_offset_, + data_mask_, + channels, + height_im, + width_im, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + batch_size, + 2 * kernel_h * kernel_w * deformable_group, + deformable_group, + height_col, + width_col, + grad_offset_, + grad_mask_); + })); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf( + "error in modulated_deformable_col2im_coord_cuda: %s\n", + cudaGetErrorString(err)); + } +} diff --git a/detectron2/layers/csrc/nms_rotated/nms_rotated.h b/detectron2/layers/csrc/nms_rotated/nms_rotated.h new file mode 100644 index 0000000000..ebdfed5133 --- /dev/null +++ b/detectron2/layers/csrc/nms_rotated/nms_rotated.h @@ -0,0 +1,38 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#pragma once +#include + +namespace detectron2 { + +at::Tensor nms_rotated_cpu( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold); + +#ifdef WITH_CUDA +at::Tensor nms_rotated_cuda( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold); +#endif + +// Interface for Python +// inline is needed to prevent multiple function definitions when this header is +// included by different cpps +inline at::Tensor nms_rotated( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold) { + assert(dets.device().is_cuda() == scores.device().is_cuda()); + if (dets.device().is_cuda()) { +#ifdef WITH_CUDA + return nms_rotated_cuda(dets, scores, iou_threshold); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + + return nms_rotated_cpu(dets, scores, iou_threshold); +} + +} // namespace detectron2 diff --git a/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp b/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp new file mode 100644 index 0000000000..2850d4ad76 --- /dev/null +++ b/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp @@ -0,0 +1,73 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include "../box_iou_rotated/box_iou_rotated_utils.h" +#include "nms_rotated.h" + +namespace detectron2 { + +template +at::Tensor nms_rotated_cpu_kernel( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold) { + // nms_rotated_cpu_kernel is modified from torchvision's nms_cpu_kernel, + // however, the code in this function is much shorter because + // we delegate the IoU computation for rotated boxes to + // the single_box_iou_rotated function in box_iou_rotated_utils.h + AT_ASSERTM(!dets.type().is_cuda(), "dets must be a CPU tensor"); + AT_ASSERTM(!scores.type().is_cuda(), "scores must be a CPU tensor"); + AT_ASSERTM( + dets.type() == scores.type(), "dets should have the same type as scores"); + + if (dets.numel() == 0) { + return at::empty({0}, dets.options().dtype(at::kLong)); + } + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + + auto ndets = dets.size(0); + at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte)); + at::Tensor keep_t = at::zeros({ndets}, dets.options().dtype(at::kLong)); + + auto suppressed = suppressed_t.data_ptr(); + auto keep = keep_t.data_ptr(); + auto order = order_t.data_ptr(); + + int64_t num_to_keep = 0; + + for (int64_t _i = 0; _i < ndets; _i++) { + auto i = order[_i]; + if (suppressed[i] == 1) { + continue; + } + + keep[num_to_keep++] = i; + + for (int64_t _j = _i + 1; _j < ndets; _j++) { + auto j = order[_j]; + if (suppressed[j] == 1) { + continue; + } + + auto ovr = single_box_iou_rotated( + dets[i].data_ptr(), dets[j].data_ptr()); + if (ovr >= iou_threshold) { + suppressed[j] = 1; + } + } + } + return keep_t.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep); +} + +at::Tensor nms_rotated_cpu( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold) { + auto result = at::empty({0}, dets.options()); + + AT_DISPATCH_FLOATING_TYPES(dets.type(), "nms_rotated", [&] { + result = nms_rotated_cpu_kernel(dets, scores, iou_threshold); + }); + return result; +} + +} // namespace detectron2 diff --git a/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.cu b/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.cu new file mode 100644 index 0000000000..9f0d85c72a --- /dev/null +++ b/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.cu @@ -0,0 +1,132 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include +#include +#include +#include "../box_iou_rotated/box_iou_rotated_utils.h" + +namespace detectron2 { + +int const threadsPerBlock = sizeof(unsigned long long) * 8; + +template +__global__ void nms_rotated_cuda_kernel( + const int n_boxes, + const float iou_threshold, + const T* dev_boxes, + unsigned long long* dev_mask) { + // nms_rotated_cuda_kernel is modified from torchvision's nms_cuda_kernel + + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = + min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); + const int col_size = + min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); + + // Compared to nms_cuda_kernel, where each box is represented with 4 values + // (x1, y1, x2, y2), each rotated box is represented with 5 values + // (x_center, y_center, width, height, angle_degrees) here. + __shared__ T block_boxes[threadsPerBlock * 5]; + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const T* cur_box = dev_boxes + cur_box_idx * 5; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + // Instead of devIoU used by original horizontal nms, here + // we use the single_box_iou_rotated function from box_iou_rotated_utils.h + if (single_box_iou_rotated(cur_box, block_boxes + i * 5) > + iou_threshold) { + t |= 1ULL << i; + } + } + const int col_blocks = at::cuda::ATenCeilDiv(n_boxes, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +at::Tensor nms_rotated_cuda( + const at::Tensor& dets, + const at::Tensor& scores, + float iou_threshold) { + // using scalar_t = float; + AT_ASSERTM(dets.type().is_cuda(), "dets must be a CUDA tensor"); + AT_ASSERTM(scores.type().is_cuda(), "scores must be a CUDA tensor"); + at::cuda::CUDAGuard device_guard(dets.device()); + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + auto dets_sorted = dets.index_select(0, order_t); + + int dets_num = dets.size(0); + + const int col_blocks = at::cuda::ATenCeilDiv(dets_num, threadsPerBlock); + + at::Tensor mask = + at::empty({dets_num * col_blocks}, dets.options().dtype(at::kLong)); + + dim3 blocks(col_blocks, col_blocks); + dim3 threads(threadsPerBlock); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + dets_sorted.type(), "nms_rotated_kernel_cuda", [&] { + nms_rotated_cuda_kernel<<>>( + dets_num, + iou_threshold, + dets_sorted.data(), + (unsigned long long*)mask.data()); + }); + + at::Tensor mask_cpu = mask.to(at::kCPU); + unsigned long long* mask_host = (unsigned long long*)mask_cpu.data(); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + at::Tensor keep = + at::empty({dets_num}, dets.options().dtype(at::kLong).device(at::kCPU)); + int64_t* keep_out = keep.data(); + + int num_to_keep = 0; + for (int i = 0; i < dets_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long* p = mask_host + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + + AT_CUDA_CHECK(cudaGetLastError()); + return order_t.index( + {keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep) + .to(order_t.device(), keep.scalar_type())}); +} + +} // namespace detectron2 diff --git a/detectron2/layers/csrc/vision.cpp b/detectron2/layers/csrc/vision.cpp new file mode 100644 index 0000000000..79b474a5ef --- /dev/null +++ b/detectron2/layers/csrc/vision.cpp @@ -0,0 +1,71 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include "ROIAlign/ROIAlign.h" +#include "ROIAlignRotated/ROIAlignRotated.h" +#include "box_iou_rotated/box_iou_rotated.h" +#include "deformable/deform_conv.h" +#include "nms_rotated/nms_rotated.h" + +namespace detectron2 { + +// similar to +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp +std::string get_compiler_version() { + std::ostringstream ss; +#if defined(__GNUC__) +#ifndef __clang__ + { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; } +#endif +#endif + +#if defined(__clang_major__) + { + ss << "clang " << __clang_major__ << "." << __clang_minor__ << "." + << __clang_patchlevel__; + } +#endif + +#if defined(_MSC_VER) + { ss << "MSVC " << _MSC_FULL_VER; } +#endif + return ss.str(); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("get_compiler_version", &get_compiler_version, "get_compiler_version"); + + m.def("box_iou_rotated", &box_iou_rotated, "IoU for rotated boxes"); + + m.def("deform_conv_forward", &deform_conv_forward, "deform_conv_forward"); + m.def( + "deform_conv_backward_input", + &deform_conv_backward_input, + "deform_conv_backward_input"); + m.def( + "deform_conv_backward_filter", + &deform_conv_backward_filter, + "deform_conv_backward_filter"); + m.def( + "modulated_deform_conv_forward", + &modulated_deform_conv_forward, + "modulated_deform_conv_forward"); + m.def( + "modulated_deform_conv_backward", + &modulated_deform_conv_backward, + "modulated_deform_conv_backward"); + + m.def("nms_rotated", &nms_rotated, "NMS for rotated boxes"); + + m.def("roi_align_forward", &ROIAlign_forward, "ROIAlign_forward"); + m.def("roi_align_backward", &ROIAlign_backward, "ROIAlign_backward"); + + m.def( + "roi_align_rotated_forward", + &ROIAlignRotated_forward, + "Forward pass for Rotated ROI-Align Operator"); + m.def( + "roi_align_rotated_backward", + &ROIAlignRotated_backward, + "Backward pass for Rotated ROI-Align Operator"); +} + +} // namespace detectron2 diff --git a/detectron2/layers/deform_conv.py b/detectron2/layers/deform_conv.py new file mode 100644 index 0000000000..2a8f26cb28 --- /dev/null +++ b/detectron2/layers/deform_conv.py @@ -0,0 +1,494 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import math +from functools import lru_cache +import torch +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +from detectron2 import _C + +from .wrappers import _NewEmptyTensorOp + + +class _DeformConv(Function): + @staticmethod + def forward( + ctx, + input, + offset, + weight, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + im2col_step=64, + ): + if input is not None and input.dim() != 4: + raise ValueError( + "Expected 4D tensor as input, got {}D tensor instead.".format(input.dim()) + ) + ctx.stride = _pair(stride) + ctx.padding = _pair(padding) + ctx.dilation = _pair(dilation) + ctx.groups = groups + ctx.deformable_groups = deformable_groups + ctx.im2col_step = im2col_step + + ctx.save_for_backward(input, offset, weight) + + output = input.new_empty( + _DeformConv._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride) + ) + + ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones + + if not input.is_cuda: + raise NotImplementedError + else: + cur_im2col_step = _DeformConv._cal_im2col_step(input.shape[0], ctx.im2col_step) + assert (input.shape[0] % cur_im2col_step) == 0, "im2col step must divide batchsize" + + _C.deform_conv_forward( + input, + weight, + offset, + output, + ctx.bufs_[0], + ctx.bufs_[1], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + cur_im2col_step, + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, offset, weight = ctx.saved_tensors + + grad_input = grad_offset = grad_weight = None + + if not grad_output.is_cuda: + raise NotImplementedError + else: + cur_im2col_step = _DeformConv._cal_im2col_step(input.shape[0], ctx.im2col_step) + assert (input.shape[0] % cur_im2col_step) == 0, "im2col step must divide batchsize" + + if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + _C.deform_conv_backward_input( + input, + offset, + grad_output, + grad_input, + grad_offset, + weight, + ctx.bufs_[0], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + cur_im2col_step, + ) + + if ctx.needs_input_grad[2]: + grad_weight = torch.zeros_like(weight) + _C.deform_conv_backward_filter( + input, + offset, + grad_output, + grad_weight, + ctx.bufs_[0], + ctx.bufs_[1], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + 1, + cur_im2col_step, + ) + + return grad_input, grad_offset, grad_weight, None, None, None, None, None, None + + @staticmethod + def _output_size(input, weight, padding, dilation, stride): + channels = weight.size(0) + output_size = (input.size(0), channels) + for d in range(input.dim() - 2): + in_size = input.size(d + 2) + pad = padding[d] + kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 + stride_ = stride[d] + output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1,) + if not all(map(lambda s: s > 0, output_size)): + raise ValueError( + "convolution input is too small (output would be {})".format( + "x".join(map(str, output_size)) + ) + ) + return output_size + + @staticmethod + @lru_cache(maxsize=128) + def _cal_im2col_step(input_size, default_size): + """ + Calculate proper im2col step size, which should be divisible by input_size and not larger + than prefer_size. Meanwhile the step size should be as large as possible to be more + efficient. So we choose the largest one among all divisors of input_size which are smaller + than prefer_size. + :param input_size: input batch size . + :param default_size: default preferred im2col step size. + :return: the largest proper step size. + """ + if input_size <= default_size: + return input_size + best_step = 1 + for step in range(2, min(int(math.sqrt(input_size)) + 1, default_size)): + if input_size % step == 0: + if input_size // step <= default_size: + return input_size // step + best_step = step + + return best_step + + +class _ModulatedDeformConv(Function): + @staticmethod + def forward( + ctx, + input, + offset, + mask, + weight, + bias=None, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + ): + ctx.stride = stride + ctx.padding = padding + ctx.dilation = dilation + ctx.groups = groups + ctx.deformable_groups = deformable_groups + ctx.with_bias = bias is not None + if not ctx.with_bias: + bias = input.new_empty(1) # fake tensor + if not input.is_cuda: + raise NotImplementedError + if ( + weight.requires_grad + or mask.requires_grad + or offset.requires_grad + or input.requires_grad + ): + ctx.save_for_backward(input, offset, mask, weight, bias) + output = input.new_empty(_ModulatedDeformConv._infer_shape(ctx, input, weight)) + ctx._bufs = [input.new_empty(0), input.new_empty(0)] + _C.modulated_deform_conv_forward( + input, + weight, + bias, + ctx._bufs[0], + offset, + mask, + output, + ctx._bufs[1], + weight.shape[2], + weight.shape[3], + ctx.stride, + ctx.stride, + ctx.padding, + ctx.padding, + ctx.dilation, + ctx.dilation, + ctx.groups, + ctx.deformable_groups, + ctx.with_bias, + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + if not grad_output.is_cuda: + raise NotImplementedError + input, offset, mask, weight, bias = ctx.saved_tensors + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + grad_mask = torch.zeros_like(mask) + grad_weight = torch.zeros_like(weight) + grad_bias = torch.zeros_like(bias) + _C.modulated_deform_conv_backward( + input, + weight, + bias, + ctx._bufs[0], + offset, + mask, + ctx._bufs[1], + grad_input, + grad_weight, + grad_bias, + grad_offset, + grad_mask, + grad_output, + weight.shape[2], + weight.shape[3], + ctx.stride, + ctx.stride, + ctx.padding, + ctx.padding, + ctx.dilation, + ctx.dilation, + ctx.groups, + ctx.deformable_groups, + ctx.with_bias, + ) + if not ctx.with_bias: + grad_bias = None + + return ( + grad_input, + grad_offset, + grad_mask, + grad_weight, + grad_bias, + None, + None, + None, + None, + None, + ) + + @staticmethod + def _infer_shape(ctx, input, weight): + n = input.size(0) + channels_out = weight.size(0) + height, width = input.shape[2:4] + kernel_h, kernel_w = weight.shape[2:4] + height_out = ( + height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1) + ) // ctx.stride + 1 + width_out = ( + width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1) + ) // ctx.stride + 1 + return n, channels_out, height_out, width_out + + +deform_conv = _DeformConv.apply +modulated_deform_conv = _ModulatedDeformConv.apply + + +class DeformConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=False, + norm=None, + activation=None, + ): + """ + Deformable convolution. + + Args: + similar to `Conv2D`. + + deformable_groups (int): number of groups used in deformable convolution. + norm (nn.Module, optional): a normalization layer + activation (callable(Tensor) -> Tensor): a callable activation function + """ + super(DeformConv, self).__init__() + + assert not bias + assert in_channels % groups == 0, "in_channels {} cannot be divisible by groups {}".format( + in_channels, groups + ) + assert ( + out_channels % groups == 0 + ), "out_channels {} cannot be divisible by groups {}".format(out_channels, groups) + + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.groups = groups + self.deformable_groups = deformable_groups + self.norm = norm + self.activation = activation + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size) + ) + self.bias = None + + nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") + + def forward(self, x, offset): + if x.numel() == 0: + # When input is empty, we want to return a empty tensor with "correct" shape, + # So that the following operations will not panic + # if they check for the shape of the tensor. + # This computes the height and width of the output tensor + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // s + 1 + for i, p, di, k, s in zip( + x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride + ) + ] + output_shape = [x.shape[0], self.weight.shape[0]] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) + + x = deform_conv( + x, + offset, + self.weight, + self.stride, + self.padding, + self.dilation, + self.groups, + self.deformable_groups, + ) + if self.norm is not None: + x = self.norm(x) + if self.activation is not None: + x = self.activation(x) + return x + + def extra_repr(self): + tmpstr = "in_channels=" + str(self.in_channels) + tmpstr += ", out_channels=" + str(self.out_channels) + tmpstr += ", kernel_size=" + str(self.kernel_size) + tmpstr += ", stride=" + str(self.stride) + tmpstr += ", padding=" + str(self.padding) + tmpstr += ", dilation=" + str(self.dilation) + tmpstr += ", groups=" + str(self.groups) + tmpstr += ", deformable_groups=" + str(self.deformable_groups) + tmpstr += ", bias=False" + return tmpstr + + +class ModulatedDeformConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=True, + norm=None, + activation=None, + ): + """ + Modulated deformable convolution. + + Args: + similar to `Conv2D`. + + deformable_groups (int): number of groups used in deformable convolution. + norm (nn.Module, optional): a normalization layer + activation (callable(Tensor) -> Tensor): a callable activation function + """ + super(ModulatedDeformConv, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.deformable_groups = deformable_groups + self.with_bias = bias + self.norm = norm + self.activation = activation + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels // groups, *self.kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.bias = None + + nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") + if self.bias is not None: + nn.init.constant_(self.bias, 0) + + def forward(self, x, offset, mask): + if x.numel() == 0: + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // s + 1 + for i, p, di, k, s in zip( + x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride + ) + ] + output_shape = [x.shape[0], self.weight.shape[0]] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) + + x = modulated_deform_conv( + x, + offset, + mask, + self.weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + self.deformable_groups, + ) + if self.norm is not None: + x = self.norm(x) + if self.activation is not None: + x = self.activation(x) + return x + + def extra_repr(self): + tmpstr = "in_channels=" + str(self.in_channels) + tmpstr += ", out_channels=" + str(self.out_channels) + tmpstr += ", kernel_size=" + str(self.kernel_size) + tmpstr += ", stride=" + str(self.stride) + tmpstr += ", padding=" + str(self.padding) + tmpstr += ", dilation=" + str(self.dilation) + tmpstr += ", groups=" + str(self.groups) + tmpstr += ", deformable_groups=" + str(self.deformable_groups) + tmpstr += ", bias=" + str(self.with_bias) + return tmpstr diff --git a/detectron2/layers/mask_ops.py b/detectron2/layers/mask_ops.py new file mode 100644 index 0000000000..dfbb09e005 --- /dev/null +++ b/detectron2/layers/mask_ops.py @@ -0,0 +1,241 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import numpy as np +import torch +from PIL import Image +from torch.nn import functional as F + +__all__ = ["paste_masks_in_image"] + + +BYTES_PER_FLOAT = 4 +# TODO: This memory limit may be too much or too little. It would be better to +# determine it based on available resources. +GPU_MEM_LIMIT = 1024 ** 3 # 1 GB memory limit + + +def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True): + """ + Args: + masks: N, 1, H, W + boxes: N, 4 + img_h, img_w (int): + skip_empty (bool): only paste masks within the region that + tightly bound all boxes, and returns the results this region only. + An important optimization for CPU. + + Returns: + if skip_empty == False, a mask of shape (N, img_h, img_w) + if skip_empty == True, a mask of shape (N, h', w'), and the slice + object for the corresponding region. + """ + # On GPU, paste all masks together (up to chunk size) + # by using the entire image to sample the masks + # Compared to pasting them one by one, + # this has more operations but is faster on COCO-scale dataset. + device = masks.device + if skip_empty: + x0_int, y0_int = torch.clamp(boxes.min(dim=0).values.floor()[:2] - 1, min=0).to( + dtype=torch.int32 + ) + x1_int = torch.clamp(boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32) + y1_int = torch.clamp(boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32) + else: + x0_int, y0_int = 0, 0 + x1_int, y1_int = img_w, img_h + x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1 + + N = masks.shape[0] + + img_y = torch.arange(y0_int, y1_int, device=device, dtype=torch.float32) + 0.5 + img_x = torch.arange(x0_int, x1_int, device=device, dtype=torch.float32) + 0.5 + img_y = (img_y - y0) / (y1 - y0) * 2 - 1 + img_x = (img_x - x0) / (x1 - x0) * 2 - 1 + # img_x, img_y have shapes (N, w), (N, h) + + gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1)) + gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1)) + grid = torch.stack([gx, gy], dim=3) + + img_masks = F.grid_sample(masks.to(dtype=torch.float32), grid, align_corners=False) + + if skip_empty: + return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int)) + else: + return img_masks[:, 0], () + + +def paste_masks_in_image(masks, boxes, image_shape, threshold=0.5): + """ + Paste a set of masks that are of a fixed resolution (e.g., 28 x 28) into an image. + The location, height, and width for pasting each mask is determined by their + corresponding bounding boxes in boxes. + + Args: + masks (tensor): Tensor of shape (Bimg, Hmask, Wmask), where Bimg is the number of + detected object instances in the image and Hmask, Wmask are the mask width and mask + height of the predicted mask (e.g., Hmask = Wmask = 28). Values are in [0, 1]. + boxes (Boxes): A Boxes of length Bimg. boxes.tensor[i] and masks[i] correspond + to the same object instance. + image_shape (tuple): height, width + threshold (float): A threshold in [0, 1] for converting the (soft) masks to + binary masks. + + Returns: + img_masks (Tensor): A tensor of shape (Bimg, Himage, Wimage), where Bimg is the + number of detected object instances and Himage, Wimage are the image width + and height. img_masks[i] is a binary mask for object instance i. + """ + assert masks.shape[-1] == masks.shape[-2], "Only square mask predictions are supported" + N = len(masks) + if N == 0: + return masks.new_empty((0,) + image_shape, dtype=torch.uint8) + + boxes = boxes.tensor + device = boxes.device + assert len(boxes) == N, boxes.shape + + img_h, img_w = image_shape + + # The actual implementation split the input into chunks, + # and paste them chunk by chunk. + if device.type == "cpu": + # CPU is most efficient when they are pasted one by one with skip_empty=True + # so that it performs minimal number of operatins. + num_chunks = N + else: + # GPU benefits from parallelism for larger chunks, but may have memory issue + num_chunks = int(np.ceil(N * img_h * img_w * BYTES_PER_FLOAT / GPU_MEM_LIMIT)) + assert ( + num_chunks <= N + ), "Default GPU_MEM_LIMIT in mask_ops.py is too small; try increasing it" + chunks = torch.chunk(torch.arange(N, device=device), num_chunks) + + img_masks = torch.zeros( + N, img_h, img_w, device=device, dtype=torch.bool if threshold >= 0 else torch.uint8 + ) + for inds in chunks: + masks_chunk, spatial_inds = _do_paste_mask( + masks[inds, None, :, :], boxes[inds], img_h, img_w, skip_empty=device.type == "cpu" + ) + + if threshold >= 0: + masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool) + else: + # for visualization and debugging + masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8) + + img_masks[(inds,) + spatial_inds] = masks_chunk + return img_masks + + +# The below are the original paste function (from Detectron1) which has +# larger quantization error. +# It is faster on CPU, while the aligned one is faster on GPU thanks to grid_sample. + + +def paste_mask_in_image_old(mask, box, img_h, img_w, threshold): + """ + Paste a single mask in an image. + This is a per-box implementation of :func:`paste_masks_in_image`. + This function has larger quantization error due to incorrect pixel + modeling and is not used any more. + + Args: + mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single + object instance. Values are in [0, 1]. + box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners + of the object instance. + img_h, img_w (int): Image height and width. + threshold (float): Mask binarization threshold in [0, 1]. + + Returns: + im_mask (Tensor): + The resized and binarized object mask pasted into the original + image plane (a tensor of shape (img_h, img_w)). + """ + # Conversion from continuous box coordinates to discrete pixel coordinates + # via truncation (cast to int32). This determines which pixels to paste the + # mask onto. + box = box.to(dtype=torch.int32) # Continuous to discrete coordinate conversion + # An example (1D) box with continuous coordinates (x0=0.7, x1=4.3) will map to + # a discrete coordinates (x0=0, x1=4). Note that box is mapped to 5 = x1 - x0 + 1 + # pixels (not x1 - x0 pixels). + samples_w = box[2] - box[0] + 1 # Number of pixel samples, *not* geometric width + samples_h = box[3] - box[1] + 1 # Number of pixel samples, *not* geometric height + + # Resample the mask from it's original grid to the new samples_w x samples_h grid + mask = Image.fromarray(mask.cpu().numpy()) + mask = mask.resize((samples_w, samples_h), resample=Image.BILINEAR) + mask = np.array(mask, copy=False) + + if threshold >= 0: + mask = np.array(mask > threshold, dtype=np.uint8) + mask = torch.from_numpy(mask) + else: + # for visualization and debugging, we also + # allow it to return an unmodified mask + mask = torch.from_numpy(mask * 255).to(torch.uint8) + + im_mask = torch.zeros((img_h, img_w), dtype=torch.uint8) + x_0 = max(box[0], 0) + x_1 = min(box[2] + 1, img_w) + y_0 = max(box[1], 0) + y_1 = min(box[3] + 1, img_h) + + im_mask[y_0:y_1, x_0:x_1] = mask[ + (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0]) + ] + return im_mask + + +# Our pixel modeling requires extrapolation for any continuous +# coordinate < 0.5 or > length - 0.5. When sampling pixels on the masks, +# we would like this extrapolation to be an interpolation between boundary values and zero, +# instead of using absolute zero or boundary values. +# Therefore `paste_mask_in_image_old` is often used with zero padding around the masks like this: +# masks, scale = pad_masks(masks[:, 0, :, :], 1) +# boxes = scale_boxes(boxes.tensor, scale) + + +def pad_masks(masks, padding): + """ + Args: + masks (tensor): A tensor of shape (B, M, M) representing B masks. + padding (int): Number of cells to pad on all sides. + + Returns: + The padded masks and the scale factor of the padding size / original size. + """ + B = masks.shape[0] + M = masks.shape[-1] + pad2 = 2 * padding + scale = float(M + pad2) / M + padded_masks = masks.new_zeros((B, M + pad2, M + pad2)) + padded_masks[:, padding:-padding, padding:-padding] = masks + return padded_masks, scale + + +def scale_boxes(boxes, scale): + """ + Args: + boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4 + coords representing the corners x0, y0, x1, y1, + scale (float): The box scaling factor. + + Returns: + Scaled boxes. + """ + w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5 + h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5 + x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5 + y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5 + + w_half *= scale + h_half *= scale + + scaled_boxes = torch.zeros_like(boxes) + scaled_boxes[:, 0] = x_c - w_half + scaled_boxes[:, 2] = x_c + w_half + scaled_boxes[:, 1] = y_c - h_half + scaled_boxes[:, 3] = y_c + h_half + return scaled_boxes diff --git a/detectron2/layers/nms.py b/detectron2/layers/nms.py new file mode 100644 index 0000000000..e29435e77b --- /dev/null +++ b/detectron2/layers/nms.py @@ -0,0 +1,146 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import torch +from torchvision.ops import boxes as box_ops +from torchvision.ops import nms # BC-compat + + +def batched_nms(boxes, scores, idxs, iou_threshold): + """ + Same as torchvision.ops.boxes.batched_nms, but safer. + """ + assert boxes.shape[-1] == 4 + # TODO may need better strategy. + # Investigate after having a fully-cuda NMS op. + if len(boxes) < 40000: + return box_ops.batched_nms(boxes, scores, idxs, iou_threshold) + + result_mask = scores.new_zeros(scores.size(), dtype=torch.bool) + for id in torch.unique(idxs).cpu().tolist(): + mask = (idxs == id).nonzero().view(-1) + keep = nms(boxes[mask], scores[mask], iou_threshold) + result_mask[mask[keep]] = True + keep = result_mask.nonzero().view(-1) + keep = keep[scores[keep].argsort(descending=True)] + return keep + + +# Note: this function (nms_rotated) might be moved into +# torchvision/ops/boxes.py in the future +def nms_rotated(boxes, scores, iou_threshold): + """ + Performs non-maximum suppression (NMS) on the rotated boxes according + to their intersection-over-union (IoU). + + Rotated NMS iteratively removes lower scoring rotated boxes which have an + IoU greater than iou_threshold with another (higher scoring) rotated box. + + Note that RotatedBox (5, 3, 4, 2, -90) covers exactly the same region as + RotatedBox (5, 3, 4, 2, 90) does, and their IoU will be 1. However, they + can be representing completely different objects in certain tasks, e.g., OCR. + + As for the question of whether rotated-NMS should treat them as faraway boxes + even though their IOU is 1, it depends on the application and/or ground truth annotation. + + As an extreme example, consider a single character v and the square box around it. + + If the angle is 0 degree, the object (text) would be read as 'v'; + + If the angle is 90 degrees, the object (text) would become '>'; + + If the angle is 180 degrees, the object (text) would become '^'; + + If the angle is 270/-90 degrees, the object (text) would become '<' + + All of these cases have IoU of 1 to each other, and rotated NMS that only + uses IoU as criterion would only keep one of them with the highest score - + which, practically, still makes sense in most cases because typically + only one of theses orientations is the correct one. Also, it does not matter + as much if the box is only used to classify the object (instead of transcribing + them with a sequential OCR recognition model) later. + + On the other hand, when we use IoU to filter proposals that are close to the + ground truth during training, we should definitely take the angle into account if + we know the ground truth is labeled with the strictly correct orientation (as in, + upside-down words are annotated with -180 degrees even though they can be covered + with a 0/90/-90 degree box, etc.) + + The way the original dataset is annotated also matters. For example, if the dataset + is a 4-point polygon dataset that does not enforce ordering of vertices/orientation, + we can estimate a minimum rotated bounding box to this polygon, but there's no way + we can tell the correct angle with 100% confidence (as shown above, there could be 4 different + rotated boxes, with angles differed by 90 degrees to each other, covering the exactly + same region). In that case we have to just use IoU to determine the box + proximity (as many detection benchmarks (even for text) do) unless there're other + assumptions we can make (like width is always larger than height, or the object is not + rotated by more than 90 degrees CCW/CW, etc.) + + In summary, not considering angles in rotated NMS seems to be a good option for now, + but we should be aware of its implications. + + Args: + boxes (Tensor[N, 5]): Rotated boxes to perform NMS on. They are expected to be in + (x_center, y_center, width, height, angle_degrees) format. + scores (Tensor[N]): Scores for each one of the rotated boxes + iou_threshold (float): Discards all overlapping rotated boxes with IoU < iou_threshold + + Returns: + keep (Tensor): int64 tensor with the indices of the elements that have been kept + by Rotated NMS, sorted in decreasing order of scores + """ + from detectron2 import _C + + return _C.nms_rotated(boxes, scores, iou_threshold) + + +# Note: this function (batched_nms_rotated) might be moved into +# torchvision/ops/boxes.py in the future +def batched_nms_rotated(boxes, scores, idxs, iou_threshold): + """ + Performs non-maximum suppression in a batched fashion. + + Each index value correspond to a category, and NMS + will not be applied between elements of different categories. + + Args: + boxes (Tensor[N, 5]): + boxes where NMS will be performed. They + are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format + scores (Tensor[N]): + scores for each one of the boxes + idxs (Tensor[N]): + indices of the categories for each one of the boxes. + iou_threshold (float): + discards all overlapping boxes + with IoU < iou_threshold + + Returns: + Tensor: + int64 tensor with the indices of the elements that have been kept + by NMS, sorted in decreasing order of scores + """ + assert boxes.shape[-1] == 5 + + if boxes.numel() == 0: + return torch.empty((0,), dtype=torch.int64, device=boxes.device) + # Strategy: in order to perform NMS independently per class, + # we add an offset to all the boxes. The offset is dependent + # only on the class idx, and is large enough so that boxes + # from different classes do not overlap + + # Note that batched_nms in torchvision/ops/boxes.py only uses max_coordinate, + # which won't handle negative coordinates correctly. + # Here by using min_coordinate we can make sure the negative coordinates are + # correctly handled. + max_coordinate = ( + torch.max(boxes[:, 0], boxes[:, 1]) + torch.max(boxes[:, 2], boxes[:, 3]) / 2 + ).max() + min_coordinate = ( + torch.min(boxes[:, 0], boxes[:, 1]) - torch.min(boxes[:, 2], boxes[:, 3]) / 2 + ).min() + offsets = idxs.to(boxes) * (max_coordinate - min_coordinate + 1) + boxes_for_nms = boxes.clone() # avoid modifying the original values in boxes + boxes_for_nms[:, :2] += offsets[:, None] + keep = nms_rotated(boxes_for_nms, scores, iou_threshold) + return keep diff --git a/detectron2/layers/roi_align.py b/detectron2/layers/roi_align.py new file mode 100644 index 0000000000..328bbab2f7 --- /dev/null +++ b/detectron2/layers/roi_align.py @@ -0,0 +1,105 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +from detectron2 import _C + + +class _ROIAlign(Function): + @staticmethod + def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio, aligned): + ctx.save_for_backward(roi) + ctx.output_size = _pair(output_size) + ctx.spatial_scale = spatial_scale + ctx.sampling_ratio = sampling_ratio + ctx.input_shape = input.size() + ctx.aligned = aligned + output = _C.roi_align_forward( + input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + rois, = ctx.saved_tensors + output_size = ctx.output_size + spatial_scale = ctx.spatial_scale + sampling_ratio = ctx.sampling_ratio + bs, ch, h, w = ctx.input_shape + grad_input = _C.roi_align_backward( + grad_output, + rois, + spatial_scale, + output_size[0], + output_size[1], + bs, + ch, + h, + w, + sampling_ratio, + ctx.aligned, + ) + return grad_input, None, None, None, None, None + + +roi_align = _ROIAlign.apply + + +class ROIAlign(nn.Module): + def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True): + """ + Args: + output_size (tuple): h, w + spatial_scale (float): scale the input boxes by this number + sampling_ratio (int): number of inputs samples to take for each output + sample. 0 to take samples densely. + aligned (bool): if False, use the legacy implementation in + Detectron. If True, align the results more perfectly. + + Note: + The meaning of aligned=True: + + Given a continuous coordinate c, its two neighboring pixel indices (in our + pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example, + c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled + from the underlying signal at continuous coordinates 0.5 and 1.5). But the original + roi_align (aligned=False) does not subtract the 0.5 when computing neighboring + pixel indices and therefore it uses pixels with a slightly incorrect alignment + (relative to our pixel model) when performing bilinear interpolation. + + With `aligned=True`, + we first appropriately scale the ROI and then shift it by -0.5 + prior to calling roi_align. This produces the correct neighbors; see + detectron2/tests/test_roi_align.py for verification. + + The difference does not make a difference to the model's performance if + ROIAlign is used together with conv layers. + """ + super(ROIAlign, self).__init__() + self.output_size = output_size + self.spatial_scale = spatial_scale + self.sampling_ratio = sampling_ratio + self.aligned = aligned + + def forward(self, input, rois): + """ + Args: + input: NCHW images + rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy. + """ + assert rois.dim() == 2 and rois.size(1) == 5 + return roi_align( + input, rois, self.output_size, self.spatial_scale, self.sampling_ratio, self.aligned + ) + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "output_size=" + str(self.output_size) + tmpstr += ", spatial_scale=" + str(self.spatial_scale) + tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) + tmpstr += ", aligned=" + str(self.aligned) + tmpstr += ")" + return tmpstr diff --git a/detectron2/layers/roi_align_rotated.py b/detectron2/layers/roi_align_rotated.py new file mode 100644 index 0000000000..57381a95c1 --- /dev/null +++ b/detectron2/layers/roi_align_rotated.py @@ -0,0 +1,88 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +from detectron2 import _C + + +class _ROIAlignRotated(Function): + @staticmethod + def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio): + ctx.save_for_backward(roi) + ctx.output_size = _pair(output_size) + ctx.spatial_scale = spatial_scale + ctx.sampling_ratio = sampling_ratio + ctx.input_shape = input.size() + output = _C.roi_align_rotated_forward( + input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + rois, = ctx.saved_tensors + output_size = ctx.output_size + spatial_scale = ctx.spatial_scale + sampling_ratio = ctx.sampling_ratio + bs, ch, h, w = ctx.input_shape + grad_input = _C.roi_align_rotated_backward( + grad_output, + rois, + spatial_scale, + output_size[0], + output_size[1], + bs, + ch, + h, + w, + sampling_ratio, + ) + return grad_input, None, None, None, None, None + + +roi_align_rotated = _ROIAlignRotated.apply + + +class ROIAlignRotated(nn.Module): + def __init__(self, output_size, spatial_scale, sampling_ratio): + """ + Args: + output_size (tuple): h, w + spatial_scale (float): scale the input boxes by this number + sampling_ratio (int): number of inputs samples to take for each output + sample. 0 to take samples densely. + + Note: + ROIAlignRotated supports continuous coordinate by default: + Given a continuous coordinate c, its two neighboring pixel indices (in our + pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example, + c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled + from the underlying signal at continuous coordinates 0.5 and 1.5). + """ + super(ROIAlignRotated, self).__init__() + self.output_size = output_size + self.spatial_scale = spatial_scale + self.sampling_ratio = sampling_ratio + + def forward(self, input, rois): + """ + Args: + input: NCHW images + rois: Bx6 boxes. First column is the index into N. + The other 5 columns are (x_ctr, y_ctr, width, height, angle_degrees). + """ + assert rois.dim() == 2 and rois.size(1) == 6 + return roi_align_rotated( + input, rois, self.output_size, self.spatial_scale, self.sampling_ratio + ) + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "output_size=" + str(self.output_size) + tmpstr += ", spatial_scale=" + str(self.spatial_scale) + tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) + tmpstr += ")" + return tmpstr diff --git a/detectron2/layers/rotated_boxes.py b/detectron2/layers/rotated_boxes.py new file mode 100644 index 0000000000..2b212c9664 --- /dev/null +++ b/detectron2/layers/rotated_boxes.py @@ -0,0 +1,24 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from __future__ import absolute_import, division, print_function, unicode_literals + +# import torch +from detectron2 import _C + + +def pairwise_iou_rotated(boxes1, boxes2): + """ + Return intersection-over-union (Jaccard index) of boxes. + + Both sets of boxes are expected to be in + (x_center, y_center, width, height, angle) format. + + Arguments: + boxes1 (Tensor[N, 5]) + boxes2 (Tensor[M, 5]) + + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + return _C.box_iou_rotated(boxes1, boxes2) diff --git a/detectron2/layers/shape_spec.py b/detectron2/layers/shape_spec.py new file mode 100644 index 0000000000..ed7f0d0826 --- /dev/null +++ b/detectron2/layers/shape_spec.py @@ -0,0 +1,20 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from collections import namedtuple + + +class ShapeSpec(namedtuple("_ShapeSpec", ["channels", "height", "width", "stride"])): + """ + A simple structure that contains basic shape specification about a tensor. + It is often used as the auxiliary inputs/outputs of models, + to obtain the shape inference ability among pytorch modules. + + Attributes: + channels: + height: + width: + stride: + """ + + def __new__(cls, *, channels=None, height=None, width=None, stride=None): + return super().__new__(cls, channels, height, width, stride) diff --git a/detectron2/layers/wrappers.py b/detectron2/layers/wrappers.py new file mode 100644 index 0000000000..bc208fdff0 --- /dev/null +++ b/detectron2/layers/wrappers.py @@ -0,0 +1,154 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Wrappers around on some nn functions, mainly to support empty tensors. + +Ideally, add support directly in PyTorch to empty tensors in those functions. + +These can be removed once https://github.com/pytorch/pytorch/issues/12013 +is implemented +""" + +import math +import torch +from torch.nn.modules.utils import _ntuple + + +def cat(tensors, dim=0): + """ + Efficient version of torch.cat that avoids a copy if there is only a single element in a list + """ + assert isinstance(tensors, (list, tuple)) + if len(tensors) == 1: + return tensors[0] + return torch.cat(tensors, dim) + + +class _NewEmptyTensorOp(torch.autograd.Function): + @staticmethod + def forward(ctx, x, new_shape): + ctx.shape = x.shape + return x.new_empty(new_shape) + + @staticmethod + def backward(ctx, grad): + shape = ctx.shape + return _NewEmptyTensorOp.apply(grad, shape), None + + +class Conv2d(torch.nn.Conv2d): + def __init__(self, *args, **kwargs): + """ + Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`: + + norm (nn.Module, optional): a normalization layer + activation (callable(Tensor) -> Tensor): a callable activation function + + It assumes that norm layer is used before activation. + """ + norm = kwargs.pop("norm", None) + activation = kwargs.pop("activation", None) + super().__init__(*args, **kwargs) + + self.norm = norm + self.activation = activation + + def forward(self, x): + if x.numel() == 0: + # When input is empty, we want to return a empty tensor with "correct" shape, + # So that the following operations will not panic + # if they check for the shape of the tensor. + # This computes the height and width of the output tensor + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // s + 1 + for i, p, di, k, s in zip( + x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride + ) + ] + output_shape = [x.shape[0], self.weight.shape[0]] + output_shape + empty = _NewEmptyTensorOp.apply(x, output_shape) + if self.training: + # https://github.com/pytorch/pytorch/issues/12013 + assert not isinstance( + self.norm, torch.nn.SyncBatchNorm + ), "SyncBatchNorm does not support empty inputs!" + + # This is to make DDP happy. + # DDP expects all workers to have gradient w.r.t the same set of parameters. + _dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 + return empty + _dummy + else: + return empty + + x = super().forward(x) + if self.norm is not None: + x = self.norm(x) + if self.activation is not None: + x = self.activation(x) + return x + + +class ConvTranspose2d(torch.nn.ConvTranspose2d): + def forward(self, x): + if x.numel() > 0: + return super(ConvTranspose2d, self).forward(x) + # get output shape + + output_shape = [ + (i - 1) * d - 2 * p + (di * (k - 1) + 1) + op + for i, p, di, k, d, op in zip( + x.shape[-2:], + self.padding, + self.dilation, + self.kernel_size, + self.stride, + self.output_padding, + ) + ] + output_shape = [x.shape[0], self.bias.shape[0]] + output_shape + # This is to make DDP happy. + # DDP expects all workers to have gradient w.r.t the same set of parameters. + _dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 + return _NewEmptyTensorOp.apply(x, output_shape) + _dummy + + +class BatchNorm2d(torch.nn.BatchNorm2d): + def forward(self, x): + if x.numel() > 0: + return super(BatchNorm2d, self).forward(x) + # get output shape + output_shape = x.shape + return _NewEmptyTensorOp.apply(x, output_shape) + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + if input.numel() > 0: + return torch.nn.functional.interpolate( + input, size, scale_factor, mode, align_corners=align_corners + ) + + def _check_size_scale_factor(dim): + if size is None and scale_factor is None: + raise ValueError("either size or scale_factor should be defined") + if size is not None and scale_factor is not None: + raise ValueError("only one of size or scale_factor should be defined") + if ( + scale_factor is not None + and isinstance(scale_factor, tuple) + and len(scale_factor) != dim + ): + raise ValueError( + "scale_factor shape must match input shape. " + "Input is {}D, scale_factor size is {}".format(dim, len(scale_factor)) + ) + + def _output_size(dim): + _check_size_scale_factor(dim) + if size is not None: + return size + scale_factors = _ntuple(dim)(scale_factor) + # math.floor might return float in py2.7 + return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)] + + output_shape = tuple(_output_size(2)) + output_shape = input.shape[:-2] + output_shape + return _NewEmptyTensorOp.apply(input, output_shape) diff --git a/detectron2/modeling/__init__.py b/detectron2/modeling/__init__.py new file mode 100644 index 0000000000..4202d6a950 --- /dev/null +++ b/detectron2/modeling/__init__.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import torch + +from detectron2.layers import ShapeSpec + +from .anchor_generator import build_anchor_generator, ANCHOR_GENERATOR_REGISTRY +from .backbone import ( + BACKBONE_REGISTRY, + FPN, + Backbone, + ResNet, + ResNetBlockBase, + build_backbone, + build_resnet_backbone, + make_stage, +) +from .meta_arch import ( + META_ARCH_REGISTRY, + SEM_SEG_HEADS_REGISTRY, + GeneralizedRCNN, + PanopticFPN, + ProposalNetwork, + RetinaNet, + SemanticSegmentor, + build_model, + build_sem_seg_head, +) +from .postprocessing import detector_postprocess +from .proposal_generator import ( + PROPOSAL_GENERATOR_REGISTRY, + build_proposal_generator, + RPN_HEAD_REGISTRY, + build_rpn_head, +) +from .roi_heads import ( + ROI_BOX_HEAD_REGISTRY, + ROI_HEADS_REGISTRY, + ROI_KEYPOINT_HEAD_REGISTRY, + ROI_MASK_HEAD_REGISTRY, + ROIHeads, + StandardROIHeads, + build_box_head, + build_keypoint_head, + build_mask_head, + build_roi_heads, +) +from .test_time_augmentation import DatasetMapperTTA, GeneralizedRCNNWithTTA + +_EXCLUDE = {"torch", "ShapeSpec"} +__all__ = [k for k in globals().keys() if k not in _EXCLUDE and not k.startswith("_")] + +assert ( + torch.Tensor([1]) == torch.Tensor([2]) +).dtype == torch.bool, "Your Pytorch is too old. Please update to contain https://github.com/pytorch/pytorch/pull/21113" diff --git a/detectron2/modeling/anchor_generator.py b/detectron2/modeling/anchor_generator.py new file mode 100644 index 0000000000..b6fc49b1ad --- /dev/null +++ b/detectron2/modeling/anchor_generator.py @@ -0,0 +1,352 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy +import math +from typing import List +import torch +from torch import nn + +from detectron2.layers import ShapeSpec +from detectron2.structures import Boxes, RotatedBoxes +from detectron2.utils.registry import Registry + +ANCHOR_GENERATOR_REGISTRY = Registry("ANCHOR_GENERATOR") +""" +Registry for modules that creates object detection anchors for feature maps. +""" + + +class BufferList(nn.Module): + """ + Similar to nn.ParameterList, but for buffers + """ + + def __init__(self, buffers=None): + super(BufferList, self).__init__() + if buffers is not None: + self.extend(buffers) + + def extend(self, buffers): + offset = len(self) + for i, buffer in enumerate(buffers): + self.register_buffer(str(offset + i), buffer) + return self + + def __len__(self): + return len(self._buffers) + + def __iter__(self): + return iter(self._buffers.values()) + + +def _create_grid_offsets(size, stride, device): + grid_height, grid_width = size + shifts_x = torch.arange(0, grid_width * stride, step=stride, dtype=torch.float32, device=device) + shifts_y = torch.arange( + 0, grid_height * stride, step=stride, dtype=torch.float32, device=device + ) + shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) + shift_x = shift_x.reshape(-1) + shift_y = shift_y.reshape(-1) + return shift_x, shift_y + + +@ANCHOR_GENERATOR_REGISTRY.register() +class DefaultAnchorGenerator(nn.Module): + """ + For a set of image sizes and feature maps, computes a set of anchors. + """ + + def __init__(self, cfg, input_shape: List[ShapeSpec]): + super().__init__() + # fmt: off + sizes = cfg.MODEL.ANCHOR_GENERATOR.SIZES + aspect_ratios = cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS + self.strides = [x.stride for x in input_shape] + # fmt: on + """ + sizes (list[list[int]]): sizes[i] is the list of anchor sizes to use + for the i-th feature map. If len(sizes) == 1, then the same list of + anchor sizes, given by sizes[0], is used for all feature maps. Anchor + sizes are given in absolute lengths in units of the input image; + they do not dynamically scale if the input image size changes. + aspect_ratios (list[list[float]]): aspect_ratios[i] is the list of + anchor aspect ratios to use for the i-th feature map. If + len(aspect_ratios) == 1, then the same list of anchor aspect ratios, + given by aspect_ratios[0], is used for all feature maps. + strides (list[int]): stride of each input feature. + """ + + self.num_features = len(self.strides) + self.cell_anchors = self._calculate_anchors(sizes, aspect_ratios) + + def _calculate_anchors(self, sizes, aspect_ratios): + # If one size (or aspect ratio) is specified and there are multiple feature + # maps, then we "broadcast" anchors of that single size (or aspect ratio) + # over all feature maps. + if len(sizes) == 1: + sizes *= self.num_features + if len(aspect_ratios) == 1: + aspect_ratios *= self.num_features + assert self.num_features == len(sizes) + assert self.num_features == len(aspect_ratios) + + cell_anchors = [ + self.generate_cell_anchors(s, a).float() for s, a in zip(sizes, aspect_ratios) + ] + + return BufferList(cell_anchors) + + @property + def box_dim(self): + """ + Returns: + int: the dimension of each anchor box. + """ + return 4 + + @property + def num_cell_anchors(self): + """ + Returns: + list[int]: Each int is the number of anchors at every pixel + location, on that feature map. + For example, if at every pixel we use anchors of 3 aspect + ratios and 5 sizes, the number of anchors is 15. + (See also ANCHOR_GENERATOR.SIZES and ANCHOR_GENERATOR.ASPECT_RATIOS in config) + + In standard RPN models, `num_cell_anchors` on every feature map is the same. + """ + return [len(cell_anchors) for cell_anchors in self.cell_anchors] + + def grid_anchors(self, grid_sizes): + anchors = [] + for size, stride, base_anchors in zip(grid_sizes, self.strides, self.cell_anchors): + shift_x, shift_y = _create_grid_offsets(size, stride, base_anchors.device) + shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) + + anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) + + return anchors + + def generate_cell_anchors(self, sizes=(32, 64, 128, 256, 512), aspect_ratios=(0.5, 1, 2)): + """ + Generate a tensor storing anchor boxes, which are continuous geometric rectangles + centered on one feature map point sample. We can later build the set of anchors + for the entire feature map by tiling these tensors; see `meth:grid_anchors`. + + Args: + sizes (tuple[float]): Absolute size of the anchors in the units of the input + image (the input received by the network, after ungoing necessary scaling). + The absolute size is given as the side length of a box. + aspect_ratios (tuple[float]]): Aspect ratios of the boxes computed as box + height / width. + + Returns: + Tensor of shape (len(sizes) * len(aspect_ratios), 4) storing anchor boxes + in XYXY format. + """ + + # This is different from the anchor generator defined in the original Faster R-CNN + # code or Detectron. They yield the same AP, however the old version defines cell + # anchors in a less natural way with a shift relative to the feature grid and + # quantization that results in slightly different sizes for different aspect ratios. + # See also https://github.com/facebookresearch/Detectron/issues/227 + + anchors = [] + for size in sizes: + area = size ** 2.0 + for aspect_ratio in aspect_ratios: + # s * s = w * h + # a = h / w + # ... some algebra ... + # w = sqrt(s * s / a) + # h = a * w + w = math.sqrt(area / aspect_ratio) + h = aspect_ratio * w + x0, y0, x1, y1 = -w / 2.0, -h / 2.0, w / 2.0, h / 2.0 + anchors.append([x0, y0, x1, y1]) + return torch.tensor(anchors) + + def forward(self, features): + """ + Args: + features (list[Tensor]): list of backbone feature maps on which to generate anchors. + + Returns: + list[list[Boxes]]: a list of #image elements. Each is a list of #feature level Boxes. + The Boxes contains anchors of this image on the specific feature level. + """ + num_images = len(features[0]) + grid_sizes = [feature_map.shape[-2:] for feature_map in features] + anchors_over_all_feature_maps = self.grid_anchors(grid_sizes) + + anchors_in_image = [] + for anchors_per_feature_map in anchors_over_all_feature_maps: + boxes = Boxes(anchors_per_feature_map) + anchors_in_image.append(boxes) + + anchors = [copy.deepcopy(anchors_in_image) for _ in range(num_images)] + return anchors + + +@ANCHOR_GENERATOR_REGISTRY.register() +class RotatedAnchorGenerator(nn.Module): + """ + The anchor generator used by Rotated RPN (RRPN). + """ + + def __init__(self, cfg, input_shape: List[ShapeSpec]): + super().__init__() + # fmt: off + sizes = cfg.MODEL.ANCHOR_GENERATOR.SIZES + aspect_ratios = cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS + angles = cfg.MODEL.ANCHOR_GENERATOR.ANGLES + self.strides = [x.stride for x in input_shape] + # fmt: on + + self.num_features = len(self.strides) + self.cell_anchors = self._calculate_anchors(sizes, aspect_ratios, angles, self.strides) + + def _calculate_anchors(self, sizes, aspect_ratios, angles, feature_strides): + """ + Args: + sizes (list[list[int]]): sizes[i] is the list of anchor sizes to use + for the i-th feature map. If len(sizes) == 1, then the same list of + anchor sizes, given by sizes[0], is used for all feature maps. Anchor + sizes are given in absolute lengths in units of the input image; + they do not dynamically scale if the input image size changes. + aspect_ratios (list[list[float]]): aspect_ratios[i] is the list of + anchor aspect ratios to use for the i-th feature map. If + len(aspect_ratios) == 1, then the same list of anchor aspect ratios, + given by aspect_ratios[0], is used for all feature maps. + angles (list[list[float]]): angles[i] is the list of + anchor angles to use for the i-th feature map. If + len(angles) == 1, then the same list of anchor angles, + given by angles[0], is used for all feature maps. + feature_strides (list[number]): list of feature map strides (with respect + to the input image) for each input feature map. + """ + + # If one size (or aspect ratio) is specified and there are multiple feature + # maps, then we "broadcast" anchors of that single size + # (or aspect ratio/angle) over all feature maps. + + if len(sizes) == 1: + sizes *= self.num_features + if len(aspect_ratios) == 1: + aspect_ratios *= self.num_features + if len(angles) == 1: + angles *= self.num_features + assert self.num_features == len(sizes) + assert self.num_features == len(aspect_ratios) + assert self.num_features == len(angles) + + cell_anchors = [ + self.generate_cell_anchors(size, aspect_ratio, angle).float() + for size, aspect_ratio, angle in zip(sizes, aspect_ratios, angles) + ] + + return BufferList(cell_anchors) + + @property + def box_dim(self): + """ + Returns: + int: the dimension of each anchor box. + """ + return 5 + + @property + def num_cell_anchors(self): + """ + Returns: + list[int]: Each int is the number of anchors at every pixel + location, on that feature map. + For example, if at every pixel we use anchors of 3 aspect + ratios, 2 sizes and 5 angles, the number of anchors is 30. + (See also ANCHOR_GENERATOR.SIZES, ANCHOR_GENERATOR.ASPECT_RATIOS + and ANCHOR_GENERATOR.ANGLES in config) + + In standard RRPN models, `num_cell_anchors` on every feature map is the same. + """ + return [len(cell_anchors) for cell_anchors in self.cell_anchors] + + def grid_anchors(self, grid_sizes): + anchors = [] + for size, stride, base_anchors in zip(grid_sizes, self.strides, self.cell_anchors): + shift_x, shift_y = _create_grid_offsets(size, stride, base_anchors.device) + zeros = torch.zeros_like(shift_x) + shifts = torch.stack((shift_x, shift_y, zeros, zeros, zeros), dim=1) + + anchors.append((shifts.view(-1, 1, 5) + base_anchors.view(1, -1, 5)).reshape(-1, 5)) + + return anchors + + def generate_cell_anchors( + self, + sizes=(32, 64, 128, 256, 512), + aspect_ratios=(0.5, 1, 2), + angles=(-90, -60, -30, 0, 30, 60, 90), + ): + """ + Generate a tensor storing anchor boxes, which are continuous geometric rectangles + centered on one feature map point sample. We can later build the set of anchors + for the entire feature map by tiling these tensors; see `meth:grid_anchors`. + + Args: + sizes (tuple[float]): Absolute size of the anchors in the units of the input + image (the input received by the network, after ungoing necessary scaling). + The absolute size is given as the side length of a box. + aspect_ratios (tuple[float]]): Aspect ratios of the boxes computed as box + height / width. + angles (tuple[float]]): Angles of boxes indicating how many degrees + the boxes are rotated counter-clockwise. + + Returns: + Tensor of shape (len(sizes) * len(aspect_ratios) * len(angles), 5) + storing anchor boxes in (x_ctr, y_ctr, w, h, angle) format. + """ + anchors = [] + for size in sizes: + area = size ** 2.0 + for aspect_ratio in aspect_ratios: + # s * s = w * h + # a = h / w + # ... some algebra ... + # w = sqrt(s * s / a) + # h = a * w + w = math.sqrt(area / aspect_ratio) + h = aspect_ratio * w + anchors.extend([0, 0, w, h, a] for a in angles) + + return torch.tensor(anchors) + + def forward(self, features): + """ + Args: + features (list[Tensor]): list of backbone feature maps on which to generate anchors. + + Returns: + list[list[RotatedBoxes]]: + a list of #image elements. Each is a list of #feature level RotatedBoxes. + The RotatedBoxes contains anchors of this image on the specific feature level. + """ + num_images = len(features[0]) + grid_sizes = [feature_map.shape[-2:] for feature_map in features] + anchors_over_all_feature_maps = self.grid_anchors(grid_sizes) + + anchors_in_image = [] + for anchors_per_feature_map in anchors_over_all_feature_maps: + boxes = RotatedBoxes(anchors_per_feature_map) + anchors_in_image.append(boxes) + + anchors = [copy.deepcopy(anchors_in_image) for _ in range(num_images)] + return anchors + + +def build_anchor_generator(cfg, input_shape): + """ + Built an anchor generator from `cfg.MODEL.ANCHOR_GENERATOR.NAME`. + """ + anchor_generator = cfg.MODEL.ANCHOR_GENERATOR.NAME + return ANCHOR_GENERATOR_REGISTRY.get(anchor_generator)(cfg, input_shape) diff --git a/detectron2/modeling/backbone/__init__.py b/detectron2/modeling/backbone/__init__.py new file mode 100644 index 0000000000..bd737d9977 --- /dev/null +++ b/detectron2/modeling/backbone/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .build import build_backbone, BACKBONE_REGISTRY # noqa F401 isort:skip + +from .backbone import Backbone +from .fpn import FPN +from .resnet import ResNet, ResNetBlockBase, build_resnet_backbone, make_stage + +# TODO can expose more resnet blocks after careful consideration diff --git a/detectron2/modeling/backbone/backbone.py b/detectron2/modeling/backbone/backbone.py new file mode 100644 index 0000000000..a530efe48f --- /dev/null +++ b/detectron2/modeling/backbone/backbone.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from abc import ABCMeta, abstractmethod +import torch.nn as nn + +from detectron2.layers import ShapeSpec + +__all__ = ["Backbone"] + + +class Backbone(nn.Module, metaclass=ABCMeta): + """ + Abstract base class for network backbones. + """ + + def __init__(self): + """ + The `__init__` method of any subclass can specify its own set of arguments. + """ + super().__init__() + + @abstractmethod + def forward(self): + """ + Subclasses must override this method, but adhere to the same return type. + + Returns: + dict[str: Tensor]: mapping from feature name (e.g., "res2") to tensor + """ + pass + + @property + def size_divisibility(self): + """ + Some backbones require the input height and width to be divisible by a + specific integer. This is typically true for encoder / decoder type networks + with lateral connection (e.g., FPN) for which feature maps need to match + dimension in the "bottom up" and "top down" paths. Set to 0 if no specific + input size divisibility is required. + """ + return 0 + + def output_shape(self): + """ + Returns: + dict[str->ShapeSpec] + """ + # this is a backward-compatible default + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + # the properties below are not used any more + + @property + def out_features(self): + """deprecated""" + return self._out_features + + @property + def out_feature_strides(self): + """deprecated""" + return {f: self._out_feature_strides[f] for f in self._out_features} + + @property + def out_feature_channels(self): + """deprecated""" + return {f: self._out_feature_channels[f] for f in self._out_features} diff --git a/detectron2/modeling/backbone/build.py b/detectron2/modeling/backbone/build.py new file mode 100644 index 0000000000..8a1bbc950b --- /dev/null +++ b/detectron2/modeling/backbone/build.py @@ -0,0 +1,26 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from detectron2.layers import ShapeSpec +from detectron2.utils.registry import Registry + +from .backbone import Backbone + +BACKBONE_REGISTRY = Registry("BACKBONE") +""" +Registry for backbones, which extract feature maps from images. +""" + + +def build_backbone(cfg, input_shape=None): + """ + Build a backbone from `cfg.MODEL.BACKBONE.NAME`. + + Returns: + an instance of :class:`Backbone` + """ + if input_shape is None: + input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN)) + + backbone_name = cfg.MODEL.BACKBONE.NAME + backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape) + assert isinstance(backbone, Backbone) + return backbone diff --git a/detectron2/modeling/backbone/fpn.py b/detectron2/modeling/backbone/fpn.py new file mode 100644 index 0000000000..657d81cd12 --- /dev/null +++ b/detectron2/modeling/backbone/fpn.py @@ -0,0 +1,244 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import math +import fvcore.nn.weight_init as weight_init +import torch.nn.functional as F +from torch import nn + +from detectron2.layers import Conv2d, ShapeSpec, get_norm + +from .backbone import Backbone +from .build import BACKBONE_REGISTRY +from .resnet import build_resnet_backbone + +__all__ = ["build_resnet_fpn_backbone", "build_retinanet_resnet_fpn_backbone", "FPN"] + + +class FPN(Backbone): + """ + This module implements Feature Pyramid Network. + It creates pyramid features built on top of some input feature maps. + """ + + def __init__( + self, bottom_up, in_features, out_channels, norm="", top_block=None, fuse_type="sum" + ): + """ + Args: + bottom_up (Backbone): module representing the bottom up subnetwork. + Must be a subclass of :class:`Backbone`. The multi-scale feature + maps generated by the bottom up network, and listed in `in_features`, + are used to generate FPN levels. + in_features (list[str]): names of the input feature maps coming + from the backbone to which FPN is attached. For example, if the + backbone produces ["res2", "res3", "res4"], any *contiguous* sublist + of these may be used; order must be from high to low resolution. + out_channels (int): number of channels in the output feature maps. + norm (str): the normalization to use. + top_block (nn.Module or None): if provided, an extra operation will + be performed on the output of the last (smallest resolution) + FPN output, and the result will extend the result list. The top_block + further downsamples the feature map. It must have an attribute + "num_levels", meaning the number of extra FPN levels added by + this block, and "in_feature", which is a string representing + its input feature (e.g., p5). + fuse_type (str): types for fusing the top down features and the lateral + ones. It can be "sum" (default), which sums up element-wise; or "avg", + which takes the element-wise mean of the two. + """ + super(FPN, self).__init__() + assert isinstance(bottom_up, Backbone) + + # Feature map strides and channels from the bottom up network (e.g. ResNet) + in_strides = [bottom_up.out_feature_strides[f] for f in in_features] + in_channels = [bottom_up.out_feature_channels[f] for f in in_features] + + _assert_strides_are_log2_contiguous(in_strides) + lateral_convs = [] + output_convs = [] + + use_bias = norm == "" + for idx, in_channels in enumerate(in_channels): + lateral_norm = get_norm(norm, out_channels) + output_norm = get_norm(norm, out_channels) + + lateral_conv = Conv2d( + in_channels, out_channels, kernel_size=1, bias=use_bias, norm=lateral_norm + ) + output_conv = Conv2d( + out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + ) + weight_init.c2_xavier_fill(lateral_conv) + weight_init.c2_xavier_fill(output_conv) + stage = int(math.log2(in_strides[idx])) + self.add_module("fpn_lateral{}".format(stage), lateral_conv) + self.add_module("fpn_output{}".format(stage), output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + self.top_block = top_block + self.in_features = in_features + self.bottom_up = bottom_up + # Return feature names are "p", like ["p2", "p3", ..., "p6"] + self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in in_strides} + # top block output feature maps. + if self.top_block is not None: + for s in range(stage, stage + self.top_block.num_levels): + self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1) + + self._out_features = list(self._out_feature_strides.keys()) + self._out_feature_channels = {k: out_channels for k in self._out_features} + self._size_divisibility = in_strides[-1] + assert fuse_type in {"avg", "sum"} + self._fuse_type = fuse_type + + @property + def size_divisibility(self): + return self._size_divisibility + + def forward(self, x): + """ + Args: + input (dict[str: Tensor]): mapping feature map name (e.g., "res5") to + feature map tensor for each feature level in high to low resolution order. + + Returns: + dict[str: Tensor]: + mapping from feature map name to FPN feature map tensor + in high to low resolution order. Returned feature names follow the FPN + paper convention: "p", where stage has stride = 2 ** stage e.g., + ["p2", "p3", ..., "p6"]. + """ + # Reverse feature maps into top-down order (from low to high resolution) + bottom_up_features = self.bottom_up(x) + x = [bottom_up_features[f] for f in self.in_features[::-1]] + results = [] + prev_features = self.lateral_convs[0](x[0]) + results.append(self.output_convs[0](prev_features)) + for features, lateral_conv, output_conv in zip( + x[1:], self.lateral_convs[1:], self.output_convs[1:] + ): + top_down_features = F.interpolate(prev_features, scale_factor=2, mode="nearest") + lateral_features = lateral_conv(features) + prev_features = lateral_features + top_down_features + if self._fuse_type == "avg": + prev_features /= 2 + results.insert(0, output_conv(prev_features)) + + if self.top_block is not None: + top_block_in_feature = bottom_up_features.get(self.top_block.in_feature, None) + if top_block_in_feature is None: + top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)] + results.extend(self.top_block(top_block_in_feature)) + assert len(self._out_features) == len(results) + return dict(zip(self._out_features, results)) + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + +def _assert_strides_are_log2_contiguous(strides): + """ + Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2". + """ + for i, stride in enumerate(strides[1:], 1): + assert stride == 2 * strides[i - 1], "Stides {} {} are not log2 contiguous".format( + stride, strides[i - 1] + ) + + +class LastLevelMaxPool(nn.Module): + """ + This module is used in the original FPN to generate a downsampled + P6 feature from P5. + """ + + def __init__(self): + super().__init__() + self.num_levels = 1 + self.in_feature = "p5" + + def forward(self, x): + return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] + + +class LastLevelP6P7(nn.Module): + """ + This module is used in RetinaNet to generate extra layers, P6 and P7 from + C5 feature. + """ + + def __init__(self, in_channels, out_channels): + super().__init__() + self.num_levels = 2 + self.in_feature = "res5" + self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) + self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) + for module in [self.p6, self.p7]: + weight_init.c2_xavier_fill(module) + + def forward(self, c5): + p6 = self.p6(c5) + p7 = self.p7(F.relu(p6)) + return [p6, p7] + + +@BACKBONE_REGISTRY.register() +def build_resnet_fpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=LastLevelMaxPool(), + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + return backbone + + +@BACKBONE_REGISTRY.register() +def build_retinanet_resnet_fpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + in_channels_p6p7 = bottom_up.out_feature_channels["res5"] + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=LastLevelP6P7(in_channels_p6p7, out_channels), + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + return backbone diff --git a/detectron2/modeling/backbone/resnet.py b/detectron2/modeling/backbone/resnet.py new file mode 100644 index 0000000000..af6c6ea5e6 --- /dev/null +++ b/detectron2/modeling/backbone/resnet.py @@ -0,0 +1,479 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import numpy as np +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.layers import ( + Conv2d, + DeformConv, + FrozenBatchNorm2d, + ModulatedDeformConv, + ShapeSpec, + get_norm, +) + +from .backbone import Backbone +from .build import BACKBONE_REGISTRY + +__all__ = [ + "ResNetBlockBase", + "BottleneckBlock", + "DeformBottleneckBlock", + "BasicStem", + "ResNet", + "make_stage", + "build_resnet_backbone", +] + + +class ResNetBlockBase(nn.Module): + def __init__(self, in_channels, out_channels, stride): + """ + The `__init__` method of any subclass should also contain these arguments. + + Args: + in_channels (int): + out_channels (int): + stride (int): + """ + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.stride = stride + + def freeze(self): + for p in self.parameters(): + p.requires_grad = False + FrozenBatchNorm2d.convert_frozen_batchnorm(self) + return self + + +class BottleneckBlock(ResNetBlockBase): + def __init__( + self, + in_channels, + out_channels, + *, + bottleneck_channels, + stride=1, + num_groups=1, + norm="BN", + stride_in_1x1=False, + dilation=1, + ): + """ + Args: + norm (str or callable): a callable that takes the number of + channels and return a `nn.Module`, or a pre-defined string + (one of {"FrozenBN", "BN", "GN"}). + stride_in_1x1 (bool): when stride==2, whether to put stride in the + first 1x1 convolution or the bottleneck 3x3 convolution. + """ + super().__init__(in_channels, out_channels, stride) + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + # The original MSRA ResNet models have stride in the first 1x1 conv + # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have + # stride in the 3x3 conv + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv2 = Conv2d( + bottleneck_channels, + bottleneck_channels, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + bias=False, + groups=num_groups, + dilation=dilation, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv3 = Conv2d( + bottleneck_channels, + out_channels, + kernel_size=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + # Zero-initialize the last normalization in each residual branch, + # so that at the beginning, the residual branch starts with zeros, + # and each residual block behaves like an identity. + # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": + # "For BN layers, the learnable scaling coefficient γ is initialized + # to be 1, except for each residual block's last BN + # where γ is initialized to be 0." + + # nn.init.constant_(self.conv3.norm.weight, 0) + # TODO this somehow hurts performance when training GN models from scratch. + # Add it as an option when we need to use this code to train a backbone. + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + + out = self.conv2(out) + out = F.relu_(out) + + out = self.conv3(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +class DeformBottleneckBlock(ResNetBlockBase): + def __init__( + self, + in_channels, + out_channels, + *, + bottleneck_channels, + stride=1, + num_groups=1, + norm="BN", + stride_in_1x1=False, + dilation=1, + deform_modulated=False, + deform_num_groups=1, + ): + """ + Similar to :class:`BottleneckBlock`, but with deformable conv in the 3x3 convolution. + """ + super().__init__(in_channels, out_channels, stride) + self.deform_modulated = deform_modulated + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + norm=get_norm(norm, bottleneck_channels), + ) + + if deform_modulated: + deform_conv_op = ModulatedDeformConv + # offset channels are 2 or 3 (if with modulated) * kernel_size * kernel_size + offset_channels = 27 + else: + deform_conv_op = DeformConv + offset_channels = 18 + + self.conv2_offset = Conv2d( + bottleneck_channels, + offset_channels * deform_num_groups, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + dilation=dilation, + ) + self.conv2 = deform_conv_op( + bottleneck_channels, + bottleneck_channels, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + bias=False, + groups=num_groups, + dilation=dilation, + deformable_groups=deform_num_groups, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv3 = Conv2d( + bottleneck_channels, + out_channels, + kernel_size=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + nn.init.constant_(self.conv2_offset.weight, 0) + nn.init.constant_(self.conv2_offset.bias, 0) + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + + if self.deform_modulated: + offset_mask = self.conv2_offset(out) + offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1) + offset = torch.cat((offset_x, offset_y), dim=1) + mask = mask.sigmoid() + out = self.conv2(out, offset, mask) + else: + offset = self.conv2_offset(out) + out = self.conv2(out, offset) + out = F.relu_(out) + + out = self.conv3(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +def make_stage(block_class, num_blocks, first_stride, **kwargs): + """ + Create a resnet stage by creating many blocks. + Args: + block_class (class): a subclass of ResNetBlockBase + num_blocks (int): + first_stride (int): the stride of the first block. The other blocks will have stride=1. + A `stride` argument will be passed to the block constructor. + kwargs: other arguments passed to the block constructor. + + Returns: + list[nn.Module]: a list of block module. + """ + blocks = [] + for i in range(num_blocks): + blocks.append(block_class(stride=first_stride if i == 0 else 1, **kwargs)) + kwargs["in_channels"] = kwargs["out_channels"] + return blocks + + +class BasicStem(nn.Module): + def __init__(self, in_channels=3, out_channels=64, norm="BN"): + """ + Args: + norm (str or callable): a callable that takes the number of + channels and return a `nn.Module`, or a pre-defined string + (one of {"FrozenBN", "BN", "GN"}). + """ + super().__init__() + self.conv1 = Conv2d( + in_channels, + out_channels, + kernel_size=7, + stride=2, + padding=3, + bias=False, + norm=get_norm(norm, out_channels), + ) + weight_init.c2_msra_fill(self.conv1) + + def forward(self, x): + x = self.conv1(x) + x = F.relu_(x) + x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) + return x + + @property + def out_channels(self): + return self.conv1.out_channels + + @property + def stride(self): + return 4 # = stride 2 conv -> stride 2 max pool + + +class ResNet(Backbone): + def __init__(self, stem, stages, num_classes=None, out_features=None): + """ + Args: + stem (nn.Module): a stem module + stages (list[list[ResNetBlock]]): several (typically 4) stages, + each contains multiple :class:`ResNetBlockBase`. + num_classes (None or int): if None, will not perform classification. + out_features (list[str]): name of the layers whose outputs should + be returned in forward. Can be anything in "stem", "linear", or "res2" ... + If None, will return the output of the last layer. + """ + super(ResNet, self).__init__() + self.stem = stem + self.num_classes = num_classes + + current_stride = self.stem.stride + self._out_feature_strides = {"stem": current_stride} + self._out_feature_channels = {"stem": self.stem.out_channels} + + self.stages_and_names = [] + for i, blocks in enumerate(stages): + for block in blocks: + assert isinstance(block, ResNetBlockBase), block + curr_channels = block.out_channels + stage = nn.Sequential(*blocks) + name = "res" + str(i + 2) + self.add_module(name, stage) + self.stages_and_names.append((stage, name)) + self._out_feature_strides[name] = current_stride = int( + current_stride * np.prod([k.stride for k in blocks]) + ) + self._out_feature_channels[name] = blocks[-1].out_channels + + if num_classes is not None: + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.linear = nn.Linear(curr_channels, num_classes) + + # Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": + # "The 1000-way fully-connected layer is initialized by + # drawing weights from a zero-mean Gaussian with standard deviation of 0.01." + nn.init.normal_(self.linear.weight, stddev=0.01) + name = "linear" + + if out_features is None: + out_features = [name] + self._out_features = out_features + assert len(self._out_features) + children = [x[0] for x in self.named_children()] + for out_feature in self._out_features: + assert out_feature in children, "Available children: {}".format(", ".join(children)) + + def forward(self, x): + outputs = {} + x = self.stem(x) + if "stem" in self._out_features: + outputs["stem"] = x + for stage, name in self.stages_and_names: + x = stage(x) + if name in self._out_features: + outputs[name] = x + if self.num_classes is not None: + x = self.avgpool(x) + x = self.linear(x) + if "linear" in self._out_features: + outputs["linear"] = x + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + +@BACKBONE_REGISTRY.register() +def build_resnet_backbone(cfg, input_shape): + """ + Create a ResNet instance from config. + + Returns: + ResNet: a :class:`ResNet` instance. + """ + # need registration of new blocks/stems? + norm = cfg.MODEL.RESNETS.NORM + stem = BasicStem( + in_channels=input_shape.channels, + out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS, + norm=norm, + ) + freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT + + if freeze_at >= 1: + for p in stem.parameters(): + p.requires_grad = False + stem = FrozenBatchNorm2d.convert_frozen_batchnorm(stem) + + # fmt: off + out_features = cfg.MODEL.RESNETS.OUT_FEATURES + depth = cfg.MODEL.RESNETS.DEPTH + num_groups = cfg.MODEL.RESNETS.NUM_GROUPS + width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP + bottleneck_channels = num_groups * width_per_group + in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS + out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS + stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 + res5_dilation = cfg.MODEL.RESNETS.RES5_DILATION + deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE + deform_modulated = cfg.MODEL.RESNETS.DEFORM_MODULATED + deform_num_groups = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS + # fmt: on + assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation) + + num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3]}[depth] + + stages = [] + + # Avoid creating variables without gradients + # It consumes extra memory and may cause allreduce to fail + out_stage_idx = [{"res2": 2, "res3": 3, "res4": 4, "res5": 5}[f] for f in out_features] + max_stage_idx = max(out_stage_idx) + for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)): + dilation = res5_dilation if stage_idx == 5 else 1 + first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2 + stage_kargs = { + "num_blocks": num_blocks_per_stage[idx], + "first_stride": first_stride, + "in_channels": in_channels, + "bottleneck_channels": bottleneck_channels, + "out_channels": out_channels, + "num_groups": num_groups, + "norm": norm, + "stride_in_1x1": stride_in_1x1, + "dilation": dilation, + } + if deform_on_per_stage[idx]: + stage_kargs["block_class"] = DeformBottleneckBlock + stage_kargs["deform_modulated"] = deform_modulated + stage_kargs["deform_num_groups"] = deform_num_groups + else: + stage_kargs["block_class"] = BottleneckBlock + blocks = make_stage(**stage_kargs) + in_channels = out_channels + out_channels *= 2 + bottleneck_channels *= 2 + + if freeze_at >= stage_idx: + for block in blocks: + block.freeze() + stages.append(blocks) + return ResNet(stem, stages, out_features=out_features) diff --git a/detectron2/modeling/box_regression.py b/detectron2/modeling/box_regression.py new file mode 100644 index 0000000000..32f2d944ea --- /dev/null +++ b/detectron2/modeling/box_regression.py @@ -0,0 +1,214 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import math +import torch + +# Value for clamping large dw and dh predictions. The heuristic is that we clamp +# such that dw and dh are no larger than what would transform a 16px box into a +# 1000px box (based on a small anchor, 16px, and a typical image size, 1000px). +_DEFAULT_SCALE_CLAMP = math.log(1000.0 / 16) + + +class Box2BoxTransform(object): + """ + The box-to-box transform defined in R-CNN. The transformation is parameterized + by 4 deltas: (dx, dy, dw, dh). The transformation scales the box's width and height + by exp(dw), exp(dh) and shifts a box's center by the offset (dx * width, dy * height). + """ + + def __init__(self, weights, scale_clamp=_DEFAULT_SCALE_CLAMP): + """ + Args: + weights (4-element tuple): Scaling factors that are applied to the + (dx, dy, dw, dh) deltas. In Fast R-CNN, these were originally set + such that the deltas have unit variance; now they are treated as + hyperparameters of the system. + scale_clamp (float): When predicting deltas, the predicted box scaling + factors (dw and dh) are clamped such that they are <= scale_clamp. + """ + self.weights = weights + self.scale_clamp = scale_clamp + + def get_deltas(self, src_boxes, target_boxes): + """ + Get box regression transformation deltas (dx, dy, dw, dh) that can be used + to transform the `src_boxes` into the `target_boxes`. That is, the relation + `target_boxes == self.apply_deltas(deltas, src_boxes)` is true (unless + any delta is too large and is clamped). + + Args: + src_boxes (Tensor): source boxes, e.g., object proposals + target_boxes (Tensor): target of the transformation, e.g., ground-truth + boxes. + """ + assert isinstance(src_boxes, torch.Tensor), type(src_boxes) + assert isinstance(target_boxes, torch.Tensor), type(target_boxes) + + src_widths = src_boxes[:, 2] - src_boxes[:, 0] + src_heights = src_boxes[:, 3] - src_boxes[:, 1] + src_ctr_x = src_boxes[:, 0] + 0.5 * src_widths + src_ctr_y = src_boxes[:, 1] + 0.5 * src_heights + + target_widths = target_boxes[:, 2] - target_boxes[:, 0] + target_heights = target_boxes[:, 3] - target_boxes[:, 1] + target_ctr_x = target_boxes[:, 0] + 0.5 * target_widths + target_ctr_y = target_boxes[:, 1] + 0.5 * target_heights + + wx, wy, ww, wh = self.weights + dx = wx * (target_ctr_x - src_ctr_x) / src_widths + dy = wy * (target_ctr_y - src_ctr_y) / src_heights + dw = ww * torch.log(target_widths / src_widths) + dh = wh * torch.log(target_heights / src_heights) + + deltas = torch.stack((dx, dy, dw, dh), dim=1) + assert (src_widths > 0).all().item(), "Input boxes to Box2BoxTransform are not valid!" + return deltas + + def apply_deltas(self, deltas, boxes): + """ + Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`. + + Args: + deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1. + deltas[i] represents k potentially different class-specific + box transformations for the single box boxes[i]. + boxes (Tensor): boxes to transform, of shape (N, 4) + """ + assert torch.isfinite(deltas).all().item() + boxes = boxes.to(deltas.dtype) + + widths = boxes[:, 2] - boxes[:, 0] + heights = boxes[:, 3] - boxes[:, 1] + ctr_x = boxes[:, 0] + 0.5 * widths + ctr_y = boxes[:, 1] + 0.5 * heights + + wx, wy, ww, wh = self.weights + dx = deltas[:, 0::4] / wx + dy = deltas[:, 1::4] / wy + dw = deltas[:, 2::4] / ww + dh = deltas[:, 3::4] / wh + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=self.scale_clamp) + dh = torch.clamp(dh, max=self.scale_clamp) + + pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] + pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] + pred_w = torch.exp(dw) * widths[:, None] + pred_h = torch.exp(dh) * heights[:, None] + + pred_boxes = torch.zeros_like(deltas) + pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w # x1 + pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h # y1 + pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w # x2 + pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h # y2 + return pred_boxes + + +class Box2BoxTransformRotated(object): + """ + The box-to-box transform defined in Rotated R-CNN. The transformation is parameterized + by 5 deltas: (dx, dy, dw, dh, da). The transformation scales the box's width and height + by exp(dw), exp(dh), shifts a box's center by the offset (dx * width, dy * height), + and rotate a box's angle by da (radians). + Note: angles of deltas are in radians while angles of boxes are in degrees. + """ + + def __init__(self, weights, scale_clamp=_DEFAULT_SCALE_CLAMP): + """ + Args: + weights (5-element tuple): Scaling factors that are applied to the + (dx, dy, dw, dh, da) deltas. These are treated as + hyperparameters of the system. + scale_clamp (float): When predicting deltas, the predicted box scaling + factors (dw and dh) are clamped such that they are <= scale_clamp. + """ + self.weights = weights + self.scale_clamp = scale_clamp + + def get_deltas(self, src_boxes, target_boxes): + """ + Get box regression transformation deltas (dx, dy, dw, dh, da) that can be used + to transform the `src_boxes` into the `target_boxes`. That is, the relation + `target_boxes == self.apply_deltas(deltas, src_boxes)` is true (unless + any delta is too large and is clamped). + + Args: + src_boxes (Tensor): Nx5 source boxes, e.g., object proposals + target_boxes (Tensor): Nx5 target of the transformation, e.g., ground-truth + boxes. + """ + assert isinstance(src_boxes, torch.Tensor), type(src_boxes) + assert isinstance(target_boxes, torch.Tensor), type(target_boxes) + + src_ctr_x, src_ctr_y, src_widths, src_heights, src_angles = torch.unbind(src_boxes, dim=1) + + target_ctr_x, target_ctr_y, target_widths, target_heights, target_angles = torch.unbind( + target_boxes, dim=1 + ) + + wx, wy, ww, wh, wa = self.weights + dx = wx * (target_ctr_x - src_ctr_x) / src_widths + dy = wy * (target_ctr_y - src_ctr_y) / src_heights + dw = ww * torch.log(target_widths / src_widths) + dh = wh * torch.log(target_heights / src_heights) + # Angles of deltas are in radians while angles of boxes are in degrees. + # the conversion to radians serve as a way to normalize the values + da = target_angles - src_angles + while len(torch.where(da < -180.0)[0]) > 0: + da[torch.where(da < -180.0)] += 360.0 + while len(torch.where(da > 180.0)[0]) > 0: + da[torch.where(da > 180.0)] -= 360.0 + da *= wa * math.pi / 180.0 + + deltas = torch.stack((dx, dy, dw, dh, da), dim=1) + assert ( + (src_widths > 0).all().item() + ), "Input boxes to Box2BoxTransformRotated are not valid!" + return deltas + + def apply_deltas(self, deltas, boxes): + """ + Apply transformation `deltas` (dx, dy, dw, dh, da) to `boxes`. + + Args: + deltas (Tensor): transformation deltas of shape (N, 5). + deltas[i] represents box transformation for the single box boxes[i]. + boxes (Tensor): boxes to transform, of shape (N, 5) + """ + assert deltas.shape[1] == 5 and boxes.shape[1] == 5 + assert torch.isfinite(deltas).all().item() + + boxes = boxes.to(deltas.dtype) + + ctr_x, ctr_y, widths, heights, angles = torch.unbind(boxes, dim=1) + wx, wy, ww, wh, wa = self.weights + dx, dy, dw, dh, da = torch.unbind(deltas, dim=1) + + dx.div_(wx) + dy.div_(wy) + dw.div_(ww) + dh.div_(wh) + da.div_(wa) + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=self.scale_clamp) + dh = torch.clamp(dh, max=self.scale_clamp) + + pred_boxes = torch.zeros_like(deltas) + pred_boxes[:, 0] = dx * widths + ctr_x # x_ctr + pred_boxes[:, 1] = dy * heights + ctr_y # y_ctr + pred_boxes[:, 2] = torch.exp(dw) * widths # width + pred_boxes[:, 3] = torch.exp(dh) * heights # height + + # Following original RRPN implementation, + # angles of deltas are in radians while angles of boxes are in degrees. + pred_angle = da * 180.0 / math.pi + angles + + while len(torch.where(pred_angle < -180.0)[0]) > 0: + pred_angle[torch.where(pred_angle < -180.0)] += 360.0 + while len(torch.where(pred_angle > 180.0)[0]) > 0: + pred_angle[torch.where(pred_angle > 180.0)] -= 360.0 + + pred_boxes[:, 4] = pred_angle + + return pred_boxes diff --git a/detectron2/modeling/matcher.py b/detectron2/modeling/matcher.py new file mode 100644 index 0000000000..9fa1209aab --- /dev/null +++ b/detectron2/modeling/matcher.py @@ -0,0 +1,128 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import torch + + +class Matcher(object): + """ + This class assigns to each predicted "element" (e.g., a box) a ground-truth + element. Each predicted element will have exactly zero or one matches; each + ground-truth element may be matched to zero or more predicted elements. + + The matching is determined by the MxN match_quality_matrix, that characterizes + how well each (ground-truth, prediction)-pair match each other. For example, + if the elements are boxes, this matrix may contain box intersection-over-union + overlap values. + + The matcher returns (a) a vector of length N containing the index of the + ground-truth element m in [0, M) that matches to prediction n in [0, N). + (b) a vector of length N containing the labels for each prediction. + """ + + def __init__(self, thresholds, labels, allow_low_quality_matches=False): + """ + Args: + thresholds (list): a list of thresholds used to stratify predictions + into levels. + labels (list): a list of values to label predictions belonging at + each level. A label can be one of {-1, 0, 1} signifying + {ignore, negative class, positive class}, respectively. + allow_low_quality_matches (bool): if True, produce additional matches + for predictions with maximum match quality lower than high_threshold. + See set_low_quality_matches_ for more details. + + For example, + thresholds = [0.3, 0.5] + labels = [0, -1, 1] + All predictions with iou < 0.3 will be marked with 0 and + thus will be considered as false positives while training. + All predictions with 0.3 <= iou < 0.5 will be marked with -1 and + thus will be ignored. + All predictions with 0.5 <= iou will be marked with 1 and + thus will be considered as true positives. + """ + # Add -inf and +inf to first and last position in thresholds + thresholds = thresholds[:] + thresholds.insert(0, -float("inf")) + thresholds.append(float("inf")) + assert all(low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])) + assert all(l in [-1, 0, 1] for l in labels) + assert len(labels) == len(thresholds) - 1 + self.thresholds = thresholds + self.labels = labels + self.allow_low_quality_matches = allow_low_quality_matches + + def __call__(self, match_quality_matrix): + """ + Args: + match_quality_matrix (Tensor[float]): an MxN tensor, containing the + pairwise quality between M ground-truth elements and N predicted + elements. All elements must be >= 0 (due to the us of `torch.nonzero` + for selecting indices in :meth:`set_low_quality_matches_`). + + Returns: + matches (Tensor[int64]): a vector of length N, where matches[i] is a matched + ground-truth index in [0, M) + match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates + whether a prediction is a true or false positive or ignored + """ + assert match_quality_matrix.dim() == 2 + if match_quality_matrix.numel() == 0: + return ( + match_quality_matrix.new_full( + (match_quality_matrix.size(1),), 0, dtype=torch.int64 + ), + match_quality_matrix.new_full( + (match_quality_matrix.size(1),), -1, dtype=torch.int8 + ), + ) + assert torch.all(match_quality_matrix >= 0) + + # match_quality_matrix is M (gt) x N (predicted) + # Max over gt elements (dim 0) to find best gt candidate for each prediction + matched_vals, matches = match_quality_matrix.max(dim=0) + + match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8) + + for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]): + low_high = (matched_vals >= low) & (matched_vals < high) + match_labels[low_high] = l + + if self.allow_low_quality_matches: + self.set_low_quality_matches_(match_labels, match_quality_matrix) + + return matches, match_labels + + def set_low_quality_matches_(self, match_labels, match_quality_matrix): + """ + Produce additional matches for predictions that have only low-quality matches. + Specifically, for each ground-truth G find the set of predictions that have + maximum overlap with it (including ties); for each prediction in that set, if + it is unmatched, then match it to the ground-truth G. + + This function implements the RPN assignment case (i) in Sec. 3.1.2 of the + Faster R-CNN paper: https://arxiv.org/pdf/1506.01497v3.pdf. + """ + # For each gt, find the prediction with which it has highest quality + highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1) + # Find the highest quality match available, even if it is low, including ties. + # Note that the matches qualities must be positive due to the use of + # `torch.nonzero`. + gt_pred_pairs_of_highest_quality = torch.nonzero( + match_quality_matrix == highest_quality_foreach_gt[:, None] + ) + # Example gt_pred_pairs_of_highest_quality: + # tensor([[ 0, 39796], + # [ 1, 32055], + # [ 1, 32070], + # [ 2, 39190], + # [ 2, 40255], + # [ 3, 40390], + # [ 3, 41455], + # [ 4, 45470], + # [ 5, 45325], + # [ 5, 46390]]) + # Each row is a (gt index, prediction index) + # Note how gt items 1, 2, 3, and 5 each have two ties + + pred_inds_to_update = gt_pred_pairs_of_highest_quality[:, 1] + match_labels[pred_inds_to_update] = 1 diff --git a/detectron2/modeling/meta_arch/__init__.py b/detectron2/modeling/meta_arch/__init__.py new file mode 100644 index 0000000000..96ef9b582c --- /dev/null +++ b/detectron2/modeling/meta_arch/__init__.py @@ -0,0 +1,11 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from .build import META_ARCH_REGISTRY, build_model # isort:skip + +from .panoptic_fpn import PanopticFPN + +# import all the meta_arch, so they will be registered +from .rcnn import GeneralizedRCNN, ProposalNetwork +from .retinanet import RetinaNet +from .semantic_seg import SEM_SEG_HEADS_REGISTRY, SemanticSegmentor, build_sem_seg_head diff --git a/detectron2/modeling/meta_arch/build.py b/detectron2/modeling/meta_arch/build.py new file mode 100644 index 0000000000..e7980b6efc --- /dev/null +++ b/detectron2/modeling/meta_arch/build.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from detectron2.utils.registry import Registry + +META_ARCH_REGISTRY = Registry("META_ARCH") # noqa F401 isort:skip +""" +Registry for meta-architectures, i.e. the whole model. +""" + + +def build_model(cfg): + """ + Built the whole model, defined by `cfg.MODEL.META_ARCHITECTURE`. + """ + meta_arch = cfg.MODEL.META_ARCHITECTURE + return META_ARCH_REGISTRY.get(meta_arch)(cfg) diff --git a/detectron2/modeling/meta_arch/panoptic_fpn.py b/detectron2/modeling/meta_arch/panoptic_fpn.py new file mode 100644 index 0000000000..fc47904ad7 --- /dev/null +++ b/detectron2/modeling/meta_arch/panoptic_fpn.py @@ -0,0 +1,216 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import torch +from torch import nn + +from detectron2.structures import ImageList + +from ..backbone import build_backbone +from ..postprocessing import detector_postprocess, sem_seg_postprocess +from ..proposal_generator import build_proposal_generator +from ..roi_heads import build_roi_heads +from .build import META_ARCH_REGISTRY +from .semantic_seg import build_sem_seg_head + +__all__ = ["PanopticFPN"] + + +@META_ARCH_REGISTRY.register() +class PanopticFPN(nn.Module): + """ + Main class for Panoptic FPN architectures (see https://arxiv.org/abd/1901.02446). + """ + + def __init__(self, cfg): + super().__init__() + + self.device = torch.device(cfg.MODEL.DEVICE) + + self.instance_loss_weight = cfg.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT + + # options when combining instance & semantic outputs + self.combine_on = cfg.MODEL.PANOPTIC_FPN.COMBINE.ENABLED + self.combine_overlap_threshold = cfg.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH + self.combine_stuff_area_limit = cfg.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT + self.combine_instances_confidence_threshold = ( + cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH + ) + + self.backbone = build_backbone(cfg) + self.proposal_generator = build_proposal_generator(cfg, self.backbone.output_shape()) + self.roi_heads = build_roi_heads(cfg, self.backbone.output_shape()) + self.sem_seg_head = build_sem_seg_head(cfg, self.backbone.output_shape()) + + pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(3, 1, 1) + pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(3, 1, 1) + self.normalizer = lambda x: (x - pixel_mean) / pixel_std + self.to(self.device) + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper`. + Each item in the list contains the inputs for one image. + + For now, each item in the list is a dict that contains: + image: Tensor, image in (C, H, W) format. + instances: Instances + sem_seg: semantic segmentation ground truth. + Other information that's included in the original dicts, such as: + "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + list[dict]: each dict is the results for one image. The dict + contains the following keys: + "instances": see :meth:`GeneralizedRCNN.forward` for its format. + "sem_seg": see :meth:`SemanticSegmentor.forward` for its format. + "panoptic_seg": available when `PANOPTIC_FPN.COMBINE.ENABLED`. + See the return value of + :func:`combine_semantic_and_instance_outputs` for its format. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [self.normalizer(x) for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + features = self.backbone(images.tensor) + + if "proposals" in batched_inputs[0]: + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + proposal_losses = {} + + if "sem_seg" in batched_inputs[0]: + gt_sem_seg = [x["sem_seg"].to(self.device) for x in batched_inputs] + gt_sem_seg = ImageList.from_tensors( + gt_sem_seg, self.backbone.size_divisibility, self.sem_seg_head.ignore_value + ).tensor + else: + gt_sem_seg = None + sem_seg_results, sem_seg_losses = self.sem_seg_head(features, gt_sem_seg) + + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + if self.proposal_generator: + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + detector_results, detector_losses = self.roi_heads( + images, features, proposals, gt_instances + ) + + if self.training: + losses = {} + losses.update(sem_seg_losses) + losses.update({k: v * self.instance_loss_weight for k, v in detector_losses.items()}) + losses.update(proposal_losses) + return losses + + processed_results = [] + for sem_seg_result, detector_result, input_per_image, image_size in zip( + sem_seg_results, detector_results, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height") + width = input_per_image.get("width") + sem_seg_r = sem_seg_postprocess(sem_seg_result, image_size, height, width) + detector_r = detector_postprocess(detector_result, height, width) + + processed_results.append({"sem_seg": sem_seg_r, "instances": detector_r}) + + if self.combine_on: + panoptic_r = combine_semantic_and_instance_outputs( + detector_r, + sem_seg_r.argmax(dim=0), + self.combine_overlap_threshold, + self.combine_stuff_area_limit, + self.combine_instances_confidence_threshold, + ) + processed_results[-1]["panoptic_seg"] = panoptic_r + return processed_results + + +def combine_semantic_and_instance_outputs( + instance_results, + semantic_results, + overlap_threshold, + stuff_area_limit, + instances_confidence_threshold, +): + """ + Implement a simple combining logic following + "combine_semantic_and_instance_predictions.py" in panopticapi + to produce panoptic segmentation outputs. + + Args: + instance_results: output of :func:`detector_postprocess`. + semantic_results: an (H, W) tensor, each is the contiguous semantic + category id + + Returns: + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment. + segments_info (list[dict]): Describe each segment in `panoptic_seg`. + Each dict contains keys "id", "category_id", "isthing". + """ + panoptic_seg = torch.zeros_like(semantic_results, dtype=torch.int32) + + # sort instance outputs by scores + sorted_inds = torch.argsort(-instance_results.scores) + + current_segment_id = 0 + segments_info = [] + + instance_masks = instance_results.pred_masks.to(dtype=torch.bool, device=panoptic_seg.device) + + # Add instances one-by-one, check for overlaps with existing ones + for inst_id in sorted_inds: + score = instance_results.scores[inst_id].item() + if score < instances_confidence_threshold: + break + mask = instance_masks[inst_id] # H,W + mask_area = mask.sum().item() + + if mask_area == 0: + continue + + intersect = (mask > 0) & (panoptic_seg > 0) + intersect_area = intersect.sum().item() + + if intersect_area * 1.0 / mask_area > overlap_threshold: + continue + + if intersect_area > 0: + mask = mask & (panoptic_seg == 0) + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + segments_info.append( + { + "id": current_segment_id, + "isthing": True, + "score": score, + "category_id": instance_results.pred_classes[inst_id].item(), + "instance_id": inst_id.item(), + } + ) + + # Add semantic results to remaining empty areas + semantic_labels = torch.unique(semantic_results).cpu().tolist() + for semantic_label in semantic_labels: + if semantic_label == 0: # 0 is a special "thing" class + continue + mask = (semantic_results == semantic_label) & (panoptic_seg == 0) + mask_area = mask.sum().item() + if mask_area < stuff_area_limit: + continue + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + segments_info.append( + { + "id": current_segment_id, + "isthing": False, + "category_id": semantic_label, + "area": mask_area, + } + ) + + return panoptic_seg, segments_info diff --git a/detectron2/modeling/meta_arch/rcnn.py b/detectron2/modeling/meta_arch/rcnn.py new file mode 100644 index 0000000000..5fefe64654 --- /dev/null +++ b/detectron2/modeling/meta_arch/rcnn.py @@ -0,0 +1,204 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import torch +from torch import nn + +from detectron2.structures import ImageList +from detectron2.utils.logger import log_first_n + +from ..backbone import build_backbone +from ..postprocessing import detector_postprocess +from ..proposal_generator import build_proposal_generator +from ..roi_heads import build_roi_heads +from .build import META_ARCH_REGISTRY + +__all__ = ["GeneralizedRCNN", "ProposalNetwork"] + + +@META_ARCH_REGISTRY.register() +class GeneralizedRCNN(nn.Module): + """ + Generalized R-CNN. Any models that contains the following three components: + 1. Per-image feature extraction (aka backbone) + 2. Region proposal generation + 3. Per-region feature extraction and prediction + """ + + def __init__(self, cfg): + super().__init__() + + self.device = torch.device(cfg.MODEL.DEVICE) + self.backbone = build_backbone(cfg) + self.proposal_generator = build_proposal_generator(cfg, self.backbone.output_shape()) + self.roi_heads = build_roi_heads(cfg, self.backbone.output_shape()) + + assert len(cfg.MODEL.PIXEL_MEAN) == len(cfg.MODEL.PIXEL_STD) + num_channels = len(cfg.MODEL.PIXEL_MEAN) + pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(num_channels, 1, 1) + pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(num_channels, 1, 1) + self.normalizer = lambda x: (x - pixel_mean) / pixel_std + self.to(self.device) + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper` . + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + + * image: Tensor, image in (C, H, W) format. + * instances (optional): groundtruth :class:`Instances` + * proposals (optional): :class:`Instances`, precomputed proposals. + + Other information that's included in the original dicts, such as: + + * "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + list[dict]: + Each dict is the output for one input image. + The dict contains one key "instances" whose value is a :class:`Instances`. + The :class:`Instances` object has the following keys: + "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints" + """ + if not self.training: + return self.inference(batched_inputs) + + images = self.preprocess_image(batched_inputs) + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + elif "targets" in batched_inputs[0]: + log_first_n( + logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10 + ) + gt_instances = [x["targets"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + + features = self.backbone(images.tensor) + + if self.proposal_generator: + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + else: + assert "proposals" in batched_inputs[0] + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + proposal_losses = {} + + _, detector_losses = self.roi_heads(images, features, proposals, gt_instances) + + losses = {} + losses.update(detector_losses) + losses.update(proposal_losses) + return losses + + def inference(self, batched_inputs, detected_instances=None, do_postprocess=True): + """ + Run inference on the given inputs. + + Args: + batched_inputs (list[dict]): same as in :meth:`forward` + detected_instances (None or list[Instances]): if not None, it + contains an `Instances` object per image. The `Instances` + object contains "pred_boxes" and "pred_classes" which are + known boxes in the image. + The inference will then skip the detection of bounding boxes, + and only predict other per-ROI outputs. + do_postprocess (bool): whether to apply post-processing on the outputs. + + Returns: + same as in :meth:`forward`. + """ + assert not self.training + + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + + if detected_instances is None: + if self.proposal_generator: + proposals, _ = self.proposal_generator(images, features, None) + else: + assert "proposals" in batched_inputs[0] + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + + results, _ = self.roi_heads(images, features, proposals, None) + else: + detected_instances = [x.to(self.device) for x in detected_instances] + results = self.roi_heads.forward_with_given_boxes(features, detected_instances) + + if do_postprocess: + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + results, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + else: + return results + + def preprocess_image(self, batched_inputs): + """ + Normalize, pad and batch the input images. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [self.normalizer(x) for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + return images + + +@META_ARCH_REGISTRY.register() +class ProposalNetwork(nn.Module): + def __init__(self, cfg): + super().__init__() + self.device = torch.device(cfg.MODEL.DEVICE) + + self.backbone = build_backbone(cfg) + self.proposal_generator = build_proposal_generator(cfg, self.backbone.output_shape()) + + pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(-1, 1, 1) + pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(-1, 1, 1) + self.normalizer = lambda x: (x - pixel_mean) / pixel_std + self.to(self.device) + + def forward(self, batched_inputs): + """ + Args: + Same as in :class:`GeneralizedRCNN.forward` + + Returns: + list[dict]: Each dict is the output for one input image. + The dict contains one key "proposals" whose value is a + :class:`Instances` with keys "proposal_boxes" and "objectness_logits". + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [self.normalizer(x) for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + features = self.backbone(images.tensor) + + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + elif "targets" in batched_inputs[0]: + log_first_n( + logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10 + ) + gt_instances = [x["targets"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + # In training, the proposals are not useful at all but we generate them anyway. + # This makes RPN-only models about 5% slower. + if self.training: + return proposal_losses + + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + proposals, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"proposals": r}) + return processed_results diff --git a/detectron2/modeling/meta_arch/retinanet.py b/detectron2/modeling/meta_arch/retinanet.py new file mode 100644 index 0000000000..2e9a1590ea --- /dev/null +++ b/detectron2/modeling/meta_arch/retinanet.py @@ -0,0 +1,430 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import math +from typing import List +import torch +from fvcore.nn import sigmoid_focal_loss_jit, smooth_l1_loss +from torch import nn + +from detectron2.layers import ShapeSpec, batched_nms, cat +from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou +from detectron2.utils.logger import log_first_n + +from ..anchor_generator import build_anchor_generator +from ..backbone import build_backbone +from ..box_regression import Box2BoxTransform +from ..matcher import Matcher +from ..postprocessing import detector_postprocess +from .build import META_ARCH_REGISTRY + +__all__ = ["RetinaNet"] + + +def permute_to_N_HWA_K(tensor, K): + """ + Transpose/reshape a tensor from (N, (A x K), H, W) to (N, (HxWxA), K) + """ + assert tensor.dim() == 4, tensor.shape + N, _, H, W = tensor.shape + tensor = tensor.view(N, -1, K, H, W) + tensor = tensor.permute(0, 3, 4, 1, 2) + tensor = tensor.reshape(N, -1, K) # Size=(N,HWA,K) + return tensor + + +def permute_all_cls_and_box_to_N_HWA_K_and_concat(box_cls, box_delta, num_classes=80): + """ + Rearrange the tensor layout from the network output, i.e.: + list[Tensor]: #lvl tensors of shape (N, A x K, Hi, Wi) + to per-image predictions, i.e.: + Tensor: of shape (N x sum(Hi x Wi x A), K) + """ + # for each feature level, permute the outputs to make them be in the + # same format as the labels. Note that the labels are computed for + # all feature levels concatenated, so we keep the same representation + # for the objectness and the box_delta + box_cls_flattened = [permute_to_N_HWA_K(x, num_classes) for x in box_cls] + box_delta_flattened = [permute_to_N_HWA_K(x, 4) for x in box_delta] + # concatenate on the first dimension (representing the feature levels), to + # take into account the way the labels were generated (with all feature maps + # being concatenated as well) + box_cls = cat(box_cls_flattened, dim=1).view(-1, num_classes) + box_delta = cat(box_delta_flattened, dim=1).view(-1, 4) + return box_cls, box_delta + + +@META_ARCH_REGISTRY.register() +class RetinaNet(nn.Module): + """ + Implement RetinaNet (https://arxiv.org/abs/1708.02002). + """ + + def __init__(self, cfg): + super().__init__() + + self.device = torch.device(cfg.MODEL.DEVICE) + + # fmt: off + self.num_classes = cfg.MODEL.RETINANET.NUM_CLASSES + self.in_features = cfg.MODEL.RETINANET.IN_FEATURES + # Loss parameters: + self.focal_loss_alpha = cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA + self.focal_loss_gamma = cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA + self.smooth_l1_loss_beta = cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA + # Inference parameters: + self.score_threshold = cfg.MODEL.RETINANET.SCORE_THRESH_TEST + self.topk_candidates = cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST + self.nms_threshold = cfg.MODEL.RETINANET.NMS_THRESH_TEST + self.max_detections_per_image = cfg.TEST.DETECTIONS_PER_IMAGE + # fmt: on + + self.backbone = build_backbone(cfg) + + backbone_shape = self.backbone.output_shape() + feature_shapes = [backbone_shape[f] for f in self.in_features] + self.head = RetinaNetHead(cfg, feature_shapes) + self.anchor_generator = build_anchor_generator(cfg, feature_shapes) + + # Matching and loss + self.box2box_transform = Box2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS) + self.matcher = Matcher( + cfg.MODEL.RETINANET.IOU_THRESHOLDS, + cfg.MODEL.RETINANET.IOU_LABELS, + allow_low_quality_matches=True, + ) + + pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(3, 1, 1) + pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(3, 1, 1) + self.normalizer = lambda x: (x - pixel_mean) / pixel_std + self.to(self.device) + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper` . + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + + * image: Tensor, image in (C, H, W) format. + * instances: Instances + + Other information that's included in the original dicts, such as: + + * "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + Returns: + dict[str: Tensor]: + mapping from a named loss to a tensor storing the loss. Used during training only. + """ + images = self.preprocess_image(batched_inputs) + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + elif "targets" in batched_inputs[0]: + log_first_n( + logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10 + ) + gt_instances = [x["targets"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + + features = self.backbone(images.tensor) + features = [features[f] for f in self.in_features] + box_cls, box_delta = self.head(features) + anchors = self.anchor_generator(features) + + if self.training: + gt_classes, gt_anchors_reg_deltas = self.get_ground_truth(anchors, gt_instances) + return self.losses(gt_classes, gt_anchors_reg_deltas, box_cls, box_delta) + else: + results = self.inference(box_cls, box_delta, anchors, images) + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + results, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + + def losses(self, gt_classes, gt_anchors_deltas, pred_class_logits, pred_anchor_deltas): + """ + Args: + For `gt_classes` and `gt_anchors_deltas` parameters, see + :meth:`RetinaNet.get_ground_truth`. + Their shapes are (N, R) and (N, R, 4), respectively, where R is + the total number of anchors across levels, i.e. sum(Hi x Wi x A) + For `pred_class_logits` and `pred_anchor_deltas`, see + :meth:`RetinaNetHead.forward`. + + Returns: + dict[str: Tensor]: + mapping from a named loss to a scalar tensor + storing the loss. Used during training only. The dict keys are: + "loss_cls" and "loss_box_reg" + """ + pred_class_logits, pred_anchor_deltas = permute_all_cls_and_box_to_N_HWA_K_and_concat( + pred_class_logits, pred_anchor_deltas, self.num_classes + ) # Shapes: (N x R, K) and (N x R, 4), respectively. + + gt_classes = gt_classes.flatten() + gt_anchors_deltas = gt_anchors_deltas.view(-1, 4) + + valid_idxs = gt_classes >= 0 + foreground_idxs = (gt_classes >= 0) & (gt_classes != self.num_classes) + num_foreground = foreground_idxs.sum() + + gt_classes_target = torch.zeros_like(pred_class_logits) + gt_classes_target[foreground_idxs, gt_classes[foreground_idxs]] = 1 + + # logits loss + loss_cls = sigmoid_focal_loss_jit( + pred_class_logits[valid_idxs], + gt_classes_target[valid_idxs], + alpha=self.focal_loss_alpha, + gamma=self.focal_loss_gamma, + reduction="sum", + ) / max(1, num_foreground) + + # regression loss + loss_box_reg = smooth_l1_loss( + pred_anchor_deltas[foreground_idxs], + gt_anchors_deltas[foreground_idxs], + beta=self.smooth_l1_loss_beta, + reduction="sum", + ) / max(1, num_foreground) + + return {"loss_cls": loss_cls, "loss_box_reg": loss_box_reg} + + @torch.no_grad() + def get_ground_truth(self, anchors, targets): + """ + Args: + anchors (list[list[Boxes]]): a list of N=#image elements. Each is a + list of #feature level Boxes. The Boxes contains anchors of + this image on the specific feature level. + targets (list[Instances]): a list of N `Instances`s. The i-th + `Instances` contains the ground-truth per-instance annotations + for the i-th input image. Specify `targets` during training only. + + Returns: + gt_classes (Tensor): + An integer tensor of shape (N, R) storing ground-truth + labels for each anchor. + R is the total number of anchors, i.e. the sum of Hi x Wi x A for all levels. + Anchors with an IoU with some target higher than the foreground threshold + are assigned their corresponding label in the [0, K-1] range. + Anchors whose IoU are below the background threshold are assigned + the label "K". Anchors whose IoU are between the foreground and background + thresholds are assigned a label "-1", i.e. ignore. + gt_anchors_deltas (Tensor): + Shape (N, R, 4). + The last dimension represents ground-truth box2box transform + targets (dx, dy, dw, dh) that map each anchor to its matched ground-truth box. + The values in the tensor are meaningful only when the corresponding + anchor is labeled as foreground. + """ + gt_classes = [] + gt_anchors_deltas = [] + anchors = [Boxes.cat(anchors_i) for anchors_i in anchors] + # list[Tensor(R, 4)], one for each image + + for anchors_per_image, targets_per_image in zip(anchors, targets): + match_quality_matrix = pairwise_iou(targets_per_image.gt_boxes, anchors_per_image) + gt_matched_idxs, anchor_labels = self.matcher(match_quality_matrix) + + # ground truth box regression + matched_gt_boxes = targets_per_image[gt_matched_idxs].gt_boxes + gt_anchors_reg_deltas_i = self.box2box_transform.get_deltas( + anchors_per_image.tensor, matched_gt_boxes.tensor + ) + + # ground truth classes + has_gt = len(targets_per_image) > 0 + if has_gt: + gt_classes_i = targets_per_image.gt_classes[gt_matched_idxs] + # Anchors with label 0 are treated as background. + gt_classes_i[anchor_labels == 0] = self.num_classes + # Anchors with label -1 are ignored. + gt_classes_i[anchor_labels == -1] = -1 + else: + gt_classes_i = torch.zeros_like(gt_matched_idxs) + self.num_classes + + gt_classes.append(gt_classes_i) + gt_anchors_deltas.append(gt_anchors_reg_deltas_i) + + return torch.stack(gt_classes), torch.stack(gt_anchors_deltas) + + def inference(self, box_cls, box_delta, anchors, images): + """ + Arguments: + box_cls, box_delta: Same as the output of :meth:`RetinaNetHead.forward` + anchors (list[list[Boxes]]): a list of #images elements. Each is a + list of #feature level Boxes. The Boxes contain anchors of this + image on the specific feature level. + images (ImageList): the input images + + Returns: + results (List[Instances]): a list of #images elements. + """ + assert len(anchors) == len(images) + results = [] + + box_cls = [permute_to_N_HWA_K(x, self.num_classes) for x in box_cls] + box_delta = [permute_to_N_HWA_K(x, 4) for x in box_delta] + # list[Tensor], one per level, each has shape (N, Hi x Wi x A, K or 4) + + for img_idx, anchors_per_image in enumerate(anchors): + image_size = images.image_sizes[img_idx] + box_cls_per_image = [box_cls_per_level[img_idx] for box_cls_per_level in box_cls] + box_reg_per_image = [box_reg_per_level[img_idx] for box_reg_per_level in box_delta] + results_per_image = self.inference_single_image( + box_cls_per_image, box_reg_per_image, anchors_per_image, tuple(image_size) + ) + results.append(results_per_image) + return results + + def inference_single_image(self, box_cls, box_delta, anchors, image_size): + """ + Single-image inference. Return bounding-box detection results by thresholding + on scores and applying non-maximum suppression (NMS). + + Arguments: + box_cls (list[Tensor]): list of #feature levels. Each entry contains + tensor of size (H x W x A, K) + box_delta (list[Tensor]): Same shape as 'box_cls' except that K becomes 4. + anchors (list[Boxes]): list of #feature levels. Each entry contains + a Boxes object, which contains all the anchors for that + image in that feature level. + image_size (tuple(H, W)): a tuple of the image height and width. + + Returns: + Same as `inference`, but for only one image. + """ + boxes_all = [] + scores_all = [] + class_idxs_all = [] + + # Iterate over every feature level + for box_cls_i, box_reg_i, anchors_i in zip(box_cls, box_delta, anchors): + # (HxWxAxK,) + box_cls_i = box_cls_i.flatten().sigmoid_() + + # Keep top k top scoring indices only. + num_topk = min(self.topk_candidates, box_reg_i.size(0)) + # torch.sort is actually faster than .topk (at least on GPUs) + predicted_prob, topk_idxs = box_cls_i.sort(descending=True) + predicted_prob = predicted_prob[:num_topk] + topk_idxs = topk_idxs[:num_topk] + + # filter out the proposals with low confidence score + keep_idxs = predicted_prob > self.score_threshold + predicted_prob = predicted_prob[keep_idxs] + topk_idxs = topk_idxs[keep_idxs] + + anchor_idxs = topk_idxs // self.num_classes + classes_idxs = topk_idxs % self.num_classes + + box_reg_i = box_reg_i[anchor_idxs] + anchors_i = anchors_i[anchor_idxs] + # predict boxes + predicted_boxes = self.box2box_transform.apply_deltas(box_reg_i, anchors_i.tensor) + + boxes_all.append(predicted_boxes) + scores_all.append(predicted_prob) + class_idxs_all.append(classes_idxs) + + boxes_all, scores_all, class_idxs_all = [ + cat(x) for x in [boxes_all, scores_all, class_idxs_all] + ] + keep = batched_nms(boxes_all, scores_all, class_idxs_all, self.nms_threshold) + keep = keep[: self.max_detections_per_image] + + result = Instances(image_size) + result.pred_boxes = Boxes(boxes_all[keep]) + result.scores = scores_all[keep] + result.pred_classes = class_idxs_all[keep] + return result + + def preprocess_image(self, batched_inputs): + """ + Normalize, pad and batch the input images. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [self.normalizer(x) for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + return images + + +class RetinaNetHead(nn.Module): + """ + The head used in RetinaNet for object classification and box regression. + It has two subnets for the two tasks, with a common structure but separate parameters. + """ + + def __init__(self, cfg, input_shape: List[ShapeSpec]): + super().__init__() + # fmt: off + in_channels = input_shape[0].channels + num_classes = cfg.MODEL.RETINANET.NUM_CLASSES + num_convs = cfg.MODEL.RETINANET.NUM_CONVS + prior_prob = cfg.MODEL.RETINANET.PRIOR_PROB + num_anchors = build_anchor_generator(cfg, input_shape).num_cell_anchors + # fmt: on + assert ( + len(set(num_anchors)) == 1 + ), "Using different number of anchors between levels is not currently supported!" + num_anchors = num_anchors[0] + + cls_subnet = [] + bbox_subnet = [] + for _ in range(num_convs): + cls_subnet.append( + nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + ) + cls_subnet.append(nn.ReLU()) + bbox_subnet.append( + nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + ) + bbox_subnet.append(nn.ReLU()) + + self.cls_subnet = nn.Sequential(*cls_subnet) + self.bbox_subnet = nn.Sequential(*bbox_subnet) + self.cls_score = nn.Conv2d( + in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1 + ) + self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=3, stride=1, padding=1) + + # Initialization + for modules in [self.cls_subnet, self.bbox_subnet, self.cls_score, self.bbox_pred]: + for layer in modules.modules(): + if isinstance(layer, nn.Conv2d): + torch.nn.init.normal_(layer.weight, mean=0, std=0.01) + torch.nn.init.constant_(layer.bias, 0) + + # Use prior in model initialization to improve stability + bias_value = -math.log((1 - prior_prob) / prior_prob) + torch.nn.init.constant_(self.cls_score.bias, bias_value) + + def forward(self, features): + """ + Arguments: + features (list[Tensor]): FPN feature map tensors in high to low resolution. + Each tensor in the list correspond to different feature levels. + + Returns: + logits (list[Tensor]): #lvl tensors, each has shape (N, AxK, Hi, Wi). + The tensor predicts the classification probability + at each spatial position for each of the A anchors and K object + classes. + bbox_reg (list[Tensor]): #lvl tensors, each has shape (N, Ax4, Hi, Wi). + The tensor predicts 4-vector (dx,dy,dw,dh) box + regression values for every anchor. These values are the + relative offset between the anchor and the ground truth box. + """ + logits = [] + bbox_reg = [] + for feature in features: + logits.append(self.cls_score(self.cls_subnet(feature))) + bbox_reg.append(self.bbox_pred(self.bbox_subnet(feature))) + return logits, bbox_reg diff --git a/detectron2/modeling/meta_arch/semantic_seg.py b/detectron2/modeling/meta_arch/semantic_seg.py new file mode 100644 index 0000000000..722a18f550 --- /dev/null +++ b/detectron2/modeling/meta_arch/semantic_seg.py @@ -0,0 +1,170 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import numpy as np +from typing import Dict +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.layers import Conv2d, ShapeSpec +from detectron2.structures import ImageList +from detectron2.utils.registry import Registry + +from ..backbone import build_backbone +from ..postprocessing import sem_seg_postprocess +from .build import META_ARCH_REGISTRY + +__all__ = ["SemanticSegmentor", "SEM_SEG_HEADS_REGISTRY", "SemSegFPNHead", "build_sem_seg_head"] + + +SEM_SEG_HEADS_REGISTRY = Registry("SEM_SEG_HEADS") +""" +Registry for semantic segmentation heads, which make semantic segmentation predictions +from feature maps. +""" + + +@META_ARCH_REGISTRY.register() +class SemanticSegmentor(nn.Module): + """ + Main class for semantic segmentation architectures. + """ + + def __init__(self, cfg): + super().__init__() + + self.device = torch.device(cfg.MODEL.DEVICE) + + self.backbone = build_backbone(cfg) + self.sem_seg_head = build_sem_seg_head(cfg, self.backbone.output_shape()) + + pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(-1, 1, 1) + pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(-1, 1, 1) + self.normalizer = lambda x: (x - pixel_mean) / pixel_std + + self.to(self.device) + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper` . + Each item in the list contains the inputs for one image. + + For now, each item in the list is a dict that contains: + image: Tensor, image in (C, H, W) format. + sem_seg: semantic segmentation ground truth + Other information that's included in the original dicts, such as: + "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + list[dict]: Each dict is the output for one input image. + The dict contains one key "sem_seg" whose value is a + Tensor of the output resolution that represents the + per-pixel segmentation prediction. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [self.normalizer(x) for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + + features = self.backbone(images.tensor) + + if "sem_seg" in batched_inputs[0]: + targets = [x["sem_seg"].to(self.device) for x in batched_inputs] + targets = ImageList.from_tensors( + targets, self.backbone.size_divisibility, self.sem_seg_head.ignore_value + ).tensor + else: + targets = None + results, losses = self.sem_seg_head(features, targets) + + if self.training: + return losses + + processed_results = [] + for result, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes): + height = input_per_image.get("height") + width = input_per_image.get("width") + r = sem_seg_postprocess(result, image_size, height, width) + processed_results.append({"sem_seg": r}) + return processed_results + + +def build_sem_seg_head(cfg, input_shape): + """ + Build a semantic segmentation head from `cfg.MODEL.SEM_SEG_HEAD.NAME`. + """ + name = cfg.MODEL.SEM_SEG_HEAD.NAME + return SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape) + + +@SEM_SEG_HEADS_REGISTRY.register() +class SemSegFPNHead(nn.Module): + """ + A semantic segmentation head described in detail in the Panoptic Feature Pyramid Networks paper + (https://arxiv.org/abs/1901.02446). It takes FPN features as input and merges information from + all levels of the FPN into single output. + """ + + def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]): + super().__init__() + + # fmt: off + self.in_features = cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + feature_strides = {k: v.stride for k, v in input_shape.items()} + feature_channels = {k: v.channels for k, v in input_shape.items()} + self.ignore_value = cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE + num_classes = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES + conv_dims = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + self.common_stride = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE + norm = cfg.MODEL.SEM_SEG_HEAD.NORM + self.loss_weight = cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT + # fmt: on + + self.scale_heads = [] + for in_feature in self.in_features: + head_ops = [] + head_length = max( + 1, int(np.log2(feature_strides[in_feature]) - np.log2(self.common_stride)) + ) + for k in range(head_length): + norm_module = nn.GroupNorm(32, conv_dims) if norm == "GN" else None + conv = Conv2d( + feature_channels[in_feature] if k == 0 else conv_dims, + conv_dims, + kernel_size=3, + stride=1, + padding=1, + bias=not norm, + norm=norm_module, + activation=F.relu, + ) + weight_init.c2_msra_fill(conv) + head_ops.append(conv) + if feature_strides[in_feature] != self.common_stride: + head_ops.append( + nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False) + ) + self.scale_heads.append(nn.Sequential(*head_ops)) + self.add_module(in_feature, self.scale_heads[-1]) + self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0) + weight_init.c2_msra_fill(self.predictor) + + def forward(self, features, targets=None): + for i, f in enumerate(self.in_features): + if i == 0: + x = self.scale_heads[i](features[f]) + else: + x = x + self.scale_heads[i](features[f]) + x = self.predictor(x) + x = F.interpolate(x, scale_factor=self.common_stride, mode="bilinear", align_corners=False) + + if self.training: + losses = {} + losses["loss_sem_seg"] = ( + F.cross_entropy(x, targets, reduction="mean", ignore_index=self.ignore_value) + * self.loss_weight + ) + return [], losses + else: + return x, {} diff --git a/detectron2/modeling/poolers.py b/detectron2/modeling/poolers.py new file mode 100644 index 0000000000..7978511e20 --- /dev/null +++ b/detectron2/modeling/poolers.py @@ -0,0 +1,194 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import math +import sys +import torch +from torch import nn +from torchvision.ops import RoIPool + +from detectron2.layers import ROIAlign, ROIAlignRotated, cat + + +def assign_boxes_to_levels(box_lists, min_level, max_level, canonical_box_size, canonical_level): + """ + Map each box in `box_lists` to a feature map level index and return the assignment + vector. + + Args: + box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, + where N is the number of images in the batch. + min_level (int): Smallest feature map level index. The input is considered index 0, + the output of stage 1 is index 1, and so. + max_level (int): Largest feature map level index. + canonical_box_size (int): A canonical box size in pixels (sqrt(box area)). + canonical_level (int): The feature map level index on which a canonically-sized box + should be placed. + + Returns: + A tensor of length M, where M is the total number of boxes aggregated over all + N batch images. The memory layout corresponds to the concatenation of boxes + from all images. Each element is the feature map index, as an offset from + `self.min_level`, for the corresponding box (so value i means the box is at + `self.min_level + i`). + """ + eps = sys.float_info.epsilon + box_sizes = torch.sqrt(cat([boxes.area() for boxes in box_lists])) + # Eqn.(1) in FPN paper + level_assignments = torch.floor( + canonical_level + torch.log2(box_sizes / canonical_box_size + eps) + ) + level_assignments = torch.clamp(level_assignments, min=min_level, max=max_level) + return level_assignments.to(torch.int64) - min_level + + +def convert_boxes_to_pooler_format(box_lists): + """ + Convert all boxes in `box_lists` to the low-level format used by ROI pooling ops + (see description under Returns). + + Args: + box_lists (list[Boxes] | list[RotatedBoxes]): + A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. + + Returns: + When input is list[Boxes]: + A tensor of shape (M, 5), where M is the total number of boxes aggregated over all + N batch images. + The 5 columns are (batch index, x0, y0, x1, y1), where batch index + is the index in [0, N) indentifying which batch image the box with corners at + (x0, y0, x1, y1) comes from. + When input is list[RotatedBoxes]: + A tensor of shape (M, 6), where M is the total number of boxes aggregated over all + N batch images. + The 6 columns are (batch index, x_ctr, y_ctr, width, height, angle_degrees), + where batch index is the index in [0, N) indentifying which batch image the + rotated box (x_ctr, y_ctr, width, height, angle_degrees) comes from. + """ + + def fmt_box_list(box_tensor, batch_index): + repeated_index = torch.full( + (len(box_tensor), 1), batch_index, dtype=box_tensor.dtype, device=box_tensor.device + ) + return cat((repeated_index, box_tensor), dim=1) + + pooler_fmt_boxes = cat( + [fmt_box_list(box_list.tensor, i) for i, box_list in enumerate(box_lists)], dim=0 + ) + + return pooler_fmt_boxes + + +class ROIPooler(nn.Module): + """ + Region of interest feature map pooler that supports pooling from one or more + feature maps. + """ + + def __init__( + self, + output_size, + scales, + sampling_ratio, + pooler_type, + canonical_box_size=224, + canonical_level=4, + ): + """ + Args: + output_size (int, tuple[int] or list[int]): output size of the pooled region, + e.g., 14 x 14. If tuple or list is given, the length must be 2. + scales (list[float]): The scale for each low-level pooling op relative to + the input image. For a feature map with stride s relative to the input + image, scale is defined as a 1 / s. + sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. + pooler_type (string): Name of the type of pooling operation that should be applied. + For instance, "ROIPool" or "ROIAlignV2". + canonical_box_size (int): A canonical box size in pixels (sqrt(box area)). The default + is heuristically defined as 224 pixels in the FPN paper (based on ImageNet + pre-training). + canonical_level (int): The feature map level index on which a canonically-sized box + should be placed. The default is defined as level 4 in the FPN paper. + """ + super(ROIPooler, self).__init__() + + if isinstance(output_size, int): + output_size = (output_size, output_size) + assert len(output_size) == 2 + assert isinstance(output_size[0], int) and isinstance(output_size[1], int) + self.output_size = output_size + + if pooler_type == "ROIAlign": + self.level_poolers = nn.ModuleList( + ROIAlign( + output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=False + ) + for scale in scales + ) + elif pooler_type == "ROIAlignV2": + self.level_poolers = nn.ModuleList( + ROIAlign( + output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=True + ) + for scale in scales + ) + elif pooler_type == "ROIPool": + self.level_poolers = nn.ModuleList( + RoIPool(output_size, spatial_scale=scale) for scale in scales + ) + elif pooler_type == "ROIAlignRotated": + self.level_poolers = nn.ModuleList( + ROIAlignRotated(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio) + for scale in scales + ) + else: + raise ValueError("Unknown pooler type: {}".format(pooler_type)) + + # Map scale (defined as 1 / stride) to its feature map level under the + # assumption that stride is a power of 2. + min_level = -math.log2(scales[0]) + max_level = -math.log2(scales[-1]) + assert math.isclose(min_level, int(min_level)) and math.isclose(max_level, int(max_level)) + self.min_level = int(min_level) + self.max_level = int(max_level) + assert 0 < self.min_level and self.min_level <= self.max_level + assert self.min_level <= canonical_level and canonical_level <= self.max_level + self.canonical_level = canonical_level + assert canonical_box_size > 0 + self.canonical_box_size = canonical_box_size + + def forward(self, x, box_lists): + """ + Args: + x (list[Tensor]): A list of feature maps with scales matching thosed used to + construct this module. + box_lists (list[Boxes] | list[RotatedBoxes]): + A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. + + Returns: + A tensor of shape (M, C, output_size, output_size) where M is the total number of + boxes aggregated over all N batch images and C is the number of channels in `x`. + """ + num_level_assignments = len(self.level_poolers) + pooler_fmt_boxes = convert_boxes_to_pooler_format(box_lists) + + if num_level_assignments == 1: + return self.level_poolers[0](x[0], pooler_fmt_boxes) + + level_assignments = assign_boxes_to_levels( + box_lists, self.min_level, self.max_level, self.canonical_box_size, self.canonical_level + ) + + num_boxes = len(pooler_fmt_boxes) + num_channels = x[0].shape[1] + output_size = self.output_size[0] + + dtype, device = x[0].dtype, x[0].device + output = torch.zeros( + (num_boxes, num_channels, output_size, output_size), dtype=dtype, device=device + ) + + for level, (x_level, pooler) in enumerate(zip(x, self.level_poolers)): + inds = torch.nonzero(level_assignments == level).squeeze(1) + pooler_fmt_boxes_level = pooler_fmt_boxes[inds] + output[inds] = pooler(x_level, pooler_fmt_boxes_level) + + return output diff --git a/detectron2/modeling/postprocessing.py b/detectron2/modeling/postprocessing.py new file mode 100644 index 0000000000..78477bf63c --- /dev/null +++ b/detectron2/modeling/postprocessing.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from torch.nn import functional as F + +from detectron2.layers import paste_masks_in_image +from detectron2.structures import Instances + + +def detector_postprocess(results, output_height, output_width, mask_threshold=0.5): + """ + Resize the output instances. + The input images are often resized when entering an object detector. + As a result, we often need the outputs of the detector in a different + resolution from its inputs. + + This function will resize the raw outputs of an R-CNN detector + to produce outputs according to the desired output resolution. + + Args: + results (Instances): the raw outputs from the detector. + `results.image_size` contains the input image resolution the detector sees. + This object might be modified in-place. + output_height, output_width: the desired output resolution. + + Returns: + Instances: the resized output from the model, based on the output resolution + """ + scale_x, scale_y = (output_width / results.image_size[1], output_height / results.image_size[0]) + results = Instances((output_height, output_width), **results.get_fields()) + + if results.has("pred_boxes"): + output_boxes = results.pred_boxes + elif results.has("proposal_boxes"): + output_boxes = results.proposal_boxes + + output_boxes.tensor[:, 0::2] *= scale_x + output_boxes.tensor[:, 1::2] *= scale_y + output_boxes.clip(results.image_size) + + results = results[output_boxes.nonempty()] + + if results.has("pred_masks"): + results.pred_masks = paste_masks_in_image( + results.pred_masks[:, 0, :, :], # N, 1, M, M + results.pred_boxes, + results.image_size, + threshold=mask_threshold, + ) + + if results.has("pred_keypoints"): + results.pred_keypoints[:, :, 0] *= scale_x + results.pred_keypoints[:, :, 1] *= scale_y + + return results + + +def sem_seg_postprocess(result, img_size, output_height, output_width): + """ + Return semantic segmentation predictions in the original resolution. + + The input images are often resized when entering semantic segmentor. Moreover, in same + cases, they also padded inside segmentor to be divisible by maximum network stride. + As a result, we often need the predictions of the segmentor in a different + resolution from its inputs. + + Args: + result (Tensor): semantic segmentation prediction logits. A tensor of shape (C, H, W), + where C is the number of classes, and H, W are the height and width of the prediction. + img_size (tuple): image size that segmentor is taking as input. + output_height, output_width: the desired output resolution. + + Returns: + semantic segmenation prediction (Tensor): A tensor of the shape + (C, output_height, output_width) that contains per-pixel soft predictions. + """ + result = result[:, : img_size[0], : img_size[1]].expand(1, -1, -1, -1) + result = F.interpolate( + result, size=(output_height, output_width), mode="bilinear", align_corners=False + )[0] + return result diff --git a/detectron2/modeling/proposal_generator/__init__.py b/detectron2/modeling/proposal_generator/__init__.py new file mode 100644 index 0000000000..9eeeb3cb15 --- /dev/null +++ b/detectron2/modeling/proposal_generator/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .build import PROPOSAL_GENERATOR_REGISTRY, build_proposal_generator +from .rpn import RPN_HEAD_REGISTRY, build_rpn_head diff --git a/detectron2/modeling/proposal_generator/build.py b/detectron2/modeling/proposal_generator/build.py new file mode 100644 index 0000000000..6d24206c4f --- /dev/null +++ b/detectron2/modeling/proposal_generator/build.py @@ -0,0 +1,21 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from detectron2.utils.registry import Registry + +PROPOSAL_GENERATOR_REGISTRY = Registry("PROPOSAL_GENERATOR") +""" +Registry for proposal generator, which produces object proposals from feature maps. +""" + +from . import rpn, rrpn # noqa F401 isort:skip + + +def build_proposal_generator(cfg, input_shape): + """ + Build a proposal generator from `cfg.MODEL.PROPOSAL_GENERATOR.NAME`. + The name can be "PrecomputedProposals" to use no proposal generator. + """ + name = cfg.MODEL.PROPOSAL_GENERATOR.NAME + if name == "PrecomputedProposals": + return None + + return PROPOSAL_GENERATOR_REGISTRY.get(name)(cfg, input_shape) diff --git a/detectron2/modeling/proposal_generator/proposal_utils.py b/detectron2/modeling/proposal_generator/proposal_utils.py new file mode 100644 index 0000000000..89e585f1e8 --- /dev/null +++ b/detectron2/modeling/proposal_generator/proposal_utils.py @@ -0,0 +1,57 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import math +import torch + +from detectron2.structures import Instances + + +def add_ground_truth_to_proposals(gt_boxes, proposals): + """ + Call `add_ground_truth_to_proposals_single_image` for all images. + + Args: + gt_boxes(list[Boxes]): list of N elements. Element i is a Boxes + representing the gound-truth for image i. + proposals (list[Instances]): list of N elements. Element i is a Instances + representing the proposals for image i. + + Returns: + list[Instances]: list of N Instances. Each is the proposals for the image, + with field "proposal_boxes" and "objectness_logits". + """ + assert gt_boxes is not None + + assert len(proposals) == len(gt_boxes) + if len(proposals) == 0: + return proposals + + return [ + add_ground_truth_to_proposals_single_image(gt_boxes_i, proposals_i) + for gt_boxes_i, proposals_i in zip(gt_boxes, proposals) + ] + + +def add_ground_truth_to_proposals_single_image(gt_boxes, proposals): + """ + Augment `proposals` with ground-truth boxes from `gt_boxes`. + + Args: + Same as `add_ground_truth_to_proposals`, but with gt_boxes and proposals + per image. + + Returns: + Same as `add_ground_truth_to_proposals`, but for only one image. + """ + device = proposals.objectness_logits.device + # Concating gt_boxes with proposals requires them to have the same fields + # Assign all ground-truth boxes an objectness logit corresponding to P(object) \approx 1. + gt_logit_value = math.log((1.0 - 1e-10) / (1 - (1.0 - 1e-10))) + + gt_logits = gt_logit_value * torch.ones(len(gt_boxes), device=device) + gt_proposal = Instances(proposals.image_size) + + gt_proposal.proposal_boxes = gt_boxes + gt_proposal.objectness_logits = gt_logits + new_proposals = Instances.cat([proposals, gt_proposal]) + + return new_proposals diff --git a/detectron2/modeling/proposal_generator/rpn.py b/detectron2/modeling/proposal_generator/rpn.py new file mode 100644 index 0000000000..bd8df0f2da --- /dev/null +++ b/detectron2/modeling/proposal_generator/rpn.py @@ -0,0 +1,188 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from typing import Dict, List +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.layers import ShapeSpec +from detectron2.utils.registry import Registry + +from ..anchor_generator import build_anchor_generator +from ..box_regression import Box2BoxTransform +from ..matcher import Matcher +from .build import PROPOSAL_GENERATOR_REGISTRY +from .rpn_outputs import RPNOutputs, find_top_rpn_proposals + +RPN_HEAD_REGISTRY = Registry("RPN_HEAD") +""" +Registry for RPN heads, which take feature maps and perform +objectness classification and bounding box regression for anchors. +""" + + +def build_rpn_head(cfg, input_shape): + """ + Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`. + """ + name = cfg.MODEL.RPN.HEAD_NAME + return RPN_HEAD_REGISTRY.get(name)(cfg, input_shape) + + +@RPN_HEAD_REGISTRY.register() +class StandardRPNHead(nn.Module): + """ + RPN classification and regression heads. Uses a 3x3 conv to produce a shared + hidden state from which one 1x1 conv predicts objectness logits for each anchor + and a second 1x1 conv predicts bounding-box deltas specifying how to deform + each anchor into an object proposal. + """ + + def __init__(self, cfg, input_shape: List[ShapeSpec]): + super().__init__() + + # Standard RPN is shared across levels: + in_channels = [s.channels for s in input_shape] + assert len(set(in_channels)) == 1, "Each level must have the same channel!" + in_channels = in_channels[0] + + # RPNHead should take the same input as anchor generator + # NOTE: it assumes that creating an anchor generator does not have unwanted side effect. + anchor_generator = build_anchor_generator(cfg, input_shape) + num_cell_anchors = anchor_generator.num_cell_anchors + box_dim = anchor_generator.box_dim + assert ( + len(set(num_cell_anchors)) == 1 + ), "Each level must have the same number of cell anchors" + num_cell_anchors = num_cell_anchors[0] + + # 3x3 conv for the hidden representation + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + # 1x1 conv for predicting objectness logits + self.objectness_logits = nn.Conv2d(in_channels, num_cell_anchors, kernel_size=1, stride=1) + # 1x1 conv for predicting box2box transform deltas + self.anchor_deltas = nn.Conv2d( + in_channels, num_cell_anchors * box_dim, kernel_size=1, stride=1 + ) + + for l in [self.conv, self.objectness_logits, self.anchor_deltas]: + nn.init.normal_(l.weight, std=0.01) + nn.init.constant_(l.bias, 0) + + def forward(self, features): + """ + Args: + features (list[Tensor]): list of feature maps + """ + pred_objectness_logits = [] + pred_anchor_deltas = [] + for x in features: + t = F.relu(self.conv(x)) + pred_objectness_logits.append(self.objectness_logits(t)) + pred_anchor_deltas.append(self.anchor_deltas(t)) + return pred_objectness_logits, pred_anchor_deltas + + +@PROPOSAL_GENERATOR_REGISTRY.register() +class RPN(nn.Module): + """ + Region Proposal Network, introduced by the Faster R-CNN paper. + """ + + def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]): + super().__init__() + + # fmt: off + self.min_box_side_len = cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE + self.in_features = cfg.MODEL.RPN.IN_FEATURES + self.nms_thresh = cfg.MODEL.RPN.NMS_THRESH + self.batch_size_per_image = cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE + self.positive_fraction = cfg.MODEL.RPN.POSITIVE_FRACTION + self.smooth_l1_beta = cfg.MODEL.RPN.SMOOTH_L1_BETA + self.loss_weight = cfg.MODEL.RPN.LOSS_WEIGHT + # fmt: on + + # Map from self.training state to train/test settings + self.pre_nms_topk = { + True: cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN, + False: cfg.MODEL.RPN.PRE_NMS_TOPK_TEST, + } + self.post_nms_topk = { + True: cfg.MODEL.RPN.POST_NMS_TOPK_TRAIN, + False: cfg.MODEL.RPN.POST_NMS_TOPK_TEST, + } + self.boundary_threshold = cfg.MODEL.RPN.BOUNDARY_THRESH + + self.anchor_generator = build_anchor_generator( + cfg, [input_shape[f] for f in self.in_features] + ) + self.box2box_transform = Box2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS) + self.anchor_matcher = Matcher( + cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS, allow_low_quality_matches=True + ) + self.rpn_head = build_rpn_head(cfg, [input_shape[f] for f in self.in_features]) + + def forward(self, images, features, gt_instances=None): + """ + Args: + images (ImageList): input images of length `N` + features (dict[str: Tensor]): input data as a mapping from feature + map name to tensor. Axis 0 represents the number of images `N` in + the input data; axes 1-3 are channels, height, and width, which may + vary between feature maps (e.g., if a feature pyramid is used). + gt_instances (list[Instances], optional): a length `N` list of `Instances`s. + Each `Instances` stores ground-truth instances for the corresponding image. + + Returns: + proposals: list[Instances] or None + loss: dict[Tensor] + """ + gt_boxes = [x.gt_boxes for x in gt_instances] if gt_instances is not None else None + del gt_instances + features = [features[f] for f in self.in_features] + pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features) + anchors = self.anchor_generator(features) + # TODO: The anchors only depend on the feature map shape; there's probably + # an opportunity for some optimizations (e.g., caching anchors). + outputs = RPNOutputs( + self.box2box_transform, + self.anchor_matcher, + self.batch_size_per_image, + self.positive_fraction, + images, + pred_objectness_logits, + pred_anchor_deltas, + anchors, + self.boundary_threshold, + gt_boxes, + self.smooth_l1_beta, + ) + + if self.training: + losses = {k: v * self.loss_weight for k, v in outputs.losses().items()} + else: + losses = {} + + with torch.no_grad(): + # Find the top proposals by applying NMS and removing boxes that + # are too small. The proposals are treated as fixed for approximate + # joint training with roi heads. This approach ignores the derivative + # w.r.t. the proposal boxes’ coordinates that are also network + # responses, so is approximate. + proposals = find_top_rpn_proposals( + outputs.predict_proposals(), + outputs.predict_objectness_logits(), + images, + self.nms_thresh, + self.pre_nms_topk[self.training], + self.post_nms_topk[self.training], + self.min_box_side_len, + self.training, + ) + # For RPN-only models, the proposals are the final output and we return them in + # high-to-low confidence order. + # For end-to-end models, the RPN proposals are an intermediate state + # and this sorting is actually not needed. But the cost is negligible. + inds = [p.objectness_logits.sort(descending=True)[1] for p in proposals] + proposals = [p[ind] for p, ind in zip(proposals, inds)] + + return proposals, losses diff --git a/detectron2/modeling/proposal_generator/rpn_outputs.py b/detectron2/modeling/proposal_generator/rpn_outputs.py new file mode 100644 index 0000000000..2d128177ab --- /dev/null +++ b/detectron2/modeling/proposal_generator/rpn_outputs.py @@ -0,0 +1,436 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import itertools +import logging +import numpy as np +import torch +import torch.nn.functional as F +from fvcore.nn import smooth_l1_loss + +from detectron2.layers import batched_nms, cat +from detectron2.structures import Boxes, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage + +from ..sampling import subsample_labels + +logger = logging.getLogger(__name__) + +# TODO: comments for future refactoring of this module +# +# From @rbg: +# This code involves a significant amount of tensor reshaping and permuting. Look for +# ways to simplify this. + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + L: number of feature maps per image on which RPN is run + A: number of cell anchors (must be the same for all feature maps) + Hi, Wi: height and width of the i-th feature map + 4: size of the box parameterization + +Naming convention: + + objectness: refers to the binary classification of an anchor as object vs. not + object. + + deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box + transform (see :class:`box_regression.Box2BoxTransform`). + + pred_objectness_logits: predicted objectness scores in [-inf, +inf]; use + sigmoid(pred_objectness_logits) to estimate P(object). + + gt_objectness_logits: ground-truth binary classification labels for objectness + + pred_anchor_deltas: predicted box2box transform deltas + + gt_anchor_deltas: ground-truth box2box transform deltas +""" + + +def find_top_rpn_proposals( + proposals, + pred_objectness_logits, + images, + nms_thresh, + pre_nms_topk, + post_nms_topk, + min_box_side_len, + training, +): + """ + For each feature map, select the `pre_nms_topk` highest scoring proposals, + apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk` + highest scoring proposals among all the feature maps if `training` is True, + otherwise, returns the highest `post_nms_topk` scoring proposals for each + feature map. + + Args: + proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 4). + All proposal predictions on the feature maps. + pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A). + images (ImageList): Input images as an :class:`ImageList`. + nms_thresh (float): IoU threshold to use for NMS + pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS. + When RPN is run on multiple feature maps (as in FPN) this number is per + feature map. + post_nms_topk (int): number of top k scoring proposals to keep after applying NMS. + When RPN is run on multiple feature maps (as in FPN) this number is total, + over all feature maps. + min_box_side_len (float): minimum proposal box side length in pixels (absolute units + wrt input images). + training (bool): True if proposals are to be used in training, otherwise False. + This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..." + comment. + + Returns: + proposals (list[Instances]): list of N Instances. The i-th Instances + stores post_nms_topk object proposals for image i. + """ + image_sizes = images.image_sizes # in (h, w) order + num_images = len(image_sizes) + device = proposals[0].device + + # 1. Select top-k anchor for every level and every image + topk_scores = [] # #lvl Tensor, each of shape N x topk + topk_proposals = [] + level_ids = [] # #lvl Tensor, each of shape (topk,) + batch_idx = torch.arange(num_images, device=device) + for level_id, proposals_i, logits_i in zip( + itertools.count(), proposals, pred_objectness_logits + ): + Hi_Wi_A = logits_i.shape[1] + num_proposals_i = min(pre_nms_topk, Hi_Wi_A) + + # sort is faster than topk (https://github.com/pytorch/pytorch/issues/22812) + # topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) + logits_i, idx = logits_i.sort(descending=True, dim=1) + topk_scores_i = logits_i[batch_idx, :num_proposals_i] + topk_idx = idx[batch_idx, :num_proposals_i] + + # each is N x topk + topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 + + topk_proposals.append(topk_proposals_i) + topk_scores.append(topk_scores_i) + level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device)) + + # 2. Concat all levels together + topk_scores = cat(topk_scores, dim=1) + topk_proposals = cat(topk_proposals, dim=1) + level_ids = cat(level_ids, dim=0) + + # 3. For each image, run a per-level NMS, and choose topk results. + results = [] + for n, image_size in enumerate(image_sizes): + boxes = Boxes(topk_proposals[n]) + scores_per_img = topk_scores[n] + boxes.clip(image_size) + + # filter empty boxes + keep = boxes.nonempty(threshold=min_box_side_len) + lvl = level_ids + if keep.sum().item() != len(boxes): + boxes, scores_per_img, lvl = boxes[keep], scores_per_img[keep], level_ids[keep] + + keep = batched_nms(boxes.tensor, scores_per_img, lvl, nms_thresh) + # In Detectron1, there was different behavior during training vs. testing. + # (https://github.com/facebookresearch/Detectron/issues/459) + # During training, topk is over the proposals from *all* images in the training batch. + # During testing, it is over the proposals for each image separately. + # As a result, the training behavior becomes batch-dependent, + # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size. + # This bug is addressed in Detectron2 to make the behavior independent of batch size. + keep = keep[:post_nms_topk] + + res = Instances(image_size) + res.proposal_boxes = boxes[keep] + res.objectness_logits = scores_per_img[keep] + results.append(res) + return results + + +def rpn_losses( + gt_objectness_logits, + gt_anchor_deltas, + pred_objectness_logits, + pred_anchor_deltas, + smooth_l1_beta, +): + """ + Args: + gt_objectness_logits (Tensor): shape (N,), each element in {-1, 0, 1} representing + ground-truth objectness labels with: -1 = ignore; 0 = not object; 1 = object. + gt_anchor_deltas (Tensor): shape (N, box_dim), row i represents ground-truth + box2box transform targets (dx, dy, dw, dh) or (dx, dy, dw, dh, da) that map anchor i to + its matched ground-truth box. + pred_objectness_logits (Tensor): shape (N,), each element is a predicted objectness + logit. + pred_anchor_deltas (Tensor): shape (N, box_dim), each row is a predicted box2box + transform (dx, dy, dw, dh) or (dx, dy, dw, dh, da) + smooth_l1_beta (float): The transition point between L1 and L2 loss in + the smooth L1 loss function. When set to 0, the loss becomes L1. When + set to +inf, the loss becomes constant 0. + + Returns: + objectness_loss, localization_loss, both unnormalized (summed over samples). + """ + pos_masks = gt_objectness_logits == 1 + localization_loss = smooth_l1_loss( + pred_anchor_deltas[pos_masks], gt_anchor_deltas[pos_masks], smooth_l1_beta, reduction="sum" + ) + + valid_masks = gt_objectness_logits >= 0 + objectness_loss = F.binary_cross_entropy_with_logits( + pred_objectness_logits[valid_masks], + gt_objectness_logits[valid_masks].to(torch.float32), + reduction="sum", + ) + return objectness_loss, localization_loss + + +class RPNOutputs(object): + def __init__( + self, + box2box_transform, + anchor_matcher, + batch_size_per_image, + positive_fraction, + images, + pred_objectness_logits, + pred_anchor_deltas, + anchors, + boundary_threshold=0, + gt_boxes=None, + smooth_l1_beta=0.0, + ): + """ + Args: + box2box_transform (Box2BoxTransform): :class:`Box2BoxTransform` instance for + anchor-proposal tranformations. + anchor_matcher (Matcher): :class:`Matcher` instance for matching anchors to + ground-truth boxes; used to determine training labels. + batch_size_per_image (int): number of proposals to sample when training + positive_fraction (float): target fraction of sampled proposals that should be positive + images (ImageList): :class:`ImageList` instance representing N input images + pred_objectness_logits (list[Tensor]): A list of L elements. + Element i is a tensor of shape (N, A, Hi, Wi) representing + the predicted objectness logits for anchors. + pred_anchor_deltas (list[Tensor]): A list of L elements. Element i is a tensor of shape + (N, A*4, Hi, Wi) representing the predicted "deltas" used to transform anchors + to proposals. + anchors (list[list[Boxes]]): A list of N elements. Each element is a list of L + Boxes. The Boxes at (n, l) stores the entire anchor array for feature map l in image + n (i.e. the cell anchors repeated over all locations in feature map (n, l)). + boundary_threshold (int): if >= 0, then anchors that extend beyond the image + boundary by more than boundary_thresh are not used in training. Set to a very large + number or < 0 to disable this behavior. Only needed in training. + gt_boxes (list[Boxes], optional): A list of N elements. Element i a Boxes storing + the ground-truth ("gt") boxes for image i. + smooth_l1_beta (float): The transition point between L1 and L2 loss in + the smooth L1 loss function. When set to 0, the loss becomes L1. When + set to +inf, the loss becomes constant 0. + """ + self.box2box_transform = box2box_transform + self.anchor_matcher = anchor_matcher + self.batch_size_per_image = batch_size_per_image + self.positive_fraction = positive_fraction + self.pred_objectness_logits = pred_objectness_logits + self.pred_anchor_deltas = pred_anchor_deltas + + self.anchors = anchors + self.gt_boxes = gt_boxes + self.num_feature_maps = len(pred_objectness_logits) + self.num_images = len(images) + self.image_sizes = images.image_sizes + self.boundary_threshold = boundary_threshold + self.smooth_l1_beta = smooth_l1_beta + + def _get_ground_truth(self): + """ + Returns: + gt_objectness_logits: list of N tensors. Tensor i is a vector whose length is the + total number of anchors in image i (i.e., len(anchors[i])). Label values are + in {-1, 0, 1}, with meanings: -1 = ignore; 0 = negative class; 1 = positive class. + gt_anchor_deltas: list of N tensors. Tensor i has shape (len(anchors[i]), 4). + """ + gt_objectness_logits = [] + gt_anchor_deltas = [] + # Concatenate anchors from all feature maps into a single Boxes per image + anchors = [Boxes.cat(anchors_i) for anchors_i in self.anchors] + for image_size_i, anchors_i, gt_boxes_i in zip(self.image_sizes, anchors, self.gt_boxes): + """ + image_size_i: (h, w) for the i-th image + anchors_i: anchors for i-th image + gt_boxes_i: ground-truth boxes for i-th image + """ + match_quality_matrix = pairwise_iou(gt_boxes_i, anchors_i) + matched_idxs, gt_objectness_logits_i = self.anchor_matcher(match_quality_matrix) + + if self.boundary_threshold >= 0: + # Discard anchors that go out of the boundaries of the image + # NOTE: This is legacy functionality that is turned off by default in Detectron2 + anchors_inside_image = anchors_i.inside_box(image_size_i, self.boundary_threshold) + gt_objectness_logits_i[~anchors_inside_image] = -1 + + if len(gt_boxes_i) == 0: + # These values won't be used anyway since the anchor is labeled as background + gt_anchor_deltas_i = torch.zeros_like(anchors_i.tensor) + else: + # TODO wasted computation for ignored boxes + matched_gt_boxes = gt_boxes_i[matched_idxs] + gt_anchor_deltas_i = self.box2box_transform.get_deltas( + anchors_i.tensor, matched_gt_boxes.tensor + ) + + gt_objectness_logits.append(gt_objectness_logits_i) + gt_anchor_deltas.append(gt_anchor_deltas_i) + + return gt_objectness_logits, gt_anchor_deltas + + def losses(self): + """ + Return the losses from a set of RPN predictions and their associated ground-truth. + + Returns: + dict[loss name -> loss value]: A dict mapping from loss name to loss value. + Loss names are: `loss_rpn_cls` for objectness classification and + `loss_rpn_loc` for proposal localization. + """ + + def resample(label): + """ + Randomly sample a subset of positive and negative examples by overwritting + the label vector to the ignore value (-1) for all elements that are not + included in the sample. + """ + pos_idx, neg_idx = subsample_labels( + label, self.batch_size_per_image, self.positive_fraction, 0 + ) + # Fill with the ignore label (-1), then set positive and negative labels + label.fill_(-1) + label.scatter_(0, pos_idx, 1) + label.scatter_(0, neg_idx, 0) + return label + + gt_objectness_logits, gt_anchor_deltas = self._get_ground_truth() + """ + gt_objectness_logits: list of N tensors. Tensor i is a vector whose length is the + total number of anchors in image i (i.e., len(anchors[i])) + gt_anchor_deltas: list of N tensors. Tensor i has shape (len(anchors[i]), B), + where B is the box dimension + """ + # Collect all objectness labels and delta targets over feature maps and images + # The final ordering is L, N, H, W, A from slowest to fastest axis. + num_anchors_per_map = [np.prod(x.shape[1:]) for x in self.pred_objectness_logits] + num_anchors_per_image = sum(num_anchors_per_map) + + # Stack to: (N, num_anchors_per_image) + gt_objectness_logits = torch.stack( + [resample(label) for label in gt_objectness_logits], dim=0 + ) + + # Log the number of positive/negative anchors per-image that's used in training + num_pos_anchors = (gt_objectness_logits == 1).sum().item() + num_neg_anchors = (gt_objectness_logits == 0).sum().item() + storage = get_event_storage() + storage.put_scalar("rpn/num_pos_anchors", num_pos_anchors / self.num_images) + storage.put_scalar("rpn/num_neg_anchors", num_neg_anchors / self.num_images) + + assert gt_objectness_logits.shape[1] == num_anchors_per_image + # Split to tuple of L tensors, each with shape (N, num_anchors_per_map) + gt_objectness_logits = torch.split(gt_objectness_logits, num_anchors_per_map, dim=1) + # Concat from all feature maps + gt_objectness_logits = cat([x.flatten() for x in gt_objectness_logits], dim=0) + + # Stack to: (N, num_anchors_per_image, B) + gt_anchor_deltas = torch.stack(gt_anchor_deltas, dim=0) + assert gt_anchor_deltas.shape[1] == num_anchors_per_image + B = gt_anchor_deltas.shape[2] # box dimension (4 or 5) + + # Split to tuple of L tensors, each with shape (N, num_anchors_per_image) + gt_anchor_deltas = torch.split(gt_anchor_deltas, num_anchors_per_map, dim=1) + # Concat from all feature maps + gt_anchor_deltas = cat([x.reshape(-1, B) for x in gt_anchor_deltas], dim=0) + + # Collect all objectness logits and delta predictions over feature maps + # and images to arrive at the same shape as the labels and targets + # The final ordering is L, N, H, W, A from slowest to fastest axis. + pred_objectness_logits = cat( + [ + # Reshape: (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N*Hi*Wi*A, ) + x.permute(0, 2, 3, 1).flatten() + for x in self.pred_objectness_logits + ], + dim=0, + ) + pred_anchor_deltas = cat( + [ + # Reshape: (N, A*B, Hi, Wi) -> (N, A, B, Hi, Wi) -> (N, Hi, Wi, A, B) + # -> (N*Hi*Wi*A, B) + x.view(x.shape[0], -1, B, x.shape[-2], x.shape[-1]) + .permute(0, 3, 4, 1, 2) + .reshape(-1, B) + for x in self.pred_anchor_deltas + ], + dim=0, + ) + + objectness_loss, localization_loss = rpn_losses( + gt_objectness_logits, + gt_anchor_deltas, + pred_objectness_logits, + pred_anchor_deltas, + self.smooth_l1_beta, + ) + normalizer = 1.0 / (self.batch_size_per_image * self.num_images) + loss_cls = objectness_loss * normalizer # cls: classification loss + loss_loc = localization_loss * normalizer # loc: localization loss + losses = {"loss_rpn_cls": loss_cls, "loss_rpn_loc": loss_loc} + + return losses + + def predict_proposals(self): + """ + Transform anchors into proposals by applying the predicted anchor deltas. + + Returns: + proposals (list[Tensor]): A list of L tensors. Tensor i has shape + (N, Hi*Wi*A, B), where B is box dimension (4 or 5). + """ + proposals = [] + # Transpose anchors from images-by-feature-maps (N, L) to feature-maps-by-images (L, N) + anchors = list(zip(*self.anchors)) + # For each feature map + for anchors_i, pred_anchor_deltas_i in zip(anchors, self.pred_anchor_deltas): + B = anchors_i[0].tensor.size(1) + N, _, Hi, Wi = pred_anchor_deltas_i.shape + # Reshape: (N, A*B, Hi, Wi) -> (N, A, B, Hi, Wi) -> (N, Hi, Wi, A, B) -> (N*Hi*Wi*A, B) + pred_anchor_deltas_i = ( + pred_anchor_deltas_i.view(N, -1, B, Hi, Wi).permute(0, 3, 4, 1, 2).reshape(-1, B) + ) + # Concatenate all anchors to shape (N*Hi*Wi*A, B) + # type(anchors_i[0]) is Boxes (B = 4) or RotatedBoxes (B = 5) + anchors_i = type(anchors_i[0]).cat(anchors_i) + proposals_i = self.box2box_transform.apply_deltas( + pred_anchor_deltas_i, anchors_i.tensor + ) + # Append feature map proposals with shape (N, Hi*Wi*A, B) + proposals.append(proposals_i.view(N, -1, B)) + return proposals + + def predict_objectness_logits(self): + """ + Return objectness logits in the same format as the proposals returned by + :meth:`predict_proposals`. + + Returns: + pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape + (N, Hi*Wi*A). + """ + pred_objectness_logits = [ + # Reshape: (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N, Hi*Wi*A) + score.permute(0, 2, 3, 1).reshape(self.num_images, -1) + for score in self.pred_objectness_logits + ] + return pred_objectness_logits diff --git a/detectron2/modeling/proposal_generator/rrpn.py b/detectron2/modeling/proposal_generator/rrpn.py new file mode 100644 index 0000000000..effac74119 --- /dev/null +++ b/detectron2/modeling/proposal_generator/rrpn.py @@ -0,0 +1,87 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +from typing import Dict +import torch + +from detectron2.layers import ShapeSpec + +from ..box_regression import Box2BoxTransformRotated +from .build import PROPOSAL_GENERATOR_REGISTRY +from .rpn import RPN +from .rrpn_outputs import RRPNOutputs, find_top_rrpn_proposals + +logger = logging.getLogger(__name__) + + +@PROPOSAL_GENERATOR_REGISTRY.register() +class RRPN(RPN): + """ + Rotated RPN subnetwork. + Please refer to https://arxiv.org/pdf/1703.01086.pdf for the original RRPN paper: + Ma, J., Shao, W., Ye, H., Wang, L., Wang, H., Zheng, Y., & Xue, X. (2018). + Arbitrary-oriented scene text detection via rotation proposals. + IEEE Transactions on Multimedia, 20(11), 3111-3122. + """ + + def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]): + super().__init__(cfg, input_shape) + self.box2box_transform = Box2BoxTransformRotated(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS) + + def forward(self, images, features, gt_instances=None): + """ + Args: + images (ImageList): input images of length `N` + features (dict[str: Tensor]): input data as a mapping from feature + map name to tensor. Axis 0 represents the number of images `N` in + the input data; axes 1-3 are channels, height, and width, which may + vary between feature maps (e.g., if a feature pyramid is used). + gt_instances (list[Instances], optional): a length `N` list of `Instances`s. + Each `Instances` stores ground-truth instances for the corresponding image. + + Returns: + proposals: list[Instances] or None + loss: dict[Tensor] + """ + gt_boxes = [x.gt_boxes for x in gt_instances] if gt_instances is not None else None + del gt_instances + features = [features[f] for f in self.in_features] + pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features) + anchors = self.anchor_generator(features) + + outputs = RRPNOutputs( + self.box2box_transform, + self.anchor_matcher, + self.batch_size_per_image, + self.positive_fraction, + images, + pred_objectness_logits, + pred_anchor_deltas, + anchors, + self.boundary_threshold, + gt_boxes, + self.smooth_l1_beta, + ) + + if self.training: + losses = outputs.losses() + else: + losses = {} + + with torch.no_grad(): + # Find the top proposals by applying NMS and removing boxes that + # are too small. The proposals are treated as fixed for approximate + # joint training with roi heads. This approach ignores the derivative + # w.r.t. the proposal boxes’ coordinates that are also network + # responses, so is approximate. + proposals = find_top_rrpn_proposals( + outputs.predict_proposals(), + outputs.predict_objectness_logits(), + images, + self.nms_thresh, + self.pre_nms_topk[self.training], + self.post_nms_topk[self.training], + self.min_box_side_len, + self.training, + ) + + return proposals, losses diff --git a/detectron2/modeling/proposal_generator/rrpn_outputs.py b/detectron2/modeling/proposal_generator/rrpn_outputs.py new file mode 100644 index 0000000000..4bbbd2b90f --- /dev/null +++ b/detectron2/modeling/proposal_generator/rrpn_outputs.py @@ -0,0 +1,240 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import itertools +import logging +import torch + +from detectron2.layers import batched_nms_rotated, cat +from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated + +from .rpn_outputs import RPNOutputs + +logger = logging.getLogger(__name__) + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + L: number of feature maps per image on which RRPN is run + A: number of cell anchors (must be the same for all feature maps) + Hi, Wi: height and width of the i-th feature map + 5: size of the box parameterization + +Naming convention: + + objectness: refers to the binary classification of an anchor as object vs. not + object. + + deltas: refers to the 5-d (dx, dy, dw, dh, da) deltas that parameterize the rotated box2box + transform (see :class:`box_regression.Box2BoxTransformRotated`). + + pred_objectness_logits: predicted objectness scores in [-inf, +inf]; use + sigmoid(pred_objectness_logits) to estimate P(object). + + gt_objectness_logits: ground-truth binary classification labels for objectness + + pred_anchor_deltas: predicted rotated box2box transform deltas + + gt_anchor_deltas: ground-truth rotated box2box transform deltas +""" + + +def find_top_rrpn_proposals( + proposals, + pred_objectness_logits, + images, + nms_thresh, + pre_nms_topk, + post_nms_topk, + min_box_side_len, + training, +): + """ + For each feature map, select the `pre_nms_topk` highest scoring proposals, + apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk` + highest scoring proposals among all the feature maps if `training` is True, + otherwise, returns the highest `post_nms_topk` scoring proposals for each + feature map. + + Args: + proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 5). + All proposal predictions on the feature maps. + pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A). + images (ImageList): Input images as an :class:`ImageList`. + nms_thresh (float): IoU threshold to use for NMS + pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS. + When RRPN is run on multiple feature maps (as in FPN) this number is per + feature map. + post_nms_topk (int): number of top k scoring proposals to keep after applying NMS. + When RRPN is run on multiple feature maps (as in FPN) this number is total, + over all feature maps. + min_box_side_len (float): minimum proposal box side length in pixels (absolute units + wrt input images). + training (bool): True if proposals are to be used in training, otherwise False. + This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..." + comment. + + Returns: + proposals (list[Instances]): list of N Instances. The i-th Instances + stores post_nms_topk object proposals for image i. + """ + image_sizes = images.image_sizes # in (h, w) order + num_images = len(image_sizes) + device = proposals[0].device + + # 1. Select top-k anchor for every level and every image + topk_scores = [] # #lvl Tensor, each of shape N x topk + topk_proposals = [] + level_ids = [] # #lvl Tensor, each of shape (topk,) + batch_idx = torch.arange(num_images, device=device) + for level_id, proposals_i, logits_i in zip( + itertools.count(), proposals, pred_objectness_logits + ): + Hi_Wi_A = logits_i.shape[1] + num_proposals_i = min(pre_nms_topk, Hi_Wi_A) + + # sort is faster than topk (https://github.com/pytorch/pytorch/issues/22812) + # topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) + logits_i, idx = logits_i.sort(descending=True, dim=1) + topk_scores_i = logits_i[batch_idx, :num_proposals_i] + topk_idx = idx[batch_idx, :num_proposals_i] + + # each is N x topk + topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 5 + + topk_proposals.append(topk_proposals_i) + topk_scores.append(topk_scores_i) + level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device)) + + # 2. Concat all levels together + topk_scores = cat(topk_scores, dim=1) + topk_proposals = cat(topk_proposals, dim=1) + level_ids = cat(level_ids, dim=0) + + # 3. For each image, run a per-level NMS, and choose topk results. + results = [] + for n, image_size in enumerate(image_sizes): + boxes = RotatedBoxes(topk_proposals[n]) + scores_per_img = topk_scores[n] + boxes.clip(image_size) + + # filter empty boxes + keep = boxes.nonempty(threshold=min_box_side_len) + lvl = level_ids + if keep.sum().item() != len(boxes): + boxes, scores_per_img, lvl = (boxes[keep], scores_per_img[keep], level_ids[keep]) + + keep = batched_nms_rotated(boxes.tensor, scores_per_img, lvl, nms_thresh) + # In Detectron1, there was different behavior during training vs. testing. + # (https://github.com/facebookresearch/Detectron/issues/459) + # During training, topk is over the proposals from *all* images in the training batch. + # During testing, it is over the proposals for each image separately. + # As a result, the training behavior becomes batch-dependent, + # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size. + # This bug is addressed in Detectron2 to make the behavior independent of batch size. + keep = keep[:post_nms_topk] + + res = Instances(image_size) + res.proposal_boxes = boxes[keep] + res.objectness_logits = scores_per_img[keep] + results.append(res) + return results + + +class RRPNOutputs(RPNOutputs): + def __init__( + self, + box2box_transform, + anchor_matcher, + batch_size_per_image, + positive_fraction, + images, + pred_objectness_logits, + pred_anchor_deltas, + anchors, + boundary_threshold=0, + gt_boxes=None, + smooth_l1_beta=0.0, + ): + """ + Args: + box2box_transform (Box2BoxTransformRotated): :class:`Box2BoxTransformRotated` + instance for anchor-proposal tranformations. + anchor_matcher (Matcher): :class:`Matcher` instance for matching anchors to + ground-truth boxes; used to determine training labels. + batch_size_per_image (int): number of proposals to sample when training + positive_fraction (float): target fraction of sampled proposals that should be positive + images (ImageList): :class:`ImageList` instance representing N input images + pred_objectness_logits (list[Tensor]): A list of L elements. + Element i is a tensor of shape (N, A, Hi, Wi) representing + the predicted objectness logits for anchors. + pred_anchor_deltas (list[Tensor]): A list of L elements. Element i is a tensor of shape + (N, A*5, Hi, Wi) representing the predicted "deltas" used to transform anchors + to proposals. + anchors (list[list[RotatedBoxes]]): A list of N elements. Each element is a list of L + RotatedBoxes. The RotatedBoxes at (n, l) stores the entire anchor array for + feature map l in image n (i.e. the cell anchors repeated over all locations in + feature map (n, l)). + boundary_threshold (int): if >= 0, then anchors that extend beyond the image + boundary by more than boundary_thresh are not used in training. Set to a very large + number or < 0 to disable this behavior. Only needed in training. + gt_boxes (list[RotatedBoxes], optional): A list of N elements. Element i a RotatedBoxes + storing the ground-truth ("gt") rotated boxes for image i. + smooth_l1_beta (float): The transition point between L1 and L2 loss in + the smooth L1 loss function. When set to 0, the loss becomes L1. When + set to +inf, the loss becomes constant 0. + """ + super(RRPNOutputs, self).__init__( + box2box_transform, + anchor_matcher, + batch_size_per_image, + positive_fraction, + images, + pred_objectness_logits, + pred_anchor_deltas, + anchors, + boundary_threshold, + gt_boxes, + smooth_l1_beta, + ) + + def _get_ground_truth(self): + """ + Returns: + gt_objectness_logits: list of N tensors. Tensor i is a vector whose length is the + total number of anchors in image i (i.e., len(anchors[i])). Label values are + in {-1, 0, 1}, with meanings: -1 = ignore; 0 = negative class; 1 = positive class. + gt_anchor_deltas: list of N tensors. Tensor i has shape (len(anchors[i]), 5). + """ + gt_objectness_logits = [] + gt_anchor_deltas = [] + # Concatenate anchors from all feature maps into a single RotatedBoxes per image + anchors = [RotatedBoxes.cat(anchors_i) for anchors_i in self.anchors] + for image_size_i, anchors_i, gt_boxes_i in zip(self.image_sizes, anchors, self.gt_boxes): + """ + image_size_i: (h, w) for the i-th image + anchors_i: anchors for i-th image + gt_boxes_i: ground-truth boxes for i-th image + """ + match_quality_matrix = pairwise_iou_rotated(gt_boxes_i, anchors_i) + matched_idxs, gt_objectness_logits_i = self.anchor_matcher(match_quality_matrix) + + if self.boundary_threshold >= 0: + # Discard anchors that go out of the boundaries of the image + # NOTE: This is legacy functionality that is turned off by default in Detectron2 + anchors_inside_image = anchors_i.inside_box(image_size_i, self.boundary_threshold) + gt_objectness_logits_i[~anchors_inside_image] = -1 + + if len(gt_boxes_i) == 0: + # These values won't be used anyway since the anchor is labeled as background + gt_anchor_deltas_i = torch.zeros_like(anchors_i.tensor) + else: + # TODO wasted computation for ignored boxes + matched_gt_boxes = gt_boxes_i[matched_idxs] + gt_anchor_deltas_i = self.box2box_transform.get_deltas( + anchors_i.tensor, matched_gt_boxes.tensor + ) + + gt_objectness_logits.append(gt_objectness_logits_i) + gt_anchor_deltas.append(gt_anchor_deltas_i) + + return gt_objectness_logits, gt_anchor_deltas diff --git a/detectron2/modeling/roi_heads/__init__.py b/detectron2/modeling/roi_heads/__init__.py new file mode 100644 index 0000000000..390bd17f0e --- /dev/null +++ b/detectron2/modeling/roi_heads/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .box_head import ROI_BOX_HEAD_REGISTRY, build_box_head +from .keypoint_head import ROI_KEYPOINT_HEAD_REGISTRY, build_keypoint_head +from .mask_head import ROI_MASK_HEAD_REGISTRY, build_mask_head +from .roi_heads import ROI_HEADS_REGISTRY, ROIHeads, StandardROIHeads, build_roi_heads + +from . import cascade_rcnn # isort:skip diff --git a/detectron2/modeling/roi_heads/box_head.py b/detectron2/modeling/roi_heads/box_head.py new file mode 100644 index 0000000000..d2fc0323ba --- /dev/null +++ b/detectron2/modeling/roi_heads/box_head.py @@ -0,0 +1,91 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import numpy as np +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.utils.registry import Registry + +ROI_BOX_HEAD_REGISTRY = Registry("ROI_BOX_HEAD") +""" +Registry for box heads, which make box predictions from per-region features. +""" + + +@ROI_BOX_HEAD_REGISTRY.register() +class FastRCNNConvFCHead(nn.Module): + """ + A head with several 3x3 conv layers (each followed by norm & relu) and + several fc layers (each followed by relu). + """ + + def __init__(self, cfg, input_shape: ShapeSpec): + """ + The following attributes are parsed from config: + num_conv, num_fc: the number of conv/fc layers + conv_dim/fc_dim: the dimension of the conv/fc layers + norm: normalization for the conv layers + """ + super().__init__() + + # fmt: off + num_conv = cfg.MODEL.ROI_BOX_HEAD.NUM_CONV + conv_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_DIM + num_fc = cfg.MODEL.ROI_BOX_HEAD.NUM_FC + fc_dim = cfg.MODEL.ROI_BOX_HEAD.FC_DIM + norm = cfg.MODEL.ROI_BOX_HEAD.NORM + # fmt: on + assert num_conv + num_fc > 0 + + self._output_size = (input_shape.channels, input_shape.height, input_shape.width) + + self.conv_norm_relus = [] + for k in range(num_conv): + conv = Conv2d( + self._output_size[0], + conv_dim, + kernel_size=3, + padding=1, + bias=not norm, + norm=get_norm(norm, conv_dim), + activation=F.relu, + ) + self.add_module("conv{}".format(k + 1), conv) + self.conv_norm_relus.append(conv) + self._output_size = (conv_dim, self._output_size[1], self._output_size[2]) + + self.fcs = [] + for k in range(num_fc): + fc = nn.Linear(np.prod(self._output_size), fc_dim) + self.add_module("fc{}".format(k + 1), fc) + self.fcs.append(fc) + self._output_size = fc_dim + + for layer in self.conv_norm_relus: + weight_init.c2_msra_fill(layer) + for layer in self.fcs: + weight_init.c2_xavier_fill(layer) + + def forward(self, x): + for layer in self.conv_norm_relus: + x = layer(x) + if len(self.fcs): + if x.dim() > 2: + x = torch.flatten(x, start_dim=1) + for layer in self.fcs: + x = F.relu(layer(x)) + return x + + @property + def output_size(self): + return self._output_size + + +def build_box_head(cfg, input_shape): + """ + Build a box head defined by `cfg.MODEL.ROI_BOX_HEAD.NAME`. + """ + name = cfg.MODEL.ROI_BOX_HEAD.NAME + return ROI_BOX_HEAD_REGISTRY.get(name)(cfg, input_shape) diff --git a/detectron2/modeling/roi_heads/cascade_rcnn.py b/detectron2/modeling/roi_heads/cascade_rcnn.py new file mode 100644 index 0000000000..2b3259c72b --- /dev/null +++ b/detectron2/modeling/roi_heads/cascade_rcnn.py @@ -0,0 +1,243 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import torch +from torch import nn +from torch.autograd.function import Function + +from detectron2.layers import ShapeSpec +from detectron2.structures import Boxes, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage + +from ..box_regression import Box2BoxTransform +from ..matcher import Matcher +from ..poolers import ROIPooler +from .box_head import build_box_head +from .fast_rcnn import FastRCNNOutputLayers, FastRCNNOutputs, fast_rcnn_inference +from .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads + + +class _ScaleGradient(Function): + @staticmethod + def forward(ctx, input, scale): + ctx.scale = scale + return input + + @staticmethod + def backward(ctx, grad_output): + return grad_output * ctx.scale, None + + +@ROI_HEADS_REGISTRY.register() +class CascadeROIHeads(StandardROIHeads): + def _init_box_head(self, cfg): + # fmt: off + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / self.feature_strides[k] for k in self.in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS + cascade_ious = cfg.MODEL.ROI_BOX_CASCADE_HEAD.IOUS + self.num_cascade_stages = len(cascade_ious) + assert len(cascade_bbox_reg_weights) == self.num_cascade_stages + assert cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG, \ + "CascadeROIHeads only support class-agnositc regression now!" + assert cascade_ious[0] == cfg.MODEL.ROI_HEADS.IOU_THRESHOLDS[0] + # fmt: on + + in_channels = [self.feature_channels[f] for f in self.in_features] + # Check all channel counts are equal + assert len(set(in_channels)) == 1, in_channels + in_channels = in_channels[0] + + self.box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + pooled_shape = ShapeSpec( + channels=in_channels, width=pooler_resolution, height=pooler_resolution + ) + + self.box_head = nn.ModuleList() + self.box_predictor = nn.ModuleList() + self.box2box_transform = [] + self.proposal_matchers = [] + for k in range(self.num_cascade_stages): + box_head = build_box_head(cfg, pooled_shape) + self.box_head.append(box_head) + self.box_predictor.append( + FastRCNNOutputLayers( + box_head.output_size, self.num_classes, cls_agnostic_bbox_reg=True + ) + ) + self.box2box_transform.append(Box2BoxTransform(weights=cascade_bbox_reg_weights[k])) + + if k == 0: + # The first matching is done by the matcher of ROIHeads (self.proposal_matcher). + self.proposal_matchers.append(None) + else: + self.proposal_matchers.append( + Matcher([cascade_ious[k]], [0, 1], allow_low_quality_matches=False) + ) + + def forward(self, images, features, proposals, targets=None): + del images + if self.training: + proposals = self.label_and_sample_proposals(proposals, targets) + + features_list = [features[f] for f in self.in_features] + + if self.training: + # Need targets to box head + losses = self._forward_box(features_list, proposals, targets) + losses.update(self._forward_mask(features_list, proposals)) + losses.update(self._forward_keypoint(features_list, proposals)) + return proposals, losses + else: + pred_instances = self._forward_box(features_list, proposals) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def _forward_box(self, features, proposals, targets=None): + head_outputs = [] + image_sizes = [x.image_size for x in proposals] + for k in range(self.num_cascade_stages): + if k > 0: + # The output boxes of the previous stage are the input proposals of the next stage + proposals = self._create_proposals_from_boxes( + head_outputs[-1].predict_boxes(), image_sizes + ) + if self.training: + proposals = self._match_and_label_boxes(proposals, k, targets) + head_outputs.append(self._run_stage(features, proposals, k)) + + if self.training: + losses = {} + storage = get_event_storage() + for stage, output in enumerate(head_outputs): + with storage.name_scope("stage{}".format(stage)): + stage_losses = output.losses() + losses.update({k + "_stage{}".format(stage): v for k, v in stage_losses.items()}) + return losses + else: + # Each is a list[Tensor] of length #image. Each tensor is Ri x (K+1) + scores_per_stage = [h.predict_probs() for h in head_outputs] + + # Average the scores across heads + scores = [ + sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages) + for scores_per_image in zip(*scores_per_stage) + ] + # Use the boxes of the last head + boxes = head_outputs[-1].predict_boxes() + pred_instances, _ = fast_rcnn_inference( + boxes, + scores, + image_sizes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_detections_per_img, + ) + return pred_instances + + @torch.no_grad() + def _match_and_label_boxes(self, proposals, stage, targets): + """ + Match proposals with groundtruth using the matcher at the given stage. + Label the proposals as foreground or background based on the match. + + Args: + proposals (list[Instances]): One Instances for each image, with + the field "proposal_boxes". + stage (int): the current stage + targets (list[Instances]): the ground truth instances + + Returns: + list[Instances]: the same proposals, but with fields "gt_classes" and "gt_boxes" + """ + num_fg_samples, num_bg_samples = [], [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + match_quality_matrix = pairwise_iou( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + # proposal_labels are 0 or 1 + matched_idxs, proposal_labels = self.proposal_matchers[stage](match_quality_matrix) + if len(targets_per_image) > 0: + gt_classes = targets_per_image.gt_classes[matched_idxs] + # Label unmatched proposals (0 label from matcher) as background (label=num_classes) + gt_classes[proposal_labels == 0] = self.num_classes + gt_boxes = targets_per_image.gt_boxes[matched_idxs] + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + gt_boxes = Boxes( + targets_per_image.gt_boxes.tensor.new_zeros((len(proposals_per_image), 4)) + ) + proposals_per_image.gt_classes = gt_classes + proposals_per_image.gt_boxes = gt_boxes + + num_fg_samples.append((proposal_labels == 1).sum().item()) + num_bg_samples.append(proposal_labels.numel() - num_fg_samples[-1]) + + # Log the number of fg/bg samples in each stage + storage = get_event_storage() + storage.put_scalar( + "stage{}/roi_head/num_fg_samples".format(stage), + sum(num_fg_samples) / len(num_fg_samples), + ) + storage.put_scalar( + "stage{}/roi_head/num_bg_samples".format(stage), + sum(num_bg_samples) / len(num_bg_samples), + ) + return proposals + + def _run_stage(self, features, proposals, stage): + """ + Args: + features (list[Tensor]): #lvl input features to ROIHeads + proposals (list[Instances]): #image Instances, with the field "proposal_boxes" + stage (int): the current stage + + Returns: + FastRCNNOutputs: the output of this stage + """ + box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals]) + # The original implementation averages the losses among heads, + # but scale up the parameter gradients of the heads. + # This is equivalent to adding the losses among heads, + # but scale down the gradients on features. + box_features = _ScaleGradient.apply(box_features, 1.0 / self.num_cascade_stages) + box_features = self.box_head[stage](box_features) + pred_class_logits, pred_proposal_deltas = self.box_predictor[stage](box_features) + del box_features + + outputs = FastRCNNOutputs( + self.box2box_transform[stage], + pred_class_logits, + pred_proposal_deltas, + proposals, + self.smooth_l1_beta, + ) + return outputs + + def _create_proposals_from_boxes(self, boxes, image_sizes): + """ + Args: + boxes (list[Tensor]): per-image predicted boxes, each of shape Ri x 4 + image_sizes (list[tuple]): list of image shapes in (h, w) + + Returns: + list[Instances]: per-image proposals with the given boxes. + """ + # Just like RPN, the proposals should not have gradients + boxes = [Boxes(b.detach()) for b in boxes] + proposals = [] + for boxes_per_image, image_size in zip(boxes, image_sizes): + boxes_per_image.clip(image_size) + if self.training: + # do not filter empty boxes at inference time, + # because the scores from each stage need to be aligned and added later + boxes_per_image = boxes_per_image[boxes_per_image.nonempty()] + prop = Instances(image_size) + prop.proposal_boxes = boxes_per_image + proposals.append(prop) + return proposals diff --git a/detectron2/modeling/roi_heads/fast_rcnn.py b/detectron2/modeling/roi_heads/fast_rcnn.py new file mode 100644 index 0000000000..67dc4fba65 --- /dev/null +++ b/detectron2/modeling/roi_heads/fast_rcnn.py @@ -0,0 +1,371 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import numpy as np +import torch +from fvcore.nn import smooth_l1_loss +from torch import nn +from torch.nn import functional as F + +from detectron2.layers import batched_nms, cat +from detectron2.structures import Boxes, Instances +from detectron2.utils.events import get_event_storage + +logger = logging.getLogger(__name__) + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + R: number of ROIs, combined over all images, in the minibatch + Ri: number of ROIs in image i + K: number of foreground classes. E.g.,there are 80 foreground classes in COCO. + +Naming convention: + + deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box + transform (see :class:`box_regression.Box2BoxTransform`). + + pred_class_logits: predicted class scores in [-inf, +inf]; use + softmax(pred_class_logits) to estimate P(class). + + gt_classes: ground-truth classification labels in [0, K], where [0, K) represent + foreground object classes and K represents the background class. + + pred_proposal_deltas: predicted box2box transform deltas for transforming proposals + to detection box predictions. + + gt_proposal_deltas: ground-truth box2box transform deltas +""" + + +def fast_rcnn_losses( + gt_classes, gt_proposal_deltas, pred_class_logits, pred_proposal_deltas, smooth_l1_beta +): + """ + When box dimension is 4: + Computes the classification and box delta losses defined in the Fast R-CNN paper. + When box dimension is 5: + Computes the same losses for Fast R-CNN with rotated boxes. + + Args: + gt_classes (Tensor): A tensor of shape (R,) storing ground-truth classification + labels in [0, K], including K fg class and 1 bg class. + gt_proposal_deltas (Tensor): + Shape (R, box_dim), row i represents ground-truth box2box transform targets + (dx, dy, dw, dh) or (dx, dy, dw, dh, da) that map object instance i to + its matched ground-truth box. + pred_class_logits (Tensor): A tensor for shape (R, K + 1) storing predicted classification + logits for the K+1-way classification problem. Each row corresponds to a predicted + object instance. + pred_proposal_deltas (Tensor): shape depends on whether we are doing + cls-agnoistic or cls-specific regression, and the box dimensions. + When box_dim is 4: + 1. cls-specific: Shape (R, 4 * K), each row stores a list of class-specific + predicted box2box transform [dx_0, dy_0, dw_0, dh_0, ..., dx_k, dy_k, dw_k, dh_k, ...] + for each class k in [0, K). (No predictions for the background class.) + 2. cls-agnostic: Shape (R, 4), the second row stores the class-agnostic (foreground) + predicted box2box transform. + When box_dim is 5: + 1. cls-specific: Shape (R, 5 * K), each row stores a list of class-specific + predicted rotated box2box transform + [dx_0, dy_0, dw_0, dh_0, da_0, ..., dx_k, dy_k, dw_k, dh_k, da_k, ...] + for each class k in [0, K). (No predictions for the background class.) + 2. cls-agnostic: Shape (R, 5), the second row stores the class-agnostic (foreground) + predicted rotated box2box transform. + smooth_l1_beta (float): The transition point between L1 and L2 loss in + the smooth L1 loss function. When set to 0, the loss becomes L1. When + set to +inf, the loss becomes constant 0. + + Returns: + loss_cls, loss_box_reg (Tensor): Scalar loss values. + """ + box_dim = gt_proposal_deltas.size(1) + cls_agnostic_bbox_reg = pred_proposal_deltas.size(1) == box_dim + device = pred_class_logits.device + + loss_cls = F.cross_entropy(pred_class_logits, gt_classes, reduction="mean") + + bg_class_ind = pred_class_logits.shape[1] - 1 + + # Box delta loss is only computed between the prediction for the gt class k + # (if 0 <= k < bg_class_ind) and the target; there is no loss defined on predictions + # for non-gt classes and background. + # Empty fg_inds produces a valid loss of zero as long as the size_average + # arg to smooth_l1_loss is False (otherwise it uses torch.mean internally + # and would produce a nan loss). + fg_inds = torch.nonzero((gt_classes >= 0) & (gt_classes < bg_class_ind)).squeeze(1) + if cls_agnostic_bbox_reg: + # pred_proposal_deltas only corresponds to foreground class for agnostic + gt_class_cols = torch.arange(box_dim, device=device) + else: + fg_gt_classes = gt_classes[fg_inds] + # pred_proposal_deltas for class k are located in columns [b * k : b * k + b], + # where b is the dimension of box representation (4 or 5) + # Note that compared to Detectron1, + # we do not perform bounding box regression for background classes. + gt_class_cols = box_dim * fg_gt_classes[:, None] + torch.arange(box_dim, device=device) + + loss_box_reg = smooth_l1_loss( + pred_proposal_deltas[fg_inds[:, None], gt_class_cols], + gt_proposal_deltas[fg_inds], + smooth_l1_beta, + reduction="sum", + ) + # The loss is normalized using the total number of regions (R), not the number + # of foreground regions even though the box regression loss is only defined on + # foreground regions. Why? Because doing so gives equal training influence to + # each foreground example. To see how, consider two different minibatches: + # (1) Contains a single foreground region + # (2) Contains 100 foreground regions + # If we normalize by the number of foreground regions, the single example in + # minibatch (1) will be given 100 times as much influence as each foreground + # example in minibatch (2). Normalizing by the total number of regions, R, + # means that the single example in minibatch (1) and each of the 100 examples + # in minibatch (2) are given equal influence. + loss_box_reg = loss_box_reg / gt_classes.numel() + + return loss_cls, loss_box_reg + + +def fast_rcnn_inference(boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image): + """ + Call `fast_rcnn_inference_single_image` for all images. + + Args: + boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic + boxes for each image. Element i has shape (Ri, K * 4) if doing + class-specific regression, or (Ri, 4) if doing class-agnostic + regression, where Ri is the number of predicted objects for image i. + This is compatible with the output of :meth:`FastRCNNOutputs.predict_boxes`. + scores (list[Tensor]): A list of Tensors of predicted class scores for each image. + Element i has shape (Ri, K + 1), where Ri is the number of predicted objects + for image i. Compatible with the output of :meth:`FastRCNNOutputs.predict_probs`. + image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch. + score_thresh (float): Only return detections with a confidence score exceeding this + threshold. + nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1]. + topk_per_image (int): The number of top scoring detections to return. Set < 0 to return + all detections. + + Returns: + instances: (list[Instances]): A list of N instances, one for each image in the batch, + that stores the topk most confidence detections. + kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates + the corresponding boxes/scores index in [0, Ri) from the input, for image i. + """ + result_per_image = [ + fast_rcnn_inference_single_image( + boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image + ) + for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes) + ] + return tuple(list(x) for x in zip(*result_per_image)) + + +def fast_rcnn_inference_single_image( + boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image +): + """ + Single-image inference. Return bounding-box detection results by thresholding + on scores and applying non-maximum suppression (NMS). + + Args: + Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes + per image. + + Returns: + Same as `fast_rcnn_inference`, but for only one image. + """ + scores = scores[:, :-1] + num_bbox_reg_classes = boxes.shape[1] // 4 + # Convert to Boxes to use the `clip` function ... + boxes = Boxes(boxes.reshape(-1, 4)) + boxes.clip(image_shape) + boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4) # R x C x 4 + + # Filter results based on detection scores + filter_mask = scores > score_thresh # R x K + # R' x 2. First column contains indices of the R predictions; + # Second column contains indices of classes. + filter_inds = filter_mask.nonzero() + if num_bbox_reg_classes == 1: + boxes = boxes[filter_inds[:, 0], 0] + else: + boxes = boxes[filter_mask] + scores = scores[filter_mask] + + # Apply per-class NMS + keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh) + keep = keep[:topk_per_image] + boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] + + result = Instances(image_shape) + result.pred_boxes = Boxes(boxes) + result.scores = scores + result.pred_classes = filter_inds[:, 1] + return result, filter_inds[:, 0] + + +class FastRCNNOutputs(object): + """ + A class that stores information about outputs of a Fast R-CNN head. + """ + + def __init__( + self, box2box_transform, pred_class_logits, pred_proposal_deltas, proposals, smooth_l1_beta + ): + """ + Args: + box2box_transform (Box2BoxTransform/Box2BoxTransformRotated): + box2box transform instance for proposal-to-detection tranformations. + pred_class_logits (Tensor): A tensor of shape (R, K + 1) storing the predicted class + logits for all R predicted object instances. + pred_proposal_deltas (Tensor): A tensor of shape (R, K * B) or (R, B) for + class-specific or class-agnostic storing the predicted deltas that + transform proposals into final box detections, where B is the box dimension (4 or 5) + proposals (list[Instances]): A list of N Instancess, where Instances i stores the + proposals for image i, in the field "proposal_boxes". + When training, each Instances must have ground-truth labels + stored in the field "gt_classes" and "gt_boxes". + smooth_l1_beta (float): The transition point between L1 and L2 loss in + the smooth L1 loss function. When set to 0, the loss becomes L1. When + set to +inf, the loss becomes constant 0. + """ + self.box2box_transform = box2box_transform + self.num_preds_per_image = [len(p) for p in proposals] + self.pred_class_logits = pred_class_logits + self.pred_proposal_deltas = pred_proposal_deltas + self.smooth_l1_beta = smooth_l1_beta + + box_type = type(proposals[0].proposal_boxes) + # cat(..., dim=0) concatenates over all images in the batch + self.proposals = box_type.cat([p.proposal_boxes for p in proposals]) + assert not self.proposals.tensor.requires_grad, "Proposals should not require gradients!" + self.image_shapes = [x.image_size for x in proposals] + + # The following fields should exist only when training. + if proposals[0].has("gt_boxes"): + self.gt_boxes = box_type.cat([p.gt_boxes for p in proposals]) + assert proposals[0].has("gt_classes") + self.gt_classes = cat([p.gt_classes for p in proposals], dim=0) + + def _log_accuracy(self): + """ + Log the accuracy metrics to EventStorage. + """ + num_instances = self.gt_classes.numel() + pred_classes = self.pred_class_logits.argmax(dim=1) + bg_class_ind = self.pred_class_logits.shape[1] - 1 + + fg_inds = (self.gt_classes >= 0) & (self.gt_classes < bg_class_ind) + num_fg = fg_inds.nonzero().numel() + fg_gt_classes = self.gt_classes[fg_inds] + fg_pred_classes = pred_classes[fg_inds] + + num_false_negative = (fg_pred_classes == bg_class_ind).nonzero().numel() + num_accurate = (pred_classes == self.gt_classes).nonzero().numel() + fg_num_accurate = (fg_pred_classes == fg_gt_classes).nonzero().numel() + + storage = get_event_storage() + storage.put_scalar("fast_rcnn/cls_accuracy", num_accurate / num_instances) + if num_fg > 0: + storage.put_scalar("fast_rcnn/fg_cls_accuracy", fg_num_accurate / num_fg) + storage.put_scalar("fast_rcnn/false_negative", num_false_negative / num_fg) + + def losses(self): + """ + Returns: + A dict of losses (scalar tensors) containing keys "loss_cls" and "loss_box_reg". + """ + self._log_accuracy() + gt_proposal_deltas = self.box2box_transform.get_deltas( + self.proposals.tensor, self.gt_boxes.tensor + ) + loss_cls, loss_box_reg = fast_rcnn_losses( + self.gt_classes, + gt_proposal_deltas, + self.pred_class_logits, + self.pred_proposal_deltas, + self.smooth_l1_beta, + ) + return {"loss_cls": loss_cls, "loss_box_reg": loss_box_reg} + + def predict_boxes(self): + """ + Returns: + list[Tensor]: A list of Tensors of predicted class-specific or class-agnostic boxes + for each image. Element i has shape (Ri, K * B) or (Ri, B), where Ri is + the number of predicted objects for image i and B is the box dimension (4 or 5) + """ + boxes = self.box2box_transform.apply_deltas( + self.pred_proposal_deltas, self.proposals.tensor + ) + return boxes.split(self.num_preds_per_image, dim=0) + + def predict_probs(self): + """ + Returns: + list[Tensor]: A list of Tensors of predicted class probabilities for each image. + Element i has shape (Ri, K + 1), where Ri is the number of predicted objects + for image i. + """ + probs = F.softmax(self.pred_class_logits, dim=-1) + return probs.split(self.num_preds_per_image, dim=0) + + def inference(self, score_thresh, nms_thresh, topk_per_image): + """ + Args: + score_thresh (float): same as fast_rcnn_inference. + nms_thresh (float): same as fast_rcnn_inference. + topk_per_image (int): same as fast_rcnn_inference. + Returns: + list[Instances]: same as fast_rcnn_inference. + list[Tensor]: same as fast_rcnn_inference. + """ + boxes = self.predict_boxes() + scores = self.predict_probs() + image_shapes = self.image_shapes + + return fast_rcnn_inference( + boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image + ) + + +class FastRCNNOutputLayers(nn.Module): + """ + Two linear layers for predicting Fast R-CNN outputs: + (1) proposal-to-detection box regression deltas + (2) classification scores + """ + + def __init__(self, input_size, num_classes, cls_agnostic_bbox_reg, box_dim=4): + """ + Args: + input_size (int): channels, or (channels, height, width) + num_classes (int): number of foreground classes + cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression + box_dim (int): the dimension of bounding boxes. + Example box dimensions: 4 for regular XYXY boxes and 5 for rotated XYWHA boxes + """ + super(FastRCNNOutputLayers, self).__init__() + + if not isinstance(input_size, int): + input_size = np.prod(input_size) + + # The prediction layer for num_classes foreground classes and one background class + # (hence + 1) + self.cls_score = nn.Linear(input_size, num_classes + 1) + num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes + self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim) + + nn.init.normal_(self.cls_score.weight, std=0.01) + nn.init.normal_(self.bbox_pred.weight, std=0.001) + for l in [self.cls_score, self.bbox_pred]: + nn.init.constant_(l.bias, 0) + + def forward(self, x): + if x.dim() > 2: + x = torch.flatten(x, start_dim=1) + scores = self.cls_score(x) + proposal_deltas = self.bbox_pred(x) + return scores, proposal_deltas diff --git a/detectron2/modeling/roi_heads/keypoint_head.py b/detectron2/modeling/roi_heads/keypoint_head.py new file mode 100644 index 0000000000..3242b381d0 --- /dev/null +++ b/detectron2/modeling/roi_heads/keypoint_head.py @@ -0,0 +1,166 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, interpolate +from detectron2.structures import heatmaps_to_keypoints +from detectron2.utils.events import get_event_storage +from detectron2.utils.registry import Registry + +_TOTAL_SKIPPED = 0 + +ROI_KEYPOINT_HEAD_REGISTRY = Registry("ROI_KEYPOINT_HEAD") +""" +Registry for keypoint heads, which make keypoint predictions from per-region features. +""" + + +def build_keypoint_head(cfg, input_shape): + """ + Build a keypoint head from `cfg.MODEL.ROI_KEYPOINT_HEAD.NAME`. + """ + name = cfg.MODEL.ROI_KEYPOINT_HEAD.NAME + return ROI_KEYPOINT_HEAD_REGISTRY.get(name)(cfg, input_shape) + + +def keypoint_rcnn_loss(pred_keypoint_logits, instances, normalizer): + """ + Arguments: + pred_keypoint_logits (Tensor): A tensor of shape (N, K, S, S) where N is the total number + of instances in the batch, K is the number of keypoints, and S is the side length + of the keypoint heatmap. The values are spatial logits. + instances (list[Instances]): A list of M Instances, where M is the batch size. + These instances are predictions from the model + that are in 1:1 correspondence with pred_keypoint_logits. + Each Instances should contain a `gt_keypoints` field containing a `structures.Keypoint` + instance. + normalizer (float): Normalize the loss by this amount. + If not specified, we normalize by the number of visible keypoints in the minibatch. + + Returns a scalar tensor containing the loss. + """ + heatmaps = [] + valid = [] + + keypoint_side_len = pred_keypoint_logits.shape[2] + for instances_per_image in instances: + if len(instances_per_image) == 0: + continue + keypoints = instances_per_image.gt_keypoints + heatmaps_per_image, valid_per_image = keypoints.to_heatmap( + instances_per_image.proposal_boxes.tensor, keypoint_side_len + ) + heatmaps.append(heatmaps_per_image.view(-1)) + valid.append(valid_per_image.view(-1)) + + if len(heatmaps): + keypoint_targets = cat(heatmaps, dim=0) + valid = cat(valid, dim=0).to(dtype=torch.uint8) + valid = torch.nonzero(valid).squeeze(1) + + # torch.mean (in binary_cross_entropy_with_logits) doesn't + # accept empty tensors, so handle it separately + if len(heatmaps) == 0 or valid.numel() == 0: + global _TOTAL_SKIPPED + _TOTAL_SKIPPED += 1 + storage = get_event_storage() + storage.put_scalar("kpts_num_skipped_batches", _TOTAL_SKIPPED, smoothing_hint=False) + return pred_keypoint_logits.sum() * 0 + + N, K, H, W = pred_keypoint_logits.shape + pred_keypoint_logits = pred_keypoint_logits.view(N * K, H * W) + + keypoint_loss = F.cross_entropy( + pred_keypoint_logits[valid], keypoint_targets[valid], reduction="sum" + ) + + # If a normalizer isn't specified, normalize by the number of visible keypoints in the minibatch + if normalizer is None: + normalizer = valid.numel() + keypoint_loss /= normalizer + + return keypoint_loss + + +def keypoint_rcnn_inference(pred_keypoint_logits, pred_instances): + """ + Post process each predicted keypoint heatmap in `pred_keypoint_logits` into (x, y, score, prob) + and add it to the `pred_instances` as a `pred_keypoints` field. + + Args: + pred_keypoint_logits (Tensor): A tensor of shape (N, K, S, S) where N is the total number + of instances in the batch, K is the number of keypoints, and S is the side length of + the keypoint heatmap. The values are spatial logits. + pred_instances (list[Instances]): A list of M Instances, where M is the batch size. + + Returns: + None. boxes will contain an extra "pred_keypoints" field. + The field is a tensor of shape (#instance, K, 3) where the last + dimension corresponds to (x, y, probability). + """ + # flatten all bboxes from all images together (list[Boxes] -> Nx4 tensor) + bboxes_flat = cat([b.pred_boxes.tensor for b in pred_instances], dim=0) + + keypoint_results = heatmaps_to_keypoints(pred_keypoint_logits.detach(), bboxes_flat.detach()) + num_instances_per_image = [len(i) for i in pred_instances] + keypoint_results = keypoint_results.split(num_instances_per_image, dim=0) + + for keypoint_results_per_image, instances_per_image in zip(keypoint_results, pred_instances): + # keypoint_results_per_image is (num instances)x(num keypoints)x(x, y, score, prob) + keypoint_xyp = keypoint_results_per_image[:, :, [0, 1, 3]] + instances_per_image.pred_keypoints = keypoint_xyp + + +@ROI_KEYPOINT_HEAD_REGISTRY.register() +class KRCNNConvDeconvUpsampleHead(nn.Module): + """ + A standard keypoint head containing a series of 3x3 convs, followed by + a transpose convolution and bilinear interpolation for upsampling. + """ + + def __init__(self, cfg, input_shape: ShapeSpec): + """ + The following attributes are parsed from config: + conv_dims: an iterable of output channel counts for each conv in the head + e.g. (512, 512, 512) for three convs outputting 512 channels. + num_keypoints: number of keypoint heatmaps to predicts, determines the number of + channels in the final output. + """ + super(KRCNNConvDeconvUpsampleHead, self).__init__() + + # fmt: off + # default up_scale to 2 (this can eventually be moved to config) + up_scale = 2 + conv_dims = cfg.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS + num_keypoints = cfg.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS + in_channels = input_shape.channels + # fmt: on + + self.blocks = [] + for idx, layer_channels in enumerate(conv_dims, 1): + module = Conv2d(in_channels, layer_channels, 3, stride=1, padding=1) + self.add_module("conv_fcn{}".format(idx), module) + self.blocks.append(module) + in_channels = layer_channels + + deconv_kernel = 4 + self.score_lowres = ConvTranspose2d( + in_channels, num_keypoints, deconv_kernel, stride=2, padding=deconv_kernel // 2 - 1 + ) + self.up_scale = up_scale + + for name, param in self.named_parameters(): + if "bias" in name: + nn.init.constant_(param, 0) + elif "weight" in name: + # Caffe2 implementation uses MSRAFill, which in fact + # corresponds to kaiming_normal_ in PyTorch + nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu") + + def forward(self, x): + for layer in self.blocks: + x = F.relu(layer(x)) + x = self.score_lowres(x) + x = interpolate(x, scale_factor=self.up_scale, mode="bilinear", align_corners=False) + return x diff --git a/detectron2/modeling/roi_heads/mask_head.py b/detectron2/modeling/roi_heads/mask_head.py new file mode 100644 index 0000000000..5fa41e04cd --- /dev/null +++ b/detectron2/modeling/roi_heads/mask_head.py @@ -0,0 +1,204 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, get_norm +from detectron2.utils.events import get_event_storage +from detectron2.utils.registry import Registry + +ROI_MASK_HEAD_REGISTRY = Registry("ROI_MASK_HEAD") +""" +Registry for mask heads, which predicts instance masks given +per-region features. +""" + + +def mask_rcnn_loss(pred_mask_logits, instances): + """ + Compute the mask prediction loss defined in the Mask R-CNN paper. + + Args: + pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask) + for class-specific or class-agnostic, where B is the total number of predicted masks + in all images, C is the number of foreground classes, and Hmask, Wmask are the height + and width of the mask predictions. The values are logits. + instances (list[Instances]): A list of N Instances, where N is the number of images + in the batch. These instances are in 1:1 + correspondence with the pred_mask_logits. The ground-truth labels (class, box, mask, + ...) associated with each instance are stored in fields. + + Returns: + mask_loss (Tensor): A scalar tensor containing the loss. + """ + cls_agnostic_mask = pred_mask_logits.size(1) == 1 + total_num_masks = pred_mask_logits.size(0) + mask_side_len = pred_mask_logits.size(2) + assert pred_mask_logits.size(2) == pred_mask_logits.size(3), "Mask prediction must be square!" + + gt_classes = [] + gt_masks = [] + for instances_per_image in instances: + if len(instances_per_image) == 0: + continue + if not cls_agnostic_mask: + gt_classes_per_image = instances_per_image.gt_classes.to(dtype=torch.int64) + gt_classes.append(gt_classes_per_image) + + gt_masks_per_image = instances_per_image.gt_masks.crop_and_resize( + instances_per_image.proposal_boxes.tensor, mask_side_len + ).to(device=pred_mask_logits.device) + # A tensor of shape (N, M, M), N=#instances in the image; M=mask_side_len + gt_masks.append(gt_masks_per_image) + + if len(gt_masks) == 0: + return pred_mask_logits.sum() * 0 + + gt_masks = cat(gt_masks, dim=0) + + if cls_agnostic_mask: + pred_mask_logits = pred_mask_logits[:, 0] + else: + indices = torch.arange(total_num_masks) + gt_classes = cat(gt_classes, dim=0) + pred_mask_logits = pred_mask_logits[indices, gt_classes] + + if gt_masks.dtype == torch.bool: + gt_masks_bool = gt_masks + else: + # Here we allow gt_masks to be float as well (depend on the implementation of rasterize()) + gt_masks_bool = gt_masks > 0.5 + + # Log the training accuracy (using gt classes and 0.5 threshold) + mask_incorrect = (pred_mask_logits > 0.0) != gt_masks_bool + mask_accuracy = 1 - (mask_incorrect.sum().item() / max(mask_incorrect.numel(), 1.0)) + num_positive = gt_masks_bool.sum().item() + false_positive = (mask_incorrect & ~gt_masks_bool).sum().item() / max( + gt_masks_bool.numel() - num_positive, 1.0 + ) + false_negative = (mask_incorrect & gt_masks_bool).sum().item() / max(num_positive, 1.0) + + storage = get_event_storage() + storage.put_scalar("mask_rcnn/accuracy", mask_accuracy) + storage.put_scalar("mask_rcnn/false_positive", false_positive) + storage.put_scalar("mask_rcnn/false_negative", false_negative) + + mask_loss = F.binary_cross_entropy_with_logits( + pred_mask_logits, gt_masks.to(dtype=torch.float32), reduction="mean" + ) + return mask_loss + + +def mask_rcnn_inference(pred_mask_logits, pred_instances): + """ + Convert pred_mask_logits to estimated foreground probability masks while also + extracting only the masks for the predicted classes in pred_instances. For each + predicted box, the mask of the same class is attached to the instance by adding a + new "pred_masks" field to pred_instances. + + Args: + pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask) + for class-specific or class-agnostic, where B is the total number of predicted masks + in all images, C is the number of foreground classes, and Hmask, Wmask are the height + and width of the mask predictions. The values are logits. + pred_instances (list[Instances]): A list of N Instances, where N is the number of images + in the batch. Each Instances must have field "pred_classes". + + Returns: + None. pred_instances will contain an extra "pred_masks" field storing a mask of size (Hmask, + Wmask) for predicted class. Note that the masks are returned as a soft (non-quantized) + masks the resolution predicted by the network; post-processing steps, such as resizing + the predicted masks to the original image resolution and/or binarizing them, is left + to the caller. + """ + cls_agnostic_mask = pred_mask_logits.size(1) == 1 + + if cls_agnostic_mask: + mask_probs_pred = pred_mask_logits.sigmoid() + else: + # Select masks corresponding to the predicted classes + num_masks = pred_mask_logits.shape[0] + class_pred = cat([i.pred_classes for i in pred_instances]) + indices = torch.arange(num_masks, device=class_pred.device) + mask_probs_pred = pred_mask_logits[indices, class_pred][:, None].sigmoid() + # mask_probs_pred.shape: (B, 1, Hmask, Wmask) + + num_boxes_per_image = [len(i) for i in pred_instances] + mask_probs_pred = mask_probs_pred.split(num_boxes_per_image, dim=0) + + for prob, instances in zip(mask_probs_pred, pred_instances): + instances.pred_masks = prob # (1, Hmask, Wmask) + + +@ROI_MASK_HEAD_REGISTRY.register() +class MaskRCNNConvUpsampleHead(nn.Module): + """ + A mask head with several conv layers, plus an upsample layer (with `ConvTranspose2d`). + """ + + def __init__(self, cfg, input_shape: ShapeSpec): + """ + The following attributes are parsed from config: + num_conv: the number of conv layers + conv_dim: the dimension of the conv layers + norm: normalization for the conv layers + """ + super(MaskRCNNConvUpsampleHead, self).__init__() + + # fmt: off + num_classes = cfg.MODEL.ROI_HEADS.NUM_CLASSES + conv_dims = cfg.MODEL.ROI_MASK_HEAD.CONV_DIM + self.norm = cfg.MODEL.ROI_MASK_HEAD.NORM + num_conv = cfg.MODEL.ROI_MASK_HEAD.NUM_CONV + input_channels = input_shape.channels + cls_agnostic_mask = cfg.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK + # fmt: on + + self.conv_norm_relus = [] + + for k in range(num_conv): + conv = Conv2d( + input_channels if k == 0 else conv_dims, + conv_dims, + kernel_size=3, + stride=1, + padding=1, + bias=not self.norm, + norm=get_norm(self.norm, conv_dims), + activation=F.relu, + ) + self.add_module("mask_fcn{}".format(k + 1), conv) + self.conv_norm_relus.append(conv) + + self.deconv = ConvTranspose2d( + conv_dims if num_conv > 0 else input_channels, + conv_dims, + kernel_size=2, + stride=2, + padding=0, + ) + + num_mask_classes = 1 if cls_agnostic_mask else num_classes + self.predictor = Conv2d(conv_dims, num_mask_classes, kernel_size=1, stride=1, padding=0) + + for layer in self.conv_norm_relus + [self.deconv]: + weight_init.c2_msra_fill(layer) + # use normal distribution initialization for mask prediction layer + nn.init.normal_(self.predictor.weight, std=0.001) + if self.predictor.bias is not None: + nn.init.constant_(self.predictor.bias, 0) + + def forward(self, x): + for layer in self.conv_norm_relus: + x = layer(x) + x = F.relu(self.deconv(x)) + return self.predictor(x) + + +def build_mask_head(cfg, input_shape): + """ + Build a mask head defined by `cfg.MODEL.ROI_MASK_HEAD.NAME`. + """ + name = cfg.MODEL.ROI_MASK_HEAD.NAME + return ROI_MASK_HEAD_REGISTRY.get(name)(cfg, input_shape) diff --git a/detectron2/modeling/roi_heads/roi_heads.py b/detectron2/modeling/roi_heads/roi_heads.py new file mode 100644 index 0000000000..07438ddcaf --- /dev/null +++ b/detectron2/modeling/roi_heads/roi_heads.py @@ -0,0 +1,816 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import numpy as np +from typing import Dict +import torch +from torch import nn + +from detectron2.layers import ShapeSpec +from detectron2.structures import Boxes, Instances, RotatedBoxes, pairwise_iou, pairwise_iou_rotated +from detectron2.utils.events import get_event_storage +from detectron2.utils.registry import Registry + +from ..backbone.resnet import BottleneckBlock, make_stage +from ..box_regression import Box2BoxTransform, Box2BoxTransformRotated +from ..matcher import Matcher +from ..poolers import ROIPooler +from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals +from ..sampling import subsample_labels +from .box_head import build_box_head +from .fast_rcnn import FastRCNNOutputLayers, FastRCNNOutputs +from .keypoint_head import build_keypoint_head, keypoint_rcnn_inference, keypoint_rcnn_loss +from .mask_head import build_mask_head, mask_rcnn_inference, mask_rcnn_loss + +ROI_HEADS_REGISTRY = Registry("ROI_HEADS") +""" +Registry for ROI heads in a generalized R-CNN model. +ROIHeads take feature maps and region proposals, and +perform per-region computation. +""" + +logger = logging.getLogger(__name__) + + +def build_roi_heads(cfg, input_shape): + """ + Build ROIHeads defined by `cfg.MODEL.ROI_HEADS.NAME`. + """ + name = cfg.MODEL.ROI_HEADS.NAME + return ROI_HEADS_REGISTRY.get(name)(cfg, input_shape) + + +def select_foreground_proposals(proposals, bg_label): + """ + Given a list of N Instances (for N images), each containing a `gt_classes` field, + return a list of Instances that contain only instances with `gt_classes != -1 && + gt_classes != bg_label`. + + Args: + proposals (list[Instances]): A list of N Instances, where N is the number of + images in the batch. + bg_label: label index of background class. + + Returns: + list[Instances]: N Instances, each contains only the selected foreground instances. + list[Tensor]: N boolean vector, correspond to the selection mask of + each instance. True for selected instances. + """ + assert isinstance(proposals, (list, tuple)) + assert isinstance(proposals[0], Instances) + assert proposals[0].has("gt_classes") + fg_proposals = [] + fg_selection_masks = [] + for proposals_per_image in proposals: + gt_classes = proposals_per_image.gt_classes + fg_selection_mask = (gt_classes != -1) & (gt_classes != bg_label) + fg_inds = fg_selection_mask.nonzero().squeeze(1) + fg_proposals.append(proposals_per_image[fg_inds]) + fg_selection_masks.append(fg_selection_mask) + return fg_proposals, fg_selection_masks + + +def select_proposals_with_visible_keypoints(proposals): + """ + Args: + proposals (list[Instances]): a list of N Instances, where N is the + number of images. + + Returns: + proposals: only contains proposals with at least one visible keypoint. + + Note that this is still slightly different from Detectron. + In Detectron, proposals for training keypoint head are re-sampled from + all the proposals with IOU>threshold & >=1 visible keypoint. + + Here, the proposals are first sampled from all proposals with + IOU>threshold, then proposals with no visible keypoint are filtered out. + This strategy seems to make no difference on Detectron and is easier to implement. + """ + ret = [] + all_num_fg = [] + for proposals_per_image in proposals: + gt_keypoints = proposals_per_image.gt_keypoints.tensor + # #fg x K x 3 + vis_mask = gt_keypoints[:, :, 2] >= 1 + xs, ys = gt_keypoints[:, :, 0], gt_keypoints[:, :, 1] + proposal_boxes = proposals_per_image.proposal_boxes.tensor.unsqueeze(dim=1) # #fg x 1 x 4 + kp_in_box = ( + (xs >= proposal_boxes[:, :, 0]) + & (xs <= proposal_boxes[:, :, 2]) + & (ys >= proposal_boxes[:, :, 1]) + & (ys <= proposal_boxes[:, :, 3]) + ) + selection = (kp_in_box & vis_mask).any(dim=1) + selection_idxs = torch.nonzero(selection).squeeze(1) + all_num_fg.append(selection_idxs.numel()) + ret.append(proposals_per_image[selection_idxs]) + + storage = get_event_storage() + storage.put_scalar("keypoint_head/num_fg_samples", np.mean(all_num_fg)) + return ret + + +class ROIHeads(torch.nn.Module): + """ + ROIHeads perform all per-region computation in an R-CNN. + + It contains logic of cropping the regions, extract per-region features, + and make per-region predictions. + + It can have many variants, implemented as subclasses of this class. + """ + + def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]): + super(ROIHeads, self).__init__() + + # fmt: off + self.batch_size_per_image = cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE + self.positive_sample_fraction = cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION + self.test_score_thresh = cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST + self.test_nms_thresh = cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST + self.test_detections_per_img = cfg.TEST.DETECTIONS_PER_IMAGE + self.in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + self.num_classes = cfg.MODEL.ROI_HEADS.NUM_CLASSES + self.proposal_append_gt = cfg.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT + self.feature_strides = {k: v.stride for k, v in input_shape.items()} + self.feature_channels = {k: v.channels for k, v in input_shape.items()} + self.cls_agnostic_bbox_reg = cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG + self.smooth_l1_beta = cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA + # fmt: on + + # Matcher to assign box proposals to gt boxes + self.proposal_matcher = Matcher( + cfg.MODEL.ROI_HEADS.IOU_THRESHOLDS, + cfg.MODEL.ROI_HEADS.IOU_LABELS, + allow_low_quality_matches=False, + ) + + # Box2BoxTransform for bounding box regression + self.box2box_transform = Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS) + + @torch.no_grad() + def label_and_sample_proposals(self, proposals, targets): + """ + Prepare some proposals to be used to train the ROI heads. + It performs box matching between `proposals` and `targets`, and assign + training labels to the lproposals. + It returns `self.batch_size_per_image` random samples from proposals and groundtruth boxes, + with a fraction of positives that is no larger than `self.positive_sample_fraction. + + Args: + See :meth:`ROIHeads.forward` + + Returns: + list[Instances]: + length `N` list of `Instances`s containing the proposals + sampled for training. Each `Instances` has the following fields: + - proposal_boxes: the proposal boxes + - gt_boxes: the ground-truth box that the proposal is assigned to + (this is only meaningful if the proposal has a label > 0; if label = 0 + then the ground-truth box is random) + Other fields such as "gt_classes", "gt_masks", that's included in `targets`. + """ + gt_boxes = [x.gt_boxes for x in targets] + # Augment proposals with ground-truth boxes. + # In the case of learned proposals (e.g., RPN), when training starts + # the proposals will be low quality due to random initialization. + # It's possible that none of these initial + # proposals have high enough overlap with the gt objects to be used + # as positive examples for the second stage components (box head, + # cls head, mask head). Adding the gt boxes to the set of proposals + # ensures that the second stage components will have some positive + # examples from the start of training. For RPN, this augmentation improves + # convergence and empirically improves box AP on COCO by about 0.5 + # points (under one tested configuration). + if self.proposal_append_gt: + proposals = add_ground_truth_to_proposals(gt_boxes, proposals) + + proposals_with_gt = [] + + num_fg_samples = [] + num_bg_samples = [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + has_gt = len(targets_per_image) > 0 + match_quality_matrix = pairwise_iou( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + matched_idxs, proposals_labels = self.proposal_matcher(match_quality_matrix) + + # Get the corresponding GT for each proposal + if has_gt: + gt_classes = targets_per_image.gt_classes[matched_idxs] + # Label unmatched proposals (0 label from matcher) as background (label=num_classes) + gt_classes[proposals_labels == 0] = self.num_classes + # Label ignore proposals (-1 label) + gt_classes[proposals_labels == -1] = -1 + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + + sampled_fg_inds, sampled_bg_inds = subsample_labels( + gt_classes, + self.batch_size_per_image, + self.positive_sample_fraction, + self.num_classes, + ) + + sampled_inds = torch.cat([sampled_fg_inds, sampled_bg_inds], dim=0) + + proposals_per_image = proposals_per_image[sampled_inds] + proposals_per_image.gt_classes = gt_classes[sampled_inds] + + # We index all the attributes of targets that start with "gt_" + # and have not been added to proposals yet (="gt_classes"). + if has_gt: + sampled_targets = matched_idxs[sampled_inds] + # NOTE: here the indexing waste some compute, because heads + # like masks, keypoints, etc, will filter the proposals again, + # (by foreground/background, or number of keypoints in the image, etc) + # so we essentially index the data twice. + for (trg_name, trg_value) in targets_per_image.get_fields().items(): + if trg_name.startswith("gt_") and not proposals_per_image.has(trg_name): + proposals_per_image.set(trg_name, trg_value[sampled_targets]) + else: + gt_boxes = Boxes( + targets_per_image.gt_boxes.tensor.new_zeros((len(sampled_inds), 4)) + ) + proposals_per_image.gt_boxes = gt_boxes + + num_fg_samples.append(sampled_fg_inds.numel()) + num_bg_samples.append(sampled_bg_inds.numel()) + proposals_with_gt.append(proposals_per_image) + + # Log the number of fg/bg samples that are selected for training ROI heads + storage = get_event_storage() + storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples)) + storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples)) + + return proposals_with_gt + + def forward(self, images, features, proposals, targets=None): + """ + Args: + images (ImageList): + features (dict[str: Tensor]): input data as a mapping from feature + map name to tensor. Axis 0 represents the number of images `N` in + the input data; axes 1-3 are channels, height, and width, which may + vary between feature maps (e.g., if a feature pyramid is used). + proposals (list[Instances]): length `N` list of `Instances`s. The i-th + `Instances` contains object proposals for the i-th input image, + with fields "proposal_boxes" and "objectness_logits". + targets (list[Instances], optional): length `N` list of `Instances`s. The i-th + `Instances` contains the ground-truth per-instance annotations + for the i-th input image. Specify `targets` during training only. + It may have the following fields: + - gt_boxes: the bounding box of each instance. + - gt_classes: the label for each instance with a category ranging in [0, #class]. + - gt_masks: the ground-truth mask of the instance. + + Returns: + results (list[Instances]): length `N` list of `Instances`s containing the + detected instances. Returned during inference only; may be [] + during training. + losses (dict[str: Tensor]): mapping from a named loss to a tensor + storing the loss. Used during training only. + """ + raise NotImplementedError() + + +@ROI_HEADS_REGISTRY.register() +class Res5ROIHeads(ROIHeads): + """ + The ROIHeads in a typical "C4" R-CNN model, where + the box and mask head share the cropping and + the per-region feature computation by a Res5 block. + """ + + def __init__(self, cfg, input_shape): + super().__init__(cfg, input_shape) + + assert len(self.in_features) == 1 + + # fmt: off + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + pooler_scales = (1.0 / self.feature_strides[self.in_features[0]], ) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + self.mask_on = cfg.MODEL.MASK_ON + # fmt: on + assert not cfg.MODEL.KEYPOINT_ON + + self.pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + + self.res5, out_channels = self._build_res5_block(cfg) + self.box_predictor = FastRCNNOutputLayers( + out_channels, self.num_classes, self.cls_agnostic_bbox_reg + ) + + if self.mask_on: + self.mask_head = build_mask_head( + cfg, + ShapeSpec(channels=out_channels, width=pooler_resolution, height=pooler_resolution), + ) + + def _build_res5_block(self, cfg): + # fmt: off + stage_channel_factor = 2 ** 3 # res5 is 8x res2 + num_groups = cfg.MODEL.RESNETS.NUM_GROUPS + width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP + bottleneck_channels = num_groups * width_per_group * stage_channel_factor + out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS * stage_channel_factor + stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 + norm = cfg.MODEL.RESNETS.NORM + assert not cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE[-1], \ + "Deformable conv is not yet supported in res5 head." + # fmt: on + + blocks = make_stage( + BottleneckBlock, + 3, + first_stride=2, + in_channels=out_channels // 2, + bottleneck_channels=bottleneck_channels, + out_channels=out_channels, + num_groups=num_groups, + norm=norm, + stride_in_1x1=stride_in_1x1, + ) + return nn.Sequential(*blocks), out_channels + + def _shared_roi_transform(self, features, boxes): + x = self.pooler(features, boxes) + return self.res5(x) + + def forward(self, images, features, proposals, targets=None): + """ + See :class:`ROIHeads.forward`. + """ + del images + + if self.training: + proposals = self.label_and_sample_proposals(proposals, targets) + del targets + + proposal_boxes = [x.proposal_boxes for x in proposals] + box_features = self._shared_roi_transform( + [features[f] for f in self.in_features], proposal_boxes + ) + feature_pooled = box_features.mean(dim=[2, 3]) # pooled to 1x1 + pred_class_logits, pred_proposal_deltas = self.box_predictor(feature_pooled) + del feature_pooled + + outputs = FastRCNNOutputs( + self.box2box_transform, + pred_class_logits, + pred_proposal_deltas, + proposals, + self.smooth_l1_beta, + ) + + if self.training: + del features + losses = outputs.losses() + if self.mask_on: + proposals, fg_selection_masks = select_foreground_proposals( + proposals, self.num_classes + ) + # Since the ROI feature transform is shared between boxes and masks, + # we don't need to recompute features. The mask loss is only defined + # on foreground proposals, so we need to select out the foreground + # features. + mask_features = box_features[torch.cat(fg_selection_masks, dim=0)] + del box_features + mask_logits = self.mask_head(mask_features) + losses["loss_mask"] = mask_rcnn_loss(mask_logits, proposals) + return [], losses + else: + pred_instances, _ = outputs.inference( + self.test_score_thresh, self.test_nms_thresh, self.test_detections_per_img + ) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def forward_with_given_boxes(self, features, instances): + """ + Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. + + Args: + features: same as in `forward()` + instances (list[Instances]): instances to predict other outputs. Expect the keys + "pred_boxes" and "pred_classes" to exist. + + Returns: + instances (Instances): + the same `Instances` object, with extra + fields such as `pred_masks` or `pred_keypoints`. + """ + assert not self.training + assert instances[0].has("pred_boxes") and instances[0].has("pred_classes") + + if self.mask_on: + features = [features[f] for f in self.in_features] + x = self._shared_roi_transform(features, [x.pred_boxes for x in instances]) + mask_logits = self.mask_head(x) + mask_rcnn_inference(mask_logits, instances) + return instances + + +@ROI_HEADS_REGISTRY.register() +class StandardROIHeads(ROIHeads): + """ + It's "standard" in a sense that there is no ROI transform sharing + or feature sharing between tasks. + The cropped rois go to separate branches (boxes and masks) directly. + This way, it is easier to make separate abstractions for different branches. + + This class is used by most models, such as FPN and C5. + To implement more models, you can subclass it and implement a different + :meth:`forward()` or a head. + """ + + def __init__(self, cfg, input_shape): + super(StandardROIHeads, self).__init__(cfg, input_shape) + self._init_box_head(cfg) + self._init_mask_head(cfg) + self._init_keypoint_head(cfg) + + def _init_box_head(self, cfg): + # fmt: off + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / self.feature_strides[k] for k in self.in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + # fmt: on + + # If StandardROIHeads is applied on multiple feature maps (as in FPN), + # then we share the same predictors and therefore the channel counts must be the same + in_channels = [self.feature_channels[f] for f in self.in_features] + # Check all channel counts are equal + assert len(set(in_channels)) == 1, in_channels + in_channels = in_channels[0] + + self.box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + # Here we split "box head" and "box predictor", which is mainly due to historical reasons. + # They are used together so the "box predictor" layers should be part of the "box head". + # New subclasses of ROIHeads do not need "box predictor"s. + self.box_head = build_box_head( + cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution) + ) + self.box_predictor = FastRCNNOutputLayers( + self.box_head.output_size, self.num_classes, self.cls_agnostic_bbox_reg + ) + + def _init_mask_head(self, cfg): + # fmt: off + self.mask_on = cfg.MODEL.MASK_ON + if not self.mask_on: + return + pooler_resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / self.feature_strides[k] for k in self.in_features) + sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE + # fmt: on + + in_channels = [self.feature_channels[f] for f in self.in_features][0] + + self.mask_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + self.mask_head = build_mask_head( + cfg, ShapeSpec(channels=in_channels, width=pooler_resolution, height=pooler_resolution) + ) + + def _init_keypoint_head(self, cfg): + # fmt: off + self.keypoint_on = cfg.MODEL.KEYPOINT_ON + if not self.keypoint_on: + return + pooler_resolution = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / self.feature_strides[k] for k in self.in_features) # noqa + sampling_ratio = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE + self.normalize_loss_by_visible_keypoints = cfg.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS # noqa + self.keypoint_loss_weight = cfg.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT + # fmt: on + + in_channels = [self.feature_channels[f] for f in self.in_features][0] + + self.keypoint_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + self.keypoint_head = build_keypoint_head( + cfg, ShapeSpec(channels=in_channels, width=pooler_resolution, height=pooler_resolution) + ) + + def forward(self, images, features, proposals, targets=None): + """ + See :class:`ROIHeads.forward`. + """ + del images + if self.training: + proposals = self.label_and_sample_proposals(proposals, targets) + del targets + + features_list = [features[f] for f in self.in_features] + + if self.training: + losses = self._forward_box(features_list, proposals) + # During training the proposals used by the box head are + # used by the mask, keypoint (and densepose) heads. + losses.update(self._forward_mask(features_list, proposals)) + losses.update(self._forward_keypoint(features_list, proposals)) + return proposals, losses + else: + pred_instances = self._forward_box(features_list, proposals) + # During inference cascaded prediction is used: the mask and keypoints heads are only + # applied to the top scoring box detections. + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def forward_with_given_boxes(self, features, instances): + """ + Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. + + This is useful for downstream tasks where a box is known, but need to obtain + other attributes (outputs of other heads). + Test-time augmentation also uses this. + + Args: + features: same as in `forward()` + instances (list[Instances]): instances to predict other outputs. Expect the keys + "pred_boxes" and "pred_classes" to exist. + + Returns: + instances (Instances): + the same `Instances` object, with extra + fields such as `pred_masks` or `pred_keypoints`. + """ + assert not self.training + assert instances[0].has("pred_boxes") and instances[0].has("pred_classes") + features = [features[f] for f in self.in_features] + + instances = self._forward_mask(features, instances) + instances = self._forward_keypoint(features, instances) + return instances + + def _forward_box(self, features, proposals): + """ + Forward logic of the box prediction branch. + + Args: + features (list[Tensor]): #level input features for box prediction + proposals (list[Instances]): the per-image object proposals with + their matching ground truth. + Each has fields "proposal_boxes", and "objectness_logits", + "gt_classes", "gt_boxes". + + Returns: + In training, a dict of losses. + In inference, a list of `Instances`, the predicted instances. + """ + box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals]) + box_features = self.box_head(box_features) + pred_class_logits, pred_proposal_deltas = self.box_predictor(box_features) + del box_features + + outputs = FastRCNNOutputs( + self.box2box_transform, + pred_class_logits, + pred_proposal_deltas, + proposals, + self.smooth_l1_beta, + ) + if self.training: + return outputs.losses() + else: + pred_instances, _ = outputs.inference( + self.test_score_thresh, self.test_nms_thresh, self.test_detections_per_img + ) + return pred_instances + + def _forward_mask(self, features, instances): + """ + Forward logic of the mask prediction branch. + + Args: + features (list[Tensor]): #level input features for mask prediction + instances (list[Instances]): the per-image instances to train/predict masks. + In training, they can be the proposals. + In inference, they can be the predicted boxes. + + Returns: + In training, a dict of losses. + In inference, update `instances` with new fields "pred_masks" and return it. + """ + if not self.mask_on: + return {} if self.training else instances + + if self.training: + # The loss is only defined on positive proposals. + proposals, _ = select_foreground_proposals(instances, self.num_classes) + proposal_boxes = [x.proposal_boxes for x in proposals] + mask_features = self.mask_pooler(features, proposal_boxes) + mask_logits = self.mask_head(mask_features) + return {"loss_mask": mask_rcnn_loss(mask_logits, proposals)} + else: + pred_boxes = [x.pred_boxes for x in instances] + mask_features = self.mask_pooler(features, pred_boxes) + mask_logits = self.mask_head(mask_features) + mask_rcnn_inference(mask_logits, instances) + return instances + + def _forward_keypoint(self, features, instances): + """ + Forward logic of the keypoint prediction branch. + + Args: + features (list[Tensor]): #level input features for keypoint prediction + instances (list[Instances]): the per-image instances to train/predict keypoints. + In training, they can be the proposals. + In inference, they can be the predicted boxes. + + Returns: + In training, a dict of losses. + In inference, update `instances` with new fields "pred_keypoints" and return it. + """ + if not self.keypoint_on: + return {} if self.training else instances + + num_images = len(instances) + + if self.training: + # The loss is defined on positive proposals with at >=1 visible keypoints. + proposals, _ = select_foreground_proposals(instances, self.num_classes) + proposals = select_proposals_with_visible_keypoints(proposals) + proposal_boxes = [x.proposal_boxes for x in proposals] + + keypoint_features = self.keypoint_pooler(features, proposal_boxes) + keypoint_logits = self.keypoint_head(keypoint_features) + + normalizer = ( + num_images + * self.batch_size_per_image + * self.positive_sample_fraction + * keypoint_logits.shape[1] + ) + loss = keypoint_rcnn_loss( + keypoint_logits, + proposals, + normalizer=None if self.normalize_loss_by_visible_keypoints else normalizer, + ) + return {"loss_keypoint": loss * self.keypoint_loss_weight} + else: + pred_boxes = [x.pred_boxes for x in instances] + keypoint_features = self.keypoint_pooler(features, pred_boxes) + keypoint_logits = self.keypoint_head(keypoint_features) + keypoint_rcnn_inference(keypoint_logits, instances) + return instances + + +@ROI_HEADS_REGISTRY.register() +class RROIHeads(StandardROIHeads): + """ + This class is used by Rotated RPN (RRPN). + For now, it just supports box_head but not mask or keypoints. + """ + + def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]): + super(RROIHeads, self).__init__(cfg, input_shape) + self.box2box_transform = Box2BoxTransformRotated( + weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS + ) + + def _init_box_head(self, cfg): + # fmt: off + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / self.feature_strides[k] for k in self.in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + # fmt: on + + # If StandardROIHeads is applied on multiple feature maps (as in FPN), + # then we share the same predictors and therefore the channel counts must be the same + in_channels = [self.feature_channels[f] for f in self.in_features] + # Check all channel counts are equal + assert len(set(in_channels)) == 1, in_channels + in_channels = in_channels[0] + + self.box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + self.box_head = build_box_head( + cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution) + ) + + self.box_predictor = FastRCNNOutputLayers( + input_size=self.box_head.output_size, + num_classes=self.num_classes, + cls_agnostic_bbox_reg=self.cls_agnostic_bbox_reg, + box_dim=5, + ) + + @torch.no_grad() + def label_and_sample_proposals(self, proposals, targets): + """ + Prepare some proposals to be used to train the RROI heads. + It performs box matching between `proposals` and `targets`, and assign + training labels to the lproposals. + It returns `self.batch_size_per_image` random samples from proposals and groundtruth boxes, + with a fraction of positives that is no larger than `self.positive_sample_fraction. + + Args: + See :meth:`StandardROIHeads.forward` + + Returns: + list[Instances]: length `N` list of `Instances`s containing the proposals + sampled for training. Each `Instances` has the following fields: + - proposal_boxes: the proposal rotated boxes + - gt_boxes: the ground-truth rotated boxes that the proposal is assigned to + (this is only meaningful if the proposal has a label > 0; if label = 0 + then the ground-truth box is random) + - other fields such as "gt_classes" and "gt_masks" that are included in `targets`. + """ + gt_boxes = [x.gt_boxes for x in targets] + # Augment proposals with ground-truth boxes. + # In the case of learned proposals (e.g., RPN), in the beginning of training + # the proposals are of low quality due to random initialization. + # It's possible that none of these initial + # proposals have high enough overlap with the gt objects to be used + # as positive examples for the second stage components (box head, + # cls head, mask head). Adding the gt boxes to the set of proposals + # ensures that the second stage components will have some positive + # examples from the start of training. For RPN, this augmentation improves + # convergence and empirically improves box AP on COCO by about 0.5 + # points (under one tested configuration). + proposals = add_ground_truth_to_proposals(gt_boxes, proposals) + + proposals_with_gt = [] + + num_fg_samples = [] + num_bg_samples = [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + has_gt = len(targets_per_image) > 0 + match_quality_matrix = pairwise_iou_rotated( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + matched_idxs, proposals_labels = self.proposal_matcher(match_quality_matrix) + + # Get the corresponding GT for each proposal + if has_gt: + gt_classes = targets_per_image.gt_classes[matched_idxs] + # Label unmatched proposals (0 label from matcher) as background (label=num_classes) + gt_classes[proposals_labels == 0] = self.num_classes + # Label ignore proposals (-1 label) + gt_classes[proposals_labels == -1] = -1 + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + + sampled_fg_inds, sampled_bg_inds = subsample_labels( + gt_classes, + self.batch_size_per_image, + self.positive_sample_fraction, + self.num_classes, + ) + + sampled_inds = torch.cat([sampled_fg_inds, sampled_bg_inds], dim=0) + + proposals_per_image = proposals_per_image[sampled_inds] + proposals_per_image.gt_classes = gt_classes[sampled_inds] + + if has_gt: + sampled_targets = matched_idxs[sampled_inds] + proposals_per_image.gt_boxes = targets_per_image.gt_boxes[sampled_targets] + else: + gt_boxes = RotatedBoxes( + targets_per_image.gt_boxes.tensor.new_zeros((len(sampled_inds), 5)) + ) + proposals_per_image.gt_boxes = gt_boxes + + num_fg_samples.append(sampled_fg_inds.numel()) + num_bg_samples.append(sampled_bg_inds.numel()) + proposals_with_gt.append(proposals_per_image) + + # Log the number of fg/bg samples that are selected for training ROI heads + storage = get_event_storage() + storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples)) + storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples)) + + return proposals_with_gt diff --git a/detectron2/modeling/sampling.py b/detectron2/modeling/sampling.py new file mode 100644 index 0000000000..47ab8f4e3f --- /dev/null +++ b/detectron2/modeling/sampling.py @@ -0,0 +1,45 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import torch + + +def subsample_labels(labels, num_samples, positive_fraction, bg_label): + """ + Return `num_samples` random samples from `labels`, with a fraction of + positives no larger than `positive_fraction`. + + Args: + labels (Tensor): (N, ) label vector with values: + -1: ignore + bg_label: background ("negative") class + otherwise: one or more foreground ("positive") classes + num_samples (int): The total number of labels with value >= 0 to return. + Values that are not sampled will be filled with -1 (ignore). + positive_fraction (float): The number of subsampled labels with values > 0 + is `min(num_positives, int(positive_fraction * num_samples))`. The number + of negatives sampled is `min(num_negatives, num_samples - num_positives_sampled)`. + In order words, if there are not enough positives, the sample is filled with + negatives. If there are also not enough negatives, then as many elements are + sampled as is possible. + bg_label (int): label index of background ("negative") class. + + Returns: + pos_idx, neg_idx (Tensor): 1D indices. The total number of indices is `num_samples` + if possible. The fraction of positive indices is `positive_fraction` if possible. + """ + positive = torch.nonzero((labels != -1) & (labels != bg_label)).squeeze(1) + negative = torch.nonzero(labels == bg_label).squeeze(1) + + num_pos = int(num_samples * positive_fraction) + # protect against not enough positive examples + num_pos = min(positive.numel(), num_pos) + num_neg = num_samples - num_pos + # protect against not enough negative examples + num_neg = min(negative.numel(), num_neg) + + # randomly select positive and negative examples + perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos] + perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg] + + pos_idx = positive[perm1] + neg_idx = negative[perm2] + return pos_idx, neg_idx diff --git a/detectron2/modeling/test_time_augmentation.py b/detectron2/modeling/test_time_augmentation.py new file mode 100644 index 0000000000..6ba205591e --- /dev/null +++ b/detectron2/modeling/test_time_augmentation.py @@ -0,0 +1,244 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy +import numpy as np +from contextlib import contextmanager +from itertools import count +import torch +from torch import nn + +from detectron2.data.detection_utils import read_image +from detectron2.data.transforms import ResizeShortestEdge +from detectron2.structures import Instances + +from .meta_arch import GeneralizedRCNN +from .postprocessing import detector_postprocess +from .roi_heads.fast_rcnn import fast_rcnn_inference_single_image + +__all__ = ["DatasetMapperTTA", "GeneralizedRCNNWithTTA"] + + +class DatasetMapperTTA: + """ + Implement test-time augmentation for detection data. + It is a callable which takes a dataset dict from a detection dataset, + and returns a list of dataset dicts where the images + are augmented from the input image by the transformations defined in the config. + This is used for test-time augmentation. + """ + + def __init__(self, cfg): + self.min_sizes = cfg.TEST.AUG.MIN_SIZES + self.max_size = cfg.TEST.AUG.MAX_SIZE + self.flip = cfg.TEST.AUG.FLIP + self.image_format = cfg.INPUT.FORMAT + + def __call__(self, dataset_dict): + """ + Args: + dict: a detection dataset dict + + Returns: + list[dict]: + a list of dataset dicts, which contain augmented version of the input image. + The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``. + """ + ret = [] + if "image" not in dataset_dict: + numpy_image = read_image(dataset_dict["file_name"], self.image_format) + else: + numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy().astype("uint8") + for min_size in self.min_sizes: + image = np.copy(numpy_image) + tfm = ResizeShortestEdge(min_size, self.max_size).get_transform(image) + resized = tfm.apply_image(image) + resized = torch.as_tensor(resized.transpose(2, 0, 1).astype("float32")) + + dic = copy.deepcopy(dataset_dict) + dic["horiz_flip"] = False + dic["image"] = resized + ret.append(dic) + + if self.flip: + dic = copy.deepcopy(dataset_dict) + dic["horiz_flip"] = True + dic["image"] = torch.flip(resized, dims=[2]) + ret.append(dic) + return ret + + +class GeneralizedRCNNWithTTA(nn.Module): + """ + A GeneralizedRCNN with test-time augmentation enabled. + Its :meth:`__call__` method has the same interface as :meth:`GeneralizedRCNN.forward`. + """ + + def __init__(self, cfg, model, tta_mapper=None, batch_size=3): + """ + Args: + cfg (CfgNode): + model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on. + tta_mapper (callable): takes a dataset dict and returns a list of + augmented versions of the dataset dict. Defaults to + `DatasetMapperTTA(cfg)`. + batch_size (int): batch the augmented images into this batch size for inference. + """ + super().__init__() + assert isinstance( + model, GeneralizedRCNN + ), "TTA is only supported on GeneralizedRCNN. Got a model of type {}".format(type(model)) + self.cfg = cfg.clone() + assert not self.cfg.MODEL.KEYPOINT_ON, "TTA for keypoint is not supported yet" + assert ( + not self.cfg.MODEL.LOAD_PROPOSALS + ), "TTA for pre-computed proposals is not supported yet" + + self.model = model + + if tta_mapper is None: + tta_mapper = DatasetMapperTTA(cfg) + self.tta_mapper = tta_mapper + self.batch_size = batch_size + + @contextmanager + def _turn_off_roi_head(self, attr): + """ + Open a context where one head in `model.roi_heads` is temporarily turned off. + Args: + attr (str): the attribute in `model.roi_heads` which can be used + to turn off a specific head, e.g., "mask_on", "keypoint_on". + """ + roi_heads = self.model.roi_heads + try: + old = getattr(roi_heads, attr) + except AttributeError: + # The head may not be implemented in certain ROIHeads + old = None + + if old is None: + yield + else: + setattr(roi_heads, attr, False) + yield + setattr(roi_heads, attr, old) + + def _batch_inference(self, batched_inputs, detected_instances=None, do_postprocess=True): + """ + Execute inference on a list of inputs, + using batch size = self.batch_size, instead of the length of the list. + + Inputs & outputs have the same format as :meth:`GeneralizedRCNN.inference` + """ + if detected_instances is None: + detected_instances = [None] * len(batched_inputs) + + outputs = [] + inputs, instances = [], [] + for idx, input, instance in zip(count(), batched_inputs, detected_instances): + inputs.append(input) + instances.append(instance) + if len(inputs) == self.batch_size or idx == len(batched_inputs) - 1: + outputs.extend( + self.model.inference( + inputs, + instances if instances[0] is not None else None, + do_postprocess=do_postprocess, + ) + ) + inputs, instances = [], [] + return outputs + + def __call__(self, batched_inputs): + """ + Same input/output format as :meth:`GeneralizedRCNN.forward` + """ + return [self._inference_one_image(x) for x in batched_inputs] + + def _inference_one_image(self, input): + """ + Args: + input (dict): one dataset dict + + Returns: + dict: one output dict + """ + augmented_inputs = self.tta_mapper(input) + + do_hflip = [k.pop("horiz_flip", False) for k in augmented_inputs] + heights = [k["height"] for k in augmented_inputs] + widths = [k["width"] for k in augmented_inputs] + assert ( + len(set(heights)) == 1 and len(set(widths)) == 1 + ), "Augmented version of the inputs should have the same original resolution!" + height = heights[0] + width = widths[0] + + # 1. Detect boxes from all augmented versions + # 1.1: forward with all augmented images + with self._turn_off_roi_head("mask_on"), self._turn_off_roi_head("keypoint_on"): + # temporarily disable mask/keypoint head + outputs = self._batch_inference(augmented_inputs, do_postprocess=False) + # 1.2: union the results + all_boxes = [] + all_scores = [] + all_classes = [] + for idx, output in enumerate(outputs): + rescaled_output = detector_postprocess(output, height, width) + pred_boxes = rescaled_output.pred_boxes.tensor + if do_hflip[idx]: + pred_boxes[:, [0, 2]] = width - pred_boxes[:, [2, 0]] + all_boxes.append(pred_boxes) + all_scores.extend(rescaled_output.scores) + all_classes.extend(rescaled_output.pred_classes) + all_boxes = torch.cat(all_boxes, dim=0).cpu() + num_boxes = len(all_boxes) + + # 1.3: select from the union of all results + num_classes = self.cfg.MODEL.ROI_HEADS.NUM_CLASSES + all_scores_2d = torch.zeros(num_boxes, num_classes, device=all_boxes.device) + for idx, cls, score in zip(count(), all_classes, all_scores): + all_scores_2d[idx, cls] = score + + merged_instances, _ = fast_rcnn_inference_single_image( + all_boxes, + all_scores_2d, + (height, width), + 1e-8, + self.cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST, + self.cfg.TEST.DETECTIONS_PER_IMAGE, + ) + + if not self.cfg.MODEL.MASK_ON: + return {"instances": merged_instances} + + # 2. Use the detected boxes to obtain masks + # 2.1: rescale the detected boxes + augmented_instances = [] + for idx, input in enumerate(augmented_inputs): + actual_height, actual_width = input["image"].shape[1:3] + scale_x = actual_width * 1.0 / width + scale_y = actual_height * 1.0 / height + pred_boxes = merged_instances.pred_boxes.clone() + pred_boxes.tensor[:, 0::2] *= scale_x + pred_boxes.tensor[:, 1::2] *= scale_y + if do_hflip[idx]: + pred_boxes.tensor[:, [0, 2]] = actual_width - pred_boxes.tensor[:, [2, 0]] + + aug_instances = Instances( + image_size=(actual_height, actual_width), + pred_boxes=pred_boxes, + pred_classes=merged_instances.pred_classes, + scores=merged_instances.scores, + ) + augmented_instances.append(aug_instances) + # 2.2: run forward on the detected boxes + outputs = self._batch_inference(augmented_inputs, augmented_instances, do_postprocess=False) + for idx, output in enumerate(outputs): + if do_hflip[idx]: + output.pred_masks = output.pred_masks.flip(dims=[3]) + # 2.3: average the predictions + all_pred_masks = torch.stack([o.pred_masks for o in outputs], dim=0) + avg_pred_masks = torch.mean(all_pred_masks, dim=0) + output = outputs[0] + output.pred_masks = avg_pred_masks + output = detector_postprocess(output, height, width) + return {"instances": output} diff --git a/detectron2/solver/__init__.py b/detectron2/solver/__init__.py new file mode 100644 index 0000000000..10f84e12d0 --- /dev/null +++ b/detectron2/solver/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .build import build_lr_scheduler, build_optimizer +from .lr_scheduler import WarmupCosineLR, WarmupMultiStepLR + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/detectron2/solver/build.py b/detectron2/solver/build.py new file mode 100644 index 0000000000..af685c93ce --- /dev/null +++ b/detectron2/solver/build.py @@ -0,0 +1,60 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from typing import Any, Dict, List +import torch + +from detectron2.config import CfgNode + +from .lr_scheduler import WarmupCosineLR, WarmupMultiStepLR + + +def build_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer: + """ + Build an optimizer from config. + """ + params: List[Dict[str, Any]] = [] + for key, value in model.named_parameters(): + if not value.requires_grad: + continue + lr = cfg.SOLVER.BASE_LR + weight_decay = cfg.SOLVER.WEIGHT_DECAY + if key.endswith("norm.weight") or key.endswith("norm.bias"): + weight_decay = cfg.SOLVER.WEIGHT_DECAY_NORM + elif key.endswith(".bias"): + # NOTE: unlike Detectron v1, we now default BIAS_LR_FACTOR to 1.0 + # and WEIGHT_DECAY_BIAS to WEIGHT_DECAY so that bias optimizer + # hyperparameters are by default exactly the same as for regular + # weights. + lr = cfg.SOLVER.BASE_LR * cfg.SOLVER.BIAS_LR_FACTOR + weight_decay = cfg.SOLVER.WEIGHT_DECAY_BIAS + params += [{"params": [value], "lr": lr, "weight_decay": weight_decay}] + + optimizer = torch.optim.SGD(params, lr, momentum=cfg.SOLVER.MOMENTUM) + return optimizer + + +def build_lr_scheduler( + cfg: CfgNode, optimizer: torch.optim.Optimizer +) -> torch.optim.lr_scheduler._LRScheduler: + """ + Build a LR scheduler from config. + """ + name = cfg.SOLVER.LR_SCHEDULER_NAME + if name == "WarmupMultiStepLR": + return WarmupMultiStepLR( + optimizer, + cfg.SOLVER.STEPS, + cfg.SOLVER.GAMMA, + warmup_factor=cfg.SOLVER.WARMUP_FACTOR, + warmup_iters=cfg.SOLVER.WARMUP_ITERS, + warmup_method=cfg.SOLVER.WARMUP_METHOD, + ) + elif name == "WarmupCosineLR": + return WarmupCosineLR( + optimizer, + cfg.SOLVER.MAX_ITER, + warmup_factor=cfg.SOLVER.WARMUP_FACTOR, + warmup_iters=cfg.SOLVER.WARMUP_ITERS, + warmup_method=cfg.SOLVER.WARMUP_METHOD, + ) + else: + raise ValueError("Unknown LR scheduler: {}".format(name)) diff --git a/detectron2/solver/lr_scheduler.py b/detectron2/solver/lr_scheduler.py new file mode 100644 index 0000000000..7c88689002 --- /dev/null +++ b/detectron2/solver/lr_scheduler.py @@ -0,0 +1,116 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import math +from bisect import bisect_right +from typing import List +import torch + +# NOTE: PyTorch's LR scheduler interface uses names that assume the LR changes +# only on epoch boundaries. We typically use iteration based schedules instead. +# As a result, "epoch" (e.g., as in self.last_epoch) should be understood to mean +# "iteration" instead. + +# FIXME: ideally this would be achieved with a CombinedLRScheduler, separating +# MultiStepLR with WarmupLR but the current LRScheduler design doesn't allow it. + + +class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler): + def __init__( + self, + optimizer: torch.optim.Optimizer, + milestones: List[int], + gamma: float = 0.1, + warmup_factor: float = 0.001, + warmup_iters: int = 1000, + warmup_method: str = "linear", + last_epoch: int = -1, + ): + if not list(milestones) == sorted(milestones): + raise ValueError( + "Milestones should be a list of" " increasing integers. Got {}", milestones + ) + self.milestones = milestones + self.gamma = gamma + self.warmup_factor = warmup_factor + self.warmup_iters = warmup_iters + self.warmup_method = warmup_method + super().__init__(optimizer, last_epoch) + + def get_lr(self) -> List[float]: + warmup_factor = _get_warmup_factor_at_iter( + self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor + ) + return [ + base_lr * warmup_factor * self.gamma ** bisect_right(self.milestones, self.last_epoch) + for base_lr in self.base_lrs + ] + + def _compute_values(self) -> List[float]: + # The new interface + return self.get_lr() + + +class WarmupCosineLR(torch.optim.lr_scheduler._LRScheduler): + def __init__( + self, + optimizer: torch.optim.Optimizer, + max_iters: int, + warmup_factor: float = 0.001, + warmup_iters: int = 1000, + warmup_method: str = "linear", + last_epoch: int = -1, + ): + self.max_iters = max_iters + self.warmup_factor = warmup_factor + self.warmup_iters = warmup_iters + self.warmup_method = warmup_method + super().__init__(optimizer, last_epoch) + + def get_lr(self) -> List[float]: + warmup_factor = _get_warmup_factor_at_iter( + self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor + ) + # Different definitions of half-cosine with warmup are possible. For + # simplicity we multiply the standard half-cosine schedule by the warmup + # factor. An alternative is to start the period of the cosine at warmup_iters + # instead of at 0. In the case that warmup_iters << max_iters the two are + # very close to each other. + return [ + base_lr + * warmup_factor + * 0.5 + * (1.0 + math.cos(math.pi * self.last_epoch / self.max_iters)) + for base_lr in self.base_lrs + ] + + def _compute_values(self) -> List[float]: + # The new interface + return self.get_lr() + + +def _get_warmup_factor_at_iter( + method: str, iter: int, warmup_iters: int, warmup_factor: float +) -> float: + """ + Return the learning rate warmup factor at a specific iteration. + See https://arxiv.org/abs/1706.02677 for more details. + + Args: + method (str): warmup method; either "constant" or "linear". + iter (int): iteration at which to calculate the warmup factor. + warmup_iters (int): the number of warmup iterations. + warmup_factor (float): the base warmup factor (the meaning changes according + to the method used). + + Returns: + float: the effective warmup factor at the given iteration. + """ + if iter >= warmup_iters: + return 1.0 + + if method == "constant": + return warmup_factor + elif method == "linear": + alpha = iter / warmup_iters + return warmup_factor * (1 - alpha) + alpha + else: + raise ValueError("Unknown warmup method: {}".format(method)) diff --git a/detectron2/structures/__init__.py b/detectron2/structures/__init__.py new file mode 100644 index 0000000000..9237e1427e --- /dev/null +++ b/detectron2/structures/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .boxes import Boxes, BoxMode, pairwise_iou +from .image_list import ImageList +from .instances import Instances +from .keypoints import Keypoints, heatmaps_to_keypoints +from .masks import BitMasks, PolygonMasks, rasterize_polygons_within_box +from .rotated_boxes import RotatedBoxes +from .rotated_boxes import pairwise_iou as pairwise_iou_rotated + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/detectron2/structures/boxes.py b/detectron2/structures/boxes.py new file mode 100644 index 0000000000..d1f1887feb --- /dev/null +++ b/detectron2/structures/boxes.py @@ -0,0 +1,291 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import numpy as np +from enum import Enum, unique +from typing import Iterator, List, Tuple, Union +import torch + +from detectron2.layers import cat + +_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray] + + +@unique +class BoxMode(Enum): + """ + Enum of different ways to represent a box. + + Attributes: + + XYXY_ABS: (x0, y0, x1, y1) in absolute floating points coordinates. + The coordinates in range [0, width or height]. + XYWH_ABS: (x0, y0, w, h) in absolute floating points coordinates. + XYXY_REL: (x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image. + XYWH_REL: (x0, y0, w, h) in range [0, 1]. They are relative to the size of the image. + """ + + XYXY_ABS = 0 + XYWH_ABS = 1 + XYXY_REL = 2 + XYWH_REL = 3 + + @staticmethod + def convert(box: _RawBoxType, from_mode: "BoxMode", to_mode: "BoxMode") -> _RawBoxType: + """ + Args: + box: can be a 4-tuple, 4-list or a Nx4 array/tensor. + from_mode, to_mode (BoxMode) + + Returns: + The converted box of the same type. + """ + if from_mode == to_mode: + return box + + original_type = type(box) + single_box = isinstance(box, (list, tuple)) + if single_box: + arr = np.array(box) + assert arr.shape == ( + 4, + ), "BoxMode.convert takes either a 4-tuple/list or a Nx4 array/tensor" + else: + arr = box + + assert to_mode.value < 2 and from_mode.value < 2, "Relative mode not yet supported!" + + original_shape = arr.shape + arr = arr.reshape(-1, 4) + if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS: + arr[:, 2] += arr[:, 0] + arr[:, 3] += arr[:, 1] + elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS: + arr[:, 2] -= arr[:, 0] + arr[:, 3] -= arr[:, 1] + else: + raise RuntimeError("Cannot be here!") + if single_box: + return original_type(arr.flatten()) + return arr.reshape(*original_shape) + + +class Boxes: + """ + This structure stores a list of boxes as a Nx4 torch.Tensor. + It supports some common methods about boxes + (`area`, `clip`, `nonempty`, etc), + and also behaves like a Tensor + (support indexing, `to(device)`, `.device`, and iteration over all boxes) + + Attributes: + tensor: float matrix of Nx4. + """ + + BoxSizeType = Union[List[int], Tuple[int, int]] + + def __init__(self, tensor: torch.Tensor): + """ + Args: + tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2). + """ + device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu") + tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device) + if tensor.numel() == 0: + tensor = torch.zeros(0, 4, dtype=torch.float32, device=device) + assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size() + + self.tensor = tensor + + def clone(self) -> "Boxes": + """ + Clone the Boxes. + + Returns: + Boxes + """ + return Boxes(self.tensor.clone()) + + def to(self, device: str) -> "Boxes": + return Boxes(self.tensor.to(device)) + + def area(self) -> torch.Tensor: + """ + Computes the area of all the boxes. + + Returns: + torch.Tensor: a vector with areas of each box. + """ + box = self.tensor + area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1]) + return area + + def clip(self, box_size: BoxSizeType) -> None: + """ + Clip (in place) the boxes by limiting x coordinates to the range [0, width] + and y coordinates to the range [0, height]. + + Args: + box_size (height, width): The clipping box's size. + """ + assert torch.isfinite(self.tensor).all() + h, w = box_size + self.tensor[:, 0].clamp_(min=0, max=w) + self.tensor[:, 1].clamp_(min=0, max=h) + self.tensor[:, 2].clamp_(min=0, max=w) + self.tensor[:, 3].clamp_(min=0, max=h) + + def nonempty(self, threshold: int = 0) -> torch.Tensor: + """ + Find boxes that are non-empty. + A box is considered empty, if either of its side is no larger than threshold. + + Returns: + Tensor: + a binary vector which represents whether each box is empty + (False) or non-empty (True). + """ + box = self.tensor + widths = box[:, 2] - box[:, 0] + heights = box[:, 3] - box[:, 1] + keep = (widths > threshold) & (heights > threshold) + return keep + + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Boxes": + """ + Returns: + Boxes: Create a new :class:`Boxes` by indexing. + + The following usage are allowed: + 1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box. + 2. `new_boxes = boxes[2:10]`: return a slice of boxes. + 3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor + with `length = len(boxes)`. Nonzero elements in the vector will be selected. + + Note that the returned Boxes might share storage with this Boxes, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return Boxes(self.tensor[item].view(1, -1)) + b = self.tensor[item] + assert b.dim() == 2, "Indexing on Boxes with {} failed to return a matrix!".format(item) + return Boxes(b) + + def __len__(self) -> int: + return self.tensor.shape[0] + + def __repr__(self) -> str: + return "Boxes(" + str(self.tensor) + ")" + + def inside_box(self, box_size: BoxSizeType, boundary_threshold: int = 0) -> torch.Tensor: + """ + Args: + box_size (height, width): Size of the reference box. + boundary_threshold (int): Boxes that extend beyond the reference box + boundary by more than boundary_threshold are considered "outside". + + Returns: + a binary vector, indicating whether each box is inside the reference box. + """ + height, width = box_size + inds_inside = ( + (self.tensor[..., 0] >= -boundary_threshold) + & (self.tensor[..., 1] >= -boundary_threshold) + & (self.tensor[..., 2] < width + boundary_threshold) + & (self.tensor[..., 3] < height + boundary_threshold) + ) + return inds_inside + + def get_centers(self) -> torch.Tensor: + """ + Returns: + The box centers in a Nx2 array of (x, y). + """ + return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2 + + @staticmethod + def cat(boxes_list: List["Boxes"]) -> "Boxes": + """ + Concatenates a list of Boxes into a single Boxes + + Arguments: + boxes_list (list[Boxes]) + + Returns: + Boxes: the concatenated Boxes + """ + assert isinstance(boxes_list, (list, tuple)) + assert len(boxes_list) > 0 + assert all(isinstance(box, Boxes) for box in boxes_list) + + cat_boxes = type(boxes_list[0])(cat([b.tensor for b in boxes_list], dim=0)) + return cat_boxes + + @property + def device(self) -> str: + return self.tensor.device + + def __iter__(self) -> Iterator[torch.Tensor]: + """ + Yield a box as a Tensor of shape (4,) at a time. + """ + yield from self.tensor + + +# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py +# with slight modifications +def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: + """ + Given two lists of boxes of size N and M, + compute the IoU (intersection over union) + between __all__ N x M pairs of boxes. + The box order must be (xmin, ymin, xmax, ymax). + + Args: + boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. + + Returns: + Tensor: IoU, sized [N,M]. + """ + area1 = boxes1.area() + area2 = boxes2.area() + + boxes1, boxes2 = boxes1.tensor, boxes2.tensor + + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + # handle empty boxes + iou = torch.where( + inter > 0, + inter / (area1[:, None] + area2 - inter), + torch.zeros(1, dtype=inter.dtype, device=inter.device), + ) + return iou + + +def matched_boxlist_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: + """ + Compute pairwise intersection over union (IOU) of two sets of matched + boxes. The box order must be (xmin, ymin, xmax, ymax). + Similar to boxlist_iou, but computes only diagonal elements of the matrix + Arguments: + boxes1: (Boxes) bounding boxes, sized [N,4]. + boxes2: (Boxes) bounding boxes, sized [N,4]. + Returns: + (tensor) iou, sized [N]. + """ + assert len(boxes1) == len(boxes2), ( + "boxlists should have the same" + "number of entries, got {}, {}".format(len(boxes1), len(boxes2)) + ) + area1 = boxes1.area() # [N] + area2 = boxes2.area() # [N] + box1, box2 = boxes1.tensor, boxes2.tensor + lt = torch.max(box1[:, :2], box2[:, :2]) # [N,2] + rb = torch.min(box1[:, 2:], box2[:, 2:]) # [N,2] + wh = (rb - lt).clamp(min=0) # [N,2] + inter = wh[:, 0] * wh[:, 1] # [N] + iou = inter / (area1 + area2 - inter) # [N] + return iou diff --git a/detectron2/structures/image_list.py b/detectron2/structures/image_list.py new file mode 100644 index 0000000000..de2603b26d --- /dev/null +++ b/detectron2/structures/image_list.py @@ -0,0 +1,96 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from __future__ import division +from typing import Any, List, Sequence, Tuple, Union +import torch +from torch.nn import functional as F + + +class ImageList(object): + """ + Structure that holds a list of images (of possibly + varying sizes) as a single tensor. + This works by padding the images to the same size, + and storing in a field the original sizes of each image + + Attributes: + image_sizes (list[tuple[int, int]]): each tuple is (h, w) + """ + + def __init__(self, tensor: torch.Tensor, image_sizes: List[Tuple[int, int]]): + """ + Arguments: + tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1 + image_sizes (list[tuple[int, int]]): Each tuple is (h, w). + """ + self.tensor = tensor + self.image_sizes = image_sizes + + def __len__(self) -> int: + return len(self.image_sizes) + + def __getitem__(self, idx: Union[int, slice]) -> torch.Tensor: + """ + Access the individual image in its original size. + + Returns: + Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1 + """ + size = self.image_sizes[idx] + return self.tensor[idx, ..., : size[0], : size[1]] # type: ignore + + def to(self, *args: Any, **kwargs: Any) -> "ImageList": + cast_tensor = self.tensor.to(*args, **kwargs) + return ImageList(cast_tensor, self.image_sizes) + + @staticmethod + def from_tensors( + tensors: Sequence[torch.Tensor], size_divisibility: int = 0, pad_value: float = 0.0 + ) -> "ImageList": + """ + Args: + tensors: a tuple or list of `torch.Tensors`, each of shape (Hi, Wi) or + (C_1, ..., C_K, Hi, Wi) where K >= 1. The Tensors will be padded with `pad_value` + so that they will have the same shape. + size_divisibility (int): If `size_divisibility > 0`, also adds padding to ensure + the common height and width is divisible by `size_divisibility` + pad_value (float): value to pad + + Returns: + an `ImageList`. + """ + assert len(tensors) > 0 + assert isinstance(tensors, (tuple, list)) + for t in tensors: + assert isinstance(t, torch.Tensor), type(t) + assert t.shape[1:-2] == tensors[0].shape[1:-2], t.shape + # per dimension maximum (H, W) or (C_1, ..., C_K, H, W) where K >= 1 among all tensors + max_size = tuple(max(s) for s in zip(*[img.shape for img in tensors])) + + if size_divisibility > 0: + import math + + stride = size_divisibility + max_size = list(max_size) # type: ignore + max_size[-2] = int(math.ceil(max_size[-2] / stride) * stride) # type: ignore + max_size[-1] = int(math.ceil(max_size[-1] / stride) * stride) # type: ignore + max_size = tuple(max_size) + + image_sizes = [im.shape[-2:] for im in tensors] + + if len(tensors) == 1: + # This seems slightly (2%) faster. + # TODO: check whether it's faster for multiple images as well + image_size = image_sizes[0] + padded = F.pad( + tensors[0], + [0, max_size[-1] - image_size[1], 0, max_size[-2] - image_size[0]], + value=pad_value, + ) + batched_imgs = padded.unsqueeze_(0) + else: + batch_shape = (len(tensors),) + max_size + batched_imgs = tensors[0].new_full(batch_shape, pad_value) + for img, pad_img in zip(tensors, batched_imgs): + pad_img[..., : img.shape[-2], : img.shape[-1]].copy_(img) + + return ImageList(batched_imgs.contiguous(), image_sizes) diff --git a/detectron2/structures/instances.py b/detectron2/structures/instances.py new file mode 100644 index 0000000000..a1c939f12f --- /dev/null +++ b/detectron2/structures/instances.py @@ -0,0 +1,184 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import itertools +from typing import Any, Dict, List, Tuple, Union +import torch + +from detectron2.layers import cat + + +class Instances: + """ + This class represents a list of instances in an image. + It stores the attributes of instances (e.g., boxes, masks, labels, scores) as "fields". + All fields must have the same `__len__` which is the number of instances. + + All other (non-field) attributes of this class are considered private: + they must start with '_' and are not modifiable by a user. + + Some basic usage: + + 1. Set/Get a field: + instances.gt_boxes = Boxes(...) + print(instances.pred_masks) + print('gt_masks' in instances) + 2. `len(instances)` returns the number of instances + 3. Indexing: `instances[indices]` will apply the indexing on all the fields + and returns a new `Instances`. + Typically, `indices` is a binary vector of length num_instances, + or a vector of integer indices. + """ + + def __init__(self, image_size: Tuple[int, int], **kwargs: Any): + """ + Args: + image_size (height, width): the spatial size of the image. + kwargs: fields to add to this `Instances`. + """ + self._image_size = image_size + self._fields: Dict[str, Any] = {} + for k, v in kwargs.items(): + self.set(k, v) + + @property + def image_size(self) -> Tuple[int, int]: + """ + Returns: + tuple: height, width + """ + return self._image_size + + def __setattr__(self, name: str, val: Any) -> None: + if name.startswith("_"): + super().__setattr__(name, val) + else: + self.set(name, val) + + def __getattr__(self, name: str) -> Any: + if name == "_fields" or name not in self._fields: + raise AttributeError("Cannot find field '{}' in the given Instances!".format(name)) + return self._fields[name] + + def set(self, name: str, value: Any) -> None: + """ + Set the field named `name` to `value`. + The length of `value` must be the number of instances, + and must agree with other existing fields in this object. + """ + data_len = len(value) + if len(self._fields): + assert ( + len(self) == data_len + ), "Adding a field of length {} to a Instances of length {}".format(data_len, len(self)) + self._fields[name] = value + + def has(self, name: str) -> bool: + """ + Returns: + bool: whether the field called `name` exists. + """ + return name in self._fields + + def remove(self, name: str) -> None: + """ + Remove the field called `name`. + """ + del self._fields[name] + + def get(self, name: str) -> Any: + """ + Returns the field called `name`. + """ + return self._fields[name] + + def get_fields(self) -> Dict[str, Any]: + """ + Returns: + dict: a dict which maps names (str) to data of the fields + + Modifying the returned dict will modify this instance. + """ + return self._fields + + # Tensor-like methods + def to(self, device: str) -> "Instances": + """ + Returns: + Instances: all fields are called with a `to(device)`, if + the field has this method. + """ + ret = Instances(self._image_size) + for k, v in self._fields.items(): + if hasattr(v, "to"): + v = v.to(device) + ret.set(k, v) + return ret + + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Instances": + """ + Args: + item: an index-like object and will be used to index all the fields. + + Returns: + If `item` is a string, return the data in the corresponding field. + Otherwise, returns an `Instances` where all fields are indexed by `item`. + """ + ret = Instances(self._image_size) + for k, v in self._fields.items(): + ret.set(k, v[item]) + return ret + + def __len__(self) -> int: + for v in self._fields.values(): + return len(v) + raise NotImplementedError("Empty Instances does not support __len__!") + + @staticmethod + def cat(instance_lists: List["Instances"]) -> "Instances": + """ + Args: + instance_lists (list[Instances]) + + Returns: + Instances + """ + assert all(isinstance(i, Instances) for i in instance_lists) + assert len(instance_lists) > 0 + if len(instance_lists) == 1: + return instance_lists[0] + + image_size = instance_lists[0].image_size + for i in instance_lists[1:]: + assert i.image_size == image_size + ret = Instances(image_size) + for k in instance_lists[0]._fields.keys(): + values = [i.get(k) for i in instance_lists] + v0 = values[0] + if isinstance(v0, torch.Tensor): + values = cat(values, dim=0) + elif isinstance(v0, list): + values = list(itertools.chain(*values)) + elif hasattr(type(v0), "cat"): + values = type(v0).cat(values) + else: + raise ValueError("Unsupported type {} for concatenation".format(type(v0))) + ret.set(k, values) + return ret + + def __str__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={}, ".format(len(self)) + s += "image_height={}, ".format(self._image_size[0]) + s += "image_width={}, ".format(self._image_size[1]) + s += "fields=[{}])".format(", ".join(self._fields.keys())) + return s + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={}, ".format(len(self)) + s += "image_height={}, ".format(self._image_size[0]) + s += "image_width={}, ".format(self._image_size[1]) + s += "fields=[" + for k, v in self._fields.items(): + s += "{} = {}, ".format(k, v) + s += "])" + return s diff --git a/detectron2/structures/keypoints.py b/detectron2/structures/keypoints.py new file mode 100644 index 0000000000..90a62ca6c3 --- /dev/null +++ b/detectron2/structures/keypoints.py @@ -0,0 +1,200 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import numpy as np +from typing import Any, List, Tuple, Union +import torch + +from detectron2.layers import interpolate + + +class Keypoints: + """ + Stores keypoint annotation data. GT Instances have a `gt_keypoints` property + containing the x,y location and visibility flag of each keypoint. This tensor has shape + (N, K, 3) where N is the number of instances and K is the number of keypoints per instance. + + The visiblity flag follows the COCO format and must be one of three integers: + * v=0: not labeled (in which case x=y=0) + * v=1: labeled but not visible + * v=2: labeled and visible + """ + + def __init__(self, keypoints: Union[torch.Tensor, np.ndarray, List[List[float]]]): + """ + Arguments: + keypoints: A Tensor, numpy array, or list of the x, y, and visibility of each keypoint. + The shape should be (N, K, 3) where N is the number of + instances, and K is the number of keypoints per instance. + """ + device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device("cpu") + keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device) + assert keypoints.dim() == 3 and keypoints.shape[2] == 3, keypoints.shape + self.tensor = keypoints + + def __len__(self) -> int: + return self.tensor.size(0) + + def to(self, *args: Any, **kwargs: Any) -> "Keypoints": + return type(self)(self.tensor.to(*args, **kwargs)) + + def to_heatmap(self, boxes: torch.Tensor, heatmap_size: int) -> torch.Tensor: + """ + Arguments: + boxes: Nx4 tensor, the boxes to draw the keypoints to + + Returns: + heatmaps: A tensor of shape (N, K) containing an integer spatial label + in the range [0, heatmap_size**2 - 1] for each keypoint in the input. + valid: A tensor of shape (N, K) containing whether each keypoint is in + the roi or not. + """ + return _keypoints_to_heatmap(self.tensor, boxes, heatmap_size) + + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Keypoints": + """ + Create a new `Keypoints` by indexing on this `Keypoints`. + + The following usage are allowed: + 1. `new_kpts = kpts[3]`: return a `Keypoints` which contains only one instance. + 2. `new_kpts = kpts[2:10]`: return a slice of key points. + 3. `new_kpts = kpts[vector]`, where vector is a torch.ByteTensor + with `length = len(kpts)`. Nonzero elements in the vector will be selected. + + Note that the returned Keypoints might share storage with this Keypoints, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return Keypoints([self.tensor[item]]) + return Keypoints(self.tensor[item]) + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.tensor)) + return s + + +# TODO make this nicer, this is a direct translation from C2 (but removing the inner loop) +def _keypoints_to_heatmap( + keypoints: torch.Tensor, rois: torch.Tensor, heatmap_size: int +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space. + + Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the + closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the + continuous-discrete conversion from Heckbert 1990 ("What is the coordinate of a pixel?"): + d = floor(c) and c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. + + Arguments: + keypoints: tensor of keypoint locations in of shape (N, K, 3). + rois: Nx4 tensor of rois in xyxy format + heatmap_size: integer side length of square heatmap. + + Returns: + heatmaps: A tensor of shape (N, K) containing an integer spatial label + in the range [0, heatmap_size**2 - 1] for each keypoint in the input. + valid: A tensor of shape (N, K) containing whether each keypoint is in + the roi or not. + """ + + if rois.numel() == 0: + return rois.new().long(), rois.new().long() + offset_x = rois[:, 0] + offset_y = rois[:, 1] + scale_x = heatmap_size / (rois[:, 2] - rois[:, 0]) + scale_y = heatmap_size / (rois[:, 3] - rois[:, 1]) + + offset_x = offset_x[:, None] + offset_y = offset_y[:, None] + scale_x = scale_x[:, None] + scale_y = scale_y[:, None] + + x = keypoints[..., 0] + y = keypoints[..., 1] + + x_boundary_inds = x == rois[:, 2][:, None] + y_boundary_inds = y == rois[:, 3][:, None] + + x = (x - offset_x) * scale_x + x = x.floor().long() + y = (y - offset_y) * scale_y + y = y.floor().long() + + x[x_boundary_inds] = heatmap_size - 1 + y[y_boundary_inds] = heatmap_size - 1 + + valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size) + vis = keypoints[..., 2] > 0 + valid = (valid_loc & vis).long() + + lin_ind = y * heatmap_size + x + heatmaps = lin_ind * valid + + return heatmaps, valid + + +@torch.no_grad() +def heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> torch.Tensor: + """ + Args: + maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W) + rois (Tensor): (#ROIs, 4) + + Extract predicted keypoint locations from heatmaps. Output has shape + (#rois, #keypoints, 4) with the last dimension corresponding to (x, y, logit, prob) + for each keypoint. + + Converts a discrete image coordinate in an NxN image to a continuous keypoint coordinate. We + maintain consistency with keypoints_to_heatmap by using the conversion from Heckbert 1990: + c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. + """ + offset_x = rois[:, 0] + offset_y = rois[:, 1] + + widths = (rois[:, 2] - rois[:, 0]).clamp(min=1) + heights = (rois[:, 3] - rois[:, 1]).clamp(min=1) + widths_ceil = widths.ceil() + heights_ceil = heights.ceil() + + num_rois, num_keypoints = maps.shape[:2] + xy_preds = maps.new_zeros(rois.shape[0], num_keypoints, 4) + + width_corrections = widths / widths_ceil + height_corrections = heights / heights_ceil + + keypoints_idx = torch.arange(num_keypoints, device=maps.device) + + for i in range(num_rois): + outsize = (int(heights_ceil[i]), int(widths_ceil[i])) + roi_map = interpolate(maps[[i]], size=outsize, mode="bicubic", align_corners=False).squeeze( + 0 + ) # #keypoints x H x W + + # softmax over the spatial region + max_score, _ = roi_map.view(num_keypoints, -1).max(1) + max_score = max_score.view(num_keypoints, 1, 1) + tmp_full_resoltuion = (roi_map - max_score).exp_() + tmp_pool_resoltuion = (maps[i] - max_score).exp_() + # Produce scores over the region H x W, but normalize with POOL_H x POOL_W + # So that the scores of objects of different absolute sizes will be more comparable + roi_map_probs = tmp_full_resoltuion / tmp_pool_resoltuion.sum((1, 2), keepdim=True) + + w = roi_map.shape[2] + pos = roi_map.view(num_keypoints, -1).argmax(1) + + x_int = pos % w + y_int = (pos - x_int) // w + + assert ( + roi_map_probs[keypoints_idx, y_int, x_int] + == roi_map_probs.view(num_keypoints, -1).max(1)[0] + ).all() + + x = (x_int.float() + 0.5) * width_corrections[i] + y = (y_int.float() + 0.5) * height_corrections[i] + + xy_preds[i, :, 0] = x + offset_x[i] + xy_preds[i, :, 1] = y + offset_y[i] + xy_preds[i, :, 2] = roi_map[keypoints_idx, y_int, x_int] + xy_preds[i, :, 3] = roi_map_probs[keypoints_idx, y_int, x_int] + + return xy_preds diff --git a/detectron2/structures/masks.py b/detectron2/structures/masks.py new file mode 100644 index 0000000000..747df9954e --- /dev/null +++ b/detectron2/structures/masks.py @@ -0,0 +1,346 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy +import numpy as np +from typing import Any, Iterator, List, Union +import pycocotools.mask as mask_utils +import torch + +from detectron2.layers.roi_align import ROIAlign + +from .boxes import Boxes + + +def polygons_to_bitmask(polygons: List[np.ndarray], height: int, width: int) -> np.ndarray: + """ + Args: + polygons (list[ndarray]): each array has shape (Nx2,) + height, width (int) + + Returns: + ndarray: a bool mask of shape (height, width) + """ + assert len(polygons) > 0, "COCOAPI does not support empty polygons" + rles = mask_utils.frPyObjects(polygons, height, width) + rle = mask_utils.merge(rles) + return mask_utils.decode(rle).astype(np.bool) + + +def rasterize_polygons_within_box( + polygons: List[np.ndarray], box: np.ndarray, mask_size: int +) -> torch.Tensor: + """ + Rasterize the polygons into a mask image and + crop the mask content in the given box. + The cropped mask is resized to (mask_size, mask_size). + + This function is used when generating training targets for mask head in Mask R-CNN. + Given original ground-truth masks for an image, new ground-truth mask + training targets in the size of `mask_size x mask_size` + must be provided for each predicted box. This function will be called to + produce such targets. + + Args: + polygons (list[ndarray[float]]): a list of polygons, which represents an instance. + box: 4-element numpy array + mask_size (int): + + Returns: + Tensor: BoolTensor of shape (mask_size, mask_size) + """ + # 1. Shift the polygons w.r.t the boxes + w, h = box[2] - box[0], box[3] - box[1] + + polygons = copy.deepcopy(polygons) + for p in polygons: + p[0::2] = p[0::2] - box[0] + p[1::2] = p[1::2] - box[1] + + # 2. Rescale the polygons to the new box size + ratio_h = mask_size / max(h, 0.1) + ratio_w = mask_size / max(w, 0.1) + + if ratio_h == ratio_w: + for p in polygons: + p *= ratio_h + else: + for p in polygons: + p[0::2] *= ratio_w + p[1::2] *= ratio_h + + # 3. Rasterize the polygons with coco api + mask = polygons_to_bitmask(polygons, mask_size, mask_size) + mask = torch.from_numpy(mask) + return mask + + +class BitMasks: + """ + This class stores the segmentation masks for all objects in one image, in + the form of bitmaps. + + Attributes: + tensor: bool Tensor of N,H,W, representing N instances in the image. + """ + + def __init__(self, tensor: Union[torch.Tensor, np.ndarray]): + """ + Args: + tensor: bool Tensor of N,H,W, representing N instances in the image. + """ + device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu") + tensor = torch.as_tensor(tensor, dtype=torch.bool, device=device) + assert tensor.dim() == 3, tensor.size() + self.image_size = tensor.shape[1:] + self.tensor = tensor + + def to(self, device: str) -> "BitMasks": + return BitMasks(self.tensor.to(device)) + + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "BitMasks": + """ + Returns: + BitMasks: Create a new :class:`BitMasks` by indexing. + + The following usage are allowed: + + 1. `new_masks = masks[3]`: return a `BitMasks` which contains only one mask. + 2. `new_masks = masks[2:10]`: return a slice of masks. + 3. `new_masks = masks[vector]`, where vector is a torch.BoolTensor + with `length = len(masks)`. Nonzero elements in the vector will be selected. + + Note that the returned object might share storage with this object, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return BitMasks(self.tensor[item].view(1, -1)) + m = self.tensor[item] + assert m.dim() == 3, "Indexing on BitMasks with {} returns a tensor with shape {}!".format( + item, m.shape + ) + return BitMasks(m) + + def __iter__(self) -> torch.Tensor: + yield from self.tensor + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.tensor)) + return s + + def __len__(self) -> int: + return self.tensor.shape[0] + + def nonempty(self) -> torch.Tensor: + """ + Find masks that are non-empty. + + Returns: + Tensor: a BoolTensor which represents + whether each mask is empty (False) or non-empty (True). + """ + return self.tensor.flatten(1).any(dim=1) + + @staticmethod + def from_polygon_masks( + polygon_masks: Union["PolygonMasks", List[List[np.ndarray]]], height: int, width: int + ) -> "BitMasks": + """ + Args: + polygon_masks (list[list[ndarray]] or PolygonMasks) + height, width (int) + """ + if isinstance(polygon_masks, PolygonMasks): + polygon_masks = polygon_masks.polygons + masks = [polygons_to_bitmask(p, height, width) for p in polygon_masks] + return BitMasks(torch.stack([torch.from_numpy(x) for x in masks])) + + def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor: + """ + Crop each bitmask by the given box, and resize results to (mask_size, mask_size). + This can be used to prepare training targets for Mask R-CNN. + It has less reconstruction error compared to rasterization with polygons. + However we observe no difference in accuracy, + but BitMasks requires more memory to store all the masks. + + Args: + boxes (Tensor): Nx4 tensor storing the boxes for each mask + mask_size (int): the size of the rasterized mask. + + Returns: + Tensor: + A bool tensor of shape (N, mask_size, mask_size), where + N is the number of predicted boxes for this image. + """ + assert len(boxes) == len(self), "{} != {}".format(len(boxes), len(self)) + device = self.tensor.device + + batch_inds = torch.arange(len(boxes), device=device).to(dtype=boxes.dtype)[:, None] + rois = torch.cat([batch_inds, boxes], dim=1) # Nx5 + + bit_masks = self.tensor.to(dtype=torch.float32) + rois = rois.to(device=device) + output = ( + ROIAlign((mask_size, mask_size), 1.0, 0, aligned=True) + .forward(bit_masks[:, None, :, :], rois) + .squeeze(1) + ) + output = output >= 0.5 + return output + + def get_bounding_boxes(self) -> None: + # not needed now + raise NotImplementedError + + +class PolygonMasks: + """ + This class stores the segmentation masks for all objects in one image, in the form of polygons. + + Attributes: + polygons: list[list[ndarray]]. Each ndarray is a float64 vector representing a polygon. + """ + + def __init__(self, polygons: List[List[Union[torch.Tensor, np.ndarray]]]): + """ + Arguments: + polygons (list[list[Tensor[float]]]): The first + level of the list correspond to individual instances, + the second level to all the polygons that compose the + instance, and the third level to the polygon coordinates. + The third level Tensor should have the format of + torch.Tensor([x0, y0, x1, y1, ..., xn, yn]) (n >= 3). + """ + assert isinstance(polygons, list) + + def _make_array(t: Union[torch.Tensor, np.ndarray]) -> torch.Tensor: + # Use float64 for higher precision, because why not? + # Always put polygons on CPU (self.to is a no-op) since they + # are supposed to be small tensors. + # May need to change this assumption if GPU placement becomes useful + if isinstance(t, torch.Tensor): + t = t.cpu().numpy() + return np.asarray(t).astype("float64") + + def process_polygons( + polygons_per_instance: List[Union[torch.Tensor, np.ndarray]] + ) -> List[torch.Tensor]: + assert isinstance(polygons_per_instance, list), type(polygons_per_instance) + # transform the polygon to a tensor + polygons_per_instance = [_make_array(p) for p in polygons_per_instance] + for polygon in polygons_per_instance: + assert len(polygon) % 2 == 0 and len(polygon) >= 6 + return polygons_per_instance + + self.polygons: List[List[torch.Tensor]] = [ + process_polygons(polygons_per_instance) for polygons_per_instance in polygons + ] + + def to(self, *args: Any, **kwargs: Any) -> "PolygonMasks": + return self + + def get_bounding_boxes(self) -> Boxes: + """ + Returns: + Boxes: tight bounding boxes around polygon masks. + """ + boxes = torch.zeros(len(self.polygons), 4, dtype=torch.float32) + for idx, polygons_per_instance in enumerate(self.polygons): + minxy = torch.as_tensor([float("inf"), float("inf")], dtype=torch.float32) + maxxy = torch.zeros(2, dtype=torch.float32) + for polygon in polygons_per_instance: + coords = torch.from_numpy(polygon).view(-1, 2).to(dtype=torch.float32) + minxy = torch.min(minxy, torch.min(coords, dim=0).values) + maxxy = torch.max(maxxy, torch.max(coords, dim=0).values) + boxes[idx, :2] = minxy + boxes[idx, 2:] = maxxy + return Boxes(boxes) + + def nonempty(self) -> torch.Tensor: + """ + Find masks that are non-empty. + + Returns: + Tensor: a BoolTensor which represents + whether each mask is empty (False) or non-empty (True). + """ + keep = [1 if len(polygon) > 0 else 0 for polygon in self.polygons] + return torch.as_tensor(keep, dtype=torch.bool) + + def __getitem__(self, item: Union[int, slice, List[int], torch.BoolTensor]) -> "PolygonMasks": + """ + Support indexing over the instances and return a `PolygonMasks` object. + `item` can be: + + 1. An integer. It will return an object with only one instance. + 2. A slice. It will return an object with the selected instances. + 3. A list[int]. It will return an object with the selected instances, + correpsonding to the indices in the list. + 4. A vector mask of type BoolTensor, whose length is num_instances. + It will return an object with the instances whose mask is nonzero. + """ + if isinstance(item, int): + selected_polygons = [self.polygons[item]] + elif isinstance(item, slice): + selected_polygons = self.polygons[item] + elif isinstance(item, list): + selected_polygons = [self.polygons[i] for i in item] + elif isinstance(item, torch.Tensor): + # Polygons is a list, so we have to move the indices back to CPU. + if item.dtype == torch.bool: + assert item.dim() == 1, item.shape + item = item.nonzero().squeeze(1).cpu().numpy().tolist() + elif item.dtype in [torch.int32, torch.int64]: + item = item.cpu().numpy().tolist() + else: + raise ValueError("Unsupported tensor dtype={} for indexing!".format(item.dtype)) + selected_polygons = [self.polygons[i] for i in item] + return PolygonMasks(selected_polygons) + + def __iter__(self) -> Iterator[List[torch.Tensor]]: + """ + Yields: + list[ndarray]: the polygons for one instance. Each Tensor is a + float64 vector representing a polygon. + """ + return iter(self.polygons) + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.polygons)) + return s + + def __len__(self) -> int: + return len(self.polygons) + + def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor: + """ + Crop each mask by the given box, and resize results to (mask_size, mask_size). + This can be used to prepare training targets for Mask R-CNN. + + Args: + boxes (Tensor): Nx4 tensor storing the boxes for each mask + mask_size (int): the size of the rasterized mask. + + Returns: + Tensor: + A bool tensor of shape (N, mask_size, mask_size), where + N is the number of predicted boxes for this image. + """ + assert len(boxes) == len(self), "{} != {}".format(len(boxes), len(self)) + + device = boxes.device + # Put boxes on the CPU, as the polygon representation is not efficient GPU-wise + # (several small tensors for representing a single instance mask) + boxes = boxes.to(torch.device("cpu")) + + results = [ + rasterize_polygons_within_box(poly, box.numpy(), mask_size) + for poly, box in zip(self.polygons, boxes) + ] + """ + poly: list[list[float]], the polygons for one instance + box: a tensor of shape (4,) + """ + if len(results) == 0: + return torch.empty(0, mask_size, mask_size, dtype=torch.bool, device=device) + return torch.stack(results, dim=0).to(device=device) diff --git a/detectron2/structures/rotated_boxes.py b/detectron2/structures/rotated_boxes.py new file mode 100644 index 0000000000..de71d3cff9 --- /dev/null +++ b/detectron2/structures/rotated_boxes.py @@ -0,0 +1,404 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from typing import Iterator, List, Union +import torch + +from detectron2.layers import cat +from detectron2.layers.rotated_boxes import pairwise_iou_rotated + +from .boxes import Boxes + + +class RotatedBoxes(Boxes): + """ + This structure stores a list of rotated boxes as a Nx5 torch.Tensor. + It supports some common methods about boxes + (`area`, `clip`, `nonempty`, etc), + and also behaves like a Tensor + (support indexing, `to(device)`, `.device`, and iteration over all boxes) + """ + + def __init__(self, tensor: torch.Tensor): + """ + Args: + tensor (Tensor[float]): a Nx5 matrix. Each row is + (x_center, y_center, width, height, angle), + in which angle is represented in degrees. + While there's no strict range restriction for it, + the recommended principal range is between (-180, 180] degrees. + + Assume we have a horizontal box B = (x_center, y_center, width, height), + where width is along the x-axis and height is along the y-axis. + The rotated box B_rot (x_center, y_center, width, height, angle) + can be seen as: + + 1. When angle == 0: + B_rot == B + 2. When angle > 0: + B_rot is obtained by rotating B w.r.t its center by |angle| degrees CCW; + 3. When angle < 0: + B_rot is obtained by rotating B w.r.t its center by |angle| degrees CW. + + Mathematically, since the right-handed coordinate system for image space + is (y, x), where y is top->down and x is left->right, the 4 vertices of the + rotated rectangle (yr_i, xr_i) (i = 1, 2, 3, 4) can be obtained from + the vertices of the horizontal rectangle (y_i, x_i) (i = 1, 2, 3, 4) + in the following way (theta = angle*pi/180 is the angle in radians, + (y_c, x_c) is the center of the rectangle): + yr_i = cos(theta) * (y_i - y_c) - sin(theta) * (x_i - x_c) + y_c, + xr_i = sin(theta) * (y_i - y_c) + cos(theta) * (x_i - x_c) + x_c, + which is the standard rigid-body rotation transformation. + + Intuitively, the angle is + (1) the rotation angle from y-axis in image space + to the height vector (top->down in the box's local coordinate system) + of the box in CCW, and + (2) the rotation angle from x-axis in image space + to the width vector (left->right in the box's local coordinate system) + of the box in CCW. + + More intuitively, consider the following horizontal box ABCD represented + in (x1, y1, x2, y2): + (3, 2, 7, 4), + covering the [3, 7] x [2, 4] region of the continuous coordinate system + which looks like this: + + O--------> x + | + | A---B + | | | + | D---C + | + v y + + Note that each capital letter represents one 0-dimensional geometric point + instead of a 'square pixel' here. + + In the example above, using (x, y) to represent a point we have: + O = (0, 0), + A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4) + + We name vector AB = vector DC as the width vector in box's local coordinate system, and + vector AD = vector BC as the height vector in box's local coordinate system. Initially, + when angle = 0 degree, they're aligned with the positive directions of x-axis and y-axis + in the image space, respectively. + + For better illustration, we denote the center of the box as E, + + O--------> x + | + | A---B + | | E | + | D---C + | + v y + + where the center E = ((3+7)/2, (2+4)/2) = (5, 3). + + Also, + width = |AB| = |CD| = 7 - 3 = 4, + height = |AD| = |BC| = 4 - 2 = 2. + + Therefore, the corresponding representation for the same shape in rotated box in + (x_center, y_center, width, height, angle) format is: + + (5, 3, 4, 2, 0), + + Now, let's consider (5, 3, 4, 2, 90), which is rotated by 90 degrees + CCW (counter-clockwise) by definition. It looks like this: + + O--------> x + | B-C + | | | + | |E| + | | | + | A-D + v y + + The center E is still located at the same point (5, 3), while the vertices + ABCD are rotated by 90 degrees CCW with regard to E: + A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5) + + Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to + vector AD or vector BC (the top->down height vector in box's local coordinate system), + or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right + width vector in box's local coordinate system). + + width = |AB| = |CD| = 5 - 1 = 4, + height = |AD| = |BC| = 6 - 4 = 2. + + Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise) + by definition? It looks like this: + + O--------> x + | D-A + | | | + | |E| + | | | + | C-B + v y + + The center E is still located at the same point (5, 3), while the vertices + ABCD are rotated by 90 degrees CW with regard to E: + A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1) + + width = |AB| = |CD| = 5 - 1 = 4, + height = |AD| = |BC| = 6 - 4 = 2. + + This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU + will be 1. However, these two will generate different RoI Pooling results and + should not be treated as an identical box. + + On the other hand, it's easy to see that (X, Y, W, H, A) is identical to + (X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be + identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is + equivalent to rotating the same shape 90 degrees CW. + + We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180): + + O--------> x + | + | C---D + | | E | + | B---A + | + v y + + A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2), + + width = |AB| = |CD| = 7 - 3 = 4, + height = |AD| = |BC| = 4 - 2 = 2. + + Finally, this is a very inaccurate (heavily quantized) illustration of + how (5, 3, 4, 2, 60) looks like in case anyone wonders: + + O--------> x + | B\ + | / C + | /E / + | A / + | `D + v y + + It's still a rectangle with center of (5, 3), width of 4 and height of 2, + but its angle (and thus orientation) is somewhere between + (5, 3, 4, 2, 0) and (5, 3, 4, 2, 90). + """ + device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu") + tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device) + if tensor.numel() == 0: + tensor = torch.zeros(0, 5, dtype=torch.float32, device=device) + assert tensor.dim() == 2 and tensor.size(-1) == 5, tensor.size() + + self.tensor = tensor + + def clone(self) -> "RotatedBoxes": + """ + Clone the RotatedBoxes. + + Returns: + RotatedBoxes + """ + return RotatedBoxes(self.tensor.clone()) + + def to(self, device: str) -> "RotatedBoxes": + return RotatedBoxes(self.tensor.to(device)) + + def area(self) -> torch.Tensor: + """ + Computes the area of all the boxes. + + Returns: + torch.Tensor: a vector with areas of each box. + """ + box = self.tensor + area = box[:, 2] * box[:, 3] + return area + + def normalize_angles(self) -> None: + """ + Restrict angles to the range of (-180, 180] degrees + """ + self.tensor[:, 4] = self.tensor[:, 4] % 360 + self.tensor[:, 4][torch.where(self.tensor[:, 4] > 180)] -= 360 + + def clip(self, box_size: Boxes.BoxSizeType, clip_angle_threshold: float = 1.0) -> None: + """ + Clip (in place) the boxes by limiting x coordinates to the range [0, width] + and y coordinates to the range [0, height]. + + For RRPN: + Only clip boxes that are almost horizontal with a tolerance of + clip_angle_threshold to maintain backward compatibility. + + Rotated boxes beyond this threshold are not clipped for two reasons: + (1) There are potentially multiple ways to clip a rotated box to make it + fit within the image. + (2) It's tricky to make the entire rectangular box fit within the image + and still be able to not leave out pixels of interest. + Therefore we rely on ops like RoIAlignRotated to safely handle this. + + Args: + box_size (height, width): The clipping box's size. + clip_angle_threshold: + Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees), + we do the clipping as horizontal boxes. + """ + h, w = box_size + + # normalize angles to be within (-180, 180] degrees + self.normalize_angles() + + idx = torch.where(torch.abs(self.tensor[:, 4]) <= clip_angle_threshold)[0] + + # convert to (x1, y1, x2, y2) + x1 = self.tensor[idx, 0] - self.tensor[idx, 2] / 2.0 + y1 = self.tensor[idx, 1] - self.tensor[idx, 3] / 2.0 + x2 = self.tensor[idx, 0] + self.tensor[idx, 2] / 2.0 + y2 = self.tensor[idx, 1] + self.tensor[idx, 3] / 2.0 + + # clip + x1.clamp_(min=0, max=w) + y1.clamp_(min=0, max=h) + x2.clamp_(min=0, max=w) + y2.clamp_(min=0, max=h) + + # convert back to (xc, yc, w, h) + self.tensor[idx, 0] = (x1 + x2) / 2.0 + self.tensor[idx, 1] = (y1 + y2) / 2.0 + # make sure widths and heights do not increase due to numerical errors + self.tensor[idx, 2] = torch.min(self.tensor[idx, 2], x2 - x1) + self.tensor[idx, 3] = torch.min(self.tensor[idx, 3], y2 - y1) + + def nonempty(self, threshold: int = 0) -> torch.Tensor: + """ + Find boxes that are non-empty. + A box is considered empty, if either of its side is no larger than threshold. + + Returns: + Tensor: a binary vector which represents + whether each box is empty (False) or non-empty (True). + """ + box = self.tensor + widths = box[:, 2] + heights = box[:, 3] + keep = (widths > threshold) & (heights > threshold) + return keep + + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "RotatedBoxes": + """ + Returns: + RotatedBoxes: Create a new :class:`RotatedBoxes` by indexing. + + The following usage are allowed: + + 1. `new_boxes = boxes[3]`: return a `RotatedBoxes` which contains only one box. + 2. `new_boxes = boxes[2:10]`: return a slice of boxes. + 3. `new_boxes = boxes[vector]`, where vector is a torch.ByteTensor + with `length = len(boxes)`. Nonzero elements in the vector will be selected. + + Note that the returned RotatedBoxes might share storage with this RotatedBoxes, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return RotatedBoxes(self.tensor[item].view(1, -1)) + b = self.tensor[item] + assert b.dim() == 2, "Indexing on RotatedBoxes with {} failed to return a matrix!".format( + item + ) + return RotatedBoxes(b) + + def __len__(self) -> int: + return self.tensor.shape[0] + + def __repr__(self) -> str: + return "RotatedBoxes(" + str(self.tensor) + ")" + + def inside_box(self, box_size: Boxes.BoxSizeType, boundary_threshold: int = 0) -> torch.Tensor: + """ + Args: + box_size (height, width): Size of the reference box covering + [0, width] x [0, height] + boundary_threshold (int): Boxes that extend beyond the reference box + boundary by more than boundary_threshold are considered "outside". + + For RRPN, it might not be necessary to call this function since it's common + for rotated box to extend to outside of the image boundaries + (the clip function only clips the near-horizontal boxes) + + Returns: + a binary vector, indicating whether each box is inside the reference box. + """ + height, width = box_size + + cnt_x = self.tensor[..., 0] + cnt_y = self.tensor[..., 1] + half_w = self.tensor[..., 2] / 2.0 + half_h = self.tensor[..., 3] / 2.0 + a = self.tensor[..., 4] + c = torch.abs(torch.cos(a * torch.pi / 180.0)) + s = torch.abs(torch.sin(a * torch.pi / 180.0)) + # This basically computes the horizontal bounding rectangle of the rotated box + max_rect_dx = c * half_w + s * half_h + max_rect_dy = c * half_h + s * half_w + + inds_inside = ( + (cnt_x - max_rect_dx >= -boundary_threshold) + & (cnt_y - max_rect_dy >= -boundary_threshold) + & (cnt_x + max_rect_dx < width + boundary_threshold) + & (cnt_y + max_rect_dy < height + boundary_threshold) + ) + + return inds_inside + + def get_centers(self) -> torch.Tensor: + """ + Returns: + The box centers in a Nx2 array of (x, y). + """ + return self.tensor[:, :2] + + @staticmethod + def cat(boxes_list: List["RotatedBoxes"]) -> "RotatedBoxes": # type: ignore + """ + Concatenates a list of RotatedBoxes into a single RotatedBoxes + + Arguments: + boxes_list (list[RotatedBoxes]) + + Returns: + RotatedBoxes: the concatenated RotatedBoxes + """ + assert isinstance(boxes_list, (list, tuple)) + assert len(boxes_list) > 0 + assert all(isinstance(box, RotatedBoxes) for box in boxes_list) + + cat_boxes = type(boxes_list[0])(cat([b.tensor for b in boxes_list], dim=0)) + return cat_boxes + + @property + def device(self) -> str: + return self.tensor.device + + def __iter__(self) -> Iterator[torch.Tensor]: + """ + Yield a box as a Tensor of shape (5,) at a time. + """ + yield from self.tensor + + +def pairwise_iou(boxes1: RotatedBoxes, boxes2: RotatedBoxes) -> None: + """ + Given two lists of rotated boxes of size N and M, + compute the IoU (intersection over union) + between __all__ N x M pairs of boxes. + The box order must be (x_center, y_center, width, height, angle). + + Args: + boxes1, boxes2 (RotatedBoxes): + two `RotatedBoxes`. Contains N & M rotated boxes, respectively. + + Returns: + Tensor: IoU, sized [N,M]. + """ + + return pairwise_iou_rotated(boxes1.tensor, boxes2.tensor) diff --git a/detectron2/utils/README.md b/detectron2/utils/README.md new file mode 100644 index 0000000000..9765b24a73 --- /dev/null +++ b/detectron2/utils/README.md @@ -0,0 +1,5 @@ +# Utility functions + +This folder contain utility functions that are not used in the +core library, but are useful for building models or training +code using the config system. diff --git a/detectron2/utils/__init__.py b/detectron2/utils/__init__.py new file mode 100644 index 0000000000..168f9979a4 --- /dev/null +++ b/detectron2/utils/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved diff --git a/detectron2/utils/collect_env.py b/detectron2/utils/collect_env.py new file mode 100644 index 0000000000..57b7c18c58 --- /dev/null +++ b/detectron2/utils/collect_env.py @@ -0,0 +1,65 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import os +import sys +from collections import defaultdict +import PIL +import torch +from tabulate import tabulate + +__all__ = ["collect_env_info"] + + +def collect_torch_env(): + try: + import torch.__config__ + + return torch.__config__.show() + except ImportError: + # compatible with older versions of pytorch + from torch.utils.collect_env import get_pretty_env_info + + return get_pretty_env_info() + + +def get_env_module(): + var_name = "DETECTRON2_ENV_MODULE" + return var_name, os.environ.get(var_name, "") + + +def collect_env_info(): + data = [] + data.append(("Python", sys.version.replace("\n", ""))) + try: + from detectron2 import _C + except ImportError: + pass + else: + data.append(("Detectron2 Compiler", _C.get_compiler_version())) + + data.append(get_env_module()) + data.append(("PyTorch", torch.__version__)) + data.append(("PyTorch Debug Build", torch.version.debug)) + + has_cuda = torch.cuda.is_available() + data.append(("CUDA available", has_cuda)) + if has_cuda: + devices = defaultdict(list) + for k in range(torch.cuda.device_count()): + devices[torch.cuda.get_device_name(k)].append(str(k)) + for name, devids in devices.items(): + data.append(("GPU " + ",".join(devids), name)) + data.append(("Pillow", PIL.__version__)) + + try: + import cv2 + + data.append(("cv2", cv2.__version__)) + except ImportError: + pass + env_str = tabulate(data) + "\n" + env_str += collect_torch_env() + return env_str + + +if __name__ == "__main__": + print(collect_env_info()) diff --git a/detectron2/utils/colormap.py b/detectron2/utils/colormap.py new file mode 100644 index 0000000000..1bf1455e4c --- /dev/null +++ b/detectron2/utils/colormap.py @@ -0,0 +1,140 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + +""" +An awesome colormap for really neat visualizations. +Copied from Detectron, and removed gray colors. +""" + +import numpy as np + +__all__ = ["colormap", "random_color"] + +# fmt: off +# RGB: +_COLORS = np.array( + [ + 0.000, 0.447, 0.741, + 0.850, 0.325, 0.098, + 0.929, 0.694, 0.125, + 0.494, 0.184, 0.556, + 0.466, 0.674, 0.188, + 0.301, 0.745, 0.933, + 0.635, 0.078, 0.184, + 0.300, 0.300, 0.300, + 0.600, 0.600, 0.600, + 1.000, 0.000, 0.000, + 1.000, 0.500, 0.000, + 0.749, 0.749, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 1.000, + 0.667, 0.000, 1.000, + 0.333, 0.333, 0.000, + 0.333, 0.667, 0.000, + 0.333, 1.000, 0.000, + 0.667, 0.333, 0.000, + 0.667, 0.667, 0.000, + 0.667, 1.000, 0.000, + 1.000, 0.333, 0.000, + 1.000, 0.667, 0.000, + 1.000, 1.000, 0.000, + 0.000, 0.333, 0.500, + 0.000, 0.667, 0.500, + 0.000, 1.000, 0.500, + 0.333, 0.000, 0.500, + 0.333, 0.333, 0.500, + 0.333, 0.667, 0.500, + 0.333, 1.000, 0.500, + 0.667, 0.000, 0.500, + 0.667, 0.333, 0.500, + 0.667, 0.667, 0.500, + 0.667, 1.000, 0.500, + 1.000, 0.000, 0.500, + 1.000, 0.333, 0.500, + 1.000, 0.667, 0.500, + 1.000, 1.000, 0.500, + 0.000, 0.333, 1.000, + 0.000, 0.667, 1.000, + 0.000, 1.000, 1.000, + 0.333, 0.000, 1.000, + 0.333, 0.333, 1.000, + 0.333, 0.667, 1.000, + 0.333, 1.000, 1.000, + 0.667, 0.000, 1.000, + 0.667, 0.333, 1.000, + 0.667, 0.667, 1.000, + 0.667, 1.000, 1.000, + 1.000, 0.000, 1.000, + 1.000, 0.333, 1.000, + 1.000, 0.667, 1.000, + 0.333, 0.000, 0.000, + 0.500, 0.000, 0.000, + 0.667, 0.000, 0.000, + 0.833, 0.000, 0.000, + 1.000, 0.000, 0.000, + 0.000, 0.167, 0.000, + 0.000, 0.333, 0.000, + 0.000, 0.500, 0.000, + 0.000, 0.667, 0.000, + 0.000, 0.833, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 0.167, + 0.000, 0.000, 0.333, + 0.000, 0.000, 0.500, + 0.000, 0.000, 0.667, + 0.000, 0.000, 0.833, + 0.000, 0.000, 1.000, + 0.000, 0.000, 0.000, + 0.143, 0.143, 0.143, + 0.857, 0.857, 0.857, + 1.000, 1.000, 1.000 + ] +).astype(np.float32).reshape(-1, 3) +# fmt: on + + +def colormap(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + + Returns: + ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1] + """ + assert maximum in [255, 1], maximum + c = _COLORS * maximum + if not rgb: + c = c[:, ::-1] + return c + + +def random_color(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + + Returns: + ndarray: a vector of 3 numbers + """ + idx = np.random.randint(0, len(_COLORS)) + ret = _COLORS[idx] * maximum + if not rgb: + ret = ret[::-1] + return ret + + +if __name__ == "__main__": + import cv2 + + size = 100 + H, W = 10, 10 + canvas = np.random.rand(H * size, W * size, 3).astype("float32") + for h in range(H): + for w in range(W): + idx = h * W + w + if idx >= len(_COLORS): + break + canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx] + cv2.imshow("a", canvas) + cv2.waitKey(0) diff --git a/detectron2/utils/comm.py b/detectron2/utils/comm.py new file mode 100644 index 0000000000..8cc7b3dac5 --- /dev/null +++ b/detectron2/utils/comm.py @@ -0,0 +1,263 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +This file contains primitives for multi-gpu communication. +This is useful when doing distributed training. +""" + +import functools +import logging +import numpy as np +import pickle +import torch +import torch.distributed as dist + +_LOCAL_PROCESS_GROUP = None +""" +A torch process group which only includes processes that on the same machine as the current process. +This variable is set when processes are spawned by `launch()` in "engine/launch.py". +""" + + +def get_world_size() -> int: + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank() -> int: + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + return dist.get_rank() + + +def get_local_rank() -> int: + """ + Returns: + The rank of the current process within the local (per-machine) process group. + """ + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + assert _LOCAL_PROCESS_GROUP is not None + return dist.get_rank(group=_LOCAL_PROCESS_GROUP) + + +def get_local_size() -> int: + """ + Returns: + The size of the per-machine process group, + i.e. the number of processes per machine. + """ + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) + + +def is_main_process() -> bool: + return get_rank() == 0 + + +def synchronize(): + """ + Helper function to synchronize (barrier) among all processes when + using distributed training + """ + if not dist.is_available(): + return + if not dist.is_initialized(): + return + world_size = dist.get_world_size() + if world_size == 1: + return + dist.barrier() + + +@functools.lru_cache() +def _get_global_gloo_group(): + """ + Return a process group based on gloo backend, containing all the ranks + The result is cached. + """ + if dist.get_backend() == "nccl": + return dist.new_group(backend="gloo") + else: + return dist.group.WORLD + + +def _serialize_to_tensor(data, group): + backend = dist.get_backend(group) + assert backend in ["gloo", "nccl"] + device = torch.device("cpu" if backend == "gloo" else "cuda") + + buffer = pickle.dumps(data) + if len(buffer) > 1024 ** 3: + logger = logging.getLogger(__name__) + logger.warning( + "Rank {} trying to all-gather {:.2f} GB of data on device {}".format( + get_rank(), len(buffer) / (1024 ** 3), device + ) + ) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to(device=device) + return tensor + + +def _pad_to_largest_tensor(tensor, group): + """ + Returns: + list[int]: size of the tensor, on each rank + Tensor: padded tensor that has the max size + """ + world_size = dist.get_world_size(group=group) + assert ( + world_size >= 1 + ), "comm.gather/all_gather must be called from ranks within the given group!" + local_size = torch.tensor([tensor.numel()], dtype=torch.int64, device=tensor.device) + size_list = [ + torch.zeros([1], dtype=torch.int64, device=tensor.device) for _ in range(world_size) + ] + dist.all_gather(size_list, local_size, group=group) + size_list = [int(size.item()) for size in size_list] + + max_size = max(size_list) + + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + if local_size != max_size: + padding = torch.zeros((max_size - local_size,), dtype=torch.uint8, device=tensor.device) + tensor = torch.cat((tensor, padding), dim=0) + return size_list, tensor + + +def all_gather(data, group=None): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors). + + Args: + data: any picklable object + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + + Returns: + list[data]: list of data gathered from each rank + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() + if dist.get_world_size(group) == 1: + return [data] + + tensor = _serialize_to_tensor(data, group) + + size_list, tensor = _pad_to_largest_tensor(tensor, group) + max_size = max(size_list) + + # receiving Tensor from all ranks + tensor_list = [ + torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list + ] + dist.all_gather(tensor_list, tensor, group=group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def gather(data, dst=0, group=None): + """ + Run gather on arbitrary picklable data (not necessarily tensors). + + Args: + data: any picklable object + dst (int): destination rank + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + + Returns: + list[data]: on dst, a list of data gathered from each rank. Otherwise, + an empty list. + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() + if dist.get_world_size(group=group) == 1: + return [data] + rank = dist.get_rank(group=group) + + tensor = _serialize_to_tensor(data, group) + size_list, tensor = _pad_to_largest_tensor(tensor, group) + + # receiving Tensor from all ranks + if rank == dst: + max_size = max(size_list) + tensor_list = [ + torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list + ] + dist.gather(tensor, tensor_list, dst=dst, group=group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + return data_list + else: + dist.gather(tensor, [], dst=dst, group=group) + return [] + + +def shared_random_seed(): + """ + Returns: + int: a random number that is the same across all workers. + If workers need a shared RNG, they can use this shared seed to + create one. + + All workers must call this function, otherwise it will deadlock. + """ + ints = np.random.randint(2 ** 31) + all_ints = all_gather(ints) + return all_ints[0] + + +def reduce_dict(input_dict, average=True): + """ + Reduce the values in the dictionary from all processes so that process with rank + 0 has the reduced results. + + Args: + input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor. + average (bool): whether to do average or sum + + Returns: + a dict with the same keys as input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.reduce(values, dst=0) + if dist.get_rank() == 0 and average: + # only main process gets accumulated, so only divide by + # world_size in this case + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict diff --git a/detectron2/utils/env.py b/detectron2/utils/env.py new file mode 100644 index 0000000000..a05057fca3 --- /dev/null +++ b/detectron2/utils/env.py @@ -0,0 +1,105 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import importlib +import importlib.util +import logging +import numpy as np +import os +import random +import sys +from datetime import datetime +import torch + +__all__ = ["seed_all_rng"] + + +def seed_all_rng(seed=None): + """ + Set the random seed for the RNG in torch, numpy and python. + + Args: + seed (int): if None, will use a strong random seed. + """ + if seed is None: + seed = ( + os.getpid() + + int(datetime.now().strftime("%S%f")) + + int.from_bytes(os.urandom(2), "big") + ) + logger = logging.getLogger(__name__) + logger.info("Using a generated random seed {}".format(seed)) + np.random.seed(seed) + torch.set_rng_state(torch.manual_seed(seed).get_state()) + random.seed(seed) + + +# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path +def _import_file(module_name, file_path, make_importable=False): + spec = importlib.util.spec_from_file_location(module_name, file_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + if make_importable: + sys.modules[module_name] = module + return module + + +def _configure_libraries(): + """ + Configurations for some libraries. + """ + # An environment option to disable `import cv2` globally, + # in case it leads to negative performance impact + disable_cv2 = int(os.environ.get("DETECTRON2_DISABLE_CV2", False)) + if disable_cv2: + sys.modules["cv2"] = None + else: + # Disable opencl in opencv since its interaction with cuda often has negative effects + # This envvar is supported after OpenCV 3.4.0 + os.environ["OPENCV_OPENCL_RUNTIME"] = "disabled" + try: + import cv2 + + if int(cv2.__version__.split(".")[0]) >= 3: + cv2.ocl.setUseOpenCL(False) + except ImportError: + pass + + +_ENV_SETUP_DONE = False + + +def setup_environment(): + """Perform environment setup work. The default setup is a no-op, but this + function allows the user to specify a Python source file or a module in + the $DETECTRON2_ENV_MODULE environment variable, that performs + custom setup work that may be necessary to their computing environment. + """ + global _ENV_SETUP_DONE + if _ENV_SETUP_DONE: + return + _ENV_SETUP_DONE = True + + _configure_libraries() + + custom_module_path = os.environ.get("DETECTRON2_ENV_MODULE") + + if custom_module_path: + setup_custom_environment(custom_module_path) + else: + # The default setup is a no-op + pass + + +def setup_custom_environment(custom_module): + """ + Load custom environment setup by importing a Python source file or a + module, and run the setup function. + """ + if custom_module.endswith(".py"): + module = _import_file("detectron2.utils.env.custom_module", custom_module) + else: + module = importlib.import_module(custom_module) + assert hasattr(module, "setup_environment") and callable(module.setup_environment), ( + "Custom environment module defined in {} does not have the " + "required callable attribute 'setup_environment'." + ).format(custom_module) + module.setup_environment() diff --git a/detectron2/utils/events.py b/detectron2/utils/events.py new file mode 100644 index 0000000000..b31e4b48d5 --- /dev/null +++ b/detectron2/utils/events.py @@ -0,0 +1,325 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import datetime +import json +import logging +import os +from collections import defaultdict +from contextlib import contextmanager +import torch +from fvcore.common.file_io import PathManager +from fvcore.common.history_buffer import HistoryBuffer + +_CURRENT_STORAGE_STACK = [] + + +def get_event_storage(): + assert len( + _CURRENT_STORAGE_STACK + ), "get_event_storage() has to be called inside a 'with EventStorage(...)' context!" + return _CURRENT_STORAGE_STACK[-1] + + +class JSONWriter: + """ + Write scalars to a json file. + + It saves scalars as one json per line (instead of a big json) for easy parsing. + + Examples parsing such a json file: + + .. code-block:: none + + $ cat metrics.json | jq -s '.[0:2]' + [ + { + "data_time": 0.008433341979980469, + "iteration": 20, + "loss": 1.9228371381759644, + "loss_box_reg": 0.050025828182697296, + "loss_classifier": 0.5316952466964722, + "loss_mask": 0.7236229181289673, + "loss_rpn_box": 0.0856662318110466, + "loss_rpn_cls": 0.48198649287223816, + "lr": 0.007173333333333333, + "time": 0.25401854515075684 + }, + { + "data_time": 0.007216215133666992, + "iteration": 40, + "loss": 1.282649278640747, + "loss_box_reg": 0.06222952902317047, + "loss_classifier": 0.30682939291000366, + "loss_mask": 0.6970193982124329, + "loss_rpn_box": 0.038663312792778015, + "loss_rpn_cls": 0.1471673548221588, + "lr": 0.007706666666666667, + "time": 0.2490077018737793 + } + ] + + $ cat metrics.json | jq '.loss_mask' + 0.7126231789588928 + 0.689423680305481 + 0.6776131987571716 + ... + + """ + + def __init__(self, json_file, window_size=20): + """ + Args: + json_file (str): path to the json file. New data will be appended if the file exists. + window_size (int): the window size of median smoothing for the scalars whose + `smoothing_hint` are True. + """ + self._file_handle = PathManager.open(json_file, "a") + self._window_size = window_size + + def write(self): + storage = get_event_storage() + to_save = {"iteration": storage.iter} + to_save.update(storage.latest_with_smoothing_hint(self._window_size)) + self._file_handle.write(json.dumps(to_save, sort_keys=True) + "\n") + self._file_handle.flush() + try: + os.fsync(self._file_handle.fileno()) + except AttributeError: + pass + + def __del__(self): + # not guaranteed to be called at exit, but probably fine + self._file_handle.close() + + +class TensorboardXWriter: + """ + Write all scalars to a tensorboard file. + """ + + def __init__(self, log_dir: str, window_size: int = 20, **kwargs): + """ + Args: + log_dir (str): The directory to save the output events + window_size (int): the scalars will be median-smoothed by this window size + kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)` + """ + self._window_size = window_size + from torch.utils.tensorboard import SummaryWriter + + self._writer = SummaryWriter(log_dir, **kwargs) + + def write(self): + storage = get_event_storage() + for k, v in storage.latest_with_smoothing_hint(self._window_size).items(): + self._writer.add_scalar(k, v, storage.iter) + + def __del__(self): + if hasattr(self, "_writer"): # doesn't exist when the code fails at import + self._writer.close() + + +class CommonMetricPrinter: + """ + Print __common__ metrics to the terminal, including + iteration time, ETA, memory, all losses, and the learning rate. + + To print something different, please implement a similar printer by yourself. + """ + + def __init__(self, max_iter): + """ + Args: + max_iter (int): the maximum number of iterations to train. + Used to compute ETA. + """ + self.logger = logging.getLogger(__name__) + self._max_iter = max_iter + + def write(self): + storage = get_event_storage() + iteration = storage.iter + + data_time, time = None, None + eta_string = "N/A" + try: + data_time = storage.history("data_time").avg(20) + time = storage.history("time").global_avg() + eta_seconds = storage.history("time").median(1000) * (self._max_iter - iteration) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + except KeyError: # they may not exist in the first few iterations (due to warmup) + pass + + try: + lr = "{:.6f}".format(storage.history("lr").latest()) + except KeyError: + lr = "N/A" + + if torch.cuda.is_available(): + max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0 + else: + max_mem_mb = None + + # NOTE: max_mem is parsed by grep in "dev/parse_results.sh" + self.logger.info( + """\ +eta: {eta} iter: {iter} {losses} \ +{time} {data_time} \ +lr: {lr} {memory}\ +""".format( + eta=eta_string, + iter=iteration, + losses=" ".join( + [ + "{}: {:.3f}".format(k, v.median(20)) + for k, v in storage.histories().items() + if "loss" in k + ] + ), + time="time: {:.4f}".format(time) if time is not None else "", + data_time="data_time: {:.4f}".format(data_time) if data_time is not None else "", + lr=lr, + memory="max_mem: {:.0f}M".format(max_mem_mb) if max_mem_mb is not None else "", + ) + ) + + +class EventStorage: + """ + The user-facing class that provides metric storage functionalities. + + In the future we may add support for storing / logging other types of data if needed. + """ + + def __init__(self, start_iter=0): + """ + Args: + start_iter (int): the iteration number to start with + """ + self._history = defaultdict(HistoryBuffer) + self._smoothing_hints = {} + self._latest_scalars = {} + self._iter = start_iter + self._current_prefix = "" + + def put_scalar(self, name, value, smoothing_hint=True): + """ + Add a scalar `value` to the `HistoryBuffer` associated with `name`. + + Args: + smoothing_hint (bool): a 'hint' on whether this scalar is noisy and should be + smoothed when logged. The hint will be accessible through + :meth:`EventStorage.smoothing_hints`. A writer may ignore the hint + and apply custom smoothing rule. + + It defaults to True because most scalars we save need to be smoothed to + provide any useful signal. + """ + name = self._current_prefix + name + history = self._history[name] + value = float(value) + history.update(value, self._iter) + self._latest_scalars[name] = value + + existing_hint = self._smoothing_hints.get(name) + if existing_hint is not None: + assert ( + existing_hint == smoothing_hint + ), "Scalar {} was put with a different smoothing_hint!".format(name) + else: + self._smoothing_hints[name] = smoothing_hint + + def put_scalars(self, *, smoothing_hint=True, **kwargs): + """ + Put multiple scalars from keyword arguments. + + Examples: + + storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothing_hint=True) + """ + for k, v in kwargs.items(): + self.put_scalar(k, v, smoothing_hint=smoothing_hint) + + def history(self, name): + """ + Returns: + HistoryBuffer: the scalar history for name + """ + ret = self._history.get(name, None) + if ret is None: + raise KeyError("No history metric available for {}!".format(name)) + return ret + + def histories(self): + """ + Returns: + dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars + """ + return self._history + + def latest(self): + """ + Returns: + dict[name -> number]: the scalars that's added in the current iteration. + """ + return self._latest_scalars + + def latest_with_smoothing_hint(self, window_size=20): + """ + Similar to :meth:`latest`, but the returned values + are either the un-smoothed original latest value, + or a median of the given window_size, + depend on whether the smoothing_hint is True. + + This provides a default behavior that other writers can use. + """ + result = {} + for k, v in self._latest_scalars.items(): + result[k] = self._history[k].median(window_size) if self._smoothing_hints[k] else v + return result + + def smoothing_hints(self): + """ + Returns: + dict[name -> bool]: the user-provided hint on whether the scalar + is noisy and needs smoothing. + """ + return self._smoothing_hints + + def step(self): + """ + User should call this function at the beginning of each iteration, to + notify the storage of the start of a new iteration. + The storage will then be able to associate the new data with the + correct iteration number. + """ + self._iter += 1 + self._latest_scalars = {} + + @property + def iter(self): + return self._iter + + @property + def iteration(self): + # for backward compatibility + return self._iter + + def __enter__(self): + _CURRENT_STORAGE_STACK.append(self) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + assert _CURRENT_STORAGE_STACK[-1] == self + _CURRENT_STORAGE_STACK.pop() + + @contextmanager + def name_scope(self, name): + """ + Yields: + A context within which all the events added to this storage + will be prefixed by the name scope. + """ + old_prefix = self._current_prefix + self._current_prefix = name.rstrip("/") + "/" + yield + self._current_prefix = old_prefix diff --git a/detectron2/utils/logger.py b/detectron2/utils/logger.py new file mode 100644 index 0000000000..a167d7de9d --- /dev/null +++ b/detectron2/utils/logger.py @@ -0,0 +1,196 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import functools +import logging +import os +import sys +from collections import Counter +from fvcore.common.file_io import PathManager +from tabulate import tabulate +from termcolor import colored + + +class _ColorfulFormatter(logging.Formatter): + def __init__(self, *args, **kwargs): + self._root_name = kwargs.pop("root_name") + "." + self._abbrev_name = kwargs.pop("abbrev_name", "") + if len(self._abbrev_name): + self._abbrev_name = self._abbrev_name + "." + super(_ColorfulFormatter, self).__init__(*args, **kwargs) + + def formatMessage(self, record): + record.name = record.name.replace(self._root_name, self._abbrev_name) + log = super(_ColorfulFormatter, self).formatMessage(record) + if record.levelno == logging.WARNING: + prefix = colored("WARNING", "red", attrs=["blink"]) + elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL: + prefix = colored("ERROR", "red", attrs=["blink", "underline"]) + else: + return log + return prefix + " " + log + + +@functools.lru_cache() # so that calling setup_logger multiple times won't add many handlers +def setup_logger( + output=None, distributed_rank=0, *, color=True, name="detectron2", abbrev_name=None +): + """ + Args: + output (str): a file name or a directory to save log. If None, will not save log file. + If ends with ".txt" or ".log", assumed to be a file name. + Otherwise, logs will be saved to `output/log.txt`. + name (str): the root module name of this logger + abbrev_name (str): an abbreviation of the module, to avoid long names in logs. + Set to "" to not log the root module in logs. + By default, will abbreviate "detectron2" to "d2" and leave other + modules unchanged. + """ + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + logger.propagate = False + + if abbrev_name is None: + abbrev_name = "d2" if name == "detectron2" else name + + plain_formatter = logging.Formatter( + "[%(asctime)s] %(name)s %(levelname)s: %(message)s", datefmt="%m/%d %H:%M:%S" + ) + # stdout logging: master only + if distributed_rank == 0: + ch = logging.StreamHandler(stream=sys.stdout) + ch.setLevel(logging.DEBUG) + if color: + formatter = _ColorfulFormatter( + colored("[%(asctime)s %(name)s]: ", "green") + "%(message)s", + datefmt="%m/%d %H:%M:%S", + root_name=name, + abbrev_name=str(abbrev_name), + ) + else: + formatter = plain_formatter + ch.setFormatter(formatter) + logger.addHandler(ch) + + # file logging: all workers + if output is not None: + if output.endswith(".txt") or output.endswith(".log"): + filename = output + else: + filename = os.path.join(output, "log.txt") + if distributed_rank > 0: + filename = filename + ".rank{}".format(distributed_rank) + PathManager.mkdirs(os.path.dirname(filename)) + + fh = logging.StreamHandler(_cached_log_stream(filename)) + fh.setLevel(logging.DEBUG) + fh.setFormatter(plain_formatter) + logger.addHandler(fh) + + return logger + + +# cache the opened file object, so that different calls to `setup_logger` +# with the same file name can safely write to the same file. +@functools.lru_cache(maxsize=None) +def _cached_log_stream(filename): + return PathManager.open(filename, "a") + + +""" +Below are some other convenient logging methods. +They are mainly adopted from +https://github.com/abseil/abseil-py/blob/master/absl/logging/__init__.py +""" + + +def _find_caller(): + """ + Returns: + str: module name of the caller + tuple: a hashable key to be used to identify different callers + """ + frame = sys._getframe(2) + while frame: + code = frame.f_code + if os.path.join("utils", "logger.") not in code.co_filename: + mod_name = frame.f_globals["__name__"] + if mod_name == "__main__": + mod_name = "detectron2" + return mod_name, (code.co_filename, frame.f_lineno, code.co_name) + frame = frame.f_back + + +_LOG_COUNTER = Counter() + + +def log_first_n(lvl, msg, n=1, *, name=None, key="caller"): + """ + Log only for the first n times. + + Args: + lvl (int): the logging level + msg (str): + n (int): + name (str): name of the logger to use. Will use the caller's module by default. + key (str or tuple[str]): the string(s) can be one of "caller" or + "message", which defines how to identify duplicated logs. + For example, if called with `n=1, key="caller"`, this function + will only log the first call from the same caller, regardless of + the message content. + If called with `n=1, key="message"`, this function will log the + same content only once, even if they are called from different places. + If called with `n=1, key=("caller", "message")`, this function + will not log only if the same caller has logged the same message before. + """ + if isinstance(key, str): + key = (key,) + assert len(key) > 0 + + caller_module, caller_key = _find_caller() + hash_key = () + if "caller" in key: + hash_key = hash_key + caller_key + if "message" in key: + hash_key = hash_key + (msg,) + + _LOG_COUNTER[hash_key] += 1 + if _LOG_COUNTER[hash_key] <= n: + logging.getLogger(name or caller_module).log(lvl, msg) + + +def log_every_n(lvl, msg, n=1, *, name=None): + """ + Log once per n times. + + Args: + lvl (int): the logging level + msg (str): + n (int): + name (str): name of the logger to use. Will use the caller's module by default. + """ + caller_module, key = _find_caller() + _LOG_COUNTER[key] += 1 + if n == 1 or _LOG_COUNTER[key] % n == 1: + logging.getLogger(name or caller_module).log(lvl, msg) + + +def create_small_table(small_dict): + """ + Create a small table using the keys of small_dict as headers. This is only + suitable for small dictionaries. + + Args: + small_dict (dict): a result dictionary of only a few items. + + Returns: + str: the table as a string. + """ + keys, values = tuple(zip(*small_dict.items())) + table = tabulate( + [values], + headers=keys, + tablefmt="pipe", + floatfmt=".3f", + stralign="center", + numalign="center", + ) + return table diff --git a/detectron2/utils/registry.py b/detectron2/utils/registry.py new file mode 100644 index 0000000000..1826420420 --- /dev/null +++ b/detectron2/utils/registry.py @@ -0,0 +1,62 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +class Registry(object): + """ + The registry that provides name -> object mapping, to support third-party users' custom modules. + + To create a registry (inside detectron2): + + .. code-block:: python + + BACKBONE_REGISTRY = Registry('BACKBONE') + + To register an object: + + .. code-block:: python + + @BACKBONE_REGISTRY.register() + class MyBackbone(): + ... + + Or: + + BACKBONE_REGISTRY.register(obj=MyBackbone) + """ + + def __init__(self, name): + """ + Args: + name (str): the name of this registry + """ + self._name = name + + self._obj_map = {} + + def _do_register(self, name, obj): + assert ( + name not in self._obj_map + ), "An object named '{}' was already registered in '{}' registry!".format(name, self._name) + self._obj_map[name] = obj + + def register(self, obj=None): + """ + Register the given object under the the name `obj.__name__`. + Can be used as either a decorator or not. See docstring of this class for usage. + """ + if obj is None: + # used as a decorator + def deco(func_or_class): + name = func_or_class.__name__ + self._do_register(name, func_or_class) + return func_or_class + + return deco + + # used as a function call + name = obj.__name__ + self._do_register(name, obj) + + def get(self, name): + ret = self._obj_map.get(name) + if ret is None: + raise KeyError("No object named '{}' found in '{}' registry!".format(name, self._name)) + return ret diff --git a/detectron2/utils/serialize.py b/detectron2/utils/serialize.py new file mode 100644 index 0000000000..cb09c517f9 --- /dev/null +++ b/detectron2/utils/serialize.py @@ -0,0 +1,29 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import cloudpickle + + +class PicklableWrapper(object): + """ + Wrap an object to make it more picklable, note that it uses + heavy weight serialization libraries that are slower than pickle. + It's best to use it only on closures (which are usually not picklable). + + This is a simplified version of + https://github.com/joblib/joblib/blob/master/joblib/externals/loky/cloudpickle_wrapper.py + """ + + def __init__(self, obj): + self._obj = obj + + def __reduce__(self): + s = cloudpickle.dumps(self._obj) + return cloudpickle.loads, (s,) + + def __call__(self, *args, **kwargs): + return self._obj(*args, **kwargs) + + def __getattr__(self, attr): + # Ensure that the wrapped object can be used seemlessly as the previous object. + if attr not in ["_obj"]: + return getattr(self._obj, attr) + return getattr(self, attr) diff --git a/detectron2/utils/video_visualizer.py b/detectron2/utils/video_visualizer.py new file mode 100644 index 0000000000..fb73adcbe1 --- /dev/null +++ b/detectron2/utils/video_visualizer.py @@ -0,0 +1,235 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import numpy as np +import pycocotools.mask as mask_util + +from detectron2.utils.visualizer import ( + ColorMode, + Visualizer, + _create_text_labels, + _PanopticPrediction, +) + +from .colormap import random_color + + +class _DetectedInstance: + """ + Used to store data about detected objects in video frame, + in order to transfer color to objects in the future frames. + + Attributes: + label (int): + bbox (tuple[float]): + mask_rle (dict): + color (tuple[float]): RGB colors in range (0, 1) + ttl (int): time-to-live for the instance. For example, if ttl=2, + the instance color can be transferred to objects in the next two frames. + """ + + __slots__ = ["label", "bbox", "mask_rle", "color", "ttl"] + + def __init__(self, label, bbox, mask_rle, color, ttl): + self.label = label + self.bbox = bbox + self.mask_rle = mask_rle + self.color = color + self.ttl = ttl + + +class VideoVisualizer: + def __init__(self, metadata, instance_mode=ColorMode.IMAGE): + """ + Args: + metadata (MetadataCatalog): image metadata. + """ + self.metadata = metadata + self._old_instances = [] + assert instance_mode in [ + ColorMode.IMAGE, + ColorMode.IMAGE_BW, + ], "Other mode not supported yet." + self._instance_mode = instance_mode + + def draw_instance_predictions(self, frame, predictions): + """ + Draw instance-level prediction results on an image. + + Args: + frame (ndarray): an RGB image of shape (H, W, C), in the range [0, 255]. + predictions (Instances): the output of an instance detection/segmentation + model. Following fields will be used to draw: + "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). + + Returns: + output (VisImage): image object with visualizations. + """ + frame_visualizer = Visualizer(frame, self.metadata) + num_instances = len(predictions) + if num_instances == 0: + return frame_visualizer.output + + boxes = predictions.pred_boxes.tensor.numpy() if predictions.has("pred_boxes") else None + scores = predictions.scores if predictions.has("scores") else None + classes = predictions.pred_classes.numpy() if predictions.has("pred_classes") else None + keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None + + if predictions.has("pred_masks"): + masks = predictions.pred_masks + # mask IOU is not yet enabled + # masks_rles = mask_util.encode(np.asarray(masks.permute(1, 2, 0), order="F")) + # assert len(masks_rles) == num_instances + else: + masks = None + + detected = [ + _DetectedInstance(classes[i], boxes[i], mask_rle=None, color=None, ttl=8) + for i in range(num_instances) + ] + colors = self._assign_colors(detected) + + labels = _create_text_labels(classes, scores, self.metadata.thing_classes) + + if self._instance_mode == ColorMode.IMAGE_BW: + # any() returns uint8 tensor + frame_visualizer.output.img = frame_visualizer._create_grayscale_image( + (masks.any(dim=0) > 0).numpy() if masks is not None else None + ) + alpha = 0.3 + else: + alpha = 0.5 + + frame_visualizer.overlay_instances( + boxes=None if masks is not None else boxes, # boxes are a bit distracting + masks=masks, + labels=labels, + keypoints=keypoints, + assigned_colors=colors, + alpha=alpha, + ) + + return frame_visualizer.output + + def draw_sem_seg(self, frame, sem_seg, area_threshold=None): + """ + Args: + sem_seg (ndarray or Tensor): semantic segmentation of shape (H, W), + each value is the integer label. + area_threhold (Optional[int]): only draw segmentations larger than the threshold + """ + # don't need to do anything special + frame_visualizer = Visualizer(frame, self.metadata) + frame_visualizer.draw_sem_seg(sem_seg, area_threshold=None) + return frame_visualizer.output + + def draw_panoptic_seg_predictions( + self, frame, panoptic_seg, segments_info, area_threshold=None, alpha=0.5 + ): + frame_visualizer = Visualizer(frame, self.metadata) + pred = _PanopticPrediction(panoptic_seg, segments_info) + + if self._instance_mode == ColorMode.IMAGE_BW: + frame_visualizer.output.img = frame_visualizer._create_grayscale_image( + pred.non_empty_mask() + ) + + # draw mask for all semantic segments first i.e. "stuff" + for mask, sinfo in pred.semantic_masks(): + category_idx = sinfo["category_id"] + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]] + except AttributeError: + mask_color = None + + frame_visualizer.draw_binary_mask( + mask, + color=mask_color, + text=self.metadata.stuff_classes[category_idx], + alpha=alpha, + area_threshold=area_threshold, + ) + + all_instances = list(pred.instance_masks()) + if len(all_instances) == 0: + return frame_visualizer.output + # draw mask for all instances second + masks, sinfo = list(zip(*all_instances)) + num_instances = len(masks) + masks_rles = mask_util.encode( + np.asarray(np.asarray(masks).transpose(1, 2, 0), dtype=np.uint8, order="F") + ) + assert len(masks_rles) == num_instances + + category_ids = [x["category_id"] for x in sinfo] + detected = [ + _DetectedInstance(category_ids[i], bbox=None, mask_rle=masks_rles[i], color=None, ttl=8) + for i in range(num_instances) + ] + colors = self._assign_colors(detected) + labels = [self.metadata.thing_classes[k] for k in category_ids] + + frame_visualizer.overlay_instances( + boxes=None, + masks=masks, + labels=labels, + keypoints=None, + assigned_colors=colors, + alpha=alpha, + ) + return frame_visualizer.output + + def _assign_colors(self, instances): + """ + Naive tracking heuristics to assign same color to the same instance, + will update the internal state of tracked instances. + + Returns: + list[tuple[float]]: list of colors. + """ + + # Compute iou with either boxes or masks: + is_crowd = np.zeros((len(instances),), dtype=np.bool) + if instances[0].bbox is None: + assert instances[0].mask_rle is not None + # use mask iou only when box iou is None + # because box seems good enough + rles_old = [x.mask_rle for x in self._old_instances] + rles_new = [x.mask_rle for x in instances] + ious = mask_util.iou(rles_old, rles_new, is_crowd) + threshold = 0.5 + else: + boxes_old = [x.bbox for x in self._old_instances] + boxes_new = [x.bbox for x in instances] + ious = mask_util.iou(boxes_old, boxes_new, is_crowd) + threshold = 0.6 + if len(ious) == 0: + ious = np.zeros((len(self._old_instances), len(instances)), dtype="float32") + + # Only allow matching instances of the same label: + for old_idx, old in enumerate(self._old_instances): + for new_idx, new in enumerate(instances): + if old.label != new.label: + ious[old_idx, new_idx] = 0 + + matched_new_per_old = np.asarray(ious).argmax(axis=1) + max_iou_per_old = np.asarray(ious).max(axis=1) + + # Try to find match for each old instance: + extra_instances = [] + for idx, inst in enumerate(self._old_instances): + if max_iou_per_old[idx] > threshold: + newidx = matched_new_per_old[idx] + if instances[newidx].color is None: + instances[newidx].color = inst.color + continue + # If an old instance does not match any new instances, + # keep it for the next frame in case it is just missed by the detector + inst.ttl -= 1 + if inst.ttl > 0: + extra_instances.append(inst) + + # Assign random color to newly-detected instances: + for inst in instances: + if inst.color is None: + inst.color = random_color(rgb=True, maximum=1) + self._old_instances = instances[:] + extra_instances + return [d.color for d in instances] diff --git a/detectron2/utils/visualizer.py b/detectron2/utils/visualizer.py new file mode 100644 index 0000000000..cd1db2a492 --- /dev/null +++ b/detectron2/utils/visualizer.py @@ -0,0 +1,974 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import colorsys +import numpy as np +from enum import Enum, unique +import cv2 +import matplotlib as mpl +import matplotlib.colors as mplc +import matplotlib.figure as mplfigure +import pycocotools.mask as mask_util +import torch +from matplotlib.backends.backend_agg import FigureCanvasAgg + +from detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks + +from .colormap import random_color + +__all__ = ["ColorMode", "VisImage", "Visualizer"] + + +_SMALL_OBJECT_AREA_THRESH = 1000 +_LARGE_MASK_AREA_THRESH = 120000 +_OFF_WHITE = (1.0, 1.0, 240.0 / 255) +_BLACK = (0, 0, 0) +_RED = (1.0, 0, 0) + +_KEYPOINT_THRESHOLD = 0.05 + + +@unique +class ColorMode(Enum): + """ + Enum of different color modes to use for instance visualizations. + + Attributes: + IMAGE: Picks a random color for every instance and overlay segmentations with low opacity. + SEGMENTATION: Let instances of the same category have similar colors, and overlay them with + high opacity. This provides more attention on the quality of segmentation. + IMAGE_BW: same as IMAGE, but convert all areas without masks to gray-scale. + """ + + IMAGE = 0 + SEGMENTATION = 1 + IMAGE_BW = 2 + + +class GenericMask: + """ + Attribute: + polygons (list[ndarray]): list[ndarray]: polygons for this mask. + Each ndarray has format [x, y, x, y, ...] + mask (ndarray): a binary mask + """ + + def __init__(self, mask_or_polygons, height, width): + self._mask = self._polygons = self._has_holes = None + self.height = height + self.width = width + + m = mask_or_polygons + if isinstance(m, dict): + # RLEs + assert "counts" in m and "size" in m + if isinstance(m["counts"], list): # uncompressed RLEs + h, w = m["size"] + assert h == height and w == width + m = mask_util.frPyObjects(m, h, w) + self._mask = mask_util.decode(m)[:, :] + return + + if isinstance(m, list): # list[ndarray] + self._polygons = [np.asarray(x).reshape(-1) for x in m] + return + + if isinstance(m, np.ndarray): # assumed to be a binary mask + assert m.shape[1] != 2, m.shape + assert m.shape == (height, width), m.shape + self._mask = m.astype("uint8") + return + + raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m))) + + @property + def mask(self): + if self._mask is None: + self._mask = self.polygons_to_mask(self._polygons) + return self._mask + + @property + def polygons(self): + if self._polygons is None: + self._polygons, self._has_holes = self.mask_to_polygons(self._mask) + return self._polygons + + @property + def has_holes(self): + if self._has_holes is None: + if self._mask is not None: + self._polygons, self._has_holes = self.mask_to_polygons(self._mask) + else: + self._has_holes = False # if original format is polygon, does not have holes + return self._has_holes + + def mask_to_polygons(self, mask): + # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level + # hierarchy. External contours (boundary) of the object are placed in hierarchy-1. + # Internal contours (holes) are placed in hierarchy-2. + # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from countours. + res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) + hierarchy = res[-1] + has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0 + res = res[-2] + res = [x.flatten() for x in res] + res = [x for x in res if len(x) >= 6] + return res, has_holes + + def polygons_to_mask(self, polygons): + rle = mask_util.frPyObjects(polygons, self.height, self.width) + rle = mask_util.merge(rle) + return mask_util.decode(rle)[:, :] + + def area(self): + return self.mask.sum() + + def bbox(self): + p = mask_util.frPyObjects(self.polygons, self.height, self.width) + p = mask_util.merge(p) + bbox = mask_util.toBbox(p) + bbox[2] += bbox[0] + bbox[3] += bbox[1] + return bbox + + +class _PanopticPrediction: + def __init__(self, panoptic_seg, segments_info): + self._seg = panoptic_seg + + self._sinfo = {s["id"]: s for s in segments_info} # seg id -> seg info + segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True) + areas = areas.numpy() + sorted_idxs = np.argsort(-areas) + self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs] + self._seg_ids = self._seg_ids.tolist() + for sid, area in zip(self._seg_ids, self._seg_areas): + if sid in self._sinfo: + self._sinfo[sid]["area"] = float(area) + + def non_empty_mask(self): + """ + Returns: + (H, W) array, a mask for all pixels that have a prediction + """ + empty_ids = [] + for id in self._seg_ids: + if id not in self._sinfo: + empty_ids.append(id) + if len(empty_ids) == 0: + return np.zeros(self._seg.shape, dtype=np.uint8) + assert ( + len(empty_ids) == 1 + ), ">1 ids corresponds to no labels. This is currently not supported" + return (self._seg != empty_ids[0]).numpy().astype(np.bool) + + def semantic_masks(self): + for sid in self._seg_ids: + sinfo = self._sinfo.get(sid) + if sinfo is None or sinfo["isthing"]: + # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions. + continue + yield (self._seg == sid).numpy().astype(np.bool), sinfo + + def instance_masks(self): + for sid in self._seg_ids: + sinfo = self._sinfo.get(sid) + if sinfo is None or not sinfo["isthing"]: + continue + mask = (self._seg == sid).numpy().astype(np.bool) + if mask.sum() > 0: + yield mask, sinfo + + +def _create_text_labels(classes, scores, class_names): + """ + Args: + classes (list[int] or None): + scores (list[float] or None): + class_names (list[str] or None): + + Returns: + list[str] or None + """ + labels = None + if class_names is not None and len(class_names) > 1: + labels = [class_names[i] for i in classes] + if scores is not None: + if labels is None: + labels = ["{:.0f}%".format(s * 100) for s in scores] + else: + labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)] + return labels + + +class VisImage: + def __init__(self, img, scale=1.0): + """ + Args: + img (ndarray): an RGB image of shape (H, W, 3). + scale (float): scale the input image + """ + self.img = img + self.scale = scale + self.width, self.height = img.shape[1], img.shape[0] + self._setup_figure(img) + + def _setup_figure(self, img): + """ + Args: + Same as in :meth:`__init__()`. + + Returns: + fig (matplotlib.pyplot.figure): top level container for all the image plot elements. + ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system. + """ + fig = mplfigure.Figure(frameon=False) + self.dpi = fig.get_dpi() + # add a small 1e-2 to avoid precision lost due to matplotlib's truncation + # (https://github.com/matplotlib/matplotlib/issues/15363) + fig.set_size_inches( + (self.width * self.scale + 1e-2) / self.dpi, + (self.height * self.scale + 1e-2) / self.dpi, + ) + self.canvas = FigureCanvasAgg(fig) + # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig) + ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + ax.axis("off") + ax.set_xlim(0.0, self.width) + ax.set_ylim(self.height) + + self.fig = fig + self.ax = ax + + def save(self, filepath): + """ + Args: + filepath (str): a string that contains the absolute path, including the file name, where + the visualized image will be saved. + """ + cv2.imwrite(filepath, self.get_image()[:, :, ::-1]) + + def get_image(self): + """ + Returns: + ndarray: the visualized image of shape (H, W, 3) (RGB) in uint8 type. + The shape is scaled w.r.t the input image using the given `scale` argument. + """ + canvas = self.canvas + s, (width, height) = canvas.print_to_buffer() + if (self.width, self.height) != (width, height): + img = cv2.resize(self.img, (width, height)) + else: + img = self.img + + # buf = io.BytesIO() # works for cairo backend + # canvas.print_rgba(buf) + # width, height = self.width, self.height + # s = buf.getvalue() + + buffer = np.frombuffer(s, dtype="uint8") + + # imshow is slow. blend manually (still quite slow) + img_rgba = buffer.reshape(height, width, 4) + rgb, alpha = np.split(img_rgba, [3], axis=2) + + try: + import numexpr as ne # fuse them with numexpr + + visualized_image = ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)") + except ImportError: + alpha = alpha.astype("float32") / 255.0 + visualized_image = img * (1 - alpha) + rgb * alpha + + visualized_image = visualized_image.astype("uint8") + + return visualized_image + + +class Visualizer: + def __init__(self, img_rgb, metadata, scale=1.0, instance_mode=ColorMode.IMAGE): + """ + Args: + img_rgb: a numpy array of shape (H, W, C), where H and W correspond to + the height and width of the image respectively. C is the number of + color channels. The image is required to be in RGB format since that + is a requirement of the Matplotlib library. The image is also expected + to be in the range [0, 255]. + metadata (MetadataCatalog): image metadata. + """ + self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8) + self.metadata = metadata + self.output = VisImage(self.img, scale=scale) + self.cpu_device = torch.device("cpu") + + # too small texts are useless, therefore clamp to 9 + self._default_font_size = max( + np.sqrt(self.output.height * self.output.width) // 90, 10 // scale + ) + self._instance_mode = instance_mode + + def draw_instance_predictions(self, predictions): + """ + Draw instance-level prediction results on an image. + + Args: + predictions (Instances): the output of an instance detection/segmentation + model. Following fields will be used to draw: + "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). + + Returns: + output (VisImage): image object with visualizations. + """ + boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None + scores = predictions.scores if predictions.has("scores") else None + classes = predictions.pred_classes if predictions.has("pred_classes") else None + labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None)) + keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None + + if predictions.has("pred_masks"): + masks = predictions.pred_masks.numpy() + masks = [GenericMask(x, self.output.height, self.output.width) for x in masks] + else: + masks = None + + if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"): + colors = [ + self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes + ] + alpha = 0.8 + else: + colors = None + alpha = 0.5 + + if self._instance_mode == ColorMode.IMAGE_BW: + self.output.img = self._create_grayscale_image( + (predictions.pred_masks.any(dim=0) > 0).numpy() + ) + alpha = 0.3 + + self.overlay_instances( + masks=masks, + boxes=boxes, + labels=labels, + keypoints=keypoints, + assigned_colors=colors, + alpha=alpha, + ) + return self.output + + def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8): + """ + Draw semantic segmentation predictions/labels. + + Args: + sem_seg (Tensor or ndarray): the segmentation of shape (H, W). + area_threshold (int): segments with less than `area_threshold` are not drawn. + alpha (float): the larger it is, the more opaque the segmentations are. + + Returns: + output (VisImage): image object with visualizations. + """ + if isinstance(sem_seg, torch.Tensor): + sem_seg = sem_seg.numpy() + labels, areas = np.unique(sem_seg, return_counts=True) + sorted_idxs = np.argsort(-areas).tolist() + labels = labels[sorted_idxs] + for label in labels: + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[label]] + except (AttributeError, IndexError): + mask_color = None + + binary_mask = (sem_seg == label).astype(np.uint8) + text = self.metadata.stuff_classes[label] + self.draw_binary_mask( + binary_mask, + color=mask_color, + edge_color=_OFF_WHITE, + text=text, + alpha=alpha, + area_threshold=area_threshold, + ) + return self.output + + def draw_panoptic_seg_predictions( + self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7 + ): + """ + Draw panoptic prediction results on an image. + + Args: + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each + segment. + segments_info (list[dict]): Describe each segment in `panoptic_seg`. + Each dict contains keys "id", "category_id", "isthing". + area_threshold (int): stuff segments with less than `area_threshold` are not drawn. + + Returns: + output (VisImage): image object with visualizations. + """ + pred = _PanopticPrediction(panoptic_seg, segments_info) + + if self._instance_mode == ColorMode.IMAGE_BW: + self.output.img = self._create_grayscale_image(pred.non_empty_mask()) + + # draw mask for all semantic segments first i.e. "stuff" + for mask, sinfo in pred.semantic_masks(): + category_idx = sinfo["category_id"] + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]] + except AttributeError: + mask_color = None + + text = self.metadata.stuff_classes[category_idx] + self.draw_binary_mask( + mask, + color=mask_color, + edge_color=_OFF_WHITE, + text=text, + alpha=alpha, + area_threshold=area_threshold, + ) + + # draw mask for all instances second + all_instances = list(pred.instance_masks()) + if len(all_instances) == 0: + return self.output + masks, sinfo = list(zip(*all_instances)) + category_ids = [x["category_id"] for x in sinfo] + + try: + scores = [x["score"] for x in sinfo] + except KeyError: + scores = None + labels = _create_text_labels(category_ids, scores, self.metadata.thing_classes) + + try: + colors = [random_color(rgb=True, maximum=1) for k in category_ids] + except AttributeError: + colors = None + self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors) + + return self.output + + def draw_dataset_dict(self, dic): + annos = dic.get("annotations", None) + if annos: + if "segmentation" in annos[0]: + masks = [x["segmentation"] for x in annos] + else: + masks = None + if "keypoints" in annos[0]: + keypts = [x["keypoints"] for x in annos] + keypts = np.array(keypts).reshape(len(annos), -1, 3) + else: + keypts = None + + boxes = [BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS) for x in annos] + + labels = [x["category_id"] for x in annos] + names = self.metadata.get("thing_classes", None) + if names: + labels = [names[i] for i in labels] + labels = [i + ("|crowd" if a.get("iscrowd", 0) else "") for i, a in zip(labels, annos)] + self.overlay_instances(labels=labels, boxes=boxes, masks=masks, keypoints=keypts) + + sem_seg = dic.get("sem_seg", None) + if sem_seg is None and "sem_seg_file_name" in dic: + sem_seg = cv2.imread(dic["sem_seg_file_name"], cv2.IMREAD_GRAYSCALE) + if sem_seg is not None: + self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5) + return self.output + + def overlay_instances( + self, + *, + boxes=None, + labels=None, + masks=None, + keypoints=None, + assigned_colors=None, + alpha=0.5 + ): + """ + Args: + boxes (Boxes or ndarray): either a :class:`Boxes` or a Nx4 numpy array + of XYXY_ABS format for the N objects in a single image. + labels (list[str]): the text to be displayed for each instance. + masks (masks-like object): Supported types are: + + * `structures.masks.PolygonMasks`, `structures.masks.BitMasks`. + * list[list[ndarray]]: contains the segmentation masks for all objects in one image. + The first level of the list corresponds to individual instances. The second + level to all the polygon that compose the instance, and the third level + to the polygon coordinates. The third level should have the format of + [x0, y0, x1, y1, ..., xn, yn] (n >= 3). + * list[ndarray]: each ndarray is a binary mask of shape (H, W). + * list[dict]: each dict is a COCO-style RLE. + keypoints (Keypoint or array like): an array-like object of shape (N, K, 3), + where the N is the number of instances and K is the number of keypoints. + The last dimension corresponds to (x, y, visibility or score). + assigned_colors (list[matplotlib.colors]): a list of colors, where each color + corresponds to each mask or box in the image. Refer to 'matplotlib.colors' + for full list of formats that the colors are accepted in. + + Returns: + output (VisImage): image object with visualizations. + """ + num_instances = None + if boxes is not None: + boxes = self._convert_boxes(boxes) + num_instances = len(boxes) + if masks is not None: + masks = self._convert_masks(masks) + if num_instances: + assert len(masks) == num_instances + else: + num_instances = len(masks) + if keypoints is not None: + if num_instances: + assert len(keypoints) == num_instances + else: + num_instances = len(keypoints) + keypoints = self._convert_keypoints(keypoints) + if labels is not None: + assert len(labels) == num_instances + if assigned_colors is None: + assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] + if num_instances == 0: + return self.output + + # Display in largest to smallest order to reduce occlusion. + areas = None + if boxes is not None: + areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1) + elif masks is not None: + areas = np.asarray([x.area() for x in masks]) + + if areas is not None: + sorted_idxs = np.argsort(-areas).tolist() + # Re-order overlapped instances in descending order. + boxes = boxes[sorted_idxs] if boxes is not None else None + labels = [labels[k] for k in sorted_idxs] if labels is not None else None + masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None + assigned_colors = [assigned_colors[idx] for idx in sorted_idxs] + keypoints = keypoints[sorted_idxs] if keypoints is not None else None + + for i in range(num_instances): + color = assigned_colors[i] + if boxes is not None: + self.draw_box(boxes[i], edge_color=color) + + if masks is not None: + for segment in masks[i].polygons: + self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha) + + if labels is not None: + # first get a box + if boxes is not None: + x0, y0, x1, y1 = boxes[i] + text_pos = (x0, y0) # if drawing boxes, put text on the box corner. + horiz_align = "left" + elif masks is not None: + x0, y0, x1, y1 = masks[i].bbox() + + # draw text in the center (defined by median) when box is not drawn + # median is less sensitive to outliers. + text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1] + horiz_align = "center" + else: + continue # drawing the box confidence for keypoints isn't very useful. + # for small objects, draw text at the side to avoid occulusion + instance_area = (y1 - y0) * (x1 - x0) + if ( + instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale + or y1 - y0 < 40 * self.output.scale + ): + if y1 >= self.output.height - 5: + text_pos = (x1, y0) + else: + text_pos = (x0, y1) + + height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width) + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + font_size = ( + np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) + * 0.5 + * self._default_font_size + ) + self.draw_text( + labels[i], + text_pos, + color=lighter_color, + horizontal_alignment=horiz_align, + font_size=font_size, + ) + + # draw keypoints + if keypoints is not None: + for keypoints_per_instance in keypoints: + self.draw_and_connect_keypoints(keypoints_per_instance) + + return self.output + + def draw_and_connect_keypoints(self, keypoints): + """ + Draws keypoints of an instance and follows the rules for keypoint connections + to draw lines between appropriate keypoints. This follows color heuristics for + line color. + + Args: + keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints + and the last dimension corresponds to (x, y, probability). + + Returns: + output (VisImage): image object with visualizations. + """ + visible = {} + for idx, keypoint in enumerate(keypoints): + # draw keypoint + x, y, prob = keypoint + if prob > _KEYPOINT_THRESHOLD: + color = tuple(x / 255 for x in _BLACK) + self.draw_circle((x, y), color=color) + keypoint_name = self.metadata.keypoint_names[idx] + visible[keypoint_name] = (x, y) + + for kp0, kp1, color in self.metadata.keypoint_connection_rules: + if kp0 in visible and kp1 in visible: + x0, y0 = visible[kp0] + x1, y1 = visible[kp1] + color = tuple(x / 255.0 for x in color) + self.draw_line([x0, x1], [y0, y1], color=color) + + # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip + # Note that this strategy is specific to person keypoints. + # For other keypoints, it should just do nothing + try: + ls_x, ls_y = visible["left_shoulder"] + rs_x, rs_y = visible["right_shoulder"] + mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2 + except KeyError: + pass + else: + # draw line from nose to mid-shoulder + nose_x, nose_y = visible.get("nose", (None, None)) + if nose_x is not None: + self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED) + + try: + # draw line from mid-shoulder to mid-hip + lh_x, lh_y = visible["left_hip"] + rh_x, rh_y = visible["right_hip"] + except KeyError: + pass + else: + mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2 + self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED) + return self.output + + """ + Primitive drawing functions: + """ + + def draw_text( + self, text, position, *, font_size=None, color="g", horizontal_alignment="center" + ): + """ + Args: + text (str): class label + position (tuple): a tuple of the x and y coordinates to place text on image. + font_size (int, optional): font of the text. If not provided, a font size + proportional to the image width is calculated and used. + color: color of the text. Refer to `matplotlib.colors` for full list + of formats that are accepted. + horizontal_alignment (str): see `matplotlib.text.Text` + + Returns: + output (VisImage): image object with text drawn. + """ + if not font_size: + font_size = self._default_font_size + + # since the text background is dark, we don't want the text to be dark + color = np.maximum(list(mplc.to_rgb(color)), 0.2) + color[np.argmax(color)] = max(0.8, np.max(color)) + + x, y = position + self.output.ax.text( + x, + y, + text, + size=font_size * self.output.scale, + family="sans-serif", + bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"}, + verticalalignment="top", + horizontalalignment=horizontal_alignment, + color=color, + zorder=10, + ) + return self.output + + def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"): + """ + Args: + box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0 + are the coordinates of the image's top left corner. x1 and y1 are the + coordinates of the image's bottom right corner. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + edge_color: color of the outline of the box. Refer to `matplotlib.colors` + for full list of formats that are accepted. + line_style (string): the string to use to create the outline of the boxes. + + Returns: + output (VisImage): image object with box drawn. + """ + x0, y0, x1, y1 = box_coord + width = x1 - x0 + height = y1 - y0 + + linewidth = max(self._default_font_size / 4, 1) + + self.output.ax.add_patch( + mpl.patches.Rectangle( + (x0, y0), + width, + height, + fill=False, + edgecolor=edge_color, + linewidth=linewidth * self.output.scale, + alpha=alpha, + linestyle=line_style, + ) + ) + return self.output + + def draw_circle(self, circle_coord, color, radius=5): + """ + Args: + circle_coord (list(int) or tuple(int)): contains the x and y coordinates + of the center of the circle. + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + radius (int): radius of the circle. + + Returns: + output (VisImage): image object with box drawn. + """ + x, y = circle_coord + self.output.ax.add_patch(mpl.patches.Circle(circle_coord, radius=radius, color=color)) + return self.output + + def draw_line(self, x_data, y_data, color): + """ + Args: + x_data (list[int]): a list containing x values of all the points being drawn. + Length of list should match the length of y_data. + y_data (list[int]): a list containing y values of all the points being drawn. + Length of list should match the length of x_data. + color: color of the line. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + + Returns: + output (VisImage): image object with line drawn. + """ + linewidth = max(self._default_font_size / 3, 1) + self.output.ax.add_line( + mpl.lines.Line2D(x_data, y_data, linewidth=linewidth * self.output.scale, color=color) + ) + return self.output + + def draw_binary_mask( + self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=4096 + ): + """ + Args: + binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and + W is the image width. Each value in the array is either a 0 or 1 value of uint8 + type. + color: color of the mask. Refer to `matplotlib.colors` for a full list of + formats that are accepted. If None, will pick a random color. + edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a + full list of formats that are accepted. + text (str): if None, will be drawn in the object's center of mass. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + area_threshold (float): a connected component small than this will not be shown. + + Returns: + output (VisImage): image object with mask drawn. + """ + if color is None: + color = random_color(rgb=True, maximum=1) + if area_threshold is None: + area_threshold = 4096 + + has_valid_segment = False + binary_mask = binary_mask.astype("uint8") # opencv needs uint8 + mask = GenericMask(binary_mask, self.output.height, self.output.width) + shape2d = (binary_mask.shape[0], binary_mask.shape[1]) + + if not mask.has_holes: + # draw polygons for regular masks + for segment in mask.polygons: + area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1])) + if area < area_threshold: + continue + has_valid_segment = True + segment = segment.reshape(-1, 2) + self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha) + else: + rgba = np.zeros(shape2d + (4,), dtype="float32") + rgba[:, :, :3] = color + rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha + has_valid_segment = True + self.output.ax.imshow(rgba) + + if text is not None and has_valid_segment: + # TODO sometimes drawn on wrong objects. the heuristics here can improve. + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8) + largest_component_id = np.argmax(stats[1:, -1]) + 1 + + # draw text on the largest component, as well as other very large components. + for cid in range(1, _num_cc): + if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH: + # median is more stable than centroid + # center = centroids[largest_component_id] + center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1] + self.draw_text(text, center, color=lighter_color) + return self.output + + def draw_polygon(self, segment, color, edge_color=None, alpha=0.5): + """ + Args: + segment: numpy array of shape Nx2, containing all the points in the polygon. + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a + full list of formats that are accepted. If not provided, a darker shade + of the polygon color will be used instead. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + + Returns: + output (VisImage): image object with polygon drawn. + """ + if edge_color is None: + # make edge color darker than the polygon color + if alpha > 0.8: + edge_color = self._change_color_brightness(color, brightness_factor=-0.7) + else: + edge_color = color + edge_color = mplc.to_rgb(edge_color) + (1,) + + polygon = mpl.patches.Polygon( + segment, + fill=True, + facecolor=mplc.to_rgb(color) + (alpha,), + edgecolor=edge_color, + linewidth=max(self._default_font_size // 15 * self.output.scale, 1), + ) + self.output.ax.add_patch(polygon) + return self.output + + """ + Internal methods: + """ + + def _jitter(self, color): + """ + Randomly modifies given color to produce a slightly different color than the color given. + + Args: + color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color + picked. The values in the list are in the [0.0, 1.0] range. + + Returns: + jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the + color after being jittered. The values in the list are in the [0.0, 1.0] range. + """ + color = mplc.to_rgb(color) + vec = np.random.rand(3) + # better to do it in another color space + vec = vec / np.linalg.norm(vec) * 0.5 + res = np.clip(vec + color, 0, 1) + return tuple(res) + + def _create_grayscale_image(self, mask=None): + """ + Create a grayscale version of the original image. + The colors in masked area, if given, will be kept. + """ + img_bw = self.img.astype("f4").mean(axis=2) + img_bw = np.stack([img_bw] * 3, axis=2) + if mask is not None: + img_bw[mask] = self.img[mask] + return img_bw + + def _change_color_brightness(self, color, brightness_factor): + """ + Depending on the brightness_factor, gives a lighter or darker color i.e. a color with + less or more saturation than the original color. + + Args: + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of + 0 will correspond to no change, a factor in [-1.0, 0) range will result in + a darker color and a factor in (0, 1.0] range will result in a ligher color. + + Returns: + modified_color (tuple[double]): a tuple containing the RGB values of the + modified color. Each value in the tuple is in the [0.0, 1.0] range. + """ + assert brightness_factor >= -1.0 and brightness_factor <= 1.0 + color = mplc.to_rgb(color) + polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color)) + modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1]) + modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness + modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness + modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2]) + return modified_color + + def _convert_boxes(self, boxes): + """ + Convert different format of boxes to a Nx4 array. + """ + if isinstance(boxes, Boxes): + return boxes.tensor.numpy() + else: + return np.asarray(boxes) + + def _convert_masks(self, masks_or_polygons): + """ + Convert different format of masks or polygons to a tuple of masks and polygons. + + Returns: + list[GenericMask]: + """ + + m = masks_or_polygons + if isinstance(m, PolygonMasks): + m = m.polygons + if isinstance(m, BitMasks): + m = m.tensor.numpy() + if isinstance(m, torch.Tensor): + m = m.numpy() + ret = [] + for x in m: + if isinstance(x, GenericMask): + ret.append(x) + else: + ret.append(GenericMask(x, self.output.height, self.output.width)) + return ret + + def _convert_keypoints(self, keypoints): + if isinstance(keypoints, Keypoints): + keypoints = keypoints.tensor + keypoints = np.asarray(keypoints) + return keypoints + + def get_output(self): + """ + Returns: + output (VisImage): the image output containing the visualizations added + to the image. + """ + return self.output diff --git a/dev/README.md b/dev/README.md new file mode 100644 index 0000000000..287f91f930 --- /dev/null +++ b/dev/README.md @@ -0,0 +1,6 @@ + +## Some scripts for developers to use, include: + +- `linter.sh`: lint the codebase before commit +- `run_{inference,instant}_tests.sh`: run inference/training for a few iterations. +- `parse_results.sh`: parse results from log file. diff --git a/dev/linter.sh b/dev/linter.sh new file mode 100755 index 0000000000..3fdb2d6665 --- /dev/null +++ b/dev/linter.sh @@ -0,0 +1,26 @@ +#!/bin/bash -ev +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +# Run this script at project root by "./dev/linter.sh" before you commit + +echo "Running isort ..." +isort -y --multi-line 3 --trailing-comma -sp . --skip datasets --skip docs --skip-glob '*/__init__.py' --atomic + +echo "Running black ..." +black -l 100 . + +echo "Running flake8 ..." +if [ -x "$(command -v flake8-3)" ]; then + flake8-3 . +else + python3 -m flake8 . +fi + +# echo "Running mypy ..." +# Pytorch does not have enough type annotations +# mypy detectron2/solver detectron2/structures detectron2/config + +echo "Running clang-format ..." +find . -regex ".*\.\(cpp\|c\|cc\|cu\|cxx\|h\|hh\|hpp\|hxx\|tcc\|mm\|m\)" -print0 | xargs -0 clang-format -i + +command -v arc > /dev/null && arc lint diff --git a/dev/parse_results.sh b/dev/parse_results.sh new file mode 100755 index 0000000000..358cac8c42 --- /dev/null +++ b/dev/parse_results.sh @@ -0,0 +1,44 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +# A shell script that parses metrics from the log file. +# Make it easier for developers to track performance of models. + +LOG="$1" + +if [[ -z "$LOG" ]]; then + echo "Usage: $0 /path/to/log/file" + exit 1 +fi + +# [12/15 11:47:32] trainer INFO: Total training time: 12:15:04.446477 (0.4900 s / it) +# [12/15 11:49:03] inference INFO: Total inference time: 0:01:25.326167 (0.13652186737060548 s / img per device, on 8 devices) + +# training time +trainspeed=$(grep -o 'Overall training.*' "$LOG" | grep -Eo '\(.*\)' | grep -o '[0-9\.]*') +echo "Training speed: $trainspeed s/it" + +# inference time: there could be multiple inference during training +inferencespeed=$(grep -o 'Total inference.*' "$LOG" | tail -n1 | grep -Eo '\(.*\)' | grep -o '[0-9\.]*' | head -n1) +echo "Inference speed: $inferencespeed s/it" + +# [12/15 11:47:18] trainer INFO: eta: 0:00:00 iter: 90000 loss: 0.5407 (0.7256) loss_classifier: 0.1744 (0.2446) loss_box_reg: 0.0838 (0.1160) loss_mask: 0.2159 (0.2722) loss_objectness: 0.0244 (0.0429) loss_rpn_box_reg: 0.0279 (0.0500) time: 0.4487 (0.4899) data: 0.0076 (0.0975) lr: 0.000200 max mem: 4161 +memory=$(grep -o 'max[_ ]mem: [0-9]*' "$LOG" | tail -n1 | grep -o '[0-9]*') +echo "Training memory: $memory MB" + +echo "Easy to copypaste:" +echo "$trainspeed","$inferencespeed","$memory" + +echo "------------------------------" + +# [12/26 17:26:32] engine.coco_evaluation: copypaste: Task: bbox +# [12/26 17:26:32] engine.coco_evaluation: copypaste: AP,AP50,AP75,APs,APm,APl +# [12/26 17:26:32] engine.coco_evaluation: copypaste: 0.0017,0.0024,0.0017,0.0005,0.0019,0.0011 +# [12/26 17:26:32] engine.coco_evaluation: copypaste: Task: segm +# [12/26 17:26:32] engine.coco_evaluation: copypaste: AP,AP50,AP75,APs,APm,APl +# [12/26 17:26:32] engine.coco_evaluation: copypaste: 0.0014,0.0021,0.0016,0.0005,0.0016,0.0011 + +echo "COCO Results:" +num_tasks=$(grep -o 'copypaste:.*Task.*' "$LOG" | sort -u | wc -l) +# each task has 3 lines +grep -o 'copypaste:.*' "$LOG" | cut -d ' ' -f 2- | tail -n $((num_tasks * 3)) diff --git a/dev/run_inference_tests.sh b/dev/run_inference_tests.sh new file mode 100755 index 0000000000..17e422d576 --- /dev/null +++ b/dev/run_inference_tests.sh @@ -0,0 +1,44 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +BIN="python tools/train_net.py" +OUTPUT="inference_test_output" +NUM_GPUS=2 + +CFG_LIST=( "${@:1}" ) + +if [ ${#CFG_LIST[@]} -eq 0 ]; then + CFG_LIST=( ./configs/quick_schedules/*inference_acc_test.yaml ) +fi + +echo "========================================================================" +echo "Configs to run:" +echo "${CFG_LIST[@]}" +echo "========================================================================" + + +for cfg in "${CFG_LIST[@]}"; do + echo "========================================================================" + echo "Running $cfg ..." + echo "========================================================================" + $BIN \ + --eval-only \ + --num-gpus $NUM_GPUS \ + --config-file "$cfg" \ + OUTPUT_DIR $OUTPUT + rm -rf $OUTPUT +done + + +echo "========================================================================" +echo "Running demo.py ..." +echo "========================================================================" +DEMO_BIN="python demo/demo.py" +COCO_DIR=datasets/coco/val2014 +mkdir -pv $OUTPUT + +set -v + +$DEMO_BIN --config-file ./configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml \ + --input $COCO_DIR/COCO_val2014_0000001933* --output $OUTPUT +rm -rf $OUTPUT diff --git a/dev/run_instant_tests.sh b/dev/run_instant_tests.sh new file mode 100755 index 0000000000..2c51de6492 --- /dev/null +++ b/dev/run_instant_tests.sh @@ -0,0 +1,27 @@ +#!/bin/bash -e +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +BIN="python tools/train_net.py" +OUTPUT="instant_test_output" +NUM_GPUS=2 + +CFG_LIST=( "${@:1}" ) +if [ ${#CFG_LIST[@]} -eq 0 ]; then + CFG_LIST=( ./configs/quick_schedules/*instant_test.yaml ) +fi + +echo "========================================================================" +echo "Configs to run:" +echo "${CFG_LIST[@]}" +echo "========================================================================" + +for cfg in "${CFG_LIST[@]}"; do + echo "========================================================================" + echo "Running $cfg ..." + echo "========================================================================" + $BIN --num-gpus $NUM_GPUS --config-file "$cfg" \ + SOLVER.IMS_PER_BATCH $(($NUM_GPUS * 2)) \ + OUTPUT_DIR "$OUTPUT" + rm -rf "$OUTPUT" +done + diff --git a/docs/.gitignore b/docs/.gitignore new file mode 100644 index 0000000000..e35d8850c9 --- /dev/null +++ b/docs/.gitignore @@ -0,0 +1 @@ +_build diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000000..d537643dd4 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,19 @@ +# Minimal makefile for Sphinx documentation +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000000..12366165e8 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,5 @@ + +# Build the docs: + +1. Install dependencies in `requirements.txt` +2. `make html` diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000000..6dacdb6c27 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,238 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +import mock + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +import sphinx_rtd_theme + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# to support markdown +from recommonmark.parser import CommonMarkParser + +import detectron2 + +sys.path.insert(0, os.path.abspath("../")) + +DEPLOY = False + + +# -- Project information ----------------------------------------------------- + +try: + import torch # noqa +except ImportError: + for m in [ + "torch", + "torchvision", + "torch.nn", + "torch.distributed", + "torch.autograd", + "torch.autograd.function", + "torch.nn.modules", + "torch.nn.modules.utils", + ]: + sys.modules[m] = mock.Mock(name=m) + +for m in ["cv2", "scipy", "portalocker"]: + sys.modules[m] = mock.Mock(name=m) +sys.modules["cv2"].__version__ = "3.4" + + +project = "detectron2" +copyright = "2019, detectron2 contributors" +author = "detectron2 contributors" + +# The short X.Y version +version = detectron2.__version__ +# The full version, including alpha/beta/rc tags +release = version + + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +needs_sphinx = "1.7" + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.napoleon", + "sphinx.ext.intersphinx", + "sphinx.ext.todo", + "sphinx.ext.coverage", + "sphinx.ext.mathjax", + "sphinx.ext.viewcode", + "sphinx.ext.githubpages", +] + +# -- Configurations for plugins ------------ +napoleon_google_docstring = True +napoleon_include_init_with_doc = True +napoleon_include_special_with_doc = True +napoleon_numpy_docstring = False +napoleon_use_rtype = False + +if DEPLOY: + intersphinx_timeout = 10 +else: + # skip this when building locally + intersphinx_timeout = 0.1 +intersphinx_mapping = { + "python": ("https://docs.python.org/3.6", None), + "numpy": ("https://docs.scipy.org/doc/numpy/", None), +} +# ------------------------- + + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +source_parsers = {".md": CommonMarkParser} + +source_suffix = [".rst", ".md"] + +# The master toctree document. +master_doc = "index" + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "build", "README.md"] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = "sphinx" + + +# -- Options for HTML output ------------------------------------------------- + +html_theme = "sphinx_rtd_theme" +html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = "detectron2doc" + + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, "detectron2.tex", "detectron2 Documentation", "detectron2 contributors", "manual") +] + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [(master_doc, "detectron2", "detectron2 Documentation", [author], 1)] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + ( + master_doc, + "detectron2", + "detectron2 Documentation", + author, + "detectron2", + "One line description of project.", + "Miscellaneous", + ) +] + + +# -- Options for todo extension ---------------------------------------------- + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = True + + +def setup(app): + from recommonmark.transform import AutoStructify + + # app.connect('autodoc-skip-member', autodoc_skip_member) + app.add_config_value( + "recommonmark_config", + { # 'url_resolver': url_resolver, + "auto_toc_tree_section": "Contents", + "enable_math": True, + "enable_inline_math": True, + "enable_eval_rst": True, + }, + True, + ) + app.add_transform(AutoStructify) diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000000..3672b1f4c1 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,16 @@ +.. detectron2 documentation master file, created by + sphinx-quickstart on Sat Sep 21 13:46:45 2019. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to detectron2's documentation! +====================================== + +Some nice intro. + +.. toctree:: + :maxdepth: 2 + + tutorials/index + notes/index + modules/index diff --git a/docs/modules/checkpoint.rst b/docs/modules/checkpoint.rst new file mode 100644 index 0000000000..616cb186c4 --- /dev/null +++ b/docs/modules/checkpoint.rst @@ -0,0 +1,7 @@ +detectron2.checkpoint package +============================= + +.. automodule:: detectron2.checkpoint + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/config.rst b/docs/modules/config.rst new file mode 100644 index 0000000000..ae96a6bcbe --- /dev/null +++ b/docs/modules/config.rst @@ -0,0 +1,7 @@ +detectron2.config package +========================= + +.. automodule:: detectron2.config + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/data.datasets.rst b/docs/modules/data.datasets.rst new file mode 100644 index 0000000000..e3ffd2f4b3 --- /dev/null +++ b/docs/modules/data.datasets.rst @@ -0,0 +1,7 @@ +detectron2.data.datasets package +================================ + +.. automodule:: detectron2.data.datasets + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/data.rst b/docs/modules/data.rst new file mode 100644 index 0000000000..cb129b1e2d --- /dev/null +++ b/docs/modules/data.rst @@ -0,0 +1,26 @@ +detectron2.data package +======================= + +.. automodule:: detectron2.data + :members: + :undoc-members: + :show-inheritance: + +detectron2.data.detection\_utils module +--------------------------------------- + +.. automodule:: detectron2.data.detection_utils + :members: + :undoc-members: + :show-inheritance: + +Subpackages +----------- + +.. toctree:: + + data.datasets + data.samplers + data.transforms + + diff --git a/docs/modules/data.samplers.rst b/docs/modules/data.samplers.rst new file mode 100644 index 0000000000..e2040902eb --- /dev/null +++ b/docs/modules/data.samplers.rst @@ -0,0 +1,7 @@ +detectron2.data.samplers package +================================ + +.. automodule:: detectron2.data.samplers + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/data.transforms.rst b/docs/modules/data.transforms.rst new file mode 100644 index 0000000000..97d9170814 --- /dev/null +++ b/docs/modules/data.transforms.rst @@ -0,0 +1,7 @@ +detectron2.data.transforms package +================================== + +.. automodule:: detectron2.data.transforms + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/engine.rst b/docs/modules/engine.rst new file mode 100644 index 0000000000..bb8b533aee --- /dev/null +++ b/docs/modules/engine.rst @@ -0,0 +1,25 @@ +detectron2.engine package +========================= + + +.. automodule:: detectron2.engine + :members: + :undoc-members: + :show-inheritance: + + +detectron2.engine.defaults module +--------------------------------- + +.. automodule:: detectron2.engine.defaults + :members: + :undoc-members: + :show-inheritance: + +detectron2.engine.hooks module +--------------------------------- + +.. automodule:: detectron2.engine.hooks + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/evaluation.rst b/docs/modules/evaluation.rst new file mode 100644 index 0000000000..d9d34ff1a2 --- /dev/null +++ b/docs/modules/evaluation.rst @@ -0,0 +1,7 @@ +detectron2.evaluation package +============================= + +.. automodule:: detectron2.evaluation + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/index.rst b/docs/modules/index.rst new file mode 100644 index 0000000000..61c20838e4 --- /dev/null +++ b/docs/modules/index.rst @@ -0,0 +1,15 @@ +API Documentation +================== + +.. toctree:: + + checkpoint + config + data + engine + evaluation + layers + modeling + solver + structures + utils diff --git a/docs/modules/layers.rst b/docs/modules/layers.rst new file mode 100644 index 0000000000..6aeb5213a4 --- /dev/null +++ b/docs/modules/layers.rst @@ -0,0 +1,7 @@ +detectron2.layers package +========================= + +.. automodule:: detectron2.layers + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/modeling.rst b/docs/modules/modeling.rst new file mode 100644 index 0000000000..c71ab2c7fb --- /dev/null +++ b/docs/modules/modeling.rst @@ -0,0 +1,7 @@ +detectron2.modeling package +=========================== + +.. automodule:: detectron2.modeling + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/solver.rst b/docs/modules/solver.rst new file mode 100644 index 0000000000..7f4a49f2eb --- /dev/null +++ b/docs/modules/solver.rst @@ -0,0 +1,7 @@ +detectron2.solver package +========================= + +.. automodule:: detectron2.solver + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/structures.rst b/docs/modules/structures.rst new file mode 100644 index 0000000000..5701c61abf --- /dev/null +++ b/docs/modules/structures.rst @@ -0,0 +1,7 @@ +detectron2.structures package +============================= + +.. automodule:: detectron2.structures + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules/utils.rst b/docs/modules/utils.rst new file mode 100644 index 0000000000..7693b3c570 --- /dev/null +++ b/docs/modules/utils.rst @@ -0,0 +1,63 @@ +detectron2.utils package +======================== + +detectron2.utils.colormap module +-------------------------------- + +.. automodule:: detectron2.utils.colormap + :members: + :undoc-members: + :show-inheritance: + +detectron2.utils.comm module +---------------------------- + +.. automodule:: detectron2.utils.comm + :members: + :undoc-members: + :show-inheritance: + + +detectron2.utils.events module +------------------------------ + +.. automodule:: detectron2.utils.events + :members: + :undoc-members: + :show-inheritance: + + +detectron2.utils.logger module +------------------------------ + +.. automodule:: detectron2.utils.logger + :members: + :undoc-members: + :show-inheritance: + + +detectron2.utils.registry module +-------------------------------- + +.. automodule:: detectron2.utils.registry + :members: + :undoc-members: + :show-inheritance: + + +detectron2.utils.video\_visualizer module +----------------------------------------- + +.. automodule:: detectron2.utils.video_visualizer + :members: + :undoc-members: + :show-inheritance: + +detectron2.utils.visualizer module +---------------------------------- + +.. automodule:: detectron2.utils.visualizer + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/notes/benchmarks.md b/docs/notes/benchmarks.md new file mode 100644 index 0000000000..8d3c3013a5 --- /dev/null +++ b/docs/notes/benchmarks.md @@ -0,0 +1,202 @@ + +# Benchmarks + +Here we benchmark the training speed of a Mask R-CNN in detectron2, +with some other popular open source Mask R-CNN implementations. + + +### Settings + +* Hardware: 8 NVIDIA V100s. +* Software: CUDA 10.0, cuDNN 7.6.4, PyTorch 1.3.0.dev20190920 (nightly build), TensorFlow 1.5.0rc2, Keras 2.2.5. +* Model: an end-to-end R-50-FPN Mask-RCNN model, using the same hyperparameter as the + [Detectron baseline config](https://github.com/facebookresearch/Detectron/blob/master/configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml). +* Metrics: We use the average throughput in iterations 100-500 to skip GPU warmup time. + Note that for R-CNN-style models, the throughput of a model typically changes during training, because + it depends on the predictions of the model. Therefore this metric is not directly comparable with + "train speed" in model zoo, which is the average speed of the entire training run. + + +### Main Results + +```eval_rst ++-----------------------------+--------------------+ +| Implementation | Throughput (img/s) | ++=============================+====================+ +| Detectron2 | 60 | ++-----------------------------+--------------------+ +| maskrcnn-benchmark_ | 51 | ++-----------------------------+--------------------+ +| tensorpack_ | 50 | ++-----------------------------+--------------------+ +| mmdetection_ | 41 | ++-----------------------------+--------------------+ +| Detectron_ | 19 | ++-----------------------------+--------------------+ +| `matterport/Mask_RCNN`__ | 14 | ++-----------------------------+--------------------+ + +.. _maskrcnn-benchmark: https://github.com/facebookresearch/maskrcnn-benchmark/ +.. _tensorpack: https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN +.. _mmdetection: https://github.com/open-mmlab/mmdetection/ +.. _Detectron: https://github.com/facebookresearch/Detectron +__ https://github.com/matterport/Mask_RCNN/ +``` + + +Details for each implementation: + +* __Detectron2__: + ``` + python tools/train_net.py --config-file configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml --num-gpus 8 + ``` + +* __maskrcnn-benchmark__: use commit `0ce8f6f` with `sed -i ‘s/torch.uint8/torch.bool/g’ **/*.py` to make it compatible with latest PyTorch. + Then, run training with + ``` + python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml + ``` + The speed we observed is faster than its model zoo, likely due to different software versions. + +* __tensorpack__: at commit `caafda`, `export TF_CUDNN_USE_AUTOTUNE=0`, then run + ``` + mpirun -np 8 ./train.py --config DATA.BASEDIR=/data/coco TRAINER=horovod BACKBONE.STRIDE_1X1=True TRAIN.STEPS_PER_EPOCH=50 --load ImageNet-R50-AlignPadding.npz + ``` + +* __mmdetection__: at commit `4d9a5f`, apply the following diff, then run + ``` + ./tools/dist_train.sh configs/mask_rcnn_r50_fpn_1x.py 8 + ``` + + The speed we observed is faster than its model zoo, likely due to different software versions. + +
+ + (diff to make it use the same architecture - click to expand) + + ```diff + diff --git i/configs/mask_rcnn_r50_fpn_1x.py w/configs/mask_rcnn_r50_fpn_1x.py + index 04f6d22..ed721f2 100644 + --- i/configs/mask_rcnn_r50_fpn_1x.py + +++ w/configs/mask_rcnn_r50_fpn_1x.py + @@ -1,14 +1,15 @@ + # model settings + model = dict( + type='MaskRCNN', + - pretrained='torchvision://resnet50', + + pretrained='open-mmlab://resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + - style='pytorch'), + + norm_cfg=dict(type="BN", requires_grad=False), + + style='caffe'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + @@ -115,7 +116,7 @@ test_cfg = dict( + dataset_type = 'CocoDataset' + data_root = 'data/coco/' + img_norm_cfg = dict( + - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + + mean=[123.675, 116.28, 103.53], std=[1.0, 1.0, 1.0], to_rgb=False) + train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + ``` +
+ +* __Detectron__: run + ``` + python tools/train_net.py --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml + ``` + +* __matterport/Mask_RCNN__: at commit `3deaec`, apply the following diff, `export TF_CUDNN_USE_AUTOTUNE=0`, then run + ``` + python coco.py train --dataset=/data/coco/ --model=imagenet + ``` + +
+ + (diff to make it use the same hyperparameters - click to expand) + + ```diff + diff --git i/mrcnn/model.py w/mrcnn/model.py + index 62cb2b0..61d7779 100644 + --- i/mrcnn/model.py + +++ w/mrcnn/model.py + @@ -2367,8 +2367,8 @@ class MaskRCNN(): + epochs=epochs, + steps_per_epoch=self.config.STEPS_PER_EPOCH, + callbacks=callbacks, + - validation_data=val_generator, + - validation_steps=self.config.VALIDATION_STEPS, + + #validation_data=val_generator, + + #validation_steps=self.config.VALIDATION_STEPS, + max_queue_size=100, + workers=workers, + use_multiprocessing=True, + diff --git i/mrcnn/parallel_model.py w/mrcnn/parallel_model.py + index d2bf53b..060172a 100644 + --- i/mrcnn/parallel_model.py + +++ w/mrcnn/parallel_model.py + @@ -32,6 +32,7 @@ class ParallelModel(KM.Model): + keras_model: The Keras model to parallelize + gpu_count: Number of GPUs. Must be > 1 + """ + + super().__init__() + self.inner_model = keras_model + self.gpu_count = gpu_count + merged_outputs = self.make_parallel() + diff --git i/samples/coco/coco.py w/samples/coco/coco.py + index 5d172b5..239ed75 100644 + --- i/samples/coco/coco.py + +++ w/samples/coco/coco.py + @@ -81,7 +81,10 @@ class CocoConfig(Config): + IMAGES_PER_GPU = 2 + + # Uncomment to train on 8 GPUs (default is 1) + - # GPU_COUNT = 8 + + GPU_COUNT = 8 + + BACKBONE = "resnet50" + + STEPS_PER_EPOCH = 50 + + TRAIN_ROIS_PER_IMAGE = 512 + + # Number of classes (including background) + NUM_CLASSES = 1 + 80 # COCO has 80 classes + @@ -496,29 +499,10 @@ if __name__ == '__main__': + # *** This training schedule is an example. Update to your needs *** + + # Training - Stage 1 + - print("Training network heads") + model.train(dataset_train, dataset_val, + learning_rate=config.LEARNING_RATE, + epochs=40, + - layers='heads', + - augmentation=augmentation) + - + - # Training - Stage 2 + - # Finetune layers from ResNet stage 4 and up + - print("Fine tune Resnet stage 4 and up") + - model.train(dataset_train, dataset_val, + - learning_rate=config.LEARNING_RATE, + - epochs=120, + - layers='4+', + - augmentation=augmentation) + - + - # Training - Stage 3 + - # Fine tune all layers + - print("Fine tune all layers") + - model.train(dataset_train, dataset_val, + - learning_rate=config.LEARNING_RATE / 10, + - epochs=160, + - layers='all', + + layers='3+', + augmentation=augmentation) + + elif args.command == "evaluate": + ``` +
diff --git a/docs/notes/changelog.md b/docs/notes/changelog.md new file mode 100644 index 0000000000..afb43fd721 --- /dev/null +++ b/docs/notes/changelog.md @@ -0,0 +1,12 @@ +# Change Log + + +### Major Changes in Code + +* 2019-10-10: initial release. + + +### Config Version Change Log + +* v1: Rename `RPN_HEAD.NAME` to `RPN.HEAD_NAME`. +* v2: A batch of rename of many configurations before release. diff --git a/docs/notes/compatibility.md b/docs/notes/compatibility.md new file mode 100644 index 0000000000..efd1afa122 --- /dev/null +++ b/docs/notes/compatibility.md @@ -0,0 +1,75 @@ +# Compatibility with Other Libraries + +## Compatibility with Detectron + +Detectron2 addresses some legacy issues left in Detectron, as a result, their models +are not compatible: +running inference with the same model weights will produce different results in the two code bases. + +The major differences regarding inference are: + +- The height and width of a box with corners (x1, y1) and (x2, y2) is now computed more naturally as + width = x2 - x1 and height = y2 - y1; + In Detectron, a "+ 1" was added both height and width. + + Note that the relevant ops in Caffe2 have [adopted this change of convention](https://github.com/pytorch/pytorch/pull/20550) + with an extra option. + So it is still possible to run inference with a Detectron2-trained model in Caffe2. + + The change in height/width calculations most notably changes: + - encoding/decoding in bounding box regression. + - non-maximum suppression. The effect here is very negligible, though. + +- RPN now uses simpler anchors with fewer quantization artifacts. + + In Detectron, the anchors were quantized and + [do not have accurate areas](https://github.com/facebookresearch/Detectron/issues/227). + In Detectron2, the anchors are center-aligned to feature grid points and not quantized. + +- Classification layers have a different ordering of class labels. + + This involves any trainable parameter with shape (..., num_categories + 1, ...). + In Detectron2, integer labels [0, K-1] correspond to the K = num_categories object categories + and the label "K" corresponds to the special "background" category. + In Detectron, label "0" means background, and labels [1, K] correspond to the K categories. + +- ROIAlign is implemented differently. The new implementation is [available in Caffe2](https://github.com/pytorch/pytorch/pull/23706). + + 1. All the ROIs are shifted by half a pixel compared to Detectron in order to create better image-feature-map alignment. + See `layers/roi_align.py` for details. + To enable the old behavior, use `ROIAlign(aligned=False)`, or `POOLER_TYPE=ROIAlign` instead of + `ROIAlignV2` (the default). + + 1. The ROIs are not required to have a minimum size of 1. + This will lead to tiny differences in the output, but should be negligible. + +- Mask inference function is different. + + In Detectron2, the "paste_mask" function is different and should be more accurate than in Detectron. This change + can improve mask AP on COCO by ~0.5% absolute. + +There are some other differences in training as well, but they won't affect +model-level compatibility. The majors ones are: + +- We fixed a [bug](https://github.com/facebookresearch/Detectron/issues/459) in + Detectron, by making `RPN.POST_NMS_TOPK_TRAIN` per-image, rather than per-batch. + The fix may lead to a small accuracy drop for a few models (e.g. keypoint + detection) and will require some parameter tuning to match the Detectron results. +- For simplicity, we change the default loss in bounding box regression to L1 loss, instead of smooth L1 loss. + We have observed that this tends to slightly decrease box AP50 while improving box AP for higher + overlap thresholds (and leading to a slight overall improvement in box AP). +- We interpret the coordinates in COCO bounding box and segmentation annotations + as coordinates in range `[0, width]` or `[0, height]`, and the coordinates in + COCO keypoint annotations are pixel indices in range `[0, width - 1]` or `[0, height - 1]`. + + +We will later share more details and rationale behind the above mentioned issues +about pixels, coordinates, and "+1"s. + + +## Compatibility with Caffe2 + +As mentioned above, despite the incompatibilities with Detectron, the relevant +ops have been implemented in Caffe2, in [PR1](https://github.com/pytorch/pytorch/pull/20550) +and [PR2](https://github.com/pytorch/pytorch/pull/23706). +Therefore, models trained with detectron2 can be used in Caffe2. diff --git a/docs/notes/index.rst b/docs/notes/index.rst new file mode 100644 index 0000000000..0c1b65d281 --- /dev/null +++ b/docs/notes/index.rst @@ -0,0 +1,9 @@ +Notes +====================================== + +.. toctree:: + :maxdepth: 2 + + benchmarks + compatibility + changelog diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000000..0a23f0b94d --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,13 @@ +termcolor +numpy +tqdm +docutils>=0.14 +Sphinx>=1.7 +recommonmark==0.4.0 +sphinx_rtd_theme +mock +matplotlib +termcolor +yacs +tabulate +git+git://github.com/facebookresearch/fvcore.git diff --git a/docs/tutorials/configs.md b/docs/tutorials/configs.md new file mode 100644 index 0000000000..33928f37ea --- /dev/null +++ b/docs/tutorials/configs.md @@ -0,0 +1,26 @@ +# Using Configs + +Detectron2's config system uses yaml and [yacs](https://github.com/rbgirshick/yacs). +In addition to the basic operations that access and update a config, we provide +the following extra functionalities: + +1. The config can have `_BASE_: base.yaml` field, which will load a base config first. + Values in the base config will be overwritten in sub-configs, if there are any conflicts. + We provided several base configs for standard model architectures. +2. We provide config versioning, for backward compatibility. + If your config file is versioned with a config line like `VERSION: 2`, + detectron2 will still recognize it even if we make rename to some keys in the future. + +### Best Practice with Configs + +1. Treat the configs you write as "code": avoid copying them or duplicating them; use "_BASE_" + instead to share common parts between configs. + +2. Keep the configs you write simple: don't include keys that do not affect the experimental setting. + +3. Keep a version number in your configs (or the base config), e.g., `VERSION: 2`, + for backward compatibility. + +4. Save a full config together with a trained model, and use it to run inference. + This is more robust to changes that may happen to the config definition + (e.g., if a default value changed). diff --git a/docs/tutorials/data_loading.md b/docs/tutorials/data_loading.md new file mode 100644 index 0000000000..3767cbe462 --- /dev/null +++ b/docs/tutorials/data_loading.md @@ -0,0 +1,81 @@ + +# Using Custom Data Loaders + +Detectron2 contains a builtin data loading pipeline. +It's good to understand how it works, in case you need to write a custom one. + +Detectron2 provides two functions +`build_detection_{train,test}_loader` that create a data loader from a given config. +Here is how `build_detection_{train,test}_loader` work: + +1. It takes the name of the dataset (e.g., "coco_2017_train") and loads a `list[dict]` representing the dataset items + in a lightweight, canonical format. These dataset items are not yet ready to be used by the model (e.g., images are + not loaded into memory, random augmentations have not been applied, etc.). + Details about the dataset format and dataset registration can be found in [datasets](datasets). +2. Each dict in this list is mapped by a function ("mapper"): + * Users can customize this mapping function by specifying the "mapper" argument in + `build_detection_{train,test}_loader`. The default mapper is [DatasetMapper]( ../modules/data.html#detectron2.data.DatasetMapper) + * There is no constraints on the output format, as long as it is accepted by the consumer of this data loader (usually the model). + * The role of the mapper is to transform the lightweight, canonical representation of a dataset item into a format + that is ready for the model to consume (including, e.g., reading images and performing random data augmentation). + The output format of the default mapper is explained below. +3. The outputs of the mapper are batched (simply into a list). +4. This batched data is the output of the data loader. Typically, it's also the input of + `model.forward()`. + + +If you want to do something different (e.g., use different sampling or batching logic), +you can write your own data loader. The only requirement is that it produces the format your model accepts. +Next, we explain the input format used by the builtin models in detectron2. + + +### Model Input Format + +The output of the default [DatasetMapper]( ../modules/data.html#detectron2.data.DatasetMapper) is a dict. +After the data loader performs batching, it becomes `list[dict]`, with one dict per image. +This will be the input format of all the builtin models. + +The dict may contain the following keys: + +* "image": `Tensor` in (C, H, W) format. +* "instances": an `Instances` object, with the following fields: + + "gt_boxes": `Boxes` object storing N boxes, one for each instance. + + "gt_classes": `Tensor`, a vector of N labels, in range [0, num_categories). + + "gt_masks": a `PolygonMasks` object storing N masks, one for each instance. + + "gt_keypoints": a `Keypoints` object storing N keypoint sets, one for each instance. +* "proposals": an `Instances` object used in Fast R-CNN style models, with the following fields: + + "proposal_boxes": `Boxes` object storing P proposal boxes. + + "objectness_logits": `Tensor`, a vector of P scores, one for each proposal. +* "height", "width": the *desired* output height and width of the image, not necessarily the same + as the height or width of the `image` when input into the model, which might be after resizing. + For example, it can be the *original* image height and width before resizing. + + If provided, the model will produce output in this resolution, + rather than in the resolution of the `image` as input into the model. This is more efficient and accurate. +* "sem_seg": `Tensor[int]` in (H, W) format. The semantic segmentation ground truth. + + +### Model Output Format + +The standard models outputs a `list[dict]`, one dict for each image. Each dict may contain: + +* "instances": [Instances](../modules/structures.html#detectron2.structures.Instances) + object with the following fields: + * "pred_boxes": [Boxes](../modules/structures.html#detectron2.structures.Boxes) object storing N boxes, one for each detected instance. + * "scores": `Tensor`, a vector of N scores. + * "pred_classes": `Tensor`, a vector of N labels in range [0, num_categories). + + "pred_masks": a `Tensor` of shape (N, H, W), masks for each detected instance. + + "pred_keypoints": a `Tensor` of shape (N, num_keypoint, 3). + Each row in the last dimension is (x, y, score). +* "sem_seg": `Tensor` of (num_categories, H, W), the semantic segmentation prediction. +* "proposals": [Instances](../modules/structures.html#detectron2.structures.Instances) + object with the following fields: + * "proposal_boxes": [Boxes](../modules/structures.html#detectron2.structures.Boxes) + object storing N boxes. + * "objectness_logits": a torch vector of N scores. +* "panoptic_seg": A tuple of (Tensor, list[dict]). The tensor has shape (H, W), where each element + represent the segment id of the pixel. Each dict describes one segment id and has the following fields: + * "id": the segment id + * "isthing": whether the segment is a thing or stuff + * "category_id": the category id of this segment. It represents the thing + class id when `isthing==True`, and the stuff class id otherwise. diff --git a/docs/tutorials/datasets.md b/docs/tutorials/datasets.md new file mode 100644 index 0000000000..abf44d5c03 --- /dev/null +++ b/docs/tutorials/datasets.md @@ -0,0 +1,152 @@ +# Using Custom Datasets + +If you want to use a custom dataset while also reusing detectron2's data loaders, +you will need to + +1. Register your dataset (i.e., tell detectron2 how to obtain your dataset). +2. Optionally, register metadata for your dataset. + +Next, we explain the above two concepts in details. + + +### Registering a Dataset + +To let detectron2 know how to obtain a dataset named "my_dataset", you will implement +a function that returns the items in your dataset and then tell detectron2 about this +function: +```python +def get_dicts(): + ... + return list[dict] in the following format + +from detectron2.data import DatasetCatalog +DatasetCatalog.register("my_dataset", get_dicts) +``` + +Here, the snippet associates a dataset "my_dataset" with a function that returns the data. +If you do not modify downstream code (i.e., you use the standard data loader and data mapper), +then the function has to return a list of dicts in detectron2's standard dataset format, described +next. + +For standard tasks +(instance detection, instance/semantic/panoptic segmentation, keypoint detection), +we use a format similar to COCO's json annotations +as the basic dataset representation. + +The format uses one dict to represent the annotations of +one image. The dict may have the following fields. +The fields are often optional, and some functions may be able to +infer certain fields from others if needed, e.g., the data loader +can load an image from "file_name" if the "image" field is not available. + ++ `file_name`: the full path to the image file. ++ `sem_seg_file_name`: the full path to the ground truth semantic segmentation file. ++ `image`: the image as a numpy array. ++ `sem_seg`: semantic segmentation ground truth in a 2D numpy array. Values in the array represent + category labels. ++ `height`, `width`: integer. The shape of image. ++ `image_id` (str): a string to identify this image. Mainly used during evaluation to identify the + image. Each dataset may use it for different purposes. ++ `annotations` (list[dict]): the per-instance annotations of every + instance in this image. Each annotation dict may contain: + + `bbox` (list[float]): list of 4 numbers representing the bounding box of the instance. + + `bbox_mode` (int): the format of bbox. + It must be a member of [detectron2.structures.BoxMode](detectron2/structures/boxes.py). + Currently supports: `BoxMode.XYXY_ABS`, `BoxMode.XYWH_ABS`. + + `category_id` (int): an integer in the range [0, num_categories) representing the category label. + The value num_categories is reserved to represent the "background" category, if applicable. + + `segmentation` (list[list[float]] or dict): + + If `list[list[float]]`, it represents a list of polygons, one for each connected component + of the object. Each `list[float]` is one simple polygon in the format of `[x1, y1, ..., xn, yn]`. + The Xs and Ys are either relative coordinates in [0, 1], or absolute coordinates, + depend on whether "bbox_mode" is relative. + + If `dict`, it represents the per-pixel segmentation mask in COCO's RLE format. + + `keypoint`s (list[float]): in the format of [x1, y1, v1,..., xn, yn, vn]. + v[i] means the visibility of this keypoint. + `n` must be equal to the number of keypoint categories. + The Xs and Ys are either relative coordinates in [0, 1], or absolute coordinates, + depend on whether "bbox_mode" is relative. + + Note that the coordinate annotations in COCO format are integers in range [0, H-1 or W-1]. + By default, detectron2 adds 0.5 to absolute keypoint coordinates to convert them from discrete + pixel indices to floating point coordinates. + + `iscrowd`: 0 or 1. Whether this instance is labeled as COCO's "crowd region". ++ `proposal_boxes` (array): 2D numpy array with shape (K, 4) representing K precomputed proposal boxes for this image. ++ `proposal_objectness_logits` (array): numpy array with shape (K, ), which corresponds to the objectness + logits of proposals in 'propopsal_boxes'. ++ `proposal_bbox_mode` (int): the format of the precomputed proposal bbox. + It must be a member of [detectron2.structures.BoxMode](detectron2/structures/boxes.py). + Default format is `BoxMode.XYXY_ABS`. + + +If your dataset is already in the COCO format, you can simply register it by +```python +from detectron2.data.datasts import register_coco_instances +register_coco_instances("my_dataset", {}, "json_annotation.json", "path/to/image/dir") +``` +which will take care of everything (including metadata) for you. + + +### "Metadata" for Datasets + +Each dataset is associated with some metadata, accessible through +`MetadataCatalog.get(dataset_name).some_metadata`. +Metadata is a key-value mapping that contains primitive information that helps interpret what's in the dataset, e.g., +names of classes, colors of classes, root of files, etc. +This information will be useful for augmentation, evaluation, visualization, logging, etc. +The structure of metadata depends on the what is needed from the corresponding downstream code. + + +If you register a new dataset through `DatasetCatalog.register`, +you may also want to add its corresponding metadata through +`MetadataCatalog.get(dataset_name).set(name, value)`, to enable any features that need metadata. +You can do it like this (using the metadata field "thing_classes" as an example): + +```python +from detectron2.data import MetadataCatalog +MetadataCatalog.get("my_dataset").thing_classes = ["person", "dog"] +``` + +Here is a list of metadata keys that are used by builtin features in detectron2. +If you add your own dataset without these metadata, some features may be +unavailable to you: + +* `thing_classes` (list[str]): Used by all instance detection/segmentation tasks. + A list of names for each instance/thing category. + If you load a COCO format dataset, it will be automatically set by the function `load_coco_json`. + +* `stuff_classes` (list[str]): Used by semantic and panoptic segmentation tasks. + A list of names for each stuff category. + +* `stuff_colors` (list[tuple(r, g, b)]): Pre-defined color (in [0, 255]) for each stuff category. + Used for visualization. If not given, random colors are used. + +* `keypoint_names` (list[str]): Used by keypoint localization. A list of names for each keypoint. + +* `keypoint_flip_map` (list[tuple[str]]): Used by the keypoint localization task. A list of pairs of names, + where each pair are the two keypoints that should be flipped if the image is + flipped during augmentation. +* `keypoint_connection_rules`: list[tuple(str, str, (r, g, b))]. Each tuple specifies a pair of keypoints + that are connected and the color to use for the line between them when visualized. + +Some additional metadata that are specific to the evaluation of certain datasets (e.g. COCO): + +* `thing_dataset_id_to_contiguous_id` (dict[int->int]): Used by all instance detection/segmentation tasks in the COCO format. + A mapping from instance class ids in the dataset to contiguous ids in range [0, #class). + Will be automatically set by the function `load_coco_json`. + +* `stuff_dataset_id_to_contiguous_id` (dict[int->int]): Used when generating prediction json files for + semantic/panoptic segmentation. + A mapping from semantic segmentation class ids in the dataset + to contiguous ids in [0, num_categories). It is useful for evaluation only. + +* `json_file`: The COCO annotation json file. Used by COCO evaluation for COCO-format datasets. +* `panoptic_root`, `panoptic_json`: Used by panoptic evaluation. +* `evaluator_type`: Used by the builtin main training script to select + evaluator. No need to use it if you write your own main script. + +NOTE: For background on the difference between "thing" and "stuff" categories, see +[On Seeing Stuff: The Perception of Materials by Humans and Machines](http://persci.mit.edu/pub_pdfs/adelson_spie_01.pdf). +In detectron2, the term "thing" is used for instance-level tasks, +and "stuff" is used for semantic segmentation tasks. +Both are used in panoptic segmentation. diff --git a/docs/tutorials/extend.md b/docs/tutorials/extend.md new file mode 100644 index 0000000000..5e63a5f27f --- /dev/null +++ b/docs/tutorials/extend.md @@ -0,0 +1,45 @@ +# Extend Detectron2's Defaults + +__Research is about doing things in new ways__. +This brings a tension in how to create abstractions in code, +which is a challenge for any research engineering project of a significant size: + +1. On one hand, it needs to have very thin abstractions to allow for the possibility of doing + everything in new ways. It should be reasonably easy to break existing + abstractions and replace them with new ones. + +2. On the other hand, such a project also needs reasonably high-level + abstractions, so that users can easily do things in standard ways, + without worrying too much about the details that only certain researchers care about. + +In detectron2, there are two types of interfaces that address this tension together: + +1. Functions and classes that take only a "config" argument (optionally with a minimal + set of extra arguments in cases of mature interfaces). + + Such functions and classes implement + the "standard default" behavior: it will read what it needs from the + config and do the "standard" thing. + Users only need to load a standard config and pass it around, without having to worry about + which arguments are used and what they all mean. + +2. Functions and classes that have well-defined explicit arguments. + + Each of these is a small building block of the entire system. + They require users' effort to stitch together, but can be stitched together in more flexible ways. + When you need to implement something different from the "standard defaults" + included in detectron2, these well-defined components can be reused. + + +If you only need the standard behavior, the [Beginner's Tutorial](getting_started) +should suffice. If you need to extend detectron2 to your own needs, +see the following tutorials for more details: + +* Detectron2 includes a few standard datasets, but you can use custom ones. See + [Use Custom Datasets](datasets). +* Detectron2 contains the standard logic that creates a data loader from a + dataset, but you can write your own as well. See [Use Custom Data Loaders](data_loading). +* Detectron2 implements many standard detection models, and provide ways for you + to overwrite its behaviors. See [Writing Models](models). +* Detectron2 provides a default training loop that is good for common training tasks. + You can customize it with hooks, or write your own loop instead. See [training](training). diff --git a/docs/tutorials/getting_started.md b/docs/tutorials/getting_started.md new file mode 120000 index 0000000000..e90bde77a3 --- /dev/null +++ b/docs/tutorials/getting_started.md @@ -0,0 +1 @@ +../../GETTING_STARTED.md \ No newline at end of file diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst new file mode 100644 index 0000000000..bd3e4e108e --- /dev/null +++ b/docs/tutorials/index.rst @@ -0,0 +1,14 @@ +Tutorials +====================================== + +.. toctree:: + :maxdepth: 2 + + install + getting_started + extend + datasets + data_loading + models + training + configs diff --git a/docs/tutorials/install.md b/docs/tutorials/install.md new file mode 120000 index 0000000000..5f52b2be3c --- /dev/null +++ b/docs/tutorials/install.md @@ -0,0 +1 @@ +../../INSTALL.md \ No newline at end of file diff --git a/docs/tutorials/models.md b/docs/tutorials/models.md new file mode 100644 index 0000000000..9489da2cfb --- /dev/null +++ b/docs/tutorials/models.md @@ -0,0 +1,32 @@ +# Using and Writing Models + +Models (and their sub-models) in detectron2 are built by +functions such as `build_model`, `build_backbone`, `build_roi_heads`: +```python +from detectron2.modeling import build_model +model = build_model(cfg) # returns a torch.nn.Module +``` + +In some cases, e.g. if you are trying to do something completely new, you may wish to implement +a model entirely from scratch within detectron2. However, in many situations you may +be interested in modifying or extending some components of an existing model. +Therefore, we also provide a registration mechanism that lets you override the +behavior of certain internal components of standard models. + +For example, to add a new backbone, import this code: +```python +from detectron2.modeling import BACKBONE_REGISTRY, Backbone +@BACKBONE_REGISTRY.register() +class NewBackBone(Backbone): + def __init__(self, cfg, input_shape): + # create your own backbone +``` +which will allow you to use `cfg.MODEL.BACKBONE.NAME = 'NewBackBone'` in your config file. + +As another example, to add new abilities to the ROI heads in the Generalized R-CNN meta-architecture, +you can implement a new +[ROIHeads](../modules/modeling.html#detectron2.modeling.ROIHeads) subclass and put it in the `ROI_HEADS_REGISTRY`. +See [densepose in detectron2](https://github.com/facebookresearch/detectron2/tree/master/projects/DensePose) +for an example. + +Other registries can be found in [API documentation](../modules/modeling.html). diff --git a/docs/tutorials/training.md b/docs/tutorials/training.md new file mode 100644 index 0000000000..615966f47c --- /dev/null +++ b/docs/tutorials/training.md @@ -0,0 +1,17 @@ +# Training + +From the previous tutorials, you may now have a custom model and data loader. + +You are free to create your own optimizer, and write the training logic: it's +usually easy with PyTorch, and allow researchers to see the entire training +logic more clearly. + +We also provide a standarized "trainer" abstraction with a +[minimal hook system](../modules/engine.html#detectron2.engine.HookBase) +that helps simplify the standard types of training. + +You can use +[SimpleTrainer().train()](../modules/engine.html#detectron2.engine.SimpleTrainer) +which does single-cost single-optimizer single-data-source training. +Or use [DefaultTrainer().train()](../modules/engine.html#detectron2.engine.defaults.DefaultTrainer) +which includes more standard behavior that one might want to opt in. diff --git a/projects/DensePose/README.md b/projects/DensePose/README.md new file mode 100644 index 0000000000..faf21786a4 --- /dev/null +++ b/projects/DensePose/README.md @@ -0,0 +1,64 @@ + +# DensePose in Detectron2 +**Dense Human Pose Estimation In The Wild** + +_Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos_ + +[[`densepose.org`](https://densepose.org)] [[`arXiv`](https://arxiv.org/abs/1802.00434)] [[`BibTeX`](#CitingDensePose)] + +Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. + +
+ +
+ +In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide tools to visualize +DensePose annotation and results. + +## Training + +To train a model one can call +```bash +python /path/to/detectron2/projects/DensePose/train_net.py --config-file +``` + +For example, to launch end-to-end DensePose-RCNN training with ResNet-50 FPN backbone on a single GPU, +one should execute: +```bash +python /path/to/detectron2/projects/DensePose/train_net.py --config-file /path/to/detectron2/projects/DensePose/configs/densepose_R_50_FPN_s1x.yaml +``` + +## Testing + +Model testing can be done in the same way as training, except for an additional flag `--eval-only` and +model location specification through `MODEL.WEIGHT model.pth` in the command line +```bash +python /path/to/detectron2/projects/DensePose/train_net.py --config-file /path/to/detectron2/projects/DensePose/configs/densepose_R_50_FPN_s1x.yaml --eval-only MODEL.WEIGHT model.pth +``` + +## Tools + +We provide tools which allow one to: + - easily view DensePose annotated data in a dataset; + - perform DensePose inference on a set of images; + - visualize DensePose model results; + +`query_db` is a tool to print or visualize DensePose data in a dataset. +Details on this tool can be found in [`TOOL_QUERY_DB.md`](doc/TOOL_QUERY_DB.md) + +`apply_net` is a tool to print or visualize DensePose results. +Details on this tool can be found in [`TOOL_APPLY_NET.md`](doc/TOOL_APPLY_NET.md) + +## Citing DensePose + +If you use DensePose, please use the following BibTeX entry. + +``` +@InProceedings{Guler2018DensePose, + title={DensePose: Dense Human Pose Estimation In The Wild}, + author={R\{i}za Alp G\"uler, Natalia Neverova, Iasonas Kokkinos}, + journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2018} +} +``` + diff --git a/projects/DensePose/apply_net.py b/projects/DensePose/apply_net.py new file mode 100644 index 0000000000..6469bb6c89 --- /dev/null +++ b/projects/DensePose/apply_net.py @@ -0,0 +1,299 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import argparse +import glob +import logging +import os +import pickle +import sys +from typing import Any, ClassVar, Dict, List +import torch + +from detectron2.config import get_cfg +from detectron2.data.detection_utils import read_image +from detectron2.engine.defaults import DefaultPredictor +from detectron2.structures.instances import Instances +from detectron2.utils.logger import setup_logger + +from densepose import add_densepose_config +from densepose.utils.logger import verbosity_to_level +from densepose.vis.base import CompoundVisualizer +from densepose.vis.bounding_box import ScoredBoundingBoxVisualizer +from densepose.vis.densepose import ( + DensePoseResultsContourVisualizer, + DensePoseResultsFineSegmentationVisualizer, + DensePoseResultsUVisualizer, + DensePoseResultsVVisualizer, +) +from densepose.vis.extractor import CompoundExtractor, create_extractor + +DOC = """Apply Net - a tool to print / visualize DensePose results +""" + +LOGGER_NAME = "apply_net" +logger = logging.getLogger(LOGGER_NAME) + +_ACTION_REGISTRY: Dict[str, "Action"] = {} + + +class Action(object): + @classmethod + def add_arguments(cls: type, parser: argparse.ArgumentParser): + parser.add_argument( + "-v", + "--verbosity", + action="count", + help="Verbose mode. Multiple -v options increase the verbosity.", + ) + + +def register_action(cls: type): + """ + Decorator for action classes to automate action registration + """ + global _ACTION_REGISTRY + _ACTION_REGISTRY[cls.COMMAND] = cls + return cls + + +class InferenceAction(Action): + @classmethod + def add_arguments(cls: type, parser: argparse.ArgumentParser): + super(InferenceAction, cls).add_arguments(parser) + parser.add_argument("cfg", metavar="", help="Config file") + parser.add_argument("model", metavar="", help="Model file") + parser.add_argument("input", metavar="", help="Input data") + + @classmethod + def execute(cls: type, args: argparse.Namespace): + logger.info(f"Loading config from {args.cfg}") + opts = [] + cfg = cls.setup_config(args.cfg, args.model, args, opts) + logger.info(f"Loading model from {args.model}") + predictor = DefaultPredictor(cfg) + logger.info(f"Loading data from {args.input}") + file_list = cls._get_input_file_list(args.input) + if len(file_list) == 0: + logger.warning(f"No input images for {args.input}") + return + context = cls.create_context(args) + for file_name in file_list: + img = read_image(file_name, format="BGR") # predictor expects BGR image. + with torch.no_grad(): + outputs = predictor(img)["instances"] + cls.execute_on_outputs(context, {"file_name": file_name, "image": img}, outputs) + cls.postexecute(context) + + @classmethod + def setup_config( + cls: type, config_fpath: str, model_fpath: str, args: argparse.Namespace, opts: List[str] + ): + cfg = get_cfg() + add_densepose_config(cfg) + cfg.merge_from_file(config_fpath) + if opts: + cfg.merge_from_list(opts) + cfg.MODEL.WEIGHTS = model_fpath + cfg.freeze() + return cfg + + @classmethod + def _get_input_file_list(cls: type, input_spec: str): + if os.path.isdir(input_spec): + file_list = [ + fname + for fname in os.listdir(input_spec) + if os.path.isfile(os.path.join(input_spec, fname)) + ] + elif os.path.isfile(input_spec): + file_list = [input_spec] + else: + file_list = glob.glob(input_spec) + return file_list + + +@register_action +class DumpAction(InferenceAction): + """ + Dump action that outputs results to a pickle file + """ + + COMMAND: ClassVar[str] = "dump" + + @classmethod + def add_parser(cls: type, subparsers: argparse._SubParsersAction): + parser = subparsers.add_parser(cls.COMMAND, help="Dump model outputs to a file.") + cls.add_arguments(parser) + parser.set_defaults(func=cls.execute) + + @classmethod + def add_arguments(cls: type, parser: argparse.ArgumentParser): + super(DumpAction, cls).add_arguments(parser) + parser.add_argument( + "--output", + metavar="", + default="results.pkl", + help="File name to save dump to", + ) + + @classmethod + def execute_on_outputs( + cls: type, context: Dict[str, Any], entry: Dict[str, Any], outputs: Instances + ): + image_fpath = entry["file_name"] + logger.info(f"Processing {image_fpath}") + entry["instances"] = outputs + context["results"].append(entry) + + @classmethod + def create_context(cls: type, args: argparse.Namespace): + context = {"results": [], "out_fname": args.output} + return context + + @classmethod + def postexecute(cls: type, context: Dict[str, Any]): + out_fname = context["out_fname"] + out_dir = os.path.dirname(out_fname) + if len(out_dir) > 0 and not os.path.exists(out_dir): + os.makedirs(out_dir) + with open(out_fname, "wb") as hFile: + pickle.dump(context["results"], hFile) + logger.info(f"Output saved to {out_fname}") + + +@register_action +class ShowAction(InferenceAction): + """ + Show action that visualizes selected entries on an image + """ + + COMMAND: ClassVar[str] = "show" + VISUALIZERS: ClassVar[Dict[str, object]] = { + "dp_contour": DensePoseResultsContourVisualizer, + "dp_segm": DensePoseResultsFineSegmentationVisualizer, + "dp_u": DensePoseResultsUVisualizer, + "dp_v": DensePoseResultsVVisualizer, + "bbox": ScoredBoundingBoxVisualizer, + } + + @classmethod + def add_parser(cls: type, subparsers: argparse._SubParsersAction): + parser = subparsers.add_parser(cls.COMMAND, help="Visualize selected entries") + cls.add_arguments(parser) + parser.set_defaults(func=cls.execute) + + @classmethod + def add_arguments(cls: type, parser: argparse.ArgumentParser): + super(ShowAction, cls).add_arguments(parser) + parser.add_argument( + "visualizations", + metavar="", + help="Comma separated list of visualizations, possible values: " + "[{}]".format(",".join(sorted(cls.VISUALIZERS.keys()))), + ) + parser.add_argument( + "--min_score", + metavar="", + default=0.8, + type=float, + help="Minimum detection score to visualize", + ) + parser.add_argument( + "--nms_thresh", metavar="", default=None, type=float, help="NMS threshold" + ) + parser.add_argument( + "--output", + metavar="", + default="outputres.png", + help="File name to save output to", + ) + + @classmethod + def setup_config( + cls: type, config_fpath: str, model_fpath: str, args: argparse.Namespace, opts: List[str] + ): + opts.append("MODEL.ROI_HEADS.SCORE_THRESH_TEST") + opts.append(str(args.min_score)) + if args.nms_thresh is not None: + opts.append("MODEL.ROI_HEADS.NMS_THRESH_TEST") + opts.append(str(args.nms_thresh)) + cfg = super(ShowAction, cls).setup_config(config_fpath, model_fpath, args, opts) + return cfg + + @classmethod + def execute_on_outputs( + cls: type, context: Dict[str, Any], entry: Dict[str, Any], outputs: Instances + ): + import cv2 + import numpy as np + + visualizer = context["visualizer"] + extractor = context["extractor"] + image_fpath = entry["file_name"] + logger.info(f"Processing {image_fpath}") + image = cv2.cvtColor(entry["image"], cv2.COLOR_BGR2GRAY) + image = np.tile(image[:, :, np.newaxis], [1, 1, 3]) + data = extractor(outputs) + image_vis = visualizer.visualize(image, data) + entry_idx = context["entry_idx"] + 1 + out_fname = cls._get_out_fname(entry_idx, context["out_fname"]) + out_dir = os.path.dirname(out_fname) + if len(out_dir) > 0 and not os.path.exists(out_dir): + os.makedirs(out_dir) + cv2.imwrite(out_fname, image_vis) + logger.info(f"Output saved to {out_fname}") + context["entry_idx"] += 1 + + @classmethod + def postexecute(cls: type, context: Dict[str, Any]): + pass + + @classmethod + def _get_out_fname(cls: type, entry_idx: int, fname_base: str): + base, ext = os.path.splitext(fname_base) + return base + ".{0:04d}".format(entry_idx) + ext + + @classmethod + def create_context(cls: type, args: argparse.Namespace) -> Dict[str, Any]: + vis_specs = args.visualizations.split(",") + visualizers = [] + extractors = [] + for vis_spec in vis_specs: + vis = cls.VISUALIZERS[vis_spec]() + visualizers.append(vis) + extractor = create_extractor(vis) + extractors.append(extractor) + visualizer = CompoundVisualizer(visualizers) + extractor = CompoundExtractor(extractors) + context = { + "extractor": extractor, + "visualizer": visualizer, + "out_fname": args.output, + "entry_idx": 0, + } + return context + + +def create_argument_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description=DOC, + formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=120), + ) + parser.set_defaults(func=lambda _: parser.print_help(sys.stdout)) + subparsers = parser.add_subparsers(title="Actions") + for _, action in _ACTION_REGISTRY.items(): + action.add_parser(subparsers) + return parser + + +def main(): + parser = create_argument_parser() + args = parser.parse_args() + verbosity = args.verbosity if hasattr(args, "verbosity") else None + global logger + logger = setup_logger(name=LOGGER_NAME) + logger.setLevel(verbosity_to_level(verbosity)) + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/projects/DensePose/configs/Base-DensePose-RCNN.yaml b/projects/DensePose/configs/Base-DensePose-RCNN.yaml new file mode 100644 index 0000000000..7909af33ab --- /dev/null +++ b/projects/DensePose/configs/Base-DensePose-RCNN.yaml @@ -0,0 +1,43 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + BACKBONE: + NAME: "build_resnet_fpn_backbone" + RESNETS: + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + FPN: + IN_FEATURES: ["res2", "res3", "res4", "res5"] + ANCHOR_GENERATOR: + SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map + ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps) + RPN: + IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"] + PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level + PRE_NMS_TOPK_TEST: 1000 # Per FPN level + # Detectron1 uses 2000 proposals per-batch, + # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue) + # which is approximately 1000 proposals per-image since the default batch size for FPN is 2. + POST_NMS_TOPK_TRAIN: 1000 + POST_NMS_TOPK_TEST: 1000 + + DENSEPOSE_ON: True + ROI_HEADS: + NAME: "DensePoseROIHeads" + IN_FEATURES: ["p2", "p3", "p4", "p5"] + NUM_CLASSES: 1 + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_FC: 2 + POOLER_RESOLUTION: 7 + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" + ROI_DENSEPOSE_HEAD: + NAME: "DensePoseV1ConvXHead" + POOLER_TYPE: "ROIAlign" +DATASETS: + TRAIN: ("densepose_coco_2014_train", "densepose_coco_2014_valminusminival") + TEST: ("densepose_coco_2014_minival",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 diff --git a/projects/DensePose/configs/densepose_R_50_FPN_s1x.yaml b/projects/DensePose/configs/densepose_R_50_FPN_s1x.yaml new file mode 100644 index 0000000000..44035d7fba --- /dev/null +++ b/projects/DensePose/configs/densepose_R_50_FPN_s1x.yaml @@ -0,0 +1,12 @@ +_BASE_: "Base-DensePose-RCNN.yaml" +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +MODEL: + WEIGHTS: "catalog://ImageNetPretrained/MSRA/R-50" +SOLVER: + MAX_ITER: 130000 + STEPS: (100000, 120000) + BASE_LR: 0.002 + IMS_PER_BATCH: 24 + WARMUP_FACTOR: 0.1 + diff --git a/projects/DensePose/configs/quick_schedules/densepose_R_50_FPN_inference_acc_test.yaml b/projects/DensePose/configs/quick_schedules/densepose_R_50_FPN_inference_acc_test.yaml new file mode 100644 index 0000000000..cd277bee8e --- /dev/null +++ b/projects/DensePose/configs/quick_schedules/densepose_R_50_FPN_inference_acc_test.yaml @@ -0,0 +1,8 @@ +_BASE_: "../densepose_R_50_FPN_s1x.yaml" +MODEL: + WEIGHTS: "detectron2://e2e_densepose_R_50_FPN_s1x/124238535/model_final_5f3d7f9875229310fdfe6649459c0157.pkl" +DATASETS: + TRAIN: () + TEST: ("densepose_coco_2014_minival_100",) +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 56.05, 0.025], ["densepose", "AP", 46.54, 0.02]] diff --git a/projects/DensePose/configs/quick_schedules/densepose_R_50_FPN_instant_test.yaml b/projects/DensePose/configs/quick_schedules/densepose_R_50_FPN_instant_test.yaml new file mode 100644 index 0000000000..dec46d5224 --- /dev/null +++ b/projects/DensePose/configs/quick_schedules/densepose_R_50_FPN_instant_test.yaml @@ -0,0 +1,14 @@ +_BASE_: "../Base-DensePose-RCNN.yaml" +MODEL: + WEIGHTS: "catalog://ImageNetPretrained/MSRA/R-50" + DENSEPOSE_ON: True + ROI_DENSEPOSE_HEAD: + NAME: "DensePoseV1ConvXHead" +DATASETS: + TRAIN: ("densepose_coco_2014_minival_100",) + TEST: ("densepose_coco_2014_minival_100",) +SOLVER: + MAX_ITER: 40 + STEPS: (30,) + BASE_LR: 0.002 + IMS_PER_BATCH: 24 diff --git a/projects/DensePose/configs/quick_schedules/densepose_R_50_FPN_training_acc_test.yaml b/projects/DensePose/configs/quick_schedules/densepose_R_50_FPN_training_acc_test.yaml new file mode 100644 index 0000000000..d780840fe7 --- /dev/null +++ b/projects/DensePose/configs/quick_schedules/densepose_R_50_FPN_training_acc_test.yaml @@ -0,0 +1,25 @@ +_BASE_: "../Base-DensePose-RCNN.yaml" +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +MODEL: + WEIGHTS: "catalog://ImageNetPretrained/MSRA/R-50" + DENSEPOSE_ON: True + ROI_HEADS: + NUM_CLASSES: 1 + ROI_BOX_HEAD: + POOLER_RESOLUTION: 7 + POOLER_SAMPLING_RATIO: 2 + ROI_DENSEPOSE_HEAD: + NAME: "DensePoseV1ConvXHead" +DATASETS: + TRAIN: ("densepose_coco_2014_minival",) + TEST: ("densepose_coco_2014_minival",) +SOLVER: + MAX_ITER: 6000 + STEPS: (5500, 5800) + BASE_LR: 0.002 + IMS_PER_BATCH: 24 + WARMUP_FACTOR: 0.1 +TEST: + EXPECTED_RESULTS: [["bbox", "AP", 58.27, 1.0], ["densepose", "AP", 42.47, 1.5]] + diff --git a/projects/DensePose/densepose/__init__.py b/projects/DensePose/densepose/__init__.py new file mode 100644 index 0000000000..48896fc7b8 --- /dev/null +++ b/projects/DensePose/densepose/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from . import dataset # just to register data +from .config import add_densepose_config +from .dataset_mapper import DatasetMapper +from .densepose_head import ROI_DENSEPOSE_HEAD_REGISTRY +from .evaluator import DensePoseCOCOEvaluator +from .roi_head import DensePoseROIHeads +from .structures import DensePoseDataRelative, DensePoseList, DensePoseTransformData diff --git a/projects/DensePose/densepose/config.py b/projects/DensePose/densepose/config.py new file mode 100644 index 0000000000..c23aa15066 --- /dev/null +++ b/projects/DensePose/densepose/config.py @@ -0,0 +1,35 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from detectron2.config import CfgNode as CN + + +def add_densepose_config(cfg): + """ + Add config for densepose head. + """ + _C = cfg + + _C.MODEL.DENSEPOSE_ON = True + + _C.MODEL.ROI_DENSEPOSE_HEAD = CN() + _C.MODEL.ROI_DENSEPOSE_HEAD.NAME = "" + _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS = 8 + # Number of parts used for point labels + _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES = 24 + _C.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL = 4 + _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM = 512 + _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL = 3 + _C.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE = 2 + _C.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE = 56 + _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_TYPE = "ROIAlignV2" + _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_RESOLUTION = 14 + _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_SAMPLING_RATIO = 2 + # Overlap threshold for an RoI to be considered foreground (if >= FG_IOU_THRESHOLD) + _C.MODEL.ROI_DENSEPOSE_HEAD.FG_IOU_THRESHOLD = 0.7 + # Loss weights for annotation masks.(14 Parts) + _C.MODEL.ROI_DENSEPOSE_HEAD.INDEX_WEIGHTS = 2.0 + # Loss weights for surface parts. (24 Parts) + _C.MODEL.ROI_DENSEPOSE_HEAD.PART_WEIGHTS = 0.3 + # Loss weights for UV regression. + _C.MODEL.ROI_DENSEPOSE_HEAD.POINT_REGRESSION_WEIGHTS = 0.1 diff --git a/projects/DensePose/densepose/dataset.py b/projects/DensePose/densepose/dataset.py new file mode 100644 index 0000000000..4100533874 --- /dev/null +++ b/projects/DensePose/densepose/dataset.py @@ -0,0 +1,37 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import os + +from detectron2.data.datasets import register_coco_instances + + +def get_densepose_metadata(): + meta = { + "thing_classes": ["person"], + "densepose_transform_src": "detectron2://densepose/UV_symmetry_transforms.mat", + "densepose_smpl_subdiv": "detectron2://densepose/SMPL_subdiv.mat", + "densepose_smpl_subdiv_transform": "detectron2://densepose/SMPL_SUBDIV_TRANSFORM.mat", + } + return meta + + +SPLITS = { + "densepose_coco_2014_train": ("coco/train2014", "coco/annotations/densepose_train2014.json"), + "densepose_coco_2014_minival": ("coco/val2014", "coco/annotations/densepose_minival2014.json"), + "densepose_coco_2014_minival_100": ( + "coco/val2014", + "coco/annotations/densepose_minival2014_100.json", + ), + "densepose_coco_2014_valminusminival": ( + "coco/val2014", + "coco/annotations/densepose_valminusminival2014.json", + ), +} + +for key, (image_root, json_file) in SPLITS.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + get_densepose_metadata(), + os.path.join("datasets", json_file), + os.path.join("datasets", image_root), + ) diff --git a/projects/DensePose/densepose/dataset_mapper.py b/projects/DensePose/densepose/dataset_mapper.py new file mode 100644 index 0000000000..b3d37c794a --- /dev/null +++ b/projects/DensePose/densepose/dataset_mapper.py @@ -0,0 +1,118 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import copy +import torch +from fvcore.common.file_io import PathManager + +from detectron2.data import MetadataCatalog +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T + +from .structures import DensePoseDataRelative, DensePoseList, DensePoseTransformData + + +class DatasetMapper: + """ + A customized version of `detectron2.data.DatasetMapperper` + """ + + def __init__(self, cfg, is_train=True): + self.tfm_gens = utils.build_transform_gen(cfg, is_train) + + # fmt: off + self.img_format = cfg.INPUT.FORMAT + self.mask_on = cfg.MODEL.MASK_ON + self.keypoint_on = cfg.MODEL.KEYPOINT_ON + self.densepose_on = cfg.MODEL.DENSEPOSE_ON + assert not cfg.MODEL.LOAD_PROPOSALS, "not supported yet" + # fmt: on + if self.keypoint_on and is_train: + # Flip only makes sense in training + self.keypoint_hflip_indices = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) + else: + self.keypoint_hflip_indices = None + + if self.densepose_on: + densepose_transform_srcs = [ + MetadataCatalog.get(ds).densepose_transform_src + for ds in cfg.DATASETS.TRAIN + cfg.DATASETS.TEST + ] + assert len(densepose_transform_srcs) > 0 + # TODO: check that DensePose transformation data is the same for + # all the datasets. Otherwise one would have to pass DB ID with + # each entry to select proper transformation data. For now, since + # all DensePose annotated data uses the same data semantics, we + # omit this check. + densepose_transform_data_fpath = PathManager.get_local_path(densepose_transform_srcs[0]) + self.densepose_transform_data = DensePoseTransformData.load( + densepose_transform_data_fpath + ) + + self.is_train = is_train + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + image, transforms = T.apply_transform_gens(self.tfm_gens, image) + image_shape = image.shape[:2] # h, w + dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) + + if not self.is_train: + dataset_dict.pop("annotations", None) + return dataset_dict + + for anno in dataset_dict["annotations"]: + if not self.mask_on: + anno.pop("segmentation", None) + if not self.keypoint_on: + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + # USER: Don't call transpose_densepose if you don't need + annos = [ + self._transform_densepose( + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ), + transforms, + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances(annos, image_shape) + + if len(annos) and "densepose" in annos[0]: + gt_densepose = [obj["densepose"] for obj in annos] + instances.gt_densepose = DensePoseList(gt_densepose, instances.gt_boxes, image_shape) + + dataset_dict["instances"] = instances[instances.gt_boxes.nonempty()] + return dataset_dict + + def _transform_densepose(self, annotation, transforms): + if not self.densepose_on: + return annotation + + # Handle densepose annotations + is_valid, reason_not_valid = DensePoseDataRelative.validate_annotation(annotation) + if is_valid: + densepose_data = DensePoseDataRelative(annotation, cleanup=True) + densepose_data.apply_transform(transforms, self.densepose_transform_data) + annotation["densepose"] = densepose_data + else: + # logger = logging.getLogger(__name__) + # logger.debug("Could not load DensePose annotation: {}".format(reason_not_valid)) + DensePoseDataRelative.cleanup_annotation(annotation) + # NOTE: annotations for certain instances may be unavailable. + # 'None' is accepted by the DensePostList data structure. + annotation["densepose"] = None + return annotation diff --git a/projects/DensePose/densepose/densepose_coco_evaluation.py b/projects/DensePose/densepose/densepose_coco_evaluation.py new file mode 100644 index 0000000000..3a882f8c11 --- /dev/null +++ b/projects/DensePose/densepose/densepose_coco_evaluation.py @@ -0,0 +1,953 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# This is a modified version of cocoeval.py where we also have the densepose evaluation. + +__author__ = "tsungyi" + +import copy +import datetime +import itertools +import logging +import numpy as np +import pickle +import time +from collections import defaultdict +import scipy.spatial.distance as ssd +from fvcore.common.file_io import PathManager +from pycocotools import mask as maskUtils +from scipy.io import loadmat + +from .structures import DensePoseResult + +logger = logging.getLogger(__name__) + + +class DensePoseCocoEval(object): + # Interface for evaluating detection on the Microsoft COCO dataset. + # + # The usage for CocoEval is as follows: + # cocoGt=..., cocoDt=... # load dataset and results + # E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object + # E.params.recThrs = ...; # set parameters as desired + # E.evaluate(); # run per image evaluation + # E.accumulate(); # accumulate per image results + # E.summarize(); # display summary metrics of results + # For example usage see evalDemo.m and http://mscoco.org/. + # + # The evaluation parameters are as follows (defaults in brackets): + # imgIds - [all] N img ids to use for evaluation + # catIds - [all] K cat ids to use for evaluation + # iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation + # recThrs - [0:.01:1] R=101 recall thresholds for evaluation + # areaRng - [...] A=4 object area ranges for evaluation + # maxDets - [1 10 100] M=3 thresholds on max detections per image + # iouType - ['segm'] set iouType to 'segm', 'bbox', 'keypoints' or 'densepose' + # iouType replaced the now DEPRECATED useSegm parameter. + # useCats - [1] if true use category labels for evaluation + # Note: if useCats=0 category labels are ignored as in proposal scoring. + # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified. + # + # evaluate(): evaluates detections on every image and every category and + # concats the results into the "evalImgs" with fields: + # dtIds - [1xD] id for each of the D detections (dt) + # gtIds - [1xG] id for each of the G ground truths (gt) + # dtMatches - [TxD] matching gt id at each IoU or 0 + # gtMatches - [TxG] matching dt id at each IoU or 0 + # dtScores - [1xD] confidence of each dt + # gtIgnore - [1xG] ignore flag for each gt + # dtIgnore - [TxD] ignore flag for each dt at each IoU + # + # accumulate(): accumulates the per-image, per-category evaluation + # results in "evalImgs" into the dictionary "eval" with fields: + # params - parameters used for evaluation + # date - date evaluation was performed + # counts - [T,R,K,A,M] parameter dimensions (see above) + # precision - [TxRxKxAxM] precision for every evaluation setting + # recall - [TxKxAxM] max recall for every evaluation setting + # Note: precision and recall==-1 for settings with no gt objects. + # + # See also coco, mask, pycocoDemo, pycocoEvalDemo + # + # Microsoft COCO Toolbox. version 2.0 + # Data, paper, and tutorials available at: http://mscoco.org/ + # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. + # Licensed under the Simplified BSD License [see coco/license.txt] + def __init__(self, cocoGt=None, cocoDt=None, iouType="densepose"): + """ + Initialize CocoEval using coco APIs for gt and dt + :param cocoGt: coco object with ground truth annotations + :param cocoDt: coco object with detection results + :return: None + """ + self.cocoGt = cocoGt # ground truth COCO API + self.cocoDt = cocoDt # detections COCO API + self.params = {} # evaluation parameters + self.evalImgs = defaultdict(list) # per-image per-category eval results [KxAxI] + self.eval = {} # accumulated evaluation results + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + self.params = Params(iouType=iouType) # parameters + self._paramsEval = {} # parameters for evaluation + self.stats = [] # result summarization + self.ious = {} # ious between all gts and dts + if cocoGt is not None: + self.params.imgIds = sorted(cocoGt.getImgIds()) + self.params.catIds = sorted(cocoGt.getCatIds()) + self.ignoreThrBB = 0.7 + self.ignoreThrUV = 0.9 + + def _loadGEval(self): + smpl_subdiv_fpath = PathManager.get_local_path("detectron2://densepose/SMPL_subdiv.mat") + pdist_transform_fpath = PathManager.get_local_path( + "detectron2://densepose/SMPL_SUBDIV_TRANSFORM.mat" + ) + pdist_matrix_fpath = PathManager.get_local_path("detectron2://densepose/Pdist_matrix.pkl") + SMPL_subdiv = loadmat(smpl_subdiv_fpath) + self.PDIST_transform = loadmat(pdist_transform_fpath) + self.PDIST_transform = self.PDIST_transform["index"].squeeze() + UV = np.array([SMPL_subdiv["U_subdiv"], SMPL_subdiv["V_subdiv"]]).squeeze() + ClosestVertInds = np.arange(UV.shape[1]) + 1 + self.Part_UVs = [] + self.Part_ClosestVertInds = [] + for i in np.arange(24): + self.Part_UVs.append(UV[:, SMPL_subdiv["Part_ID_subdiv"].squeeze() == (i + 1)]) + self.Part_ClosestVertInds.append( + ClosestVertInds[SMPL_subdiv["Part_ID_subdiv"].squeeze() == (i + 1)] + ) + + with open(pdist_matrix_fpath, "rb") as hFile: + arrays = pickle.load(hFile, encoding="latin1") + self.Pdist_matrix = arrays["Pdist_matrix"] + self.Part_ids = np.array(SMPL_subdiv["Part_ID_subdiv"].squeeze()) + # Mean geodesic distances for parts. + self.Mean_Distances = np.array([0, 0.351, 0.107, 0.126, 0.237, 0.173, 0.142, 0.128, 0.150]) + # Coarse Part labels. + self.CoarseParts = np.array( + [0, 1, 1, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8] + ) + + def _prepare(self): + """ + Prepare ._gts and ._dts for evaluation based on params + :return: None + """ + + def _toMask(anns, coco): + # modify ann['segmentation'] by reference + for ann in anns: + rle = coco.annToRLE(ann) + ann["segmentation"] = rle + + def _getIgnoreRegion(iid, coco): + img = coco.imgs[iid] + + if "ignore_regions_x" not in img.keys(): + return None + + if len(img["ignore_regions_x"]) == 0: + return None + + rgns_merged = [] + for region_x, region_y in zip(img["ignore_regions_x"], img["ignore_regions_y"]): + rgns = [iter(region_x), iter(region_y)] + rgns_merged.append([next(it) for it in itertools.cycle(rgns)]) + rles = maskUtils.frPyObjects(rgns_merged, img["height"], img["width"]) + rle = maskUtils.merge(rles) + return maskUtils.decode(rle) + + def _checkIgnore(dt, iregion): + if iregion is None: + return True + + bb = np.array(dt["bbox"]).astype(np.int) + x1, y1, x2, y2 = bb[0], bb[1], bb[0] + bb[2], bb[1] + bb[3] + x2 = min([x2, iregion.shape[1]]) + y2 = min([y2, iregion.shape[0]]) + + if bb[2] * bb[3] == 0: + return False + + crop_iregion = iregion[y1:y2, x1:x2] + + if crop_iregion.sum() == 0: + return True + + if "densepose" not in dt.keys(): # filtering boxes + return crop_iregion.sum() / bb[2] / bb[3] < self.ignoreThrBB + + # filtering UVs + ignoremask = np.require(crop_iregion, requirements=["F"]) + uvmask = np.require( + np.asarray(dt["densepose"][0] > 0), dtype=np.uint8, requirements=["F"] + ) + uvmask_ = maskUtils.encode(uvmask) + ignoremask_ = maskUtils.encode(ignoremask) + uviou = maskUtils.iou([uvmask_], [ignoremask_], [1])[0] + return uviou < self.ignoreThrUV + + p = self.params + + if p.useCats: + gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) + dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) + else: + gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds)) + dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds)) + + # if iouType == 'uv', add point gt annotations + if p.iouType == "densepose": + self._loadGEval() + + # convert ground truth to mask if iouType == 'segm' + if p.iouType == "segm": + _toMask(gts, self.cocoGt) + _toMask(dts, self.cocoDt) + + # set ignore flag + for gt in gts: + gt["ignore"] = gt["ignore"] if "ignore" in gt else 0 + gt["ignore"] = "iscrowd" in gt and gt["iscrowd"] + if p.iouType == "keypoints": + gt["ignore"] = (gt["num_keypoints"] == 0) or gt["ignore"] + if p.iouType == "densepose": + gt["ignore"] = ("dp_x" in gt) == 0 + + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + self._igrgns = defaultdict(list) + + for gt in gts: + iid = gt["image_id"] + if iid not in self._igrgns.keys(): + self._igrgns[iid] = _getIgnoreRegion(iid, self.cocoGt) + if _checkIgnore(gt, self._igrgns[iid]): + self._gts[iid, gt["category_id"]].append(gt) + for dt in dts: + if _checkIgnore(dt, self._igrgns[dt["image_id"]]): + self._dts[dt["image_id"], dt["category_id"]].append(dt) + + self.evalImgs = defaultdict(list) # per-image per-category evaluation results + self.eval = {} # accumulated evaluation results + + def evaluate(self): + """ + Run per image evaluation on given images and store results (a list of dict) in self.evalImgs + :return: None + """ + tic = time.time() + logger.info("Running per image DensePose evaluation... {}".format(self.params.iouType)) + p = self.params + # add backward compatibility if useSegm is specified in params + if p.useSegm is not None: + p.iouType = "segm" if p.useSegm == 1 else "bbox" + logger.info("useSegm (deprecated) is not None. Running DensePose evaluation") + p.imgIds = list(np.unique(p.imgIds)) + if p.useCats: + p.catIds = list(np.unique(p.catIds)) + p.maxDets = sorted(p.maxDets) + self.params = p + + self._prepare() + # loop through images, area range, max detection number + catIds = p.catIds if p.useCats else [-1] + + if p.iouType in ["segm", "bbox"]: + computeIoU = self.computeIoU + elif p.iouType == "keypoints": + computeIoU = self.computeOks + elif p.iouType == "densepose": + computeIoU = self.computeOgps + + self.ious = { + (imgId, catId): computeIoU(imgId, catId) for imgId in p.imgIds for catId in catIds + } + + evaluateImg = self.evaluateImg + maxDet = p.maxDets[-1] + self.evalImgs = [ + evaluateImg(imgId, catId, areaRng, maxDet) + for catId in catIds + for areaRng in p.areaRng + for imgId in p.imgIds + ] + self._paramsEval = copy.deepcopy(self.params) + toc = time.time() + logger.info("DensePose evaluation DONE (t={:0.2f}s).".format(toc - tic)) + + def computeIoU(self, imgId, catId): + p = self.params + if p.useCats: + gt = self._gts[imgId, catId] + dt = self._dts[imgId, catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] + if len(gt) == 0 and len(dt) == 0: + return [] + inds = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in inds] + if len(dt) > p.maxDets[-1]: + dt = dt[0 : p.maxDets[-1]] + + if p.iouType == "segm": + g = [g["segmentation"] for g in gt] + d = [d["segmentation"] for d in dt] + elif p.iouType == "bbox": + g = [g["bbox"] for g in gt] + d = [d["bbox"] for d in dt] + else: + raise Exception("unknown iouType for iou computation") + + # compute iou between each dt and gt region + iscrowd = [int(o["iscrowd"]) for o in gt] + ious = maskUtils.iou(d, g, iscrowd) + return ious + + def computeOks(self, imgId, catId): + p = self.params + # dimention here should be Nxm + gts = self._gts[imgId, catId] + dts = self._dts[imgId, catId] + inds = np.argsort([-d["score"] for d in dts], kind="mergesort") + dts = [dts[i] for i in inds] + if len(dts) > p.maxDets[-1]: + dts = dts[0 : p.maxDets[-1]] + # if len(gts) == 0 and len(dts) == 0: + if len(gts) == 0 or len(dts) == 0: + return [] + ious = np.zeros((len(dts), len(gts))) + sigmas = ( + np.array( + [ + 0.26, + 0.25, + 0.25, + 0.35, + 0.35, + 0.79, + 0.79, + 0.72, + 0.72, + 0.62, + 0.62, + 1.07, + 1.07, + 0.87, + 0.87, + 0.89, + 0.89, + ] + ) + / 10.0 + ) + vars = (sigmas * 2) ** 2 + k = len(sigmas) + # compute oks between each detection and ground truth object + for j, gt in enumerate(gts): + # create bounds for ignore regions(double the gt bbox) + g = np.array(gt["keypoints"]) + xg = g[0::3] + yg = g[1::3] + vg = g[2::3] + k1 = np.count_nonzero(vg > 0) + bb = gt["bbox"] + x0 = bb[0] - bb[2] + x1 = bb[0] + bb[2] * 2 + y0 = bb[1] - bb[3] + y1 = bb[1] + bb[3] * 2 + for i, dt in enumerate(dts): + d = np.array(dt["keypoints"]) + xd = d[0::3] + yd = d[1::3] + if k1 > 0: + # measure the per-keypoint distance if keypoints visible + dx = xd - xg + dy = yd - yg + else: + # measure minimum distance to keypoints in (x0,y0) & (x1,y1) + z = np.zeros((k)) + dx = np.max((z, x0 - xd), axis=0) + np.max((z, xd - x1), axis=0) + dy = np.max((z, y0 - yd), axis=0) + np.max((z, yd - y1), axis=0) + e = (dx ** 2 + dy ** 2) / vars / (gt["area"] + np.spacing(1)) / 2 + if k1 > 0: + e = e[vg > 0] + ious[i, j] = np.sum(np.exp(-e)) / e.shape[0] + return ious + + def computeOgps(self, imgId, catId): + p = self.params + # dimention here should be Nxm + g = self._gts[imgId, catId] + d = self._dts[imgId, catId] + inds = np.argsort([-d_["score"] for d_ in d], kind="mergesort") + d = [d[i] for i in inds] + if len(d) > p.maxDets[-1]: + d = d[0 : p.maxDets[-1]] + # if len(gts) == 0 and len(dts) == 0: + if len(g) == 0 or len(d) == 0: + return [] + ious = np.zeros((len(d), len(g))) + # compute opgs between each detection and ground truth object + # sigma = self.sigma #0.255 # dist = 0.3m corresponds to ogps = 0.5 + # 1 # dist = 0.3m corresponds to ogps = 0.96 + # 1.45 # dist = 1.7m (person height) corresponds to ogps = 0.5) + for j, gt in enumerate(g): + if not gt["ignore"]: + g_ = gt["bbox"] + for i, dt in enumerate(d): + # + dy = int(dt["bbox"][3]) + dx = int(dt["bbox"][2]) + dp_x = np.array(gt["dp_x"]) * g_[2] / 255.0 + dp_y = np.array(gt["dp_y"]) * g_[3] / 255.0 + py = (dp_y + g_[1] - dt["bbox"][1]).astype(np.int) + px = (dp_x + g_[0] - dt["bbox"][0]).astype(np.int) + # + pts = np.zeros(len(px)) + pts[px >= dx] = -1 + pts[py >= dy] = -1 + pts[px < 0] = -1 + pts[py < 0] = -1 + if len(pts) < 1: + ogps = 0.0 + elif np.max(pts) == -1: + ogps = 0.0 + else: + px[pts == -1] = 0 + py[pts == -1] = 0 + (densepose_shape, densepose_data_encoded), densepose_bbox_xywh = dt[ + "densepose" + ] + densepose_data = DensePoseResult.decode_png_data( + densepose_shape, densepose_data_encoded + ) + assert densepose_data.shape[2] == dx, ( + "DensePoseData width {} should be equal to " + "detection bounding box width {}".format(densepose_data.shape[2], dx) + ) + assert densepose_data.shape[1] == dy, ( + "DensePoseData height {} should be equal to " + "detection bounding box height {}".format(densepose_data.shape[1], dy) + ) + ipoints = densepose_data[0, py, px] + upoints = densepose_data[1, py, px] / 255.0 # convert from uint8 by /255. + vpoints = densepose_data[2, py, px] / 255.0 + ipoints[pts == -1] = 0 + # Find closest vertices in subsampled mesh. + cVerts, cVertsGT = self.findAllClosestVerts(gt, upoints, vpoints, ipoints) + # Get pairwise geodesic distances between gt and estimated mesh points. + dist = self.getDistances(cVertsGT, cVerts) + # Compute the Ogps measure. + # Find the mean geodesic normalization distance for + # each GT point, based on which part it is on. + Current_Mean_Distances = self.Mean_Distances[ + self.CoarseParts[self.Part_ids[cVertsGT[cVertsGT > 0].astype(int) - 1]] + ] + # Compute gps + ogps_values = np.exp(-(dist ** 2) / (2 * (Current_Mean_Distances ** 2))) + # + if len(dist) > 0: + ogps = np.sum(ogps_values) / len(dist) + ious[i, j] = ogps + + gbb = [gt["bbox"] for gt in g] + dbb = [dt["bbox"] for dt in d] + + # compute iou between each dt and gt region + iscrowd = [int(o["iscrowd"]) for o in g] + ious_bb = maskUtils.iou(dbb, gbb, iscrowd) + return ious, ious_bb + + def evaluateImg(self, imgId, catId, aRng, maxDet): + """ + perform evaluation for single category and image + :return: dict (single image results) + """ + + p = self.params + if p.useCats: + gt = self._gts[imgId, catId] + dt = self._dts[imgId, catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] + if len(gt) == 0 and len(dt) == 0: + return None + + for g in gt: + # g['_ignore'] = g['ignore'] + if g["ignore"] or (g["area"] < aRng[0] or g["area"] > aRng[1]): + g["_ignore"] = True + else: + g["_ignore"] = False + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g["_ignore"] for g in gt], kind="mergesort") + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o["iscrowd"]) for o in gt] + # load computed ious + if p.iouType == "densepose": + # print('Checking the length', len(self.ious[imgId, catId])) + # if len(self.ious[imgId, catId]) == 0: + # print(self.ious[imgId, catId]) + ious = ( + self.ious[imgId, catId][0][:, gtind] + if len(self.ious[imgId, catId]) > 0 + else self.ious[imgId, catId] + ) + ioubs = ( + self.ious[imgId, catId][1][:, gtind] + if len(self.ious[imgId, catId]) > 0 + else self.ious[imgId, catId] + ) + else: + ious = ( + self.ious[imgId, catId][:, gtind] + if len(self.ious[imgId, catId]) > 0 + else self.ious[imgId, catId] + ) + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + gtm = np.zeros((T, G)) + dtm = np.zeros((T, D)) + gtIg = np.array([g["_ignore"] for g in gt]) + dtIg = np.zeros((T, D)) + if np.all(gtIg) and p.iouType == "densepose": + dtIg = np.logical_or(dtIg, True) + + if len(ious) > 0: # and not p.iouType == 'densepose': + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t, 1 - 1e-10]) + m = -1 + for gind, _g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1: + break + # continue to next gt unless better match made + if ious[dind, gind] < iou: + continue + if ious[dind, gind] == 0.0: + continue + # if match successful and best so far, store appropriately + iou = ious[dind, gind] + m = gind + # if match made store id of match for both dt and gt + if m == -1: + continue + dtIg[tind, dind] = gtIg[m] + dtm[tind, dind] = gt[m]["id"] + gtm[tind, m] = d["id"] + + if p.iouType == "densepose": + if not len(ioubs) == 0: + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + if dtm[tind, dind] == 0: + ioub = 0.8 + m = -1 + for gind, _g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # continue to next gt unless better match made + if ioubs[dind, gind] < ioub: + continue + # if match successful and best so far, store appropriately + ioub = ioubs[dind, gind] + m = gind + # if match made store id of match for both dt and gt + if m > -1: + dtIg[:, dind] = gtIg[m] + if gtIg[m]: + dtm[tind, dind] = gt[m]["id"] + gtm[tind, m] = d["id"] + # set unmatched detections outside of area range to ignore + a = np.array([d["area"] < aRng[0] or d["area"] > aRng[1] for d in dt]).reshape((1, len(dt))) + dtIg = np.logical_or(dtIg, np.logical_and(dtm == 0, np.repeat(a, T, 0))) + # store results for given image and category + # print('Done with the function', len(self.ious[imgId, catId])) + return { + "image_id": imgId, + "category_id": catId, + "aRng": aRng, + "maxDet": maxDet, + "dtIds": [d["id"] for d in dt], + "gtIds": [g["id"] for g in gt], + "dtMatches": dtm, + "gtMatches": gtm, + "dtScores": [d["score"] for d in dt], + "gtIgnore": gtIg, + "dtIgnore": dtIg, + } + + def accumulate(self, p=None): + """ + Accumulate per image evaluation results and store the result in self.eval + :param p: input params for evaluation + :return: None + """ + logger.info("Accumulating evaluation results...") + tic = time.time() + if not self.evalImgs: + logger.info("Please run evaluate() first") + # allows input customized parameters + if p is None: + p = self.params + p.catIds = p.catIds if p.useCats == 1 else [-1] + T = len(p.iouThrs) + R = len(p.recThrs) + K = len(p.catIds) if p.useCats else 1 + A = len(p.areaRng) + M = len(p.maxDets) + precision = -np.ones((T, R, K, A, M)) # -1 for the precision of absent categories + recall = -np.ones((T, K, A, M)) + + # create dictionary for future indexing + logger.info("Categories: {}".format(p.catIds)) + _pe = self._paramsEval + catIds = _pe.catIds if _pe.useCats else [-1] + setK = set(catIds) + setA = set(map(tuple, _pe.areaRng)) + setM = set(_pe.maxDets) + setI = set(_pe.imgIds) + # get inds to evaluate + k_list = [n for n, k in enumerate(p.catIds) if k in setK] + m_list = [m for n, m in enumerate(p.maxDets) if m in setM] + a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA] + i_list = [n for n, i in enumerate(p.imgIds) if i in setI] + I0 = len(_pe.imgIds) + A0 = len(_pe.areaRng) + # retrieve E at each category, area range, and max number of detections + for k, k0 in enumerate(k_list): + Nk = k0 * A0 * I0 + for a, a0 in enumerate(a_list): + Na = a0 * I0 + for m, maxDet in enumerate(m_list): + E = [self.evalImgs[Nk + Na + i] for i in i_list] + E = [e for e in E if e is not None] + if len(E) == 0: + continue + dtScores = np.concatenate([e["dtScores"][0:maxDet] for e in E]) + + # different sorting method generates slightly different results. + # mergesort is used to be consistent as Matlab implementation. + inds = np.argsort(-dtScores, kind="mergesort") + + dtm = np.concatenate([e["dtMatches"][:, 0:maxDet] for e in E], axis=1)[:, inds] + dtIg = np.concatenate([e["dtIgnore"][:, 0:maxDet] for e in E], axis=1)[:, inds] + gtIg = np.concatenate([e["gtIgnore"] for e in E]) + npig = np.count_nonzero(gtIg == 0) + if npig == 0: + continue + tps = np.logical_and(dtm, np.logical_not(dtIg)) + fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg)) + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + nd = len(tp) + rc = tp / npig + pr = tp / (fp + tp + np.spacing(1)) + q = np.zeros((R,)) + + if nd: + recall[t, k, a, m] = rc[-1] + else: + recall[t, k, a, m] = 0 + + # numpy is slow without cython optimization for accessing elements + # use python array gets significant speed improvement + pr = pr.tolist() + q = q.tolist() + + for i in range(nd - 1, 0, -1): + if pr[i] > pr[i - 1]: + pr[i - 1] = pr[i] + + inds = np.searchsorted(rc, p.recThrs, side="left") + try: + for ri, pi in enumerate(inds): + q[ri] = pr[pi] + except Exception: + pass + precision[t, :, k, a, m] = np.array(q) + logger.info( + "Final: max precision {}, min precision {}".format(np.max(precision), np.min(precision)) + ) + self.eval = { + "params": p, + "counts": [T, R, K, A, M], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + } + toc = time.time() + logger.info("DONE (t={:0.2f}s).".format(toc - tic)) + + def summarize(self): + """ + Compute and display summary metrics for evaluation results. + Note this functin can *only* be applied on the default parameter setting + """ + + def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): + p = self.params + iStr = " {:<18} {} @[ {}={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" + titleStr = "Average Precision" if ap == 1 else "Average Recall" + typeStr = "(AP)" if ap == 1 else "(AR)" + measure = "IoU" + if self.params.iouType == "keypoints": + measure = "OKS" + elif self.params.iouType == "densepose": + measure = "OGPS" + iouStr = ( + "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) + if iouThr is None + else "{:0.2f}".format(iouThr) + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(np.abs(iouThr - p.iouThrs) < 0.001)[0] + s = s[t] + s = s[:, :, :, aind, mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval["recall"] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, aind, mind] + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + logger.info(iStr.format(titleStr, typeStr, measure, iouStr, areaRng, maxDets, mean_s)) + return mean_s + + def _summarizeDets(): + stats = np.zeros((12,)) + stats[0] = _summarize(1) + stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) + return stats + + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=0.5) + stats[2] = _summarize(1, maxDets=20, iouThr=0.75) + stats[3] = _summarize(1, maxDets=20, areaRng="medium") + stats[4] = _summarize(1, maxDets=20, areaRng="large") + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=0.5) + stats[7] = _summarize(0, maxDets=20, iouThr=0.75) + stats[8] = _summarize(0, maxDets=20, areaRng="medium") + stats[9] = _summarize(0, maxDets=20, areaRng="large") + return stats + + def _summarizeUvs(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=self.params.maxDets[0]) + stats[1] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.5) + stats[2] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.75) + stats[3] = _summarize(1, maxDets=self.params.maxDets[0], areaRng="medium") + stats[4] = _summarize(1, maxDets=self.params.maxDets[0], areaRng="large") + stats[5] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0], iouThr=0.5) + stats[7] = _summarize(0, maxDets=self.params.maxDets[0], iouThr=0.75) + stats[8] = _summarize(0, maxDets=self.params.maxDets[0], areaRng="medium") + stats[9] = _summarize(0, maxDets=self.params.maxDets[0], areaRng="large") + return stats + + def _summarizeUvsOld(): + stats = np.zeros((18,)) + stats[0] = _summarize(1, maxDets=self.params.maxDets[0]) + stats[1] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.5) + stats[2] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.55) + stats[3] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.60) + stats[4] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.65) + stats[5] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.70) + stats[6] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.75) + stats[7] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.80) + stats[8] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.85) + stats[9] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.90) + stats[10] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.95) + stats[11] = _summarize(1, maxDets=self.params.maxDets[0], areaRng="medium") + stats[12] = _summarize(1, maxDets=self.params.maxDets[0], areaRng="large") + stats[13] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[14] = _summarize(0, maxDets=self.params.maxDets[0], iouThr=0.5) + stats[15] = _summarize(0, maxDets=self.params.maxDets[0], iouThr=0.75) + stats[16] = _summarize(0, maxDets=self.params.maxDets[0], areaRng="medium") + stats[17] = _summarize(0, maxDets=self.params.maxDets[0], areaRng="large") + return stats + + if not self.eval: + raise Exception("Please run accumulate() first") + iouType = self.params.iouType + if iouType in ["segm", "bbox"]: + summarize = _summarizeDets + elif iouType in ["keypoints"]: + summarize = _summarizeKps + elif iouType in ["densepose"]: + summarize = _summarizeUvs + self.stats = summarize() + + def __str__(self): + self.summarize() + + # ================ functions for dense pose ============================== + def findAllClosestVerts(self, gt, U_points, V_points, Index_points): + # + I_gt = np.array(gt["dp_I"]) + U_gt = np.array(gt["dp_U"]) + V_gt = np.array(gt["dp_V"]) + # + # print(I_gt) + # + ClosestVerts = np.ones(Index_points.shape) * -1 + for i in np.arange(24): + # + if sum(Index_points == (i + 1)) > 0: + UVs = np.array( + [U_points[Index_points == (i + 1)], V_points[Index_points == (i + 1)]] + ) + Current_Part_UVs = self.Part_UVs[i] + Current_Part_ClosestVertInds = self.Part_ClosestVertInds[i] + D = ssd.cdist(Current_Part_UVs.transpose(), UVs.transpose()).squeeze() + ClosestVerts[Index_points == (i + 1)] = Current_Part_ClosestVertInds[ + np.argmin(D, axis=0) + ] + # + ClosestVertsGT = np.ones(Index_points.shape) * -1 + for i in np.arange(24): + if sum(I_gt == (i + 1)) > 0: + UVs = np.array([U_gt[I_gt == (i + 1)], V_gt[I_gt == (i + 1)]]) + Current_Part_UVs = self.Part_UVs[i] + Current_Part_ClosestVertInds = self.Part_ClosestVertInds[i] + D = ssd.cdist(Current_Part_UVs.transpose(), UVs.transpose()).squeeze() + ClosestVertsGT[I_gt == (i + 1)] = Current_Part_ClosestVertInds[np.argmin(D, axis=0)] + # + return ClosestVerts, ClosestVertsGT + + def getDistances(self, cVertsGT, cVerts): + + ClosestVertsTransformed = self.PDIST_transform[cVerts.astype(int) - 1] + ClosestVertsGTTransformed = self.PDIST_transform[cVertsGT.astype(int) - 1] + # + ClosestVertsTransformed[cVerts < 0] = 0 + ClosestVertsGTTransformed[cVertsGT < 0] = 0 + # + cVertsGT = ClosestVertsGTTransformed + cVerts = ClosestVertsTransformed + # + n = 27554 + dists = [] + for d in range(len(cVertsGT)): + if cVertsGT[d] > 0: + if cVerts[d] > 0: + i = cVertsGT[d] - 1 + j = cVerts[d] - 1 + if j == i: + dists.append(0) + elif j > i: + ccc = i + i = j + j = ccc + i = n - i - 1 + j = n - j - 1 + k = (n * (n - 1) / 2) - (n - i) * ((n - i) - 1) / 2 + j - i - 1 + k = (n * n - n) / 2 - k - 1 + dists.append(self.Pdist_matrix[int(k)][0]) + else: + i = n - i - 1 + j = n - j - 1 + k = (n * (n - 1) / 2) - (n - i) * ((n - i) - 1) / 2 + j - i - 1 + k = (n * n - n) / 2 - k - 1 + dists.append(self.Pdist_matrix[int(k)][0]) + else: + dists.append(np.inf) + return np.array(dists).squeeze() + + +class Params: + """ + Params for coco evaluation api + """ + + def setDetParams(self): + self.imgIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + self.iouThrs = np.linspace(0.5, 0.95, np.round((0.95 - 0.5) / 0.05) + 1, endpoint=True) + self.recThrs = np.linspace(0.0, 1.00, np.round((1.00 - 0.0) / 0.01) + 1, endpoint=True) + self.maxDets = [1, 10, 100] + self.areaRng = [ + [0 ** 2, 1e5 ** 2], + [0 ** 2, 32 ** 2], + [32 ** 2, 96 ** 2], + [96 ** 2, 1e5 ** 2], + ] + self.areaRngLbl = ["all", "small", "medium", "large"] + self.useCats = 1 + + def setKpParams(self): + self.imgIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + self.iouThrs = np.linspace(0.5, 0.95, np.round((0.95 - 0.5) / 0.05) + 1, endpoint=True) + self.recThrs = np.linspace(0.0, 1.00, np.round((1.00 - 0.0) / 0.01) + 1, endpoint=True) + self.maxDets = [20] + self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] + self.areaRngLbl = ["all", "medium", "large"] + self.useCats = 1 + + def setUvParams(self): + self.imgIds = [] + self.catIds = [] + self.iouThrs = np.linspace(0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True) + self.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True) + self.maxDets = [20] + self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] + self.areaRngLbl = ["all", "medium", "large"] + self.useCats = 1 + + def __init__(self, iouType="segm"): + if iouType == "segm" or iouType == "bbox": + self.setDetParams() + elif iouType == "keypoints": + self.setKpParams() + elif iouType == "densepose": + self.setUvParams() + else: + raise Exception("iouType not supported") + self.iouType = iouType + # useSegm is deprecated + self.useSegm = None diff --git a/projects/DensePose/densepose/densepose_head.py b/projects/DensePose/densepose/densepose_head.py new file mode 100644 index 0000000000..8cb5bf6f12 --- /dev/null +++ b/projects/DensePose/densepose/densepose_head.py @@ -0,0 +1,626 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.layers import Conv2d, ConvTranspose2d, interpolate +from detectron2.structures.boxes import matched_boxlist_iou +from detectron2.utils.registry import Registry + +from .structures import DensePoseOutput + +ROI_DENSEPOSE_HEAD_REGISTRY = Registry("ROI_DENSEPOSE_HEAD") + + +def initialize_module_params(module): + for name, param in module.named_parameters(): + if "bias" in name: + nn.init.constant_(param, 0) + elif "weight" in name: + nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu") + + +@ROI_DENSEPOSE_HEAD_REGISTRY.register() +class DensePoseV1ConvXHead(nn.Module): + def __init__(self, cfg, input_channels): + super(DensePoseV1ConvXHead, self).__init__() + # fmt: off + hidden_dim = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM + kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL + self.n_stacked_convs = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS + # fmt: on + pad_size = kernel_size // 2 + n_channels = input_channels + for i in range(self.n_stacked_convs): + layer = Conv2d(n_channels, hidden_dim, kernel_size, stride=1, padding=pad_size) + layer_name = self._get_layer_name(i) + self.add_module(layer_name, layer) + n_channels = hidden_dim + self.n_out_channels = n_channels + initialize_module_params(self) + + def forward(self, features): + x = features + output = x + for i in range(self.n_stacked_convs): + layer_name = self._get_layer_name(i) + x = getattr(self, layer_name)(x) + x = F.relu(x) + output = x + return output + + def _get_layer_name(self, i): + layer_name = "body_conv_fcn{}".format(i + 1) + return layer_name + + +class DensePosePredictor(nn.Module): + + NUM_ANN_INDICES = 15 + + def __init__(self, cfg, input_channels): + super(DensePosePredictor, self).__init__() + dim_in = input_channels + dim_out_ann_index = self.NUM_ANN_INDICES + dim_out_patches = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES + 1 + kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL + self.ann_index_lowres = ConvTranspose2d( + dim_in, dim_out_ann_index, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + self.index_uv_lowres = ConvTranspose2d( + dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + self.u_lowres = ConvTranspose2d( + dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + self.v_lowres = ConvTranspose2d( + dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + self.scale_factor = cfg.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE + initialize_module_params(self) + + def forward(self, head_outputs): + ann_index_lowres = self.ann_index_lowres(head_outputs) + index_uv_lowres = self.index_uv_lowres(head_outputs) + u_lowres = self.u_lowres(head_outputs) + v_lowres = self.v_lowres(head_outputs) + + def interp2d(input): + return interpolate( + input, scale_factor=self.scale_factor, mode="bilinear", align_corners=False + ) + + ann_index = interp2d(ann_index_lowres) + index_uv = interp2d(index_uv_lowres) + u = interp2d(u_lowres) + v = interp2d(v_lowres) + return (ann_index, index_uv, u, v), (ann_index_lowres, index_uv_lowres, u_lowres, v_lowres) + + +class DensePoseDataFilter(object): + def __init__(self, cfg): + self.iou_threshold = cfg.MODEL.ROI_DENSEPOSE_HEAD.FG_IOU_THRESHOLD + + @torch.no_grad() + def __call__(self, proposals_with_targets): + """ + Filters proposals with targets to keep only the ones relevant for + DensePose training + proposals: list(Instances), each element of the list corresponds to + various instances (proposals, GT for boxes and densepose) for one + image + """ + proposals_filtered = [] + for proposals_per_image in proposals_with_targets: + if not hasattr(proposals_per_image, "gt_densepose"): + continue + assert hasattr(proposals_per_image, "gt_boxes") + assert hasattr(proposals_per_image, "proposal_boxes") + gt_boxes = proposals_per_image.gt_boxes + est_boxes = proposals_per_image.proposal_boxes + # apply match threshold for densepose head + iou = matched_boxlist_iou(gt_boxes, est_boxes) + iou_select = iou > self.iou_threshold + proposals_per_image = proposals_per_image[iou_select] + assert len(proposals_per_image.gt_boxes) == len(proposals_per_image.proposal_boxes) + # filter out any target without densepose annotation + gt_densepose = proposals_per_image.gt_densepose + assert len(proposals_per_image.gt_boxes) == len(proposals_per_image.gt_densepose) + selected_indices = [ + i for i, dp_target in enumerate(gt_densepose) if dp_target is not None + ] + if len(selected_indices) != len(gt_densepose): + proposals_per_image = proposals_per_image[selected_indices] + assert len(proposals_per_image.gt_boxes) == len(proposals_per_image.proposal_boxes) + assert len(proposals_per_image.gt_boxes) == len(proposals_per_image.gt_densepose) + proposals_filtered.append(proposals_per_image) + return proposals_filtered + + +def build_densepose_head(cfg, input_channels): + head_name = cfg.MODEL.ROI_DENSEPOSE_HEAD.NAME + return ROI_DENSEPOSE_HEAD_REGISTRY.get(head_name)(cfg, input_channels) + + +def build_densepose_predictor(cfg, input_channels): + predictor = DensePosePredictor(cfg, input_channels) + return predictor + + +def build_densepose_data_filter(cfg): + dp_filter = DensePoseDataFilter(cfg) + return dp_filter + + +def densepose_inference(densepose_outputs, detections): + """ + Infer dense pose estimate based on outputs from the DensePose head + and detections. The estimate for each detection instance is stored in its + "pred_densepose" attribute. + + Args: + densepose_outputs (tuple(`torch.Tensor`)): iterable containing 4 elements: + - s (:obj: `torch.Tensor`): segmentation tensor of size (N, A, H, W), + - i (:obj: `torch.Tensor`): classification tensor of size (N, C, H, W), + - u (:obj: `torch.Tensor`): U coordinates for each class of size (N, C, H, W), + - v (:obj: `torch.Tensor`): V coordinates for each class of size (N, C, H, W), + where N is the total number of detections in a batch, + A is the number of segmentations classes (e.g. 15 for coarse body parts), + C is the number of labels (e.g. 25 for fine body parts), + W is the resolution along the X axis + H is the resolution along the Y axis + detections (list[Instances]): A list of N Instances, where N is the number of images + in the batch. Instances are modified by this method: "pred_densepose" attribute + is added to each instance, the attribute contains the corresponding + DensePoseOutput object. + """ + + # DensePose outputs: segmentation, body part indices, U, V + s, index_uv, u, v = densepose_outputs + k = 0 + for detection in detections: + n_i = len(detection) + s_i = s[k : k + n_i] + index_uv_i = index_uv[k : k + n_i] + u_i = u[k : k + n_i] + v_i = v[k : k + n_i] + densepose_output_i = DensePoseOutput(s_i, index_uv_i, u_i, v_i) + detection.pred_densepose = densepose_output_i + k += n_i + + +def _linear_interpolation_utilities(v_norm, v0_src, size_src, v0_dst, size_dst, size_z): + """ + Computes utility values for linear interpolation at points v. + The points are given as normalized offsets in the source interval + (v0_src, v0_src + size_src), more precisely: + v = v0_src + v_norm * size_src / 256.0 + The computed utilities include lower points v_lo, upper points v_hi, + interpolation weights v_w and flags j_valid indicating whether the + points falls into the destination interval (v0_dst, v0_dst + size_dst). + + Args: + v_norm (:obj: `torch.Tensor`): tensor of size N containing + normalized point offsets + v0_src (:obj: `torch.Tensor`): tensor of size N containing + left bounds of source intervals for normalized points + size_src (:obj: `torch.Tensor`): tensor of size N containing + source interval sizes for normalized points + v0_dst (:obj: `torch.Tensor`): tensor of size N containing + left bounds of destination intervals + size_dst (:obj: `torch.Tensor`): tensor of size N containing + destination interval sizes + size_z (int): interval size for data to be interpolated + + Returns: + v_lo (:obj: `torch.Tensor`): int tensor of size N containing + indices of lower values used for interpolation, all values are + integers from [0, size_z - 1] + v_hi (:obj: `torch.Tensor`): int tensor of size N containing + indices of upper values used for interpolation, all values are + integers from [0, size_z - 1] + v_w (:obj: `torch.Tensor`): float tensor of size N containing + interpolation weights + j_valid (:obj: `torch.Tensor`): uint8 tensor of size N containing + 0 for points outside the estimation interval + (v0_est, v0_est + size_est) and 1 otherwise + """ + v = v0_src + v_norm * size_src / 256.0 + j_valid = (v - v0_dst >= 0) * (v - v0_dst < size_dst) + v_grid = (v - v0_dst) * size_z / size_dst + v_lo = v_grid.floor().long().clamp(min=0, max=size_z - 1) + v_hi = (v_lo + 1).clamp(max=size_z - 1) + v_grid = torch.min(v_hi.float(), v_grid) + v_w = v_grid - v_lo.float() + return v_lo, v_hi, v_w, j_valid + + +def _grid_sampling_utilities( + zh, zw, bbox_xywh_est, bbox_xywh_gt, index_gt, x_norm, y_norm, index_bbox +): + """ + Prepare tensors used in grid sampling. + + Args: + z_est (:obj: `torch.Tensor`): tensor of size (N,C,H,W) with estimated + values of Z to be extracted for the points X, Y and channel + indices I + bbox_xywh_est (:obj: `torch.Tensor`): tensor of size (N, 4) containing + estimated bounding boxes in format XYWH + bbox_xywh_gt (:obj: `torch.Tensor`): tensor of size (N, 4) containing + matched ground truth bounding boxes in format XYWH + index_gt (:obj: `torch.Tensor`): tensor of size K with point labels for + ground truth points + x_norm (:obj: `torch.Tensor`): tensor of size K with X normalized + coordinates of ground truth points. Image X coordinates can be + obtained as X = Xbbox + x_norm * Wbbox / 255 + y_norm (:obj: `torch.Tensor`): tensor of size K with Y normalized + coordinates of ground truth points. Image Y coordinates can be + obtained as Y = Ybbox + y_norm * Hbbox / 255 + index_bbox (:obj: `torch.Tensor`): tensor of size K with bounding box + indices for each ground truth point. The values are thus in + [0, N-1] + + Returns: + j_valid (:obj: `torch.Tensor`): uint8 tensor of size M containing + 0 for points to be discarded and 1 for points to be selected + y_lo (:obj: `torch.Tensor`): int tensor of indices of upper values + in z_est for each point + y_hi (:obj: `torch.Tensor`): int tensor of indices of lower values + in z_est for each point + x_lo (:obj: `torch.Tensor`): int tensor of indices of left values + in z_est for each point + x_hi (:obj: `torch.Tensor`): int tensor of indices of right values + in z_est for each point + w_ylo_xlo (:obj: `torch.Tensor`): float tensor of size M; + contains upper-left value weight for each point + w_ylo_xhi (:obj: `torch.Tensor`): float tensor of size M; + contains upper-right value weight for each point + w_yhi_xlo (:obj: `torch.Tensor`): float tensor of size M; + contains lower-left value weight for each point + w_yhi_xhi (:obj: `torch.Tensor`): float tensor of size M; + contains lower-right value weight for each point + """ + + x0_gt, y0_gt, w_gt, h_gt = bbox_xywh_gt[index_bbox].unbind(dim=1) + x0_est, y0_est, w_est, h_est = bbox_xywh_est[index_bbox].unbind(dim=1) + x_lo, x_hi, x_w, jx_valid = _linear_interpolation_utilities( + x_norm, x0_gt, w_gt, x0_est, w_est, zw + ) + y_lo, y_hi, y_w, jy_valid = _linear_interpolation_utilities( + y_norm, y0_gt, h_gt, y0_est, h_est, zh + ) + j_valid = jx_valid * jy_valid + + w_ylo_xlo = (1.0 - x_w) * (1.0 - y_w) + w_ylo_xhi = x_w * (1.0 - y_w) + w_yhi_xlo = (1.0 - x_w) * y_w + w_yhi_xhi = x_w * y_w + + return j_valid, y_lo, y_hi, x_lo, x_hi, w_ylo_xlo, w_ylo_xhi, w_yhi_xlo, w_yhi_xhi + + +def _extract_at_points_packed( + z_est, + index_bbox_valid, + slice_index_uv, + y_lo, + y_hi, + x_lo, + x_hi, + w_ylo_xlo, + w_ylo_xhi, + w_yhi_xlo, + w_yhi_xhi, +): + """ + Extract ground truth values z_gt for valid point indices and estimated + values z_est using bilinear interpolation over top-left (y_lo, x_lo), + top-right (y_lo, x_hi), bottom-left (y_hi, x_lo) and bottom-right + (y_hi, x_hi) values in z_est with corresponding weights: + w_ylo_xlo, w_ylo_xhi, w_yhi_xlo and w_yhi_xhi. + Use slice_index_uv to slice dim=1 in z_est + """ + z_est_sampled = ( + z_est[index_bbox_valid, slice_index_uv, y_lo, x_lo] * w_ylo_xlo + + z_est[index_bbox_valid, slice_index_uv, y_lo, x_hi] * w_ylo_xhi + + z_est[index_bbox_valid, slice_index_uv, y_hi, x_lo] * w_yhi_xlo + + z_est[index_bbox_valid, slice_index_uv, y_hi, x_hi] * w_yhi_xhi + ) + return z_est_sampled + + +def _resample_data( + z, bbox_xywh_src, bbox_xywh_dst, wout, hout, mode="nearest", padding_mode="zeros" +): + """ + Args: + z (:obj: `torch.Tensor`): tensor of size (N,C,H,W) with data to be + resampled + bbox_xywh_src (:obj: `torch.Tensor`): tensor of size (N,4) containing + source bounding boxes in format XYWH + bbox_xywh_dst (:obj: `torch.Tensor`): tensor of size (N,4) containing + destination bounding boxes in format XYWH + Return: + zresampled (:obj: `torch.Tensor`): tensor of size (N, C, Hout, Wout) + with resampled values of z, where D is the discretization size + """ + n = bbox_xywh_src.size(0) + assert n == bbox_xywh_dst.size(0), ( + "The number of " + "source ROIs for resampling ({}) should be equal to the number " + "of destination ROIs ({})".format(bbox_xywh_src.size(0), bbox_xywh_dst.size(0)) + ) + x0src, y0src, wsrc, hsrc = bbox_xywh_src.unbind(dim=1) + x0dst, y0dst, wdst, hdst = bbox_xywh_dst.unbind(dim=1) + x0dst_norm = 2 * (x0dst - x0src) / wsrc - 1 + y0dst_norm = 2 * (y0dst - y0src) / hsrc - 1 + x1dst_norm = 2 * (x0dst + wdst - x0src) / wsrc - 1 + y1dst_norm = 2 * (y0dst + hdst - y0src) / hsrc - 1 + grid_w = torch.arange(wout, device=z.device, dtype=torch.float) / wout + grid_h = torch.arange(hout, device=z.device, dtype=torch.float) / hout + grid_w_expanded = grid_w[None, None, :].expand(n, hout, wout) + grid_h_expanded = grid_h[None, :, None].expand(n, hout, wout) + dx_expanded = (x1dst_norm - x0dst_norm)[:, None, None].expand(n, hout, wout) + dy_expanded = (y1dst_norm - y0dst_norm)[:, None, None].expand(n, hout, wout) + x0_expanded = x0dst_norm[:, None, None].expand(n, hout, wout) + y0_expanded = y0dst_norm[:, None, None].expand(n, hout, wout) + grid_x = grid_w_expanded * dx_expanded + x0_expanded + grid_y = grid_h_expanded * dy_expanded + y0_expanded + grid = torch.stack((grid_x, grid_y), dim=3) + # resample Z from (N, C, H, W) into (N, C, Hout, Wout) + zresampled = F.grid_sample(z, grid, mode=mode, padding_mode=padding_mode, align_corners=True) + return zresampled + + +def _extract_single_tensors_from_matches_one_image( + proposals_targets, bbox_with_dp_offset, bbox_global_offset +): + i_gt_all = [] + x_norm_all = [] + y_norm_all = [] + u_gt_all = [] + v_gt_all = [] + s_gt_all = [] + bbox_xywh_gt_all = [] + bbox_xywh_est_all = [] + # Ibbox_all == k should be true for all data that corresponds + # to bbox_xywh_gt[k] and bbox_xywh_est[k] + # index k here is global wrt images + i_bbox_all = [] + # at offset k (k is global) contains index of bounding box data + # within densepose output tensor + i_with_dp = [] + + boxes_xywh_est = proposals_targets.proposal_boxes.clone() + boxes_xywh_gt = proposals_targets.gt_boxes.clone() + n_i = len(boxes_xywh_est) + assert n_i == len(boxes_xywh_gt) + + if n_i: + boxes_xywh_est.tensor[:, 2] -= boxes_xywh_est.tensor[:, 0] + boxes_xywh_est.tensor[:, 3] -= boxes_xywh_est.tensor[:, 1] + boxes_xywh_gt.tensor[:, 2] -= boxes_xywh_gt.tensor[:, 0] + boxes_xywh_gt.tensor[:, 3] -= boxes_xywh_gt.tensor[:, 1] + if hasattr(proposals_targets, "gt_densepose"): + densepose_gt = proposals_targets.gt_densepose + for k, box_xywh_est, box_xywh_gt, dp_gt in zip( + range(n_i), boxes_xywh_est.tensor, boxes_xywh_gt.tensor, densepose_gt + ): + if (dp_gt is not None) and (len(dp_gt.x) > 0): + i_gt_all.append(dp_gt.i) + x_norm_all.append(dp_gt.x) + y_norm_all.append(dp_gt.y) + u_gt_all.append(dp_gt.u) + v_gt_all.append(dp_gt.v) + s_gt_all.append(dp_gt.segm.unsqueeze(0)) + bbox_xywh_gt_all.append(box_xywh_gt.view(-1, 4)) + bbox_xywh_est_all.append(box_xywh_est.view(-1, 4)) + i_bbox_k = torch.full_like(dp_gt.i, bbox_with_dp_offset + len(i_with_dp)) + i_bbox_all.append(i_bbox_k) + i_with_dp.append(bbox_global_offset + k) + return ( + i_gt_all, + x_norm_all, + y_norm_all, + u_gt_all, + v_gt_all, + s_gt_all, + bbox_xywh_gt_all, + bbox_xywh_est_all, + i_bbox_all, + i_with_dp, + ) + + +def _extract_single_tensors_from_matches(proposals_with_targets): + i_img = [] + i_gt_all = [] + x_norm_all = [] + y_norm_all = [] + u_gt_all = [] + v_gt_all = [] + s_gt_all = [] + bbox_xywh_gt_all = [] + bbox_xywh_est_all = [] + i_bbox_all = [] + i_with_dp_all = [] + n = 0 + for i, proposals_targets_per_image in enumerate(proposals_with_targets): + n_i = proposals_targets_per_image.proposal_boxes.tensor.size(0) + if not n_i: + continue + i_gt_img, x_norm_img, y_norm_img, u_gt_img, v_gt_img, s_gt_img, bbox_xywh_gt_img, bbox_xywh_est_img, i_bbox_img, i_with_dp_img = _extract_single_tensors_from_matches_one_image( # noqa + proposals_targets_per_image, len(i_with_dp_all), n + ) + i_gt_all.extend(i_gt_img) + x_norm_all.extend(x_norm_img) + y_norm_all.extend(y_norm_img) + u_gt_all.extend(u_gt_img) + v_gt_all.extend(v_gt_img) + s_gt_all.extend(s_gt_img) + bbox_xywh_gt_all.extend(bbox_xywh_gt_img) + bbox_xywh_est_all.extend(bbox_xywh_est_img) + i_bbox_all.extend(i_bbox_img) + i_with_dp_all.extend(i_with_dp_img) + i_img.extend([i] * len(i_with_dp_img)) + n += n_i + # concatenate all data into a single tensor + if (n > 0) and (len(i_with_dp_all) > 0): + i_gt = torch.cat(i_gt_all, 0).long() + x_norm = torch.cat(x_norm_all, 0) + y_norm = torch.cat(y_norm_all, 0) + u_gt = torch.cat(u_gt_all, 0) + v_gt = torch.cat(v_gt_all, 0) + s_gt = torch.cat(s_gt_all, 0) + bbox_xywh_gt = torch.cat(bbox_xywh_gt_all, 0) + bbox_xywh_est = torch.cat(bbox_xywh_est_all, 0) + i_bbox = torch.cat(i_bbox_all, 0).long() + else: + i_gt = None + x_norm = None + y_norm = None + u_gt = None + v_gt = None + s_gt = None + bbox_xywh_gt = None + bbox_xywh_est = None + i_bbox = None + return ( + i_img, + i_with_dp_all, + bbox_xywh_est, + bbox_xywh_gt, + i_gt, + x_norm, + y_norm, + u_gt, + v_gt, + s_gt, + i_bbox, + ) + + +class DensePoseLosses(object): + def __init__(self, cfg): + # fmt: off + self.heatmap_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE + self.w_points = cfg.MODEL.ROI_DENSEPOSE_HEAD.POINT_REGRESSION_WEIGHTS + self.w_part = cfg.MODEL.ROI_DENSEPOSE_HEAD.PART_WEIGHTS + self.w_segm = cfg.MODEL.ROI_DENSEPOSE_HEAD.INDEX_WEIGHTS + # fmt: on + + def __call__(self, proposals_with_gt, densepose_outputs): + losses = {} + # densepose outputs are computed for all images and all bounding boxes; + # i.e. if a batch has 4 images with (3, 1, 2, 1) proposals respectively, + # the outputs will have size(0) == 3+1+2+1 == 7 + s, index_uv, u, v = densepose_outputs + assert u.size(2) == v.size(2) + assert u.size(3) == v.size(3) + assert u.size(2) == index_uv.size(2) + assert u.size(3) == index_uv.size(3) + + with torch.no_grad(): + index_uv_img, i_with_dp, bbox_xywh_est, bbox_xywh_gt, index_gt_all, x_norm, y_norm, u_gt_all, v_gt_all, s_gt, index_bbox = _extract_single_tensors_from_matches( # noqa + proposals_with_gt + ) + n_batch = len(i_with_dp) + + # NOTE: we need to keep the same computation graph on all the GPUs to + # perform reduction properly. Hence even if we have no data on one + # of the GPUs, we still need to generate the computation graph. + # Add fake (zero) loss in the form Tensor.sum() * 0 + if not n_batch: + losses["loss_densepose_U"] = u.sum() * 0 + losses["loss_densepose_V"] = v.sum() * 0 + losses["loss_densepose_I"] = index_uv.sum() * 0 + losses["loss_densepose_S"] = s.sum() * 0 + return losses + + zh = u.size(2) + zw = u.size(3) + + j_valid, y_lo, y_hi, x_lo, x_hi, w_ylo_xlo, w_ylo_xhi, w_yhi_xlo, w_yhi_xhi = _grid_sampling_utilities( # noqa + zh, zw, bbox_xywh_est, bbox_xywh_gt, index_gt_all, x_norm, y_norm, index_bbox + ) + + j_valid_fg = j_valid * (index_gt_all > 0) + + u_gt = u_gt_all[j_valid_fg] + u_est_all = _extract_at_points_packed( + u[i_with_dp], + index_bbox, + index_gt_all, + y_lo, + y_hi, + x_lo, + x_hi, + w_ylo_xlo, + w_ylo_xhi, + w_yhi_xlo, + w_yhi_xhi, + ) + u_est = u_est_all[j_valid_fg] + + v_gt = v_gt_all[j_valid_fg] + v_est_all = _extract_at_points_packed( + v[i_with_dp], + index_bbox, + index_gt_all, + y_lo, + y_hi, + x_lo, + x_hi, + w_ylo_xlo, + w_ylo_xhi, + w_yhi_xlo, + w_yhi_xhi, + ) + v_est = v_est_all[j_valid_fg] + + index_uv_gt = index_gt_all[j_valid] + index_uv_est_all = _extract_at_points_packed( + index_uv[i_with_dp], + index_bbox, + slice(None), + y_lo, + y_hi, + x_lo, + x_hi, + w_ylo_xlo[:, None], + w_ylo_xhi[:, None], + w_yhi_xlo[:, None], + w_yhi_xhi[:, None], + ) + index_uv_est = index_uv_est_all[j_valid, :] + + # Resample everything to the estimated data size, no need to resample + # S_est then: + s_est = s[i_with_dp] + with torch.no_grad(): + s_gt = _resample_data( + s_gt.unsqueeze(1), + bbox_xywh_gt, + bbox_xywh_est, + self.heatmap_size, + self.heatmap_size, + mode="nearest", + padding_mode="zeros", + ).squeeze(1) + + # add point-based losses: + u_loss = F.smooth_l1_loss(u_est, u_gt, reduction="sum") * self.w_points + losses["loss_densepose_U"] = u_loss + v_loss = F.smooth_l1_loss(v_est, v_gt, reduction="sum") * self.w_points + losses["loss_densepose_V"] = v_loss + index_uv_loss = F.cross_entropy(index_uv_est, index_uv_gt.long()) * self.w_part + losses["loss_densepose_I"] = index_uv_loss + s_loss = F.cross_entropy(s_est, s_gt.long()) * self.w_segm + losses["loss_densepose_S"] = s_loss + return losses + + +def build_densepose_losses(cfg): + losses = DensePoseLosses(cfg) + return losses diff --git a/projects/DensePose/densepose/evaluator.py b/projects/DensePose/densepose/evaluator.py new file mode 100644 index 0000000000..725f8f98a5 --- /dev/null +++ b/projects/DensePose/densepose/evaluator.py @@ -0,0 +1,136 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# File: + +import contextlib +import copy +import io +import itertools +import json +import logging +import os +from collections import OrderedDict +import torch +from pycocotools import coco +from pycocotools.coco import COCO + +from detectron2.data import MetadataCatalog +from detectron2.evaluation import DatasetEvaluator +from detectron2.structures import BoxMode +from detectron2.utils.comm import all_gather, is_main_process, synchronize +from detectron2.utils.logger import create_small_table + +from .densepose_coco_evaluation import DensePoseCocoEval + +coco.unicode = str + + +class DensePoseCOCOEvaluator(DatasetEvaluator): + def __init__(self, dataset_name, distributed, output_dir=None): + self._distributed = distributed + self._output_dir = output_dir + + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + self._metadata = MetadataCatalog.get(dataset_name) + with contextlib.redirect_stdout(io.StringIO()): + self._coco_api = COCO(self._metadata.json_file) + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + The :class:`Instances` object needs to have `densepose` field. + """ + for input, output in zip(inputs, outputs): + instances = output["instances"].to(self._cpu_device) + + boxes = instances.pred_boxes.tensor.clone() + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + instances.pred_densepose = instances.pred_densepose.to_result(boxes) + + json_results = prediction_to_json(instances, input["image_id"]) + self._predictions.extend(json_results) + + def evaluate(self): + if self._distributed: + synchronize() + self._predictions = all_gather(self._predictions) + self._predictions = list(itertools.chain(*self._predictions)) + if not is_main_process(): + return + + return copy.deepcopy(self._eval_predictions()) + + def _eval_predictions(self): + """ + Evaluate self._predictions on densepose. + Return results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_densepose_results.json") + with open(file_path, "w") as f: + json.dump(self._predictions, f) + f.flush() + os.fsync(f.fileno()) + + self._logger.info("Evaluating predictions ...") + res = OrderedDict() + res["densepose"] = _evaluate_predictions_on_coco(self._coco_api, self._predictions) + return res + + +def prediction_to_json(instances, img_id): + """ + Args: + instances (Instances): the output of the model + img_id (str): the image id in COCO + + Returns: + list[dict]: the results in densepose evaluation format + """ + scores = instances.scores.tolist() + + results = [] + for k in range(len(instances)): + densepose = instances.pred_densepose[k] + result = { + "image_id": img_id, + "category_id": 1, # densepose only has one class + "bbox": densepose[1], + "score": scores[k], + "densepose": densepose, + } + results.append(result) + return results + + +def _evaluate_predictions_on_coco(coco_gt, coco_results): + metrics = ["AP", "AP50", "AP75", "APm", "APl"] + + logger = logging.getLogger(__name__) + + if len(coco_results) == 0: # cocoapi does not handle empty results very well + logger.warn("No predictions from the model! Set scores to -1") + return {metric: -1 for metric in metrics} + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = DensePoseCocoEval(coco_gt, coco_dt, "densepose") + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + # the standard metrics + results = {metric: float(coco_eval.stats[idx] * 100) for idx, metric in enumerate(metrics)} + logger.info("Evaluation results for densepose: \n" + create_small_table(results)) + return results diff --git a/projects/DensePose/densepose/roi_head.py b/projects/DensePose/densepose/roi_head.py new file mode 100644 index 0000000000..56a92c7c0c --- /dev/null +++ b/projects/DensePose/densepose/roi_head.py @@ -0,0 +1,108 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# File: + +import torch + +from detectron2.modeling import ROI_HEADS_REGISTRY, StandardROIHeads +from detectron2.modeling.poolers import ROIPooler + +# Is this function good to expose as an API? +from detectron2.modeling.roi_heads.roi_heads import select_foreground_proposals + +from .densepose_head import ( + build_densepose_data_filter, + build_densepose_head, + build_densepose_losses, + build_densepose_predictor, + densepose_inference, +) + + +@ROI_HEADS_REGISTRY.register() +class DensePoseROIHeads(StandardROIHeads): + """ + A Standard ROIHeads which contains an addition of DensePose head. + """ + + def __init__(self, cfg, input_shape): + super().__init__(cfg, input_shape) + self._init_densepose_head(cfg) + + def _init_densepose_head(self, cfg): + # fmt: off + self.densepose_on = cfg.MODEL.DENSEPOSE_ON + if not self.densepose_on: + return + self.densepose_data_filter = build_densepose_data_filter(cfg) + dp_pooler_resolution = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_RESOLUTION + dp_pooler_scales = tuple(1.0 / self.feature_strides[k] for k in self.in_features) + dp_pooler_sampling_ratio = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_SAMPLING_RATIO + dp_pooler_type = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_TYPE + # fmt: on + in_channels = [self.feature_channels[f] for f in self.in_features][0] + self.densepose_pooler = ROIPooler( + output_size=dp_pooler_resolution, + scales=dp_pooler_scales, + sampling_ratio=dp_pooler_sampling_ratio, + pooler_type=dp_pooler_type, + ) + self.densepose_head = build_densepose_head(cfg, in_channels) + self.densepose_predictor = build_densepose_predictor( + cfg, self.densepose_head.n_out_channels + ) + self.densepose_losses = build_densepose_losses(cfg) + + def _forward_densepose(self, features, instances): + """ + Forward logic of the densepose prediction branch. + + Args: + features (list[Tensor]): #level input features for densepose prediction + instances (list[Instances]): the per-image instances to train/predict densepose. + In training, they can be the proposals. + In inference, they can be the predicted boxes. + + Returns: + In training, a dict of losses. + In inference, update `instances` with new fields "densepose" and return it. + """ + if not self.densepose_on: + return {} if self.training else instances + + if self.training: + proposals, _ = select_foreground_proposals(instances, self.num_classes) + proposals_dp = self.densepose_data_filter(proposals) + if len(proposals_dp) > 0: + proposal_boxes = [x.proposal_boxes for x in proposals_dp] + features_dp = self.densepose_pooler(features, proposal_boxes) + densepose_head_outputs = self.densepose_head(features_dp) + densepose_outputs, _ = self.densepose_predictor(densepose_head_outputs) + densepose_loss_dict = self.densepose_losses(proposals_dp, densepose_outputs) + return densepose_loss_dict + else: + pred_boxes = [x.pred_boxes for x in instances] + features_dp = self.densepose_pooler(features, pred_boxes) + if len(features_dp) > 0: + densepose_head_outputs = self.densepose_head(features_dp) + densepose_outputs, _ = self.densepose_predictor(densepose_head_outputs) + else: + # If no detection occured instances + # set densepose_outputs to empty tensors + empty_tensor = torch.zeros(size=(0, 0, 0, 0), device=features_dp.device) + densepose_outputs = tuple([empty_tensor] * 4) + + densepose_inference(densepose_outputs, instances) + return instances + + def forward(self, images, features, proposals, targets=None): + features_list = [features[f] for f in self.in_features] + + instances, losses = super().forward(images, features, proposals, targets) + del targets, images + + if self.training: + losses.update(self._forward_densepose(features_list, instances)) + else: + instances = self._forward_densepose(features_list, instances) + return instances, losses diff --git a/projects/DensePose/densepose/structures.py b/projects/DensePose/densepose/structures.py new file mode 100644 index 0000000000..9d3465ba1f --- /dev/null +++ b/projects/DensePose/densepose/structures.py @@ -0,0 +1,519 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import base64 +import numpy as np +from io import BytesIO +import torch +from PIL import Image +from torch.nn import functional as F + + +class DensePoseTransformData(object): + + # Horizontal symmetry label transforms used for horizontal flip + MASK_LABEL_SYMMETRIES = [0, 1, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10, 13, 12, 14] + # fmt: off + POINT_LABEL_SYMMETRIES = [ 0, 1, 2, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15, 18, 17, 20, 19, 22, 21, 24, 23] # noqa + # fmt: on + + def __init__(self, uv_symmetries): + self.mask_label_symmetries = DensePoseTransformData.MASK_LABEL_SYMMETRIES + self.point_label_symmetries = DensePoseTransformData.POINT_LABEL_SYMMETRIES + self.uv_symmetries = uv_symmetries + + @staticmethod + def load(fpath): + import scipy.io + + uv_symmetry_map = scipy.io.loadmat(fpath) + uv_symmetry_map_torch = {} + for key in ["U_transforms", "V_transforms"]: + map_src = uv_symmetry_map[key] + uv_symmetry_map_torch[key] = [] + for i in range(uv_symmetry_map[key].shape[1]): + uv_symmetry_map_torch[key].append( + torch.from_numpy(map_src[0, i]).to(dtype=torch.float) + ) + transform_data = DensePoseTransformData(uv_symmetry_map_torch) + return transform_data + + +class DensePoseDataRelative(object): + """ + Dense pose relative annotations that can be applied to any bounding box: + x - normalized X coordinates [0, 255] of annotated points + y - normalized Y coordinates [0, 255] of annotated points + i - body part labels 0,...,24 for annotated points + u - body part U coordinates [0, 1] for annotated points + v - body part V coordinates [0, 1] for annotated points + segm - 256x256 segmentation mask with values 0,...,14 + To obtain absolute x and y data wrt some bounding box one needs to first + divide the data by 256, multiply by the respective bounding box size + and add bounding box offset: + x_img = x0 + x_norm * w / 256.0 + y_img = y0 + y_norm * h / 256.0 + Segmentation masks are typically sampled to get image-based masks. + """ + + # Key for normalized X coordinates in annotation dict + X_KEY = "dp_x" + # Key for normalized Y coordinates in annotation dict + Y_KEY = "dp_y" + # Key for U part coordinates in annotation dict + U_KEY = "dp_U" + # Key for V part coordinates in annotation dict + V_KEY = "dp_V" + # Key for I point labels in annotation dict + I_KEY = "dp_I" + # Key for segmentation mask in annotation dict + S_KEY = "dp_masks" + # Number of body parts in segmentation masks + N_BODY_PARTS = 14 + # Number of parts in point labels + N_PART_LABELS = 24 + MASK_SIZE = 256 + + def __init__(self, annotation, cleanup=False): + is_valid, reason_not_valid = DensePoseDataRelative.validate_annotation(annotation) + assert is_valid, "Invalid DensePose annotations: {}".format(reason_not_valid) + self.x = torch.as_tensor(annotation[DensePoseDataRelative.X_KEY]) + self.y = torch.as_tensor(annotation[DensePoseDataRelative.Y_KEY]) + self.i = torch.as_tensor(annotation[DensePoseDataRelative.I_KEY]) + self.u = torch.as_tensor(annotation[DensePoseDataRelative.U_KEY]) + self.v = torch.as_tensor(annotation[DensePoseDataRelative.V_KEY]) + self.segm = DensePoseDataRelative.extract_segmentation_mask(annotation) + self.device = torch.device("cpu") + if cleanup: + DensePoseDataRelative.cleanup_annotation(annotation) + + def to(self, device): + if self.device == device: + return self + new_data = DensePoseDataRelative.__new__(DensePoseDataRelative) + new_data.x = self.x + new_data.x = self.x.to(device) + new_data.y = self.y.to(device) + new_data.i = self.i.to(device) + new_data.u = self.u.to(device) + new_data.v = self.v.to(device) + new_data.segm = self.segm.to(device) + new_data.device = device + return new_data + + @staticmethod + def extract_segmentation_mask(annotation): + import pycocotools.mask as mask_utils + + poly_specs = annotation[DensePoseDataRelative.S_KEY] + segm = torch.zeros((DensePoseDataRelative.MASK_SIZE,) * 2, dtype=torch.float32) + for i in range(DensePoseDataRelative.N_BODY_PARTS): + poly_i = poly_specs[i] + if poly_i: + mask_i = mask_utils.decode(poly_i) + segm[mask_i > 0] = i + 1 + return segm + + @staticmethod + def validate_annotation(annotation): + for key in [ + DensePoseDataRelative.X_KEY, + DensePoseDataRelative.Y_KEY, + DensePoseDataRelative.I_KEY, + DensePoseDataRelative.U_KEY, + DensePoseDataRelative.V_KEY, + DensePoseDataRelative.S_KEY, + ]: + if key not in annotation: + return False, "no {key} data in the annotation".format(key=key) + return True, None + + @staticmethod + def cleanup_annotation(annotation): + for key in [ + DensePoseDataRelative.X_KEY, + DensePoseDataRelative.Y_KEY, + DensePoseDataRelative.I_KEY, + DensePoseDataRelative.U_KEY, + DensePoseDataRelative.V_KEY, + DensePoseDataRelative.S_KEY, + ]: + if key in annotation: + del annotation[key] + + def apply_transform(self, transforms, densepose_transform_data): + self._transform_pts(transforms, densepose_transform_data) + self._transform_segm(transforms, densepose_transform_data) + + def _transform_pts(self, transforms, dp_transform_data): + import detectron2.data.transforms as T + + # NOTE: This assumes that HorizFlipTransform is the only one that does flip + do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1 + if do_hflip: + self.x = self.segm.size(1) - self.x + self._flip_iuv_semantics(dp_transform_data) + + def _flip_iuv_semantics(self, dp_transform_data): + i_old = self.i.clone() + uv_symmetries = dp_transform_data.uv_symmetries + pt_label_symmetries = dp_transform_data.point_label_symmetries + for i in range(self.N_PART_LABELS): + if pt_label_symmetries[i + 1] != i + 1: + annot_indices_i = i_old == i + 1 + self.i[annot_indices_i] = pt_label_symmetries[i + 1] + u_loc = (self.u[annot_indices_i] * 255).long() + v_loc = (self.v[annot_indices_i] * 255).long() + self.u[annot_indices_i] = uv_symmetries["U_transforms"][i][v_loc, u_loc] + self.v[annot_indices_i] = uv_symmetries["V_transforms"][i][v_loc, u_loc] + + def _transform_segm(self, transforms, dp_transform_data): + import detectron2.data.transforms as T + + # NOTE: This assumes that HorizFlipTransform is the only one that does flip + do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1 + if do_hflip: + self.segm = torch.flip(self.segm, [1]) + self._flip_segm_semantics(dp_transform_data) + + def _flip_segm_semantics(self, dp_transform_data): + old_segm = self.segm.clone() + mask_label_symmetries = dp_transform_data.mask_label_symmetries + for i in range(self.N_BODY_PARTS): + if mask_label_symmetries[i + 1] != i + 1: + self.segm[old_segm == i + 1] = mask_label_symmetries[i + 1] + + +def normalized_coords_transform(x0, y0, w, h): + """ + Coordinates transform that maps top left corner to (-1, -1) and bottom + right corner to (1, 1). Used for torch.grid_sample to initialize the + grid + """ + + def f(p): + return (2 * (p[0] - x0) / w - 1, 2 * (p[1] - y0) / h - 1) + + return f + + +class DensePoseOutput(object): + def __init__(self, S, I, U, V): + self.S = S + self.I = I # noqa: E741 + self.U = U + self.V = V + self._check_output_dims(S, I, U, V) + + def _check_output_dims(self, S, I, U, V): + assert ( + len(S.size()) == 4 + ), "Segmentation output should have 4 " "dimensions (NCHW), but has size {}".format( + S.size() + ) + assert ( + len(I.size()) == 4 + ), "Segmentation output should have 4 " "dimensions (NCHW), but has size {}".format( + S.size() + ) + assert ( + len(U.size()) == 4 + ), "Segmentation output should have 4 " "dimensions (NCHW), but has size {}".format( + S.size() + ) + assert ( + len(V.size()) == 4 + ), "Segmentation output should have 4 " "dimensions (NCHW), but has size {}".format( + S.size() + ) + assert len(S) == len(I), ( + "Number of output segmentation planes {} " + "should be equal to the number of output part index " + "planes {}".format(len(S), len(I)) + ) + assert S.size()[2:] == I.size()[2:], ( + "Output segmentation plane size {} " + "should be equal to the output part index " + "plane size {}".format(S.size()[2:], I.size()[2:]) + ) + assert I.size() == U.size(), ( + "Part index output shape {} " + "should be the same as U coordinates output shape {}".format(I.size(), U.size()) + ) + assert I.size() == V.size(), ( + "Part index output shape {} " + "should be the same as V coordinates output shape {}".format(I.size(), V.size()) + ) + + def resize(self, image_size_hw): + # do nothing - outputs are invariant to resize + pass + + def _crop(self, S, I, U, V, bbox_old_xywh, bbox_new_xywh): + """ + Resample S, I, U, V from bbox_old to the cropped bbox_new + """ + x0old, y0old, wold, hold = bbox_old_xywh + x0new, y0new, wnew, hnew = bbox_new_xywh + tr_coords = normalized_coords_transform(x0old, y0old, wold, hold) + topleft = (x0new, y0new) + bottomright = (x0new + wnew, y0new + hnew) + topleft_norm = tr_coords(topleft) + bottomright_norm = tr_coords(bottomright) + hsize = S.size(1) + wsize = S.size(2) + grid = torch.meshgrid( + torch.arange( + topleft_norm[1], + bottomright_norm[1], + (bottomright_norm[1] - topleft_norm[1]) / hsize, + )[:hsize], + torch.arange( + topleft_norm[0], + bottomright_norm[0], + (bottomright_norm[0] - topleft_norm[0]) / wsize, + )[:wsize], + ) + grid = torch.stack(grid, dim=2).to(S.device) + assert ( + grid.size(0) == hsize + ), "Resampled grid expected " "height={}, actual height={}".format(hsize, grid.size(0)) + assert grid.size(1) == wsize, "Resampled grid expected " "width={}, actual width={}".format( + wsize, grid.size(1) + ) + S_new = F.grid_sample( + S.unsqueeze(0), + torch.unsqueeze(grid, 0), + mode="bilinear", + padding_mode="border", + align_corners=True, + ).squeeze(0) + I_new = F.grid_sample( + I.unsqueeze(0), + torch.unsqueeze(grid, 0), + mode="bilinear", + padding_mode="border", + align_corners=True, + ).squeeze(0) + U_new = F.grid_sample( + U.unsqueeze(0), + torch.unsqueeze(grid, 0), + mode="bilinear", + padding_mode="border", + align_corners=True, + ).squeeze(0) + V_new = F.grid_sample( + V.unsqueeze(0), + torch.unsqueeze(grid, 0), + mode="bilinear", + padding_mode="border", + align_corners=True, + ).squeeze(0) + return S_new, I_new, U_new, V_new + + def crop(self, indices_cropped, bboxes_old, bboxes_new): + """ + Crop outputs for selected bounding boxes to the new bounding boxes. + """ + # VK: cropping is ignored for now + # for i, ic in enumerate(indices_cropped): + # self.S[ic], self.I[ic], self.U[ic], self.V[ic] = \ + # self._crop(self.S[ic], self.I[ic], self.U[ic], self.V[ic], + # bboxes_old[i], bboxes_new[i]) + pass + + def to_result(self, boxes_xywh): + """ + Convert DensePose outputs to results format. Results are more compact, + but cannot be resampled any more + """ + result = DensePoseResult(boxes_xywh, self.S, self.I, self.U, self.V) + return result + + def __getitem__(self, item): + if isinstance(item, int): + S_selected = self.S[item].unsqueeze(0) + I_selected = self.I[item].unsqueeze(0) + U_selected = self.U[item].unsqueeze(0) + V_selected = self.V[item].unsqueeze(0) + else: + S_selected = self.S[item] + I_selected = self.I[item] + U_selected = self.U[item] + V_selected = self.V[item] + return DensePoseOutput(S_selected, I_selected, U_selected, V_selected) + + def __str__(self): + s = "DensePoseOutput S {}, I {}, U {}, V {}".format( + list(self.S.size()), list(self.I.size()), list(self.U.size()), list(self.V.size()) + ) + return s + + def __len__(self): + return self.S.size(0) + + +class DensePoseResult(object): + def __init__(self, boxes_xywh, S, I, U, V): + self.results = [] + self.boxes_xywh = boxes_xywh.cpu().tolist() + assert len(boxes_xywh.size()) == 2 + assert boxes_xywh.size(1) == 4 + for i, box_xywh in enumerate(boxes_xywh): + result_i = self._output_to_result(box_xywh, S[[i]], I[[i]], U[[i]], V[[i]]) + result_numpy_i = result_i.cpu().numpy() + result_encoded_i = DensePoseResult.encode_png_data(result_numpy_i) + result_encoded_with_shape_i = (result_numpy_i.shape, result_encoded_i) + self.results.append(result_encoded_with_shape_i) + + def __str__(self): + s = "DensePoseResult: N={} [{}]".format( + len(self.results), ", ".join([str(list(r[0])) for r in self.results]) + ) + return s + + def _output_to_result(self, box_xywh, S, I, U, V): + x, y, w, h = box_xywh + w = max(int(w), 1) + h = max(int(h), 1) + result = torch.zeros([3, h, w], dtype=torch.uint8, device=U.device) + assert ( + len(S.size()) == 4 + ), "AnnIndex tensor size should have {} " "dimensions but has {}".format(4, len(S.size())) + s_bbox = F.interpolate(S, (h, w), mode="bilinear", align_corners=False).argmax(dim=1) + assert ( + len(I.size()) == 4 + ), "IndexUV tensor size should have {} " "dimensions but has {}".format(4, len(S.size())) + i_bbox = ( + F.interpolate(I, (h, w), mode="bilinear", align_corners=False).argmax(dim=1) + * (s_bbox > 0).long() + ).squeeze(0) + assert len(U.size()) == 4, "U tensor size should have {} " "dimensions but has {}".format( + 4, len(U.size()) + ) + u_bbox = F.interpolate(U, (h, w), mode="bilinear", align_corners=False) + assert len(V.size()) == 4, "V tensor size should have {} " "dimensions but has {}".format( + 4, len(V.size()) + ) + v_bbox = F.interpolate(V, (h, w), mode="bilinear", align_corners=False) + result[0] = i_bbox + for part_id in range(1, u_bbox.size(1)): + result[1][i_bbox == part_id] = ( + (u_bbox[0, part_id][i_bbox == part_id] * 255).clamp(0, 255).to(torch.uint8) + ) + result[2][i_bbox == part_id] = ( + (v_bbox[0, part_id][i_bbox == part_id] * 255).clamp(0, 255).to(torch.uint8) + ) + assert ( + result.size(1) == h + ), "Results height {} should be equal" "to bounding box height {}".format(result.size(1), h) + assert ( + result.size(2) == w + ), "Results width {} should be equal" "to bounding box width {}".format(result.size(2), w) + return result + + @staticmethod + def encode_png_data(arr): + """ + Encode array data as a PNG image using the highest compression rate + @param arr [in] Data stored in an array of size (3, M, N) of type uint8 + @return Base64-encoded string containing PNG-compressed data + """ + assert len(arr.shape) == 3, "Expected a 3D array as an input," " got a {0}D array".format( + len(arr.shape) + ) + assert arr.shape[0] == 3, "Expected first array dimension of size 3," " got {0}".format( + arr.shape[0] + ) + assert arr.dtype == np.uint8, "Expected an array of type np.uint8, " " got {0}".format( + arr.dtype + ) + data = np.moveaxis(arr, 0, -1) + im = Image.fromarray(data) + fstream = BytesIO() + im.save(fstream, format="png", optimize=True) + s = base64.encodebytes(fstream.getvalue()).decode() + return s + + @staticmethod + def decode_png_data(shape, s): + """ + Decode array data from a string that contains PNG-compressed data + @param Base64-encoded string containing PNG-compressed data + @return Data stored in an array of size (3, M, N) of type uint8 + """ + fstream = BytesIO(base64.decodebytes(s.encode())) + im = Image.open(fstream) + data = np.moveaxis(np.array(im.getdata(), dtype=np.uint8), -1, 0) + return data.reshape(shape) + + def __len__(self): + return len(self.results) + + def __getitem__(self, item): + result_encoded = self.results[item] + bbox_xywh = self.boxes_xywh[item] + return result_encoded, bbox_xywh + + +class DensePoseList(object): + + _TORCH_DEVICE_CPU = torch.device("cpu") + + def __init__(self, densepose_datas, boxes_xyxy_abs, image_size_hw, device=_TORCH_DEVICE_CPU): + assert len(densepose_datas) == len(boxes_xyxy_abs), ( + "Attempt to initialize DensePoseList with {} DensePose datas " + "and {} boxes".format(len(densepose_datas), len(boxes_xyxy_abs)) + ) + self.densepose_datas = [] + for densepose_data in densepose_datas: + assert isinstance(densepose_data, DensePoseDataRelative) or densepose_data is None, ( + "Attempt to initialize DensePoseList with DensePose datas " + "of type {}, expected DensePoseDataRelative".format(type(densepose_data)) + ) + densepose_data_ondevice = ( + densepose_data.to(device) if densepose_data is not None else None + ) + self.densepose_datas.append(densepose_data_ondevice) + self.boxes_xyxy_abs = boxes_xyxy_abs.to(device) + self.image_size_hw = image_size_hw + self.device = device + + def to(self, device): + if self.device == device: + return self + return DensePoseList(self.densepose_datas, self.boxes_xyxy_abs, self.image_size_hw, device) + + def __iter__(self): + return iter(self.densepose_datas) + + def __len__(self): + return len(self.densepose_datas) + + def __repr__(self): + s = self.__class__.__name__ + "(" + s += "num_instances={}, ".format(len(self.densepose_datas)) + s += "image_width={}, ".format(self.image_size_hw[1]) + s += "image_height={})".format(self.image_size_hw[0]) + return s + + def __getitem__(self, item): + if isinstance(item, int): + densepose_data_rel = self.densepose_datas[item] + return densepose_data_rel + elif isinstance(item, slice): + densepose_datas_rel = self.densepose_datas[item] + boxes_xyxy_abs = self.boxes_xyxy_abs[item] + return DensePoseList( + densepose_datas_rel, boxes_xyxy_abs, self.image_size_hw, self.device + ) + elif isinstance(item, torch.Tensor) and (item.dtype == torch.bool): + densepose_datas_rel = [self.densepose_datas[i] for i, x in enumerate(item) if x > 0] + boxes_xyxy_abs = self.boxes_xyxy_abs[item] + return DensePoseList( + densepose_datas_rel, boxes_xyxy_abs, self.image_size_hw, self.device + ) + else: + densepose_datas_rel = [self.densepose_datas[i] for i in item] + boxes_xyxy_abs = self.boxes_xyxy_abs[item] + return DensePoseList( + densepose_datas_rel, boxes_xyxy_abs, self.image_size_hw, self.device + ) diff --git a/projects/DensePose/densepose/utils/dbhelper.py b/projects/DensePose/densepose/utils/dbhelper.py new file mode 100644 index 0000000000..ef7e316cf3 --- /dev/null +++ b/projects/DensePose/densepose/utils/dbhelper.py @@ -0,0 +1,145 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from typing import Any, Dict, List, Optional, Tuple + + +class EntrySelector(object): + """ + Base class for entry selectors + """ + + @staticmethod + def from_string(spec: str) -> "EntrySelector": + if spec == "*": + return AllEntrySelector() + return FieldEntrySelector(spec) + + +class AllEntrySelector(EntrySelector): + """ + Selector that accepts all entries + """ + + SPECIFIER = "*" + + def __call__(self, entry): + return True + + +class FieldEntrySelector(EntrySelector): + """ + Selector that accepts only entries that match provided field + specifier(s). Only a limited set of specifiers is supported for now: + ::=[] + ::=[] + is a valid identifier + ::= "int" | "str" + ::= "=" + ::= "," + ::= ":" + ::= | + ::= + ::= "-" + is a string without spaces and special symbols + (e.g. , , , ) + """ + + _SPEC_DELIM = "," + _TYPE_DELIM = ":" + _RANGE_DELIM = "-" + _EQUAL = "=" + _ERROR_PREFIX = "Invalid field selector specifier" + + class _FieldEntryValuePredicate(object): + """ + Predicate that checks strict equality for the specified entry field + """ + + def __init__(self, name: str, typespec: str, value: str): + import builtins + + self.name = name + self.type = getattr(builtins, typespec) if typespec is not None else str + self.value = value + + def __call__(self, entry): + return entry[self.name] == self.type(self.value) + + class _FieldEntryRangePredicate(object): + """ + Predicate that checks whether an entry field falls into the specified range + """ + + def __init__(self, name: str, typespec: str, vmin: str, vmax: str): + import builtins + + self.name = name + self.type = getattr(builtins, typespec) if typespec is not None else str + self.vmin = vmin + self.vmax = vmax + + def __call__(self, entry): + return (entry[self.name] >= self.type(self.vmin)) and ( + entry[self.name] <= self.type(self.vmax) + ) + + def __init__(self, spec: str): + self._predicates = self._parse_specifier_into_predicates(spec) + + def __call__(self, entry: Dict[str, Any]): + for predicate in self._predicates: + if not predicate(entry): + return False + return True + + def _parse_specifier_into_predicates(self, spec: str) -> List["_FieldEntryPredicate"]: + predicates = [] + specs = spec.split(self._SPEC_DELIM) + for subspec in specs: + eq_idx = subspec.find(self._EQUAL) + if eq_idx > 0: + field_name_with_type = subspec[:eq_idx] + field_name, field_type = self._parse_field_name_type(field_name_with_type) + field_value_or_range = subspec[eq_idx + 1 :] + if self._is_range_spec(field_value_or_range): + vmin, vmax = self._get_range_spec(field_value_or_range) + predicate = FieldEntrySelector._FieldEntryRangePredicate( + field_name, field_type, vmin, vmax + ) + else: + predicate = FieldEntrySelector._FieldEntryValuePredicate( + field_name, field_type, field_value_or_range + ) + predicates.append(predicate) + elif eq_idx == 0: + self._parse_error(f'"{subspec}", field name is empty!') + else: + self._parse_error(f'"{subspec}", should have format ' "=!") + return predicates + + def _parse_field_name_type(self, field_name_with_type: str) -> Tuple[str, Optional[str]]: + type_delim_idx = field_name_with_type.find(self._TYPE_DELIM) + if type_delim_idx > 0: + field_name = field_name_with_type[:type_delim_idx] + field_type = field_name_with_type[type_delim_idx + 1 :] + elif type_delim_idx == 0: + self._parse_error(f'"{field_name_with_type}", field name is empty!') + else: + field_name = field_name_with_type + field_type = None + return field_name, field_type + + def _is_range_spec(self, field_value_or_range): + delim_idx = field_value_or_range.find(self._RANGE_DELIM) + return delim_idx > 0 + + def _get_range_spec(self, field_value_or_range): + if self._is_range_spec(field_value_or_range): + delim_idx = field_value_or_range.find(self._RANGE_DELIM) + vmin = field_value_or_range[:delim_idx] + vmax = field_value_or_range[delim_idx + 1 :] + return vmin, vmax + else: + self._parse_error('"field_value_or_range", range of values expected!') + + def _parse_error(self, msg): + raise ValueError(f"{self._ERROR_PREFIX}: {msg}") diff --git a/projects/DensePose/densepose/utils/logger.py b/projects/DensePose/densepose/utils/logger.py new file mode 100644 index 0000000000..e3fa45e0c0 --- /dev/null +++ b/projects/DensePose/densepose/utils/logger.py @@ -0,0 +1,13 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging + + +def verbosity_to_level(verbosity): + if verbosity is not None: + if verbosity == 0: + return logging.WARNING + elif verbosity == 1: + return logging.INFO + elif verbosity >= 2: + return logging.DEBUG + return logging.WARNING diff --git a/projects/DensePose/densepose/vis/base.py b/projects/DensePose/densepose/vis/base.py new file mode 100644 index 0000000000..b3f1ae5aff --- /dev/null +++ b/projects/DensePose/densepose/vis/base.py @@ -0,0 +1,190 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import numpy as np +import cv2 +import torch + +Image = np.ndarray +Boxes = torch.Tensor + + +class MatrixVisualizer(object): + """ + Base visualizer for matrix data + """ + + def __init__( + self, + inplace=True, + cmap=cv2.COLORMAP_PARULA, + val_scale=1.0, + alpha=0.7, + interp_method_matrix=cv2.INTER_LINEAR, + interp_method_mask=cv2.INTER_NEAREST, + ): + self.inplace = inplace + self.cmap = cmap + self.val_scale = val_scale + self.alpha = alpha + self.interp_method_matrix = interp_method_matrix + self.interp_method_mask = interp_method_mask + + def visualize(self, image_bgr, mask, matrix, bbox_xywh): + self._check_image(image_bgr) + self._check_mask_matrix(mask, matrix) + if self.inplace: + image_target_bgr = image_bgr + else: + image_target_bgr = image_bgr * 0 + x, y, w, h = [int(v) for v in bbox_xywh] + if w <= 0 or h <= 0: + return image_bgr + mask, matrix = self._resize(mask, matrix, w, h) + mask_bg = np.tile((mask == 0)[:, :, np.newaxis], [1, 1, 3]) + matrix_scaled = matrix.astype(np.float32) * self.val_scale + _EPSILON = 1e-6 + if np.any(matrix_scaled > 255 + _EPSILON): + logger = logging.getLogger(__name__) + logger.warning( + f"Matrix has values > {255 + _EPSILON} after " f"scaling, clipping to [0..255]" + ) + matrix_scaled_8u = matrix_scaled.clip(0, 255).astype(np.uint8) + matrix_vis = cv2.applyColorMap(matrix_scaled_8u, self.cmap) + matrix_vis[mask_bg] = image_target_bgr[y : y + h, x : x + w, :][mask_bg] + image_target_bgr[y : y + h, x : x + w, :] = ( + image_target_bgr[y : y + h, x : x + w, :] * (1.0 - self.alpha) + matrix_vis * self.alpha + ) + return image_target_bgr.astype(np.uint8) + + def _resize(self, mask, matrix, w, h): + if (w != mask.shape[1]) or (h != mask.shape[0]): + mask = cv2.resize(mask, (w, h), self.interp_method_mask) + if (w != matrix.shape[1]) or (h != matrix.shape[0]): + matrix = cv2.resize(matrix, (w, h), self.interp_method_matrix) + return mask, matrix + + def _check_image(self, image_rgb): + assert len(image_rgb.shape) == 3 + assert image_rgb.shape[2] == 3 + assert image_rgb.dtype == np.uint8 + + def _check_mask_matrix(self, mask, matrix): + assert len(matrix.shape) == 2 + assert len(mask.shape) == 2 + assert mask.dtype == np.uint8 + + +class RectangleVisualizer(object): + + _COLOR_GREEN = (18, 127, 15) + + def __init__(self, color=_COLOR_GREEN, thickness=1): + self.color = color + self.thickness = thickness + + def visualize(self, image_bgr, bbox_xywh, color=None, thickness=None): + x, y, w, h = bbox_xywh + color = color or self.color + thickness = thickness or self.thickness + cv2.rectangle(image_bgr, (int(x), int(y)), (int(x + w), int(y + h)), color, thickness) + return image_bgr + + +class PointsVisualizer(object): + + _COLOR_GREEN = (18, 127, 15) + + def __init__(self, color_bgr=_COLOR_GREEN, r=5): + self.color_bgr = color_bgr + self.r = r + + def visualize(self, image_bgr, pts_xy, colors_bgr=None, rs=None): + for j, pt_xy in enumerate(pts_xy): + x, y = pt_xy + color_bgr = colors_bgr[j] if colors_bgr is not None else self.color_bgr + r = rs[j] if rs is not None else self.r + cv2.circle(image_bgr, (x, y), r, color_bgr, -1) + return image_bgr + + +class TextVisualizer(object): + + _COLOR_GRAY = (218, 227, 218) + _COLOR_WHITE = (255, 255, 255) + + def __init__( + self, + font_face=cv2.FONT_HERSHEY_SIMPLEX, + font_color_bgr=_COLOR_GRAY, + font_scale=0.35, + font_line_type=cv2.LINE_AA, + font_line_thickness=1, + fill_color_bgr=_COLOR_WHITE, + fill_color_transparency=1.0, + frame_color_bgr=_COLOR_WHITE, + frame_color_transparency=1.0, + frame_thickness=1, + ): + self.font_face = font_face + self.font_color_bgr = font_color_bgr + self.font_scale = font_scale + self.font_line_type = font_line_type + self.font_line_thickness = font_line_thickness + self.fill_color_bgr = fill_color_bgr + self.fill_color_transparency = fill_color_transparency + self.frame_color_bgr = frame_color_bgr + self.frame_color_transparency = frame_color_transparency + self.frame_thickness = frame_thickness + + def visualize(self, image_bgr, txt, topleft_xy): + txt_w, txt_h = self.get_text_size_wh(txt) + topleft_xy = tuple(map(int, topleft_xy)) + x, y = topleft_xy + if self.frame_color_transparency < 1.0: + t = self.frame_thickness + image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :] = ( + image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :] + * self.frame_color_transparency + + np.array(self.frame_color_bgr) * (1.0 - self.frame_color_transparency) + ).astype(np.float) + if self.fill_color_transparency < 1.0: + image_bgr[y : y + txt_h, x : x + txt_w, :] = ( + image_bgr[y : y + txt_h, x : x + txt_w, :] * self.fill_color_transparency + + np.array(self.fill_color_bgr) * (1.0 - self.fill_color_transparency) + ).astype(np.float) + cv2.putText( + image_bgr, + txt, + topleft_xy, + self.font_face, + self.font_scale, + self.font_color_bgr, + self.font_line_thickness, + self.font_line_type, + ) + return image_bgr + + def get_text_size_wh(self, txt): + ((txt_w, txt_h), _) = cv2.getTextSize( + txt, self.font_face, self.font_scale, self.font_line_thickness + ) + return txt_w, txt_h + + +class CompoundVisualizer(object): + def __init__(self, visualizers): + self.visualizers = visualizers + + def visualize(self, image_bgr, data): + assert len(data) == len(self.visualizers), ( + "The number of datas {} should match the number of visualizers" + " {}".format(len(data), len(self.visualizers)) + ) + image = image_bgr + for i, visualizer in enumerate(self.visualizers): + image = visualizer.visualize(image, data[i]) + return image + + def __str__(self): + visualizer_str = ", ".join([str(v) for v in self.visualizers]) + return "Compound Visualizer [{}]".format(visualizer_str) diff --git a/projects/DensePose/densepose/vis/bounding_box.py b/projects/DensePose/densepose/vis/bounding_box.py new file mode 100644 index 0000000000..7fc6efd119 --- /dev/null +++ b/projects/DensePose/densepose/vis/bounding_box.py @@ -0,0 +1,36 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .base import RectangleVisualizer, TextVisualizer + + +class BoundingBoxVisualizer(object): + def __init__(self): + self.rectangle_visualizer = RectangleVisualizer() + + def visualize(self, image_bgr, boxes_xywh): + for bbox_xywh in boxes_xywh: + image_bgr = self.rectangle_visualizer.visualize(image_bgr, bbox_xywh) + return image_bgr + + +class ScoredBoundingBoxVisualizer(object): + def __init__(self, bbox_visualizer_params=None, score_visualizer_params=None): + if bbox_visualizer_params is None: + bbox_visualizer_params = {} + if score_visualizer_params is None: + score_visualizer_params = {} + self.visualizer_bbox = RectangleVisualizer(**bbox_visualizer_params) + self.visualizer_score = TextVisualizer(**score_visualizer_params) + + def visualize(self, image_bgr, scored_bboxes): + boxes_xywh, box_scores = scored_bboxes + assert len(boxes_xywh) == len(box_scores), ( + "Number of bounding boxes {} should be equal to the number of " + "scores".format(len(boxes_xywh), len(box_scores)) + ) + for i, box_xywh in enumerate(boxes_xywh): + score_i = box_scores[i] + image_bgr = self.visualizer_bbox.visualize(image_bgr, box_xywh) + score_txt = "{0:6.4f}".format(score_i) + topleft_xy = box_xywh[0], box_xywh[1] + image_bgr = self.visualizer_score.visualize(image_bgr, score_txt, topleft_xy) + return image_bgr diff --git a/projects/DensePose/densepose/vis/densepose.py b/projects/DensePose/densepose/vis/densepose.py new file mode 100644 index 0000000000..ba561cac55 --- /dev/null +++ b/projects/DensePose/densepose/vis/densepose.py @@ -0,0 +1,581 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import numpy as np +from typing import Iterable, Optional, Tuple +import cv2 + +from ..structures import DensePoseDataRelative, DensePoseOutput, DensePoseResult +from .base import Boxes, Image, MatrixVisualizer, PointsVisualizer + + +class DensePoseResultsVisualizer(object): + def visualize(self, image_bgr: Image, densepose_result: Optional[DensePoseResult]) -> Image: + if densepose_result is None: + return image_bgr + context = self.create_visualization_context(image_bgr) + for i, result_encoded_w_shape in enumerate(densepose_result.results): + iuv_arr = DensePoseResult.decode_png_data(*result_encoded_w_shape) + bbox_xywh = densepose_result.boxes_xywh[i] + self.visualize_iuv_arr(context, iuv_arr, bbox_xywh) + image_bgr = self.context_to_image_bgr(context) + return image_bgr + + +class DensePoseMaskedColormapResultsVisualizer(DensePoseResultsVisualizer): + def __init__( + self, + data_extractor, + segm_extractor, + inplace=True, + cmap=cv2.COLORMAP_PARULA, + alpha=0.7, + val_scale=1.0, + ): + self.mask_visualizer = MatrixVisualizer( + inplace=inplace, cmap=cmap, val_scale=val_scale, alpha=alpha + ) + self.data_extractor = data_extractor + self.segm_extractor = segm_extractor + + def create_visualization_context(self, image_bgr: Image): + return image_bgr + + def context_to_image_bgr(self, context): + return context + + def get_image_bgr_from_context(self, context): + return context + + def visualize_iuv_arr(self, context, iuv_arr, bbox_xywh): + image_bgr = self.get_image_bgr_from_context(context) + matrix = self.data_extractor(iuv_arr) + segm = self.segm_extractor(iuv_arr) + mask = np.zeros(matrix.shape, dtype=np.uint8) + mask[segm > 0] = 1 + image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh) + return image_bgr + + +def _extract_i_from_iuvarr(iuv_arr): + return iuv_arr[0, :, :] + + +def _extract_u_from_iuvarr(iuv_arr): + return iuv_arr[1, :, :] + + +def _extract_v_from_iuvarr(iuv_arr): + return iuv_arr[2, :, :] + + +class DensePoseResultsMplContourVisualizer(DensePoseResultsVisualizer): + def __init__(self, levels=10, **kwargs): + self.levels = levels + self.plot_args = kwargs + + def create_visualization_context(self, image_bgr: Image): + import matplotlib.pyplot as plt + from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas + + context = {} + context["image_bgr"] = image_bgr + dpi = 100 + height_inches = float(image_bgr.shape[0]) / dpi + width_inches = float(image_bgr.shape[1]) / dpi + fig = plt.figure(figsize=(width_inches, height_inches), dpi=dpi) + plt.axes([0, 0, 1, 1]) + plt.axis("off") + context["fig"] = fig + canvas = FigureCanvas(fig) + context["canvas"] = canvas + extent = (0, image_bgr.shape[1], image_bgr.shape[0], 0) + plt.imshow(image_bgr[:, :, ::-1], extent=extent) + return context + + def context_to_image_bgr(self, context): + fig = context["fig"] + w, h = map(int, fig.get_size_inches() * fig.get_dpi()) + canvas = context["canvas"] + canvas.draw() + image_1d = np.fromstring(canvas.tostring_rgb(), dtype="uint8") + image_rgb = image_1d.reshape(h, w, 3) + image_bgr = image_rgb[:, :, ::-1].copy() + return image_bgr + + def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh: Boxes) -> Image: + import matplotlib.pyplot as plt + + u = _extract_u_from_iuvarr(iuv_arr).astype(float) / 255.0 + v = _extract_v_from_iuvarr(iuv_arr).astype(float) / 255.0 + extent = ( + bbox_xywh[0], + bbox_xywh[0] + bbox_xywh[2], + bbox_xywh[1], + bbox_xywh[1] + bbox_xywh[3], + ) + plt.contour(u, self.levels, extent=extent, **self.plot_args) + plt.contour(v, self.levels, extent=extent, **self.plot_args) + + +class DensePoseResultsCustomContourVisualizer(DensePoseResultsVisualizer): + """ + Contour visualization using marching squares + """ + + def __init__(self, levels=10, **kwargs): + # TODO: colormap is hardcoded + cmap = cv2.COLORMAP_PARULA + if isinstance(levels, int): + self.levels = np.linspace(0, 1, levels) + else: + self.levels = levels + if "linewidths" in kwargs: + self.linewidths = kwargs["linewidths"] + else: + self.linewidths = [1] * len(self.levels) + self.plot_args = kwargs + img_colors_bgr = cv2.applyColorMap((self.levels * 255).astype(np.uint8), cmap) + self.level_colors_bgr = [ + [int(v) for v in img_color_bgr.ravel()] for img_color_bgr in img_colors_bgr + ] + + def create_visualization_context(self, image_bgr: Image): + return image_bgr + + def context_to_image_bgr(self, context): + return context + + def get_image_bgr_from_context(self, context): + return context + + def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh: Boxes) -> Image: + image_bgr = self.get_image_bgr_from_context(context) + segm = _extract_i_from_iuvarr(iuv_arr) + u = _extract_u_from_iuvarr(iuv_arr).astype(float) / 255.0 + v = _extract_v_from_iuvarr(iuv_arr).astype(float) / 255.0 + self._contours(image_bgr, u, segm, bbox_xywh) + self._contours(image_bgr, v, segm, bbox_xywh) + + def _contours(self, image_bgr, arr, segm, bbox_xywh): + for part_idx in range(1, DensePoseDataRelative.N_PART_LABELS + 1): + mask = segm == part_idx + if not np.any(mask): + continue + arr_min = np.amin(arr[mask]) + arr_max = np.amax(arr[mask]) + I, J = np.nonzero(mask) + i0 = np.amin(I) + i1 = np.amax(I) + 1 + j0 = np.amin(J) + j1 = np.amax(J) + 1 + if (j1 == j0 + 1) or (i1 == i0 + 1): + continue + Nw = arr.shape[1] - 1 + Nh = arr.shape[0] - 1 + for level_idx, level in enumerate(self.levels): + if (level < arr_min) or (level > arr_max): + continue + vp = arr[i0:i1, j0:j1] >= level + bin_codes = vp[:-1, :-1] + vp[1:, :-1] * 2 + vp[1:, 1:] * 4 + vp[:-1, 1:] * 8 + mp = mask[i0:i1, j0:j1] + bin_mask_codes = mp[:-1, :-1] + mp[1:, :-1] * 2 + mp[1:, 1:] * 4 + mp[:-1, 1:] * 8 + it = np.nditer(bin_codes, flags=["multi_index"]) + color_bgr = self.level_colors_bgr[level_idx] + linewidth = self.linewidths[level_idx] + while not it.finished: + if (it[0] != 0) and (it[0] != 15): + i, j = it.multi_index + if bin_mask_codes[i, j] != 0: + self._draw_line( + image_bgr, + arr, + mask, + level, + color_bgr, + linewidth, + it[0], + it.multi_index, + bbox_xywh, + Nw, + Nh, + (i0, j0), + ) + it.iternext() + + def _draw_line( + self, + image_bgr, + arr, + mask, + v, + color_bgr, + linewidth, + bin_code, + multi_idx, + bbox_xywh, + Nw, + Nh, + offset, + ): + lines = self._bin_code_2_lines(arr, v, bin_code, multi_idx, Nw, Nh, offset) + x0, y0, w, h = bbox_xywh + x1 = x0 + w + y1 = y0 + h + for line in lines: + x0r, y0r = line[0] + x1r, y1r = line[1] + pt0 = (int(x0 + x0r * (x1 - x0)), int(y0 + y0r * (y1 - y0))) + pt1 = (int(x0 + x1r * (x1 - x0)), int(y0 + y1r * (y1 - y0))) + cv2.line(image_bgr, pt0, pt1, color_bgr, linewidth) + + def _bin_code_2_lines(self, arr, v, bin_code, multi_idx, Nw, Nh, offset): + i0, j0 = offset + i, j = multi_idx + i += i0 + j += j0 + v0, v1, v2, v3 = arr[i, j], arr[i + 1, j], arr[i + 1, j + 1], arr[i, j + 1] + x0i = float(j) / Nw + y0j = float(i) / Nh + He = 1.0 / Nh + We = 1.0 / Nw + if (bin_code == 1) or (bin_code == 14): + a = (v - v0) / (v1 - v0) + b = (v - v0) / (v3 - v0) + pt1 = (x0i, y0j + a * He) + pt2 = (x0i + b * We, y0j) + return [(pt1, pt2)] + elif (bin_code == 2) or (bin_code == 13): + a = (v - v0) / (v1 - v0) + b = (v - v1) / (v2 - v1) + pt1 = (x0i, y0j + a * He) + pt2 = (x0i + b * We, y0j + He) + return [(pt1, pt2)] + elif (bin_code == 3) or (bin_code == 12): + a = (v - v0) / (v3 - v0) + b = (v - v1) / (v2 - v1) + pt1 = (x0i + a * We, y0j) + pt2 = (x0i + b * We, y0j + He) + return [(pt1, pt2)] + elif (bin_code == 4) or (bin_code == 11): + a = (v - v1) / (v2 - v1) + b = (v - v3) / (v2 - v3) + pt1 = (x0i + a * We, y0j + He) + pt2 = (x0i + We, y0j + b * He) + return [(pt1, pt2)] + elif (bin_code == 6) or (bin_code == 9): + a = (v - v0) / (v1 - v0) + b = (v - v3) / (v2 - v3) + pt1 = (x0i, y0j + a * He) + pt2 = (x0i + We, y0j + b * He) + return [(pt1, pt2)] + elif (bin_code == 7) or (bin_code == 8): + a = (v - v0) / (v3 - v0) + b = (v - v3) / (v2 - v3) + pt1 = (x0i + a * We, y0j) + pt2 = (x0i + We, y0j + b * He) + return [(pt1, pt2)] + elif bin_code == 5: + a1 = (v - v0) / (v1 - v0) + b1 = (v - v1) / (v2 - v1) + pt11 = (x0i, y0j + a1 * He) + pt12 = (x0i + b1 * We, y0j + He) + a2 = (v - v0) / (v3 - v0) + b2 = (v - v3) / (v2 - v3) + pt21 = (x0i + a2 * We, y0j) + pt22 = (x0i + We, y0j + b2 * He) + return [(pt11, pt12), (pt21, pt22)] + elif bin_code == 10: + a1 = (v - v0) / (v3 - v0) + b1 = (v - v0) / (v1 - v0) + pt11 = (x0i + a1 * We, y0j) + pt12 = (x0i, y0j + b1 * He) + a2 = (v - v1) / (v2 - v1) + b2 = (v - v3) / (v2 - v3) + pt21 = (x0i + a2 * We, y0j + He) + pt22 = (x0i + We, y0j + b2 * He) + return [(pt11, pt12), (pt21, pt22)] + return [] + + +try: + import matplotlib + + matplotlib.use("Agg") + DensePoseResultsContourVisualizer = DensePoseResultsMplContourVisualizer +except ModuleNotFoundError: + logger = logging.getLogger(__name__) + logger.warning("Could not import matplotlib, using custom contour visualizer") + DensePoseResultsContourVisualizer = DensePoseResultsCustomContourVisualizer + + +class DensePoseResultsFineSegmentationVisualizer(DensePoseMaskedColormapResultsVisualizer): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7): + super(DensePoseResultsFineSegmentationVisualizer, self).__init__( + _extract_i_from_iuvarr, + _extract_i_from_iuvarr, + inplace, + cmap, + alpha, + val_scale=255.0 / DensePoseDataRelative.N_PART_LABELS, + ) + + +class DensePoseResultsUVisualizer(DensePoseMaskedColormapResultsVisualizer): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7): + super(DensePoseResultsUVisualizer, self).__init__( + _extract_u_from_iuvarr, _extract_i_from_iuvarr, inplace, cmap, alpha, val_scale=1.0 + ) + + +class DensePoseResultsVVisualizer(DensePoseMaskedColormapResultsVisualizer): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7): + super(DensePoseResultsVVisualizer, self).__init__( + _extract_v_from_iuvarr, _extract_i_from_iuvarr, inplace, cmap, alpha, val_scale=1.0 + ) + + +class DensePoseOutputsFineSegmentationVisualizer(object): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7): + self.mask_visualizer = MatrixVisualizer( + inplace=inplace, + cmap=cmap, + val_scale=255.0 / DensePoseDataRelative.N_PART_LABELS, + alpha=alpha, + ) + + def visualize( + self, image_bgr: Image, dp_output_with_bboxes: Optional[Tuple[DensePoseOutput, Boxes]] + ) -> Image: + if dp_output_with_bboxes is None: + return image_bgr + densepose_output, bboxes_xywh = dp_output_with_bboxes + S = densepose_output.S + I = densepose_output.I # noqa + U = densepose_output.U + V = densepose_output.V + N = S.size(0) + assert N == I.size(0), ( + "densepose outputs S {} and I {}" + " should have equal first dim size".format(S.size(), I.size()) + ) + assert N == U.size(0), ( + "densepose outputs S {} and U {}" + " should have equal first dim size".format(S.size(), U.size()) + ) + assert N == V.size(0), ( + "densepose outputs S {} and V {}" + " should have equal first dim size".format(S.size(), V.size()) + ) + assert N == len(bboxes_xywh), ( + "number of bounding boxes {}" + " should be equal to first dim size of outputs {}".format(len(bboxes_xywh), N) + ) + for n in range(N): + Sn = S[n].argmax(dim=0) + In = I[n].argmax(dim=0) * (Sn > 0).long() + matrix = In.cpu().numpy().astype(np.uint8) + mask = np.zeros(matrix.shape, dtype=np.uint8) + mask[matrix > 0] = 1 + bbox_xywh = bboxes_xywh[n] + image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh) + return image_bgr + + +class DensePoseOutputsUVisualizer(object): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7): + self.mask_visualizer = MatrixVisualizer( + inplace=inplace, cmap=cmap, val_scale=1.0, alpha=alpha + ) + + def visualize( + self, image_bgr: Image, dp_output_with_bboxes: Optional[Tuple[DensePoseOutput, Boxes]] + ) -> Image: + if dp_output_with_bboxes is None: + return image_bgr + densepose_output, bboxes_xywh = dp_output_with_bboxes + assert isinstance( + densepose_output, DensePoseOutput + ), "DensePoseOutput expected, {} encountered".format(type(densepose_output)) + S = densepose_output.S + I = densepose_output.I # noqa + U = densepose_output.U + V = densepose_output.V + N = S.size(0) + assert N == I.size(0), ( + "densepose outputs S {} and I {}" + " should have equal first dim size".format(S.size(), I.size()) + ) + assert N == U.size(0), ( + "densepose outputs S {} and U {}" + " should have equal first dim size".format(S.size(), U.size()) + ) + assert N == V.size(0), ( + "densepose outputs S {} and V {}" + " should have equal first dim size".format(S.size(), V.size()) + ) + assert N == len(bboxes_xywh), ( + "number of bounding boxes {}" + " should be equal to first dim size of outputs {}".format(len(bboxes_xywh), N) + ) + for n in range(N): + Sn = S[n].argmax(dim=0) + In = I[n].argmax(dim=0) * (Sn > 0).long() + segmentation = In.cpu().numpy().astype(np.uint8) + mask = np.zeros(segmentation.shape, dtype=np.uint8) + mask[segmentation > 0] = 1 + Un = U[n].cpu().numpy().astype(np.float32) + Uvis = np.zeros(segmentation.shape, dtype=np.float32) + for partId in range(Un.shape[0]): + Uvis[segmentation == partId] = Un[partId][segmentation == partId].clip(0, 1) * 255 + bbox_xywh = bboxes_xywh[n] + image_bgr = self.mask_visualizer.visualize(image_bgr, mask, Uvis, bbox_xywh) + return image_bgr + + +class DensePoseOutputsVVisualizer(object): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7): + self.mask_visualizer = MatrixVisualizer( + inplace=inplace, cmap=cmap, val_scale=1.0, alpha=alpha + ) + + def visualize( + self, image_bgr: Image, dp_output_with_bboxes: Optional[Tuple[DensePoseOutput, Boxes]] + ) -> Image: + if dp_output_with_bboxes is None: + return image_bgr + densepose_output, bboxes_xywh = dp_output_with_bboxes + assert isinstance( + densepose_output, DensePoseOutput + ), "DensePoseOutput expected, {} encountered".format(type(densepose_output)) + S = densepose_output.S + I = densepose_output.I # noqa + U = densepose_output.U + V = densepose_output.V + N = S.size(0) + assert N == I.size(0), ( + "densepose outputs S {} and I {}" + " should have equal first dim size".format(S.size(), I.size()) + ) + assert N == U.size(0), ( + "densepose outputs S {} and U {}" + " should have equal first dim size".format(S.size(), U.size()) + ) + assert N == V.size(0), ( + "densepose outputs S {} and V {}" + " should have equal first dim size".format(S.size(), V.size()) + ) + assert N == len(bboxes_xywh), ( + "number of bounding boxes {}" + " should be equal to first dim size of outputs {}".format(len(bboxes_xywh), N) + ) + for n in range(N): + Sn = S[n].argmax(dim=0) + In = I[n].argmax(dim=0) * (Sn > 0).long() + segmentation = In.cpu().numpy().astype(np.uint8) + mask = np.zeros(segmentation.shape, dtype=np.uint8) + mask[segmentation > 0] = 1 + Vn = V[n].cpu().numpy().astype(np.float32) + Vvis = np.zeros(segmentation.shape, dtype=np.float32) + for partId in range(Vn.size(0)): + Vvis[segmentation == partId] = Vn[partId][segmentation == partId].clip(0, 1) * 255 + bbox_xywh = bboxes_xywh[n] + image_bgr = self.mask_visualizer.visualize(image_bgr, mask, Vvis, bbox_xywh) + return image_bgr + + +class DensePoseDataCoarseSegmentationVisualizer(object): + """ + Visualizer for ground truth segmentation + """ + + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7): + self.mask_visualizer = MatrixVisualizer( + inplace=inplace, + cmap=cmap, + val_scale=255.0 / DensePoseDataRelative.N_BODY_PARTS, + alpha=alpha, + ) + + def visualize( + self, + image_bgr: Image, + bbox_densepose_datas: Optional[Tuple[Iterable[Boxes], Iterable[DensePoseDataRelative]]], + ) -> Image: + if bbox_densepose_datas is None: + return image_bgr + for bbox_xywh, densepose_data in zip(*bbox_densepose_datas): + matrix = densepose_data.segm.numpy() + mask = np.zeros(matrix.shape, dtype=np.uint8) + mask[matrix > 0] = 1 + image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh.numpy()) + return image_bgr + + +class DensePoseDataPointsVisualizer(object): + def __init__(self, densepose_data_to_value_fn=None, cmap=cv2.COLORMAP_PARULA): + self.points_visualizer = PointsVisualizer() + self.densepose_data_to_value_fn = densepose_data_to_value_fn + self.cmap = cmap + + def visualize( + self, + image_bgr: Image, + bbox_densepose_datas: Optional[Tuple[Iterable[Boxes], Iterable[DensePoseDataRelative]]], + ) -> Image: + if bbox_densepose_datas is None: + return image_bgr + for bbox_xywh, densepose_data in zip(*bbox_densepose_datas): + x0, y0, w, h = bbox_xywh.numpy() + x = densepose_data.x.numpy() * w / 255.0 + x0 + y = densepose_data.y.numpy() * h / 255.0 + y0 + pts_xy = zip(x, y) + if self.densepose_data_to_value_fn is None: + image_bgr = self.points_visualizer.visualize(image_bgr, pts_xy) + else: + v = self.densepose_data_to_value_fn(densepose_data) + img_colors_bgr = cv2.applyColorMap(v, self.cmap) + colors_bgr = [ + [int(v) for v in img_color_bgr.ravel()] for img_color_bgr in img_colors_bgr + ] + image_bgr = self.points_visualizer.visualize(image_bgr, pts_xy, colors_bgr) + return image_bgr + + +def _densepose_data_u_for_cmap(densepose_data): + u = np.clip(densepose_data.u.numpy(), 0, 1) * 255.0 + return u.astype(np.uint8) + + +def _densepose_data_v_for_cmap(densepose_data): + v = np.clip(densepose_data.v.numpy(), 0, 1) * 255.0 + return v.astype(np.uint8) + + +def _densepose_data_i_for_cmap(densepose_data): + i = ( + np.clip(densepose_data.i.numpy(), 0.0, DensePoseDataRelative.N_PART_LABELS) + * 255.0 + / DensePoseDataRelative.N_PART_LABELS + ) + return i.astype(np.uint8) + + +class DensePoseDataPointsUVisualizer(DensePoseDataPointsVisualizer): + def __init__(self): + super(DensePoseDataPointsUVisualizer, self).__init__( + densepose_data_to_value_fn=_densepose_data_u_for_cmap + ) + + +class DensePoseDataPointsVVisualizer(DensePoseDataPointsVisualizer): + def __init__(self): + super(DensePoseDataPointsVVisualizer, self).__init__( + densepose_data_to_value_fn=_densepose_data_v_for_cmap + ) + + +class DensePoseDataPointsIVisualizer(DensePoseDataPointsVisualizer): + def __init__(self): + super(DensePoseDataPointsIVisualizer, self).__init__( + densepose_data_to_value_fn=_densepose_data_i_for_cmap + ) diff --git a/projects/DensePose/densepose/vis/extractor.py b/projects/DensePose/densepose/vis/extractor.py new file mode 100644 index 0000000000..b715a4451e --- /dev/null +++ b/projects/DensePose/densepose/vis/extractor.py @@ -0,0 +1,152 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +from typing import Sequence +import torch + +from detectron2.layers.nms import batched_nms +from detectron2.structures.instances import Instances + +from densepose.vis.bounding_box import BoundingBoxVisualizer, ScoredBoundingBoxVisualizer +from densepose.vis.densepose import DensePoseResultsVisualizer + +from .base import CompoundVisualizer + +Scores = Sequence[float] + + +def extract_scores_from_instances(instances: Instances, select=None): + if instances.has("scores"): + return instances.scores if select is None else instances.scores[select] + return None + + +def extract_boxes_xywh_from_instances(instances: Instances, select=None): + if instances.has("pred_boxes"): + boxes_xywh = instances.pred_boxes.tensor.clone() + boxes_xywh[:, 2] -= boxes_xywh[:, 0] + boxes_xywh[:, 3] -= boxes_xywh[:, 1] + return boxes_xywh if select is None else boxes_xywh[select] + return None + + +def create_extractor(visualizer: object): + """ + Create an extractor for the provided visualizer + """ + if isinstance(visualizer, CompoundVisualizer): + extractors = [create_extractor(v) for v in visualizer.visualizers] + return CompoundExtractor(extractors) + elif isinstance(visualizer, DensePoseResultsVisualizer): + return DensePoseResultExtractor() + elif isinstance(visualizer, ScoredBoundingBoxVisualizer): + return CompoundExtractor([extract_boxes_xywh_from_instances, extract_scores_from_instances]) + elif isinstance(visualizer, BoundingBoxVisualizer): + return extract_boxes_xywh_from_instances + else: + logger = logging.getLogger(__name__) + logger.error(f"Could not create extractor for {visualizer}") + return None + + +class BoundingBoxExtractor(object): + """ + Extracts bounding boxes from instances + """ + + def __call__(self, instances: Instances): + boxes_xywh = extract_boxes_xywh_from_instances(instances) + return boxes_xywh + + +class ScoredBoundingBoxExtractor(object): + """ + Extracts bounding boxes from instances + """ + + def __call__(self, instances: Instances, select=None): + scores = extract_scores_from_instances(instances) + boxes_xywh = extract_boxes_xywh_from_instances(instances) + if (scores is None) or (boxes_xywh is None): + return (boxes_xywh, scores) + if select is not None: + scores = scores[select] + boxes_xywh = boxes_xywh[select] + return (boxes_xywh, scores) + + +class DensePoseResultExtractor(object): + """ + Extracts DensePose result from instances + """ + + def __call__(self, instances: Instances, select=None): + boxes_xywh = extract_boxes_xywh_from_instances(instances) + if instances.has("pred_densepose") and (boxes_xywh is not None): + dpout = instances.pred_densepose + if select is not None: + dpout = dpout[select] + boxes_xywh = boxes_xywh[select] + return dpout.to_result(boxes_xywh) + else: + return None + + +class CompoundExtractor(object): + """ + Extracts data for CompoundVisualizer + """ + + def __init__(self, extractors): + self.extractors = extractors + + def __call__(self, instances: Instances, select=None): + datas = [] + for extractor in self.extractors: + data = extractor(instances, select) + datas.append(data) + return datas + + +class NmsFilteredExtractor(object): + """ + Extracts data in the format accepted by NmsFilteredVisualizer + """ + + def __init__(self, extractor, iou_threshold): + self.extractor = extractor + self.iou_threshold = iou_threshold + + def __call__(self, instances: Instances, select=None): + scores = extract_scores_from_instances(instances) + boxes_xywh = extract_boxes_xywh_from_instances(instances) + if boxes_xywh is None: + return None + select_local_idx = batched_nms( + boxes_xywh, + scores, + torch.zeros(len(scores), dtype=torch.int32), + iou_threshold=self.iou_threshold, + ).squeeze() + select_local = torch.zeros(len(boxes_xywh), dtype=torch.bool, device=boxes_xywh.device) + select_local[select_local_idx] = True + select = select_local if select is None else (select & select_local) + return self.extractor(instances, select=select) + + +class ScoreThresholdedExtractor(object): + """ + Extracts data in the format accepted by ScoreThresholdedVisualizer + """ + + def __init__(self, extractor, min_score): + self.extractor = extractor + self.min_score = min_score + + def __call__(self, instances: Instances, select=None): + scores = extract_scores_from_instances(instances) + if scores is None: + return None + select_local = scores > self.min_score + select = select_local if select is None else (select & select_local) + data = self.extractor(instances, select=select) + return data diff --git a/projects/DensePose/doc/TOOL_APPLY_NET.md b/projects/DensePose/doc/TOOL_APPLY_NET.md new file mode 100644 index 0000000000..5c38c7c3fc --- /dev/null +++ b/projects/DensePose/doc/TOOL_APPLY_NET.md @@ -0,0 +1,93 @@ + +# Apply Net + +`apply_net` is a tool to print or visualize DensePose results on a set of images. +It has two modes: `dump` to save DensePose model results to a pickle file +and `show` to visualize them on images. + +## Dump Mode + +The general command form is: +```bash +python apply_net.py dump [-h] [-v] [--output ] +``` + +There are three mandatory arguments: + - ``, configuration file for a given model; + - ``, model file with trained parameters + - ``, input image file name, pattern or folder + +One can additionally provide `--output` argument to define the output file name, +which defaults to `output.pkl`. + + +Examples: + +1. Dump results of a DensePose model with ResNet-50 FPN backbone for images + in a folder `images` to file `dump.pkl`: +```bash +python apply_net.py dump configs/densepose_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl images --output dump.pkl -v +``` + +2. Dump results of a DensePose model with ResNet-50 FPN backbone for images + with file name matching a pattern `image*.jpg` to file `results.pkl`: +```bash +python apply_net.py dump configs/densepose_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl "image*.jpg" --output results.pkl -v +``` + + +## Visualization Mode + +The general command form is: +```bash +python apply_net.py show [-h] [-v] [--min_score ] [--nms_thresh ] [--output ] +``` + +There are four mandatory arguments: + - ``, configuration file for a given model; + - ``, model file with trained parameters + - ``, input image file name, pattern or folder + - ``, visualizations specifier; currently available visualizations are: + * `bbox` - bounding boxes of detected persons; + * `dp_segm` - segmentation masks for detected persons; + * `dp_u` - each body part is colored according to the estimated values of the + U coordinate in part parametrization; + * `dp_v` - each body part is colored according to the estimated values of the + V coordinate in part parametrization; + * `dp_contour` - plots contours with color-coded U and V coordinates + + +One can additionally provide the following optional arguments: + - `--min_score` to only show detections with sufficient scores that are not lower than provided value + - `--nms_thresh` to additionally apply non-maximum suppression to detections at a given threshold + - `--output` to define visualization file name template, which defaults to `output.png`. + To distinguish output file names for different images, the tool appends 1-based entry index, + e.g. output.0001.png, output.0002.png, etc... + + +The following examples show how to output results of a DensePose model +with ResNet-50 FPN backbone using different visualizations for image `image.jpg`: + +1. Show bounding box and segmentation: +```bash +python apply_net.py show configs/densepose_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_segm -v +``` +![Bounding Box + Segmentation Visualization](images/res_bbox_dp_segm.png) + +2. Show bounding box and estimated U coordinates for body parts: +```bash +python apply_net.py show configs/densepose_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_u -v +``` +![Bounding Box + U Coordinate Visualization](images/res_bbox_dp_u.png) + +3. Show bounding box and estimated V coordinates for body parts: +```bash +python apply_net.py show configs/densepose_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg bbox,dp_v -v +``` +![Bounding Box + V Coordinate Visualization](images/res_bbox_dp_v.png) + +4. Show bounding box and estimated U and V coordinates via contour plots: +```bash +python apply_net.py show configs/densepose_R_50_FPN_s1x.yaml DensePose_ResNet50_FPN_s1x-e2e.pkl image.jpg dp_contour,bbox -v +``` +![Bounding Box + Contour Visualization](images/res_bbox_dp_contour.png) diff --git a/projects/DensePose/doc/TOOL_QUERY_DB.md b/projects/DensePose/doc/TOOL_QUERY_DB.md new file mode 100644 index 0000000000..0003c3b4bf --- /dev/null +++ b/projects/DensePose/doc/TOOL_QUERY_DB.md @@ -0,0 +1,105 @@ + +# Query Dataset + +`query_db` is a tool to print or visualize DensePose data from a dataset. +It has two modes: `print` and `show` to output dataset entries to standard +output or to visualize them on images. + +## Print Mode + +The general command form is: +```bash +python query_db.py print [-h] [-v] [--max-entries N] +``` + +There are two mandatory arguments: + - ``, DensePose dataset specification, from which to select + the entries (e.g. `densepose_coco_2014_train`). + - ``, dataset entry selector which can be a single specification, + or a comma-separated list of specifications of the form + `field[:type]=value` for exact match with the value + or `field[:type]=min-max` for a range of values + +One can additionally limit the maximum number of entries to output +by providing `--max-entries` argument. + +Examples: + +1. Output at most 10 first entries from the `densepose_coco_2014_train` dataset: +```bash +python query_db.py print densepose_coco_2014_train \* --max-entries 10 -v +``` + +2. Output all entries with `file_name` equal to `COCO_train2014_000000000036.jpg`: +```bash +python query_db.py print densepose_coco_2014_train file_name=COCO_train2014_000000000036.jpg -v +``` + +3. Output all entries with `image_id` between 36 and 156: +```bash +python query_db.py print densepose_coco_2014_train image_id:int=36-156 -v +``` + +## Visualization Mode + +The general command form is: +```bash +python query_db.py show [-h] [-v] [--max-entries N] [--output ] +``` + +There are three mandatory arguments: + - ``, DensePose dataset specification, from which to select + the entries (e.g. `densepose_coco_2014_train`). + - ``, dataset entry selector which can be a single specification, + or a comma-separated list of specifications of the form + `field[:type]=value` for exact match with the value + or `field[:type]=min-max` for a range of values + - ``, visualizations specifier; currently available visualizations are: + * `bbox` - bounding boxes of annotated persons; + * `dp_i` - annotated points colored according to the containing part; + * `dp_pts` - annotated points in green color; + * `dp_segm` - segmentation masks for annotated persons; + * `dp_u` - annotated points colored according to their U coordinate in part parametrization; + * `dp_v` - annotated points colored according to their V coordinate in part parametrization; + +One can additionally provide one of the two optional arguments: + - `--max_entries` to limit the maximum number of entries to visualize + - `--output` to provide visualization file name template, which defaults + to `output.png`. To distinguish file names for different dataset + entries, the tool appends 1-based entry index to the output file name, + e.g. output.0001.png, output.0002.png, etc. + +The following examples show how to output different visualizations for image with `id = 322` +from `densepose_coco_2014_train` dataset: + +1. Show bounding box and segmentation: +```bash +python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_segm -v +``` +![Bounding Box + Segmentation Visualization](images/vis_bbox_dp_segm.png) + +2. Show bounding box and points colored according to the containing part: +```bash +python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_i -v +``` +![Bounding Box + Point Label Visualization](images/vis_bbox_dp_i.png) + +3. Show bounding box and annotated points in green color: +```bash +python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_segm -v +``` +![Bounding Box + Point Visualization](images/vis_bbox_dp_pts.png) + +4. Show bounding box and annotated points colored according to their U coordinate in part parametrization: +```bash +python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_u -v +``` +![Bounding Box + Point U Visualization](images/vis_bbox_dp_u.png) + +5. Show bounding box and annotated points colored according to their V coordinate in part parametrization: +```bash +python query_db.py show densepose_coco_2014_train image_id:int=322 bbox,dp_v -v +``` +![Bounding Box + Point V Visualization](images/vis_bbox_dp_v.png) + + diff --git a/projects/DensePose/doc/images/res_bbox_dp_contour.jpg b/projects/DensePose/doc/images/res_bbox_dp_contour.jpg new file mode 100644 index 0000000000000000000000000000000000000000..8f0c195c237d8ca70e16f5827b8a3b6e456844a4 GIT binary patch literal 91492 zcmb4JWl$VlupQi;;J!GETae)H5-h<9OK^8v+--4pf`uT#U4kwW+#zUy1SbT4`F^~r z_xJTwO-)VLy{D&prtY27=Vke28^BjlP*wmC5D);Re+Tfg2FL`KZ4RA-xj)bH|r5+zI*gzC9ZVh7HfRQlj zvoSx&yXmI^c0&edv(c7rur!nyCgMMg9pAmeqAd7yXKA&tGc$xQ{u&~J`ys*tLNRmf z@N|~mN5aOyB?!u@%x69e0DPjlfZ;KcRq*mZmdmj9?6}O`cjK1X<)x(c{=}?02A;+MOsnK_48ns0wmw{%KN2+?^oniksa%429 z4e?SncZ{F%vy>i=jQs6mo7i>~ZwgG7QMy#TlYaSK^1E)~!}a@kH+S~YnK5}9SmaFu 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za-qUpl^~L!{=7n1DbOgw_3{P?(&`cph`=hel^HTWiRjNFB>ZJFNd#KkLFo<5W{(;H zOIdgfMg`tN_c`~EK)2@gvO~~s7E?4x)?m?Tys`&7r3SmVgPfM2f~a{Oga$|hx~C{^TsA3)e{)uhyQ+p9W#KzR z5B0GPm<_@-xM{WeSD?k}e_5qV&xpXUQc$86JmiIw&`7i1hkETv< OCY)o|2 Dict[str, Any]: + context = {} + return context + + +@register_action +class PrintAction(EntrywiseAction): + """ + Print action that outputs selected entries to stdout + """ + + COMMAND: ClassVar[str] = "print" + + @classmethod + def add_parser(cls: type, subparsers: argparse._SubParsersAction): + parser = subparsers.add_parser(cls.COMMAND, help="Output selected entries to stdout. ") + cls.add_arguments(parser) + parser.set_defaults(func=cls.execute) + + @classmethod + def add_arguments(cls: type, parser: argparse.ArgumentParser): + super(PrintAction, cls).add_arguments(parser) + + @classmethod + def execute_on_entry(cls: type, entry: Dict[str, Any], context: Dict[str, Any]): + import pprint + + printer = pprint.PrettyPrinter(indent=2, width=200, compact=True) + printer.pprint(entry) + + +@register_action +class ShowAction(EntrywiseAction): + """ + Show action that visualizes selected entries on an image + """ + + COMMAND: ClassVar[str] = "show" + VISUALIZERS: ClassVar[Dict[str, object]] = { + "dp_segm": DensePoseDataCoarseSegmentationVisualizer(), + "dp_i": DensePoseDataPointsIVisualizer(), + "dp_u": DensePoseDataPointsUVisualizer(), + "dp_v": DensePoseDataPointsVVisualizer(), + "dp_pts": DensePoseDataPointsVisualizer(), + "bbox": BoundingBoxVisualizer(), + } + + @classmethod + def add_parser(cls: type, subparsers: argparse._SubParsersAction): + parser = subparsers.add_parser(cls.COMMAND, help="Visualize selected entries") + cls.add_arguments(parser) + parser.set_defaults(func=cls.execute) + + @classmethod + def add_arguments(cls: type, parser: argparse.ArgumentParser): + super(ShowAction, cls).add_arguments(parser) + parser.add_argument( + "visualizations", + metavar="", + help="Comma separated list of visualizations, possible values: " + "[{}]".format(",".join(sorted(cls.VISUALIZERS.keys()))), + ) + parser.add_argument( + "--output", + metavar="", + default="output.png", + help="File name to save output to", + ) + + @classmethod + def execute_on_entry(cls: type, entry: Dict[str, Any], context: Dict[str, Any]): + import cv2 + import numpy as np + + image_fpath = entry["file_name"] + image = cv2.imread(image_fpath, cv2.IMREAD_GRAYSCALE) + image = np.tile(image[:, :, np.newaxis], [1, 1, 3]) + datas = cls._extract_data_for_visualizers_from_entry(context["vis_specs"], entry) + visualizer = context["visualizer"] + image_vis = visualizer.visualize(image, datas) + entry_idx = context["entry_idx"] + 1 + out_fname = cls._get_out_fname(entry_idx, context["out_fname"]) + cv2.imwrite(out_fname, image_vis) + logger.info(f"Output saved to {out_fname}") + context["entry_idx"] += 1 + + @classmethod + def _get_out_fname(cls: type, entry_idx: int, fname_base: str): + base, ext = os.path.splitext(fname_base) + return base + ".{0:04d}".format(entry_idx) + ext + + @classmethod + def create_context(cls: type, args: argparse.Namespace) -> Dict[str, Any]: + vis_specs = args.visualizations.split(",") + visualizers = [] + for vis_spec in vis_specs: + vis = cls.VISUALIZERS[vis_spec] + visualizers.append(vis) + context = { + "vis_specs": vis_specs, + "visualizer": CompoundVisualizer(visualizers), + "out_fname": args.output, + "entry_idx": 0, + } + return context + + @classmethod + def _extract_data_for_visualizers_from_entry( + cls: type, vis_specs: List[str], entry: Dict[str, Any] + ): + dp_list = [] + bbox_list = [] + for annotation in entry["annotations"]: + is_valid, _ = DensePoseDataRelative.validate_annotation(annotation) + if not is_valid: + continue + bbox = torch.as_tensor(annotation["bbox"]) + bbox_list.append(bbox) + dp_data = DensePoseDataRelative(annotation) + dp_list.append(dp_data) + datas = [] + for vis_spec in vis_specs: + datas.append(bbox_list if "bbox" == vis_spec else (bbox_list, dp_list)) + return datas + + +def setup_dataset(dataset_name): + logger.info("Loading dataset {}".format(dataset_name)) + start = timer() + dataset = DatasetCatalog.get(dataset_name) + stop = timer() + logger.info("Loaded dataset {} in {:.3f}s".format(dataset_name, stop - start)) + return dataset + + +def create_argument_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description=DOC, + formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=120), + ) + parser.set_defaults(func=lambda _: parser.print_help(sys.stdout)) + subparsers = parser.add_subparsers(title="Actions") + for _, action in _ACTION_REGISTRY.items(): + action.add_parser(subparsers) + return parser + + +def main(): + parser = create_argument_parser() + args = parser.parse_args() + verbosity = args.verbosity if hasattr(args, "verbosity") else None + global logger + logger = setup_logger(name=LOGGER_NAME) + logger.setLevel(verbosity_to_level(verbosity)) + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/projects/DensePose/train_net.py b/projects/DensePose/train_net.py new file mode 100644 index 0000000000..8dc9deb874 --- /dev/null +++ b/projects/DensePose/train_net.py @@ -0,0 +1,81 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +DensePose Training Script. + +This script is similar to the training script in detectron2/tools. + +It is an example of how a user might use detectron2 for a new project. +""" + +import os + +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.data import build_detection_test_loader, build_detection_train_loader +from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch +from detectron2.evaluation import COCOEvaluator, DatasetEvaluators, verify_results +from detectron2.utils.logger import setup_logger + +from densepose import DatasetMapper, DensePoseCOCOEvaluator, add_densepose_config + + +class Trainer(DefaultTrainer): + @classmethod + def build_evaluator(cls, cfg, dataset_name): + output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") + evaluators = [COCOEvaluator(dataset_name, cfg, True, output_folder)] + if cfg.MODEL.DENSEPOSE_ON: + evaluators.append(DensePoseCOCOEvaluator(dataset_name, True, output_folder)) + return DatasetEvaluators(evaluators) + + @classmethod + def build_test_loader(cls, cfg, dataset_name): + return build_detection_test_loader(cfg, dataset_name, mapper=DatasetMapper(cfg, False)) + + @classmethod + def build_train_loader(cls, cfg): + return build_detection_train_loader(cfg, mapper=DatasetMapper(cfg, True)) + + +def setup(args): + cfg = get_cfg() + add_densepose_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + default_setup(cfg, args) + # Setup logger for "densepose" module + setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="densepose") + return cfg + + +def main(args): + cfg = setup(args) + + if args.eval_only: + model = Trainer.build_model(cfg) + DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( + cfg.MODEL.WEIGHTS, resume=args.resume + ) + res = Trainer.test(cfg, model) + if comm.is_main_process(): + verify_results(cfg, res) + return res + + trainer = Trainer(cfg) + trainer.resume_or_load(resume=args.resume) + return trainer.train() + + +if __name__ == "__main__": + args = default_argument_parser().parse_args() + print("Command Line Args:", args) + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + ) diff --git a/projects/README.md b/projects/README.md new file mode 100644 index 0000000000..60fae36e89 --- /dev/null +++ b/projects/README.md @@ -0,0 +1,9 @@ + +Here are a few research projects that are built on detectron2. +They are examples of how to use detectron2 as a library, to make your projects more +maintainable. + ++ [DensePose: Dense Human Pose Estimation In The Wild](DensePose) ++ [Scale-Aware Trident Networks for Object Detection](TridentNet) ++ TensorMask: A Foundation for Dense Object Segmentation. (Coming Soon) ++ Mesh R-CNN. (Coming Soon) diff --git a/projects/TridentNet/README.md b/projects/TridentNet/README.md new file mode 100644 index 0000000000..c0889665b4 --- /dev/null +++ b/projects/TridentNet/README.md @@ -0,0 +1,48 @@ + +# TridentNet in Detectron2 +**Scale-Aware Trident Networks for Object Detection** + +Yanghao Li\*, Yuntao Chen\*, Naiyan Wang, Zhaoxiang Zhang + +[[`arXiv`](https://arxiv.org/abs/1802.00434)] [[`BibTeX`](#CitingTridentNet)] + +

+ +In this repository, we implement TridentNet-Fast in the Detectron2 framework. Trident Network (TridentNet) aims to generate scale-specific feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields. TridentNet-Fast is a fast approximation version of TridentNet that could achieve significant improvements without any additional parameters and computational cost. + +## Training + +To train a model one can call +```bash +python /path/to/detectron2/projects/TridentNet/train_net.py --config-file +``` + +For example, to launch end-to-end TridentNet training with ResNet-50 backbone on 8 GPUs, +one should execute: +```bash +python /path/to/detectron2/projects/TridentNet/train_net.py --config-file /path/to/detectron2/projects/TridentNet/configs/tridentnet_fast_R_50_C4_1x.yaml --num_gpus 8 +``` + +## Testing + +Model testing can be done in the same way as training, except for an additional flag `--eval-only` and +model location specification through `MODEL.WEIGHT model.pth` in the command line +```bash +python /path/to/detectron2/projects/TridentNet/train_net.py --config-file /path/to/detectron2/projects/TridentNet/configs/tridentnet_fast_R_50_C4_1x.yaml --eval-only MODEL.WEIGHT model.pth +``` + +## Citing TridentNet + +If you use TridentNet, please use the following BibTeX entry. + +``` +@InProceedings{li2019scale, + title={Scale-Aware Trident Networks for Object Detection}, + author={Li, Yanghao and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang}, + journal={The International Conference on Computer Vision (ICCV)}, + year={2019} +} +``` + diff --git a/projects/TridentNet/configs/Base-TridentNet-Fast-C4.yaml b/projects/TridentNet/configs/Base-TridentNet-Fast-C4.yaml new file mode 100644 index 0000000000..1dd2a15af8 --- /dev/null +++ b/projects/TridentNet/configs/Base-TridentNet-Fast-C4.yaml @@ -0,0 +1,28 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + BACKBONE: + NAME: "build_trident_resnet_backbone" + ROI_HEADS: + NAME: "TridentRes5ROIHeads" + POSITIVE_FRACTION: 0.5 + BATCH_SIZE_PER_IMAGE: 128 + PROPOSAL_APPEND_GT: False + PROPOSAL_GENERATOR: + NAME: "TridentRPN" + RPN: + POST_NMS_TOPK_TRAIN: 500 + TRIDENT: + NUM_BRANCH: 3 + BRANCH_DILATIONS: [1, 2, 3] + TEST_BRANCH_IDX: 1 + TRIDENT_STAGE: "res4" +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) diff --git a/projects/TridentNet/configs/tridentnet_fast_R_101_C4_3x.yaml b/projects/TridentNet/configs/tridentnet_fast_R_101_C4_3x.yaml new file mode 100644 index 0000000000..bc83c2f9e7 --- /dev/null +++ b/projects/TridentNet/configs/tridentnet_fast_R_101_C4_3x.yaml @@ -0,0 +1,9 @@ +_BASE_: "Base-TridentNet-Fast-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl" + MASK_ON: False + RESNETS: + DEPTH: 101 +SOLVER: + STEPS: (210000, 250000) + MAX_ITER: 270000 diff --git a/projects/TridentNet/configs/tridentnet_fast_R_50_C4_1x.yaml b/projects/TridentNet/configs/tridentnet_fast_R_50_C4_1x.yaml new file mode 100644 index 0000000000..fda2cb6622 --- /dev/null +++ b/projects/TridentNet/configs/tridentnet_fast_R_50_C4_1x.yaml @@ -0,0 +1,6 @@ +_BASE_: "Base-TridentNet-Fast-C4.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + MASK_ON: False + RESNETS: + DEPTH: 50 diff --git a/projects/TridentNet/train_net.py b/projects/TridentNet/train_net.py new file mode 100644 index 0000000000..34cde48971 --- /dev/null +++ b/projects/TridentNet/train_net.py @@ -0,0 +1,68 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +TridentNet Training Script. + +This script is a simplified version of the training script in detectron2/tools. +""" + +import os + +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch +from detectron2.evaluation import COCOEvaluator, verify_results + +from tridentnet import add_tridentnet_config + + +class Trainer(DefaultTrainer): + @classmethod + def build_evaluator(cls, cfg, dataset_name, output_folder=None): + if output_folder is None: + output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") + return COCOEvaluator(dataset_name, cfg, True, output_folder) + + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg() + add_tridentnet_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + default_setup(cfg, args) + return cfg + + +def main(args): + cfg = setup(args) + + if args.eval_only: + model = Trainer.build_model(cfg) + DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( + cfg.MODEL.WEIGHTS, resume=args.resume + ) + res = Trainer.test(cfg, model) + if comm.is_main_process(): + verify_results(cfg, res) + return res + + trainer = Trainer(cfg) + trainer.resume_or_load(resume=args.resume) + return trainer.train() + + +if __name__ == "__main__": + args = default_argument_parser().parse_args() + print("Command Line Args:", args) + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + ) diff --git a/projects/TridentNet/tridentnet/__init__.py b/projects/TridentNet/tridentnet/__init__.py new file mode 100644 index 0000000000..2fcdeb45a0 --- /dev/null +++ b/projects/TridentNet/tridentnet/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .config import add_tridentnet_config +from .trident_backbone import ( + TridentBottleneckBlock, + build_trident_resnet_backbone, + make_trident_stage, +) +from .trident_rpn import TridentRPN +from .trident_rcnn import TridentRes5ROIHeads, TridentStandardROIHeads diff --git a/projects/TridentNet/tridentnet/config.py b/projects/TridentNet/tridentnet/config.py new file mode 100644 index 0000000000..5a484a0013 --- /dev/null +++ b/projects/TridentNet/tridentnet/config.py @@ -0,0 +1,26 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from detectron2.config import CfgNode as CN + + +def add_tridentnet_config(cfg): + """ + Add config for tridentnet. + """ + _C = cfg + + _C.MODEL.TRIDENT = CN() + + # Number of branches for TridentNet. + _C.MODEL.TRIDENT.NUM_BRANCH = 3 + # Specfiy the dilations for each branch. + _C.MODEL.TRIDENT.BRANCH_DILATIONS = [1, 2, 3] + # Specify the stage for applying trident blocks. Default stage is Res4 according to the + # TridentNet paper. + _C.MODEL.TRIDENT.TRIDENT_STAGE = "res4" + # Specify the test branch index TridentNet Fast inference: + # - use -1 to aggreate results of all branches during inference. + # - otherwise, only using specified branch for fast inference. Recommended setting is + # to use the middle branch. + _C.MODEL.TRIDENT.TEST_BRANCH_IDX = 1 diff --git a/projects/TridentNet/tridentnet/trident_backbone.py b/projects/TridentNet/tridentnet/trident_backbone.py new file mode 100644 index 0000000000..e65d9d3f32 --- /dev/null +++ b/projects/TridentNet/tridentnet/trident_backbone.py @@ -0,0 +1,223 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn.functional as F + +from detectron2.layers import Conv2d, FrozenBatchNorm2d, get_norm +from detectron2.modeling import BACKBONE_REGISTRY, ResNet, ResNetBlockBase, make_stage +from detectron2.modeling.backbone.resnet import BasicStem, BottleneckBlock, DeformBottleneckBlock + +from .trident_conv import TridentConv + +__all__ = ["TridentBottleneckBlock", "make_trident_stage", "build_trident_resnet_backbone"] + + +class TridentBottleneckBlock(ResNetBlockBase): + def __init__( + self, + in_channels, + out_channels, + *, + bottleneck_channels, + stride=1, + num_groups=1, + norm="BN", + stride_in_1x1=False, + num_branch=3, + dilations=(1, 2, 3), + concat_output=False, + test_branch_idx=-1, + ): + """ + Args: + num_branch (int): the number of branches in TridentNet. + dilations (tuple): the dilations of multple branches in TridentNet. + concat_output (bool): if concatenate outputs of multiple branches in TridentNet. + Use 'True' for the last trident block. + """ + super().__init__(in_channels, out_channels, stride) + + assert num_branch == len(dilations) + + self.num_branch = num_branch + self.concat_output = concat_output + self.test_branch_idx = test_branch_idx + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv2 = TridentConv( + bottleneck_channels, + bottleneck_channels, + kernel_size=3, + stride=stride_3x3, + paddings=dilations, + bias=False, + groups=num_groups, + dilations=dilations, + num_branch=num_branch, + test_branch_idx=test_branch_idx, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv3 = Conv2d( + bottleneck_channels, + out_channels, + kernel_size=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + def forward(self, x): + num_branch = self.num_branch if self.training or self.test_branch_idx == -1 else 1 + if not isinstance(x, list): + x = [x] * num_branch + out = [self.conv1(b) for b in x] + out = [F.relu_(b) for b in out] + + out = self.conv2(out) + out = [F.relu_(b) for b in out] + + out = [self.conv3(b) for b in out] + + if self.shortcut is not None: + shortcut = [self.shortcut(b) for b in x] + else: + shortcut = x + + out = [out_b + shortcut_b for out_b, shortcut_b in zip(out, shortcut)] + out = [F.relu_(b) for b in out] + if self.concat_output: + out = torch.cat(out) + return out + + +def make_trident_stage(block_class, num_blocks, first_stride, **kwargs): + """ + Create a resnet stage by creating many blocks for TridentNet. + """ + blocks = [] + for i in range(num_blocks - 1): + blocks.append(block_class(stride=first_stride if i == 0 else 1, **kwargs)) + kwargs["in_channels"] = kwargs["out_channels"] + blocks.append(block_class(stride=1, concat_output=True, **kwargs)) + return blocks + + +@BACKBONE_REGISTRY.register() +def build_trident_resnet_backbone(cfg, input_shape): + """ + Create a ResNet instance from config for TridentNet. + + Returns: + ResNet: a :class:`ResNet` instance. + """ + # need registration of new blocks/stems? + norm = cfg.MODEL.RESNETS.NORM + stem = BasicStem( + in_channels=input_shape.channels, + out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS, + norm=norm, + ) + freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT + + if freeze_at >= 1: + for p in stem.parameters(): + p.requires_grad = False + stem = FrozenBatchNorm2d.convert_frozen_batchnorm(stem) + + # fmt: off + out_features = cfg.MODEL.RESNETS.OUT_FEATURES + depth = cfg.MODEL.RESNETS.DEPTH + num_groups = cfg.MODEL.RESNETS.NUM_GROUPS + width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP + bottleneck_channels = num_groups * width_per_group + in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS + out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS + stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 + res5_dilation = cfg.MODEL.RESNETS.RES5_DILATION + deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE + deform_modulated = cfg.MODEL.RESNETS.DEFORM_MODULATED + deform_num_groups = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS + num_branch = cfg.MODEL.TRIDENT.NUM_BRANCH + branch_dilations = cfg.MODEL.TRIDENT.BRANCH_DILATIONS + trident_stage = cfg.MODEL.TRIDENT.TRIDENT_STAGE + test_branch_idx = cfg.MODEL.TRIDENT.TEST_BRANCH_IDX + # fmt: on + assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation) + + num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3]}[depth] + + stages = [] + + res_stage_idx = {"res2": 2, "res3": 3, "res4": 4, "res5": 5} + out_stage_idx = [res_stage_idx[f] for f in out_features] + trident_stage_idx = res_stage_idx[trident_stage] + max_stage_idx = max(out_stage_idx) + for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)): + dilation = res5_dilation if stage_idx == 5 else 1 + first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2 + stage_kargs = { + "num_blocks": num_blocks_per_stage[idx], + "first_stride": first_stride, + "in_channels": in_channels, + "bottleneck_channels": bottleneck_channels, + "out_channels": out_channels, + "num_groups": num_groups, + "norm": norm, + "stride_in_1x1": stride_in_1x1, + "dilation": dilation, + } + if stage_idx == trident_stage_idx: + assert not deform_on_per_stage[ + idx + ], "Not support deformable conv in Trident blocks yet." + stage_kargs["block_class"] = TridentBottleneckBlock + stage_kargs["num_branch"] = num_branch + stage_kargs["dilations"] = branch_dilations + stage_kargs["test_branch_idx"] = test_branch_idx + stage_kargs.pop("dilation") + elif deform_on_per_stage[idx]: + stage_kargs["block_class"] = DeformBottleneckBlock + stage_kargs["deform_modulated"] = deform_modulated + stage_kargs["deform_num_groups"] = deform_num_groups + else: + stage_kargs["block_class"] = BottleneckBlock + blocks = ( + make_trident_stage(**stage_kargs) + if stage_idx == trident_stage_idx + else make_stage(**stage_kargs) + ) + in_channels = out_channels + out_channels *= 2 + bottleneck_channels *= 2 + + if freeze_at >= stage_idx: + for block in blocks: + block.freeze() + stages.append(blocks) + return ResNet(stem, stages, out_features=out_features) diff --git a/projects/TridentNet/tridentnet/trident_conv.py b/projects/TridentNet/tridentnet/trident_conv.py new file mode 100644 index 0000000000..7e2d5252bd --- /dev/null +++ b/projects/TridentNet/tridentnet/trident_conv.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.modules.utils import _pair + +from detectron2.layers.wrappers import _NewEmptyTensorOp + + +class TridentConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + paddings=0, + dilations=1, + groups=1, + num_branch=1, + test_branch_idx=-1, + bias=False, + norm=None, + activation=None, + ): + super(TridentConv, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.num_branch = num_branch + self.stride = _pair(stride) + self.groups = groups + self.with_bias = bias + if isinstance(paddings, int): + paddings = [paddings] * self.num_branch + if isinstance(dilations, int): + dilations = [dilations] * self.num_branch + self.paddings = [_pair(padding) for padding in paddings] + self.dilations = [_pair(dilation) for dilation in dilations] + self.test_branch_idx = test_branch_idx + self.norm = norm + self.activation = activation + + assert len({self.num_branch, len(self.paddings), len(self.dilations)}) == 1 + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels // groups, *self.kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.bias = None + + nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") + if self.bias is not None: + nn.init.constant_(self.bias, 0) + + def forward(self, inputs): + num_branch = self.num_branch if self.training or self.test_branch_idx == -1 else 1 + assert len(inputs) == num_branch + + if inputs[0].numel() == 0: + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // s + 1 + for i, p, di, k, s in zip( + inputs[0].shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride + ) + ] + output_shape = [input[0].shape[0], self.weight.shape[0]] + output_shape + return [_NewEmptyTensorOp.apply(input, output_shape) for input in inputs] + + if self.training or self.test_branch_idx == -1: + outputs = [ + F.conv2d(input, self.weight, self.bias, self.stride, padding, dilation, self.groups) + for input, dilation, padding in zip(inputs, self.dilations, self.paddings) + ] + else: + outputs = [ + F.conv2d( + inputs[0], + self.weight, + self.bias, + self.stride, + self.paddings[self.test_branch_idx], + self.dilations[self.test_branch_idx], + self.groups, + ) + ] + + if self.norm is not None: + outputs = [self.norm(x) for x in outputs] + if self.activation is not None: + outputs = [self.activation(x) for x in outputs] + return outputs + + def extra_repr(self): + tmpstr = "in_channels=" + str(self.in_channels) + tmpstr += ", out_channels=" + str(self.out_channels) + tmpstr += ", kernel_size=" + str(self.kernel_size) + tmpstr += ", num_branch=" + str(self.num_branch) + tmpstr += ", test_branch_idx=" + str(self.test_branch_idx) + tmpstr += ", stride=" + str(self.stride) + tmpstr += ", paddings=" + str(self.paddings) + tmpstr += ", dilations=" + str(self.dilations) + tmpstr += ", groups=" + str(self.groups) + tmpstr += ", bias=" + str(self.with_bias) + return tmpstr diff --git a/projects/TridentNet/tridentnet/trident_rcnn.py b/projects/TridentNet/tridentnet/trident_rcnn.py new file mode 100644 index 0000000000..f458b25c44 --- /dev/null +++ b/projects/TridentNet/tridentnet/trident_rcnn.py @@ -0,0 +1,110 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from detectron2.layers import batched_nms +from detectron2.modeling import ROI_HEADS_REGISTRY, StandardROIHeads +from detectron2.modeling.roi_heads.roi_heads import Res5ROIHeads +from detectron2.structures import Instances + + +def merge_branch_instances(instances, num_branch, nms_thrsh, topk_per_image): + """ + Merge detection results from different branches of TridentNet. + Return detection results by applying non-maximum suppression (NMS) on bounding boxes + and keep the unsuppressed boxes and other instances (e.g mask) if any. + + Args: + instances (list[Instances]): A list of N * num_branch instances that store detection + results. Contain N images and each image has num_branch instances. + num_branch (int): Number of branches used for merging detection results for each image. + nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1]. + topk_per_image (int): The number of top scoring detections to return. Set < 0 to return + all detections. + + Returns: + results: (list[Instances]): A list of N instances, one for each image in the batch, + that stores the topk most confidence detections after merging results from multiple + branches. + """ + if num_branch == 1: + return instances + + batch_size = len(instances) // num_branch + results = [] + for i in range(batch_size): + instance = Instances.cat([instances[i + batch_size * j] for j in range(num_branch)]) + + # Apply per-class NMS + keep = batched_nms( + instance.pred_boxes.tensor, instance.scores, instance.pred_classes, nms_thrsh + ) + keep = keep[:topk_per_image] + result = instance[keep] + + results.append(result) + + return results + + +@ROI_HEADS_REGISTRY.register() +class TridentRes5ROIHeads(Res5ROIHeads): + """ + The TridentNet ROIHeads in a typical "C4" R-CNN model. + See :class:`Res5ROIHeads`. + """ + + def __init__(self, cfg, input_shape): + super().__init__(cfg, input_shape) + + self.num_branch = cfg.MODEL.TRIDENT.NUM_BRANCH + self.trident_fast = cfg.MODEL.TRIDENT.TEST_BRANCH_IDX != -1 + + def forward(self, images, features, proposals, targets=None): + """ + See :class:`Res5ROIHeads.forward`. + """ + num_branch = self.num_branch if self.training or not self.trident_fast else 1 + all_targets = targets * num_branch if targets is not None else None + pred_instances, losses = super().forward(images, features, proposals, all_targets) + del images, all_targets, targets + + if self.training: + return pred_instances, losses + else: + pred_instances = merge_branch_instances( + pred_instances, num_branch, self.test_nms_thresh, self.test_detections_per_img + ) + + return pred_instances, {} + + +@ROI_HEADS_REGISTRY.register() +class TridentStandardROIHeads(StandardROIHeads): + """ + The `StandardROIHeads` for TridentNet. + See :class:`StandardROIHeads`. + """ + + def __init__(self, cfg, input_shape): + super(TridentStandardROIHeads, self).__init__(cfg, input_shape) + + self.num_branch = cfg.MODEL.TRIDENT.NUM_BRANCH + self.trident_fast = cfg.MODEL.TRIDENT.TEST_BRANCH_IDX != -1 + + def forward(self, images, features, proposals, targets=None): + """ + See :class:`Res5ROIHeads.forward`. + """ + # Use 1 branch if using trident_fast during inference. + num_branch = self.num_branch if self.training or not self.trident_fast else 1 + # Duplicate targets for all branches in TridentNet. + all_targets = targets * num_branch if targets is not None else None + pred_instances, losses = super().forward(images, features, proposals, all_targets) + del images, all_targets, targets + + if self.training: + return pred_instances, losses + else: + pred_instances = merge_branch_instances( + pred_instances, num_branch, self.test_nms_thresh, self.test_detections_per_img + ) + + return pred_instances, {} diff --git a/projects/TridentNet/tridentnet/trident_rpn.py b/projects/TridentNet/tridentnet/trident_rpn.py new file mode 100644 index 0000000000..c30137f312 --- /dev/null +++ b/projects/TridentNet/tridentnet/trident_rpn.py @@ -0,0 +1,32 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import torch + +from detectron2.modeling import PROPOSAL_GENERATOR_REGISTRY +from detectron2.modeling.proposal_generator.rpn import RPN +from detectron2.structures import ImageList + + +@PROPOSAL_GENERATOR_REGISTRY.register() +class TridentRPN(RPN): + """ + Trident RPN subnetwork. + """ + + def __init__(self, cfg, input_shape): + super(TridentRPN, self).__init__(cfg, input_shape) + + self.num_branch = cfg.MODEL.TRIDENT.NUM_BRANCH + self.trident_fast = cfg.MODEL.TRIDENT.TEST_BRANCH_IDX != -1 + + def forward(self, images, features, gt_instances=None): + """ + See :class:`RPN.forward`. + """ + num_branch = self.num_branch if self.training or not self.trident_fast else 1 + # Duplicate images and gt_instances for all branches in TridentNet. + all_images = ImageList( + torch.cat([images.tensor] * num_branch), images.image_sizes * num_branch + ) + all_gt_instances = gt_instances * num_branch if gt_instances is not None else None + + return super(TridentRPN, self).forward(all_images, features, all_gt_instances) diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000000..684e1310d0 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,23 @@ +[isort] +line_length=100 +multi_line_output=4 +known_standard_library=numpy,setuptools +known_myself=detectron2 +known_third_party=fvcore,matplotlib,cv2,torch,torchvision,PIL,pycocotools,yacs,termcolor,cityscapesscripts,tabulate,tqdm,scipy,lvis,torchvision +no_lines_before=STDLIB,THIRDPARTY +sections=FUTURE,STDLIB,THIRDPARTY,myself,FIRSTPARTY,LOCALFOLDER +default_section=FIRSTPARTY + +[mypy] +python_version=3.6 +ignore_missing_imports = True +warn_unused_configs = True +disallow_untyped_defs = True +check_untyped_defs = True +warn_unused_ignores = True +warn_redundant_casts = True +show_column_numbers = True +follow_imports = silent +allow_redefinition = True +; Require all functions to be annotated +disallow_incomplete_defs = True diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000..1b313e60a8 --- /dev/null +++ b/setup.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import glob +import os +from setuptools import find_packages, setup +import torch +from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension + + +def get_extensions(): + this_dir = os.path.dirname(os.path.abspath(__file__)) + extensions_dir = os.path.join(this_dir, "detectron2", "layers", "csrc") + + main_source = os.path.join(extensions_dir, "vision.cpp") + sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp")) + source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu")) + + sources = [main_source] + sources + + extension = CppExtension + + extra_compile_args = {"cxx": []} + define_macros = [] + + if torch.cuda.is_available() and CUDA_HOME is not None: + extension = CUDAExtension + sources += source_cuda + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + + # It's better if pytorch can do this by default .. + CC = os.environ.get("CC", None) + if CC is not None: + extra_compile_args["nvcc"].append("-ccbin={}".format(CC)) + + sources = [os.path.join(extensions_dir, s) for s in sources] + + include_dirs = [extensions_dir] + + ext_modules = [ + extension( + "detectron2._C", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + + return ext_modules + + +setup( + name="detectron2", + version="0.1", + author="FAIR", + url="unknown", + description="object detection in pytorch", + packages=find_packages(exclude=("configs", "tests")), + install_requires=[ + "termcolor>=1.1", + "Pillow", + "yacs>=0.1.6", + "tabulate", + "cloudpickle", + "matplotlib", + "tqdm>4.29.0", + "shapely", + ], + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, +) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000000..168f9979a4 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved diff --git a/tests/test_anchor_generator.py b/tests/test_anchor_generator.py new file mode 100644 index 0000000000..e122655239 --- /dev/null +++ b/tests/test_anchor_generator.py @@ -0,0 +1,90 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import unittest +import torch + +from detectron2.config import get_cfg +from detectron2.layers import ShapeSpec +from detectron2.modeling.anchor_generator import DefaultAnchorGenerator, RotatedAnchorGenerator + +logger = logging.getLogger(__name__) + + +class TestAnchorGenerator(unittest.TestCase): + def test_default_anchor_generator(self): + cfg = get_cfg() + cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]] + cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]] + + anchor_generator = DefaultAnchorGenerator(cfg, [ShapeSpec(stride=4)]) + + # only the last two dimensions of features matter here + num_images = 2 + features = {"stage3": torch.rand(num_images, 96, 1, 2)} + anchors = anchor_generator([features["stage3"]]) + expected_anchor_tensor = torch.tensor( + [ + [-32.0, -8.0, 32.0, 8.0], + [-16.0, -16.0, 16.0, 16.0], + [-8.0, -32.0, 8.0, 32.0], + [-64.0, -16.0, 64.0, 16.0], + [-32.0, -32.0, 32.0, 32.0], + [-16.0, -64.0, 16.0, 64.0], + [-28.0, -8.0, 36.0, 8.0], # -28.0 == -32.0 + STRIDE (4) + [-12.0, -16.0, 20.0, 16.0], + [-4.0, -32.0, 12.0, 32.0], + [-60.0, -16.0, 68.0, 16.0], + [-28.0, -32.0, 36.0, 32.0], + [-12.0, -64.0, 20.0, 64.0], + ] + ) + + for i in range(num_images): + assert torch.allclose(anchors[i][0].tensor, expected_anchor_tensor) + + def test_rrpn_anchor_generator(self): + cfg = get_cfg() + cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]] + cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]] + cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [[0, 45]] + anchor_generator = RotatedAnchorGenerator(cfg, [ShapeSpec(stride=4)]) + + # only the last two dimensions of features matter here + num_images = 2 + features = {"stage3": torch.rand(num_images, 96, 1, 2)} + anchors = anchor_generator([features["stage3"]]) + expected_anchor_tensor = torch.tensor( + [ + [0.0, 0.0, 64.0, 16.0, 0.0], + [0.0, 0.0, 64.0, 16.0, 45.0], + [0.0, 0.0, 32.0, 32.0, 0.0], + [0.0, 0.0, 32.0, 32.0, 45.0], + [0.0, 0.0, 16.0, 64.0, 0.0], + [0.0, 0.0, 16.0, 64.0, 45.0], + [0.0, 0.0, 128.0, 32.0, 0.0], + [0.0, 0.0, 128.0, 32.0, 45.0], + [0.0, 0.0, 64.0, 64.0, 0.0], + [0.0, 0.0, 64.0, 64.0, 45.0], + [0.0, 0.0, 32.0, 128.0, 0.0], + [0.0, 0.0, 32.0, 128.0, 45.0], + [4.0, 0.0, 64.0, 16.0, 0.0], # 4.0 == 0.0 + STRIDE (4) + [4.0, 0.0, 64.0, 16.0, 45.0], + [4.0, 0.0, 32.0, 32.0, 0.0], + [4.0, 0.0, 32.0, 32.0, 45.0], + [4.0, 0.0, 16.0, 64.0, 0.0], + [4.0, 0.0, 16.0, 64.0, 45.0], + [4.0, 0.0, 128.0, 32.0, 0.0], + [4.0, 0.0, 128.0, 32.0, 45.0], + [4.0, 0.0, 64.0, 64.0, 0.0], + [4.0, 0.0, 64.0, 64.0, 45.0], + [4.0, 0.0, 32.0, 128.0, 0.0], + [4.0, 0.0, 32.0, 128.0, 45.0], + ] + ) + + for i in range(num_images): + assert torch.allclose(anchors[i][0].tensor, expected_anchor_tensor) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_box2box_transform.py b/tests/test_box2box_transform.py new file mode 100644 index 0000000000..d9e7aaf6ee --- /dev/null +++ b/tests/test_box2box_transform.py @@ -0,0 +1,58 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import unittest +import torch + +from detectron2.modeling.box_regression import Box2BoxTransform, Box2BoxTransformRotated + +logger = logging.getLogger(__name__) + + +def random_boxes(mean_box, stdev, N): + return torch.rand(N, 4) * stdev + torch.tensor(mean_box, dtype=torch.float) + + +class TestBox2BoxTransform(unittest.TestCase): + def test_reconstruction(self): + weights = (5, 5, 10, 10) + b2b_tfm = Box2BoxTransform(weights=weights) + src_boxes = random_boxes([10, 10, 20, 20], 1, 10) + dst_boxes = random_boxes([10, 10, 20, 20], 1, 10) + + devices = [torch.device("cpu")] + if torch.cuda.is_available(): + devices.append(torch.device("cuda")) + for device in devices: + src_boxes = src_boxes.to(device=device) + dst_boxes = dst_boxes.to(device=device) + deltas = b2b_tfm.get_deltas(src_boxes, dst_boxes) + dst_boxes_reconstructed = b2b_tfm.apply_deltas(deltas, src_boxes) + assert torch.allclose(dst_boxes, dst_boxes_reconstructed) + + +def random_rotated_boxes(mean_box, std_length, std_angle, N): + return torch.cat( + [torch.rand(N, 4) * std_length, torch.rand(N, 1) * std_angle], dim=1 + ) + torch.tensor(mean_box, dtype=torch.float) + + +class TestBox2BoxTransformRotated(unittest.TestCase): + def test_reconstruction(self): + weights = (5, 5, 10, 10, 1) + b2b_transform = Box2BoxTransformRotated(weights=weights) + src_boxes = random_rotated_boxes([10, 10, 20, 20, -30], 5, 60.0, 10) + dst_boxes = random_rotated_boxes([10, 10, 20, 20, -30], 5, 60.0, 10) + + devices = [torch.device("cpu")] + if torch.cuda.is_available(): + devices.append(torch.device("cuda")) + for device in devices: + src_boxes = src_boxes.to(device=device) + dst_boxes = dst_boxes.to(device=device) + deltas = b2b_transform.get_deltas(src_boxes, dst_boxes) + dst_boxes_reconstructed = b2b_transform.apply_deltas(deltas, src_boxes) + assert torch.allclose(dst_boxes, dst_boxes_reconstructed, atol=1e-5) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_boxes.py b/tests/test_boxes.py new file mode 100644 index 0000000000..7694fa1361 --- /dev/null +++ b/tests/test_boxes.py @@ -0,0 +1,63 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import numpy as np +import unittest +import torch + +from detectron2.structures import Boxes, BoxMode, pairwise_iou + + +class TestBoxMode(unittest.TestCase): + def _convert_xy_to_wh(self, x): + return BoxMode.convert(x, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + + def test_box_convert_list(self): + for tp in [list, tuple]: + box = tp([5, 5, 10, 10]) + output = self._convert_xy_to_wh(box) + self.assertTrue(output == tp([5, 5, 5, 5])) + + with self.assertRaises(Exception): + self._convert_xy_to_wh([box]) + + def test_box_convert_array(self): + box = np.asarray([[5, 5, 10, 10], [1, 1, 2, 3]]) + output = self._convert_xy_to_wh(box) + self.assertTrue((output[0] == [5, 5, 5, 5]).all()) + self.assertTrue((output[1] == [1, 1, 1, 2]).all()) + + def test_box_convert_tensor(self): + box = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]]) + output = self._convert_xy_to_wh(box).numpy() + self.assertTrue((output[0] == [5, 5, 5, 5]).all()) + self.assertTrue((output[1] == [1, 1, 1, 2]).all()) + + +class TestBoxIOU(unittest.TestCase): + def test_pairwise_iou(self): + boxes1 = torch.tensor([[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]) + + boxes2 = torch.tensor( + [ + [0.0, 0.0, 1.0, 1.0], + [0.0, 0.0, 0.5, 1.0], + [0.0, 0.0, 1.0, 0.5], + [0.0, 0.0, 0.5, 0.5], + [0.5, 0.5, 1.0, 1.0], + [0.5, 0.5, 1.5, 1.5], + ] + ) + + expected_ious = torch.tensor( + [ + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + ] + ) + + ious = pairwise_iou(Boxes(boxes1), Boxes(boxes2)) + + assert torch.allclose(ious, expected_ious) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_checkpoint.py b/tests/test_checkpoint.py new file mode 100644 index 0000000000..725b488fda --- /dev/null +++ b/tests/test_checkpoint.py @@ -0,0 +1,48 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import unittest +from collections import OrderedDict +import torch +from torch import nn + +from detectron2.checkpoint.c2_model_loading import align_and_update_state_dicts +from detectron2.utils.logger import setup_logger + + +class TestCheckpointer(unittest.TestCase): + def setUp(self): + setup_logger() + + def create_complex_model(self): + m = nn.Module() + m.block1 = nn.Module() + m.block1.layer1 = nn.Linear(2, 3) + m.layer2 = nn.Linear(3, 2) + m.res = nn.Module() + m.res.layer2 = nn.Linear(3, 2) + + state_dict = OrderedDict() + state_dict["layer1.weight"] = torch.rand(3, 2) + state_dict["layer1.bias"] = torch.rand(3) + state_dict["layer2.weight"] = torch.rand(2, 3) + state_dict["layer2.bias"] = torch.rand(2) + state_dict["res.layer2.weight"] = torch.rand(2, 3) + state_dict["res.layer2.bias"] = torch.rand(2) + return m, state_dict + + def test_complex_model_loaded(self): + for add_data_parallel in [False, True]: + model, state_dict = self.create_complex_model() + if add_data_parallel: + model = nn.DataParallel(model) + model_sd = model.state_dict() + + align_and_update_state_dicts(model_sd, state_dict) + for loaded, stored in zip(model_sd.values(), state_dict.values()): + # different tensor references + self.assertFalse(id(loaded) == id(stored)) + # same content + self.assertTrue(loaded.equal(stored)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_config.py b/tests/test_config.py new file mode 100644 index 0000000000..d79f07c498 --- /dev/null +++ b/tests/test_config.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + + +import os +import tempfile +import unittest + +from detectron2.config import downgrade_config, get_cfg, upgrade_config + +_V0_CFG = """ +MODEL: + RPN_HEAD: + NAME: "TEST" +VERSION: 0 +""" + +_V1_CFG = """ +MODEL: + WEIGHT: "/path/to/weight" +""" + + +class TestConfigVersioning(unittest.TestCase): + def test_upgrade_downgrade_consistency(self): + cfg = get_cfg() + # check that custom is preserved + cfg.USER_CUSTOM = 1 + + down = downgrade_config(cfg, to_version=0) + up = upgrade_config(down) + self.assertTrue(up == cfg) + + def _merge_cfg_str(self, cfg, merge_str): + f = tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) + try: + f.write(merge_str) + f.close() + cfg.merge_from_file(f.name) + finally: + os.remove(f.name) + return cfg + + def test_auto_upgrade(self): + cfg = get_cfg() + latest_ver = cfg.VERSION + cfg.USER_CUSTOM = 1 + + self._merge_cfg_str(cfg, _V0_CFG) + + self.assertEqual(cfg.MODEL.RPN.HEAD_NAME, "TEST") + self.assertEqual(cfg.VERSION, latest_ver) + + def test_guess_v1(self): + cfg = get_cfg() + latest_ver = cfg.VERSION + self._merge_cfg_str(cfg, _V1_CFG) + self.assertEqual(cfg.VERSION, latest_ver) diff --git a/tests/test_data_transform.py b/tests/test_data_transform.py new file mode 100644 index 0000000000..8fdfe8056e --- /dev/null +++ b/tests/test_data_transform.py @@ -0,0 +1,79 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import logging +import numpy as np +import unittest + +from detectron2.config import get_cfg +from detectron2.data import detection_utils +from detectron2.data import transforms as T +from detectron2.utils.logger import setup_logger + +logger = logging.getLogger(__name__) + + +class TestTransforms(unittest.TestCase): + def setUp(self): + setup_logger() + + def test_crop_polygons(self): + # Ensure that shapely produce an extra vertex at the end + import shapely.geometry as geometry + + polygon = np.asarray([3, 3.5, 11, 10.0, 38, 98, 15.0, 100.0]).reshape(-1, 2) + g = geometry.Polygon(polygon) + coords = np.asarray(g.exterior.coords) + self.assertEqual(coords[0].tolist(), coords[-1].tolist()) + + def test_apply_rotated_boxes(self): + np.random.seed(125) + cfg = get_cfg() + is_train = True + transform_gen = detection_utils.build_transform_gen(cfg, is_train) + image = np.random.rand(200, 300) + image, transforms = T.apply_transform_gens(transform_gen, image) + image_shape = image.shape[:2] # h, w + assert image_shape == (800, 1200) + annotation = {"bbox": [179, 97, 62, 40, -56]} + + boxes = np.array([annotation["bbox"]], dtype=np.float64) # boxes.shape = (1, 5) + transformed_bbox = transforms.apply_rotated_box(boxes)[0] + + expected_bbox = np.array([484, 388, 248, 160, 56], dtype=np.float64) + err_msg = "transformed_bbox = {}, expected {}".format(transformed_bbox, expected_bbox) + assert np.allclose(transformed_bbox, expected_bbox), err_msg + + def test_apply_rotated_boxes_unequal_scaling_factor(self): + np.random.seed(125) + h, w = 400, 200 + newh, neww = 800, 800 + image = np.random.rand(h, w) + transform_gen = [] + transform_gen.append(T.Resize(shape=(newh, neww))) + image, transforms = T.apply_transform_gens(transform_gen, image) + image_shape = image.shape[:2] # h, w + assert image_shape == (newh, neww) + + boxes = np.array( + [ + [150, 100, 40, 20, 0], + [150, 100, 40, 20, 30], + [150, 100, 40, 20, 90], + [150, 100, 40, 20, -90], + ], + dtype=np.float64, + ) + transformed_boxes = transforms.apply_rotated_box(boxes) + + expected_bboxes = np.array( + [ + [600, 200, 160, 40, 0], + [600, 200, 144.22205102, 52.91502622, 49.10660535], + [600, 200, 80, 80, 90], + [600, 200, 80, 80, -90], + ], + dtype=np.float64, + ) + err_msg = "transformed_boxes = {}, expected {}".format(transformed_boxes, expected_bboxes) + assert np.allclose(transformed_boxes, expected_bboxes), err_msg diff --git a/tests/test_fast_rcnn.py b/tests/test_fast_rcnn.py new file mode 100644 index 0000000000..17df9d9560 --- /dev/null +++ b/tests/test_fast_rcnn.py @@ -0,0 +1,102 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import unittest +import torch + +from detectron2.config import get_cfg +from detectron2.modeling.box_regression import Box2BoxTransform, Box2BoxTransformRotated +from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers, FastRCNNOutputs +from detectron2.structures import Boxes, Instances, RotatedBoxes +from detectron2.utils.events import EventStorage + +logger = logging.getLogger(__name__) + + +class FastRCNNTest(unittest.TestCase): + def test_fast_rcnn(self): + torch.manual_seed(132) + cfg = get_cfg() + cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5) + box2box_transform = Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS) + + box_head_output_size = 8 + num_classes = 5 + cls_agnostic_bbox_reg = False + + box_predictor = FastRCNNOutputLayers( + box_head_output_size, num_classes, cls_agnostic_bbox_reg, box_dim=4 + ) + feature_pooled = torch.rand(2, box_head_output_size) + pred_class_logits, pred_proposal_deltas = box_predictor(feature_pooled) + image_shape = (10, 10) + proposal_boxes = torch.tensor([[0.8, 1.1, 3.2, 2.8], [2.3, 2.5, 7, 8]], dtype=torch.float32) + gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32) + result = Instances(image_shape) + result.proposal_boxes = Boxes(proposal_boxes) + result.gt_boxes = Boxes(gt_boxes) + result.gt_classes = torch.tensor([1, 2]) + proposals = [] + proposals.append(result) + smooth_l1_beta = cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA + + outputs = FastRCNNOutputs( + box2box_transform, pred_class_logits, pred_proposal_deltas, proposals, smooth_l1_beta + ) + with EventStorage(): # capture events in a new storage to discard them + losses = outputs.losses() + + expected_losses = { + "loss_cls": torch.tensor(1.7951188087), + "loss_box_reg": torch.tensor(4.0357131958), + } + for name in expected_losses.keys(): + assert torch.allclose(losses[name], expected_losses[name]) + + def test_fast_rcnn_rotated(self): + torch.manual_seed(132) + cfg = get_cfg() + cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5, 1) + box2box_transform = Box2BoxTransformRotated(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS) + + box_head_output_size = 8 + num_classes = 5 + cls_agnostic_bbox_reg = False + + box_predictor = FastRCNNOutputLayers( + box_head_output_size, num_classes, cls_agnostic_bbox_reg, box_dim=5 + ) + feature_pooled = torch.rand(2, box_head_output_size) + pred_class_logits, pred_proposal_deltas = box_predictor(feature_pooled) + image_shape = (10, 10) + proposal_boxes = torch.tensor( + [[2, 1.95, 2.4, 1.7, 0], [4.65, 5.25, 4.7, 5.5, 0]], dtype=torch.float32 + ) + gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32) + result = Instances(image_shape) + result.proposal_boxes = RotatedBoxes(proposal_boxes) + result.gt_boxes = RotatedBoxes(gt_boxes) + result.gt_classes = torch.tensor([1, 2]) + proposals = [] + proposals.append(result) + smooth_l1_beta = cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA + + outputs = FastRCNNOutputs( + box2box_transform, pred_class_logits, pred_proposal_deltas, proposals, smooth_l1_beta + ) + with EventStorage(): # capture events in a new storage to discard them + losses = outputs.losses() + + # Note: the expected losses are slightly different even if + # the boxes are essentially the same as in the FastRCNNOutput test, because + # bbox_pred in FastRCNNOutputLayers have different Linear layers/initialization + # between the two cases. + expected_losses = { + "loss_cls": torch.tensor(1.7920907736), + "loss_box_reg": torch.tensor(4.0410838127), + } + for name in expected_losses.keys(): + assert torch.allclose(losses[name], expected_losses[name]) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_mask_ops.py b/tests/test_mask_ops.py new file mode 100644 index 0000000000..cc92e13948 --- /dev/null +++ b/tests/test_mask_ops.py @@ -0,0 +1,174 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import contextlib +import io +import numpy as np +import os +import unittest +from collections import defaultdict +import torch +import tqdm +from fvcore.common.benchmark import benchmark +from pycocotools.coco import COCO +from tabulate import tabulate +from torch.nn import functional as F + +from detectron2.data import MetadataCatalog +from detectron2.layers.mask_ops import ( + pad_masks, + paste_mask_in_image_old, + paste_masks_in_image, + scale_boxes, +) +from detectron2.structures import BitMasks, Boxes, BoxMode, PolygonMasks +from detectron2.structures.masks import polygons_to_bitmask + + +def iou_between_full_image_bit_masks(a, b): + intersect = (a & b).sum() + union = (a | b).sum() + return intersect / union + + +def rasterize_polygons_with_grid_sample(full_image_bit_mask, box, mask_size, threshold=0.5): + x0, y0, x1, y1 = box[0], box[1], box[2], box[3] + + img_h, img_w = full_image_bit_mask.shape + + mask_y = np.arange(0.0, mask_size) + 0.5 # mask y sample coords in [0.5, mask_size - 0.5] + mask_x = np.arange(0.0, mask_size) + 0.5 # mask x sample coords in [0.5, mask_size - 0.5] + mask_y = (mask_y) / (mask_size) * (y1 - y0) + y0 + mask_x = (mask_x) / (mask_size) * (x1 - x0) + x0 + + mask_x = (mask_x - 0.5) / (img_w - 1) * 2 + -1 + mask_y = (mask_y - 0.5) / (img_h - 1) * 2 + -1 + gy, gx = torch.meshgrid(torch.from_numpy(mask_y), torch.from_numpy(mask_x)) + ind = torch.stack([gx, gy], dim=-1).to(dtype=torch.float32) + + full_image_bit_mask = torch.from_numpy(full_image_bit_mask) + mask = F.grid_sample( + full_image_bit_mask[None, None, :, :].to(dtype=torch.float32), + ind[None, :, :, :], + align_corners=True, + ) + + return mask[0, 0] >= threshold + + +class TestMaskCropPaste(unittest.TestCase): + def setUp(self): + json_file = MetadataCatalog.get("coco_2017_val_100").json_file + if not os.path.isfile(json_file): + raise unittest.SkipTest("{} not found".format(json_file)) + with contextlib.redirect_stdout(io.StringIO()): + self.coco = COCO(json_file) + + def test_crop_paste_consistency(self): + """ + rasterize_polygons_within_box (used in training) + and + paste_masks_in_image (used in inference) + should be inverse operations to each other. + + This function runs several implementation of the above two operations and prints + the reconstruction error. + """ + + anns = self.coco.loadAnns(self.coco.getAnnIds(iscrowd=False)) # avoid crowd annotations + + selected_anns = anns[:100] + + ious = [] + for ann in tqdm.tqdm(selected_anns): + results = self.process_annotation(ann) + ious.append([k[2] for k in results]) + + ious = np.array(ious) + mean_ious = ious.mean(axis=0) + table = [] + res_dic = defaultdict(dict) + for row, iou in zip(results, mean_ious): + table.append((row[0], row[1], iou)) + res_dic[row[0]][row[1]] = iou + print(tabulate(table, headers=["rasterize", "paste", "iou"], tablefmt="simple")) + # assert that the reconstruction is good: + self.assertTrue(res_dic["polygon"]["aligned"] > 0.94) + self.assertTrue(res_dic["roialign"]["aligned"] > 0.95) + + def process_annotation(self, ann, mask_side_len=28): + # Parse annotation data + img_info = self.coco.loadImgs(ids=[ann["image_id"]])[0] + height, width = img_info["height"], img_info["width"] + gt_polygons = [np.array(p, dtype=np.float64) for p in ann["segmentation"]] + gt_bbox = BoxMode.convert(np.array(ann["bbox"]), BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + gt_bit_mask = polygons_to_bitmask(gt_polygons, height, width) + + # Run rasterize .. + torch_gt_bbox = torch.from_numpy(gt_bbox[None, :]).to(dtype=torch.float32) + box_bitmasks = { + "polygon": PolygonMasks([gt_polygons]).crop_and_resize(torch_gt_bbox, mask_side_len)[0], + "gridsample": rasterize_polygons_with_grid_sample(gt_bit_mask, gt_bbox, mask_side_len), + "roialign": BitMasks(torch.from_numpy(gt_bit_mask[None, :, :])).crop_and_resize( + torch_gt_bbox, mask_side_len + )[0], + } + + # Run paste .. + results = defaultdict(dict) + for k, box_bitmask in box_bitmasks.items(): + padded_bitmask, scale = pad_masks(box_bitmask[None, :, :], 1) + scaled_boxes = scale_boxes(torch_gt_bbox, scale) + + r = results[k] + r["old"] = paste_mask_in_image_old( + padded_bitmask[0], scaled_boxes[0], height, width, threshold=0.5 + ) + r["aligned"] = paste_masks_in_image( + box_bitmask[None, :, :], Boxes(gt_bbox[None, :]), (height, width) + )[0] + + table = [] + for rasterize_method, r in results.items(): + for paste_method, mask in r.items(): + mask = np.asarray(mask) + iou = iou_between_full_image_bit_masks(gt_bit_mask.astype("uint8"), mask) + table.append((rasterize_method, paste_method, iou)) + return table + + +def benchmark_paste(): + S = 800 + H, W = image_shape = (S, S) + N = 64 + torch.manual_seed(42) + masks = torch.rand(N, 28, 28) + + center = torch.rand(N, 2) * 600 + 100 + wh = torch.clamp(torch.randn(N, 2) * 40 + 200, min=50) + x0y0 = torch.clamp(center - wh * 0.5, min=0.0) + x1y1 = torch.clamp(center + wh * 0.5, max=S) + boxes = Boxes(torch.cat([x0y0, x1y1], axis=1)) + + def func(device, n=3): + m = masks.to(device=device) + b = boxes.to(device=device) + + def bench(): + for _ in range(n): + paste_masks_in_image(m, b, image_shape) + if device.type == "cuda": + torch.cuda.synchronize() + + return bench + + specs = [{"device": torch.device("cpu"), "n": 3}] + if torch.cuda.is_available(): + specs.append({"device": torch.device("cuda"), "n": 3}) + + benchmark(func, "paste_masks", specs, num_iters=10, warmup_iters=2) + + +if __name__ == "__main__": + benchmark_paste() + unittest.main() diff --git a/tests/test_nms_rotated.py b/tests/test_nms_rotated.py new file mode 100644 index 0000000000..d04a99c15d --- /dev/null +++ b/tests/test_nms_rotated.py @@ -0,0 +1,159 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from __future__ import absolute_import, division, print_function, unicode_literals +import unittest +import torch +from torchvision import ops + +from detectron2.layers import batched_nms, batched_nms_rotated, nms_rotated + + +class TestNMSRotated(unittest.TestCase): + def reference_horizontal_nms(self, boxes, scores, iou_threshold): + """ + Args: + box_scores (N, 5): boxes in corner-form and probabilities. + (Note here 5 == 4 + 1, i.e., 4-dim horizontal box + 1-dim prob) + iou_threshold: intersection over union threshold. + Returns: + picked: a list of indexes of the kept boxes + """ + picked = [] + _, indexes = scores.sort(descending=True) + while len(indexes) > 0: + current = indexes[0] + picked.append(current.item()) + if len(indexes) == 1: + break + current_box = boxes[current, :] + indexes = indexes[1:] + rest_boxes = boxes[indexes, :] + iou = ops.box_iou(rest_boxes, current_box.unsqueeze(0)).squeeze(1) + indexes = indexes[iou <= iou_threshold] + + return torch.as_tensor(picked) + + def _create_tensors(self, N): + boxes = torch.rand(N, 4) * 100 + # Note: the implementation of this function in torchvision is: + # boxes[:, 2:] += torch.rand(N, 2) * 100 + # but it does not guarantee non-negative widths/heights constraints: + # boxes[:, 2] >= boxes[:, 0] and boxes[:, 3] >= boxes[:, 1]: + boxes[:, 2:] += boxes[:, :2] + scores = torch.rand(N) + return boxes, scores + + def test_bactched_nms_rotated_0_degree_cpu(self): + # torch.manual_seed(0) + N = 2000 + num_classes = 50 + boxes, scores = self._create_tensors(N) + idxs = torch.randint(0, num_classes, (N,)) + rotated_boxes = torch.zeros(N, 5) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] + rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] + err_msg = "Rotated NMS with 0 degree is incompatible with horizontal NMS for IoU={}" + for iou in [0.2, 0.5, 0.8]: + backup = boxes.clone() + keep_ref = batched_nms(boxes, scores, idxs, iou) + assert torch.allclose(boxes, backup), "boxes modified by batched_nms" + backup = rotated_boxes.clone() + keep = batched_nms_rotated(rotated_boxes, scores, idxs, iou) + assert torch.allclose( + rotated_boxes, backup + ), "rotated_boxes modified by batched_nms_rotated" + assert torch.equal(keep, keep_ref), err_msg.format(iou) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable") + def test_bactched_nms_rotated_0_degree_cuda(self): + # torch.manual_seed(0) + N = 2000 + num_classes = 50 + boxes, scores = self._create_tensors(N) + idxs = torch.randint(0, num_classes, (N,)) + rotated_boxes = torch.zeros(N, 5) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] + rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] + err_msg = "Rotated NMS with 0 degree is incompatible with horizontal NMS for IoU={}" + for iou in [0.2, 0.5, 0.8]: + backup = boxes.clone() + keep_ref = batched_nms(boxes.cuda(), scores.cuda(), idxs, iou) + assert torch.allclose(boxes, backup), "boxes modified by batched_nms" + backup = rotated_boxes.clone() + keep = batched_nms_rotated(rotated_boxes.cuda(), scores.cuda(), idxs, iou) + assert torch.allclose( + rotated_boxes, backup + ), "rotated_boxes modified by batched_nms_rotated" + assert torch.equal(keep, keep_ref), err_msg.format(iou) + + def test_nms_rotated_0_degree_cpu(self): + N = 1000 + boxes, scores = self._create_tensors(N) + rotated_boxes = torch.zeros(N, 5) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] + rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] + err_msg = "Rotated NMS incompatible between CPU and reference implementation for IoU={}" + for iou in [0.5]: + keep_ref = self.reference_horizontal_nms(boxes, scores, iou) + keep = nms_rotated(rotated_boxes, scores, iou) + assert torch.equal(keep, keep_ref), err_msg.format(iou) + + def test_nms_rotated_90_degrees_cpu(self): + N = 1000 + boxes, scores = self._create_tensors(N) + rotated_boxes = torch.zeros(N, 5) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + # Note for rotated_boxes[:, 2] and rotated_boxes[:, 3]: + # widths and heights are intentionally swapped here for 90 degrees case + # so that the reference horizontal nms could be used + rotated_boxes[:, 2] = boxes[:, 3] - boxes[:, 1] + rotated_boxes[:, 3] = boxes[:, 2] - boxes[:, 0] + + rotated_boxes[:, 4] = torch.ones(N) * 90 + err_msg = "Rotated NMS incompatible between CPU and reference implementation for IoU={}" + for iou in [0.2, 0.5, 0.8]: + keep_ref = self.reference_horizontal_nms(boxes, scores, iou) + keep = nms_rotated(rotated_boxes, scores, iou) + assert torch.equal(keep, keep_ref), err_msg.format(iou) + + def test_nms_rotated_180_degrees_cpu(self): + N = 1000 + boxes, scores = self._create_tensors(N) + rotated_boxes = torch.zeros(N, 5) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] + rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] + rotated_boxes[:, 4] = torch.ones(N) * 180 + err_msg = "Rotated NMS incompatible between CPU and reference implementation for IoU={}" + for iou in [0.2, 0.5, 0.8]: + keep_ref = self.reference_horizontal_nms(boxes, scores, iou) + keep = nms_rotated(rotated_boxes, scores, iou) + assert torch.equal(keep, keep_ref), err_msg.format(iou) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable") + def test_nms_rotated_0_degree_cuda(self): + N = 1000 + boxes, scores = self._create_tensors(N) + rotated_boxes = torch.zeros(N, 5) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] + rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] + err_msg = "Rotated NMS incompatible between CPU and CUDA for IoU={}" + + for iou in [0.2, 0.5, 0.8]: + r_cpu = nms_rotated(rotated_boxes, scores, iou) + r_cuda = nms_rotated(rotated_boxes.cuda(), scores.cuda(), iou) + + assert torch.equal(r_cpu, r_cuda.cpu()), err_msg.format(iou) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_roi_align.py b/tests/test_roi_align.py new file mode 100644 index 0000000000..a745678808 --- /dev/null +++ b/tests/test_roi_align.py @@ -0,0 +1,86 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import numpy as np +import unittest +import cv2 +import torch + +from detectron2.layers.roi_align import ROIAlign + + +class ROIAlignTest(unittest.TestCase): + def test_forward_output(self): + input = np.arange(25).reshape(5, 5).astype("float32") + """ + 0 1 2 3 4 + 5 6 7 8 9 + 10 11 12 13 14 + 15 16 17 18 19 + 20 21 22 23 24 + """ + + output = self._simple_roialign(input, [1, 1, 3, 3], (4, 4), aligned=False) + output_correct = self._simple_roialign(input, [1, 1, 3, 3], (4, 4), aligned=True) + + # without correction: + old_results = [ + [7.5, 8, 8.5, 9], + [10, 10.5, 11, 11.5], + [12.5, 13, 13.5, 14], + [15, 15.5, 16, 16.5], + ] + + # with 0.5 correction: + correct_results = [ + [4.5, 5.0, 5.5, 6.0], + [7.0, 7.5, 8.0, 8.5], + [9.5, 10.0, 10.5, 11.0], + [12.0, 12.5, 13.0, 13.5], + ] + # This is an upsampled version of [[6, 7], [11, 12]] + + self.assertTrue(np.allclose(output.flatten(), np.asarray(old_results).flatten())) + self.assertTrue( + np.allclose(output_correct.flatten(), np.asarray(correct_results).flatten()) + ) + + # Also see similar issues in tensorflow at + # https://github.com/tensorflow/tensorflow/issues/26278 + + def test_resize(self): + H, W = 30, 30 + input = np.random.rand(H, W).astype("float32") * 100 + box = [10, 10, 20, 20] + output = self._simple_roialign(input, box, (5, 5), aligned=True) + + input2x = cv2.resize(input, (W // 2, H // 2), interpolation=cv2.INTER_LINEAR) + box2x = [x / 2 for x in box] + output2x = self._simple_roialign(input2x, box2x, (5, 5), aligned=True) + diff = np.abs(output2x - output) + self.assertTrue(diff.max() < 1e-4) + + def _simple_roialign(self, img, box, resolution, aligned=True): + """ + RoiAlign with scale 1.0 and 0 sample ratio. + """ + if isinstance(resolution, int): + resolution = (resolution, resolution) + op = ROIAlign(resolution, 1.0, 0, aligned=aligned) + input = torch.from_numpy(img[None, None, :, :].astype("float32")) + + rois = [0] + list(box) + rois = torch.from_numpy(np.asarray(rois)[None, :].astype("float32")) + output = op.forward(input, rois).numpy() + if torch.cuda.is_available(): + output_cuda = op.forward(input.cuda(), rois.cuda()).cpu().numpy() + self.assertTrue(np.allclose(output, output_cuda)) + return output[0, 0] + + def test_empty_box(self): + img = np.random.rand(5, 5) + box = [3, 4, 5, 4] + o = self._simple_roialign(img, box, 7) + self.assertTrue((o == 0).all()) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_roi_align_rotated.py b/tests/test_roi_align_rotated.py new file mode 100644 index 0000000000..d7900678ea --- /dev/null +++ b/tests/test_roi_align_rotated.py @@ -0,0 +1,176 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import unittest +import cv2 +import torch +from torch.autograd import Variable, gradcheck + +from detectron2.layers.roi_align import ROIAlign +from detectron2.layers.roi_align_rotated import ROIAlignRotated + +logger = logging.getLogger(__name__) + + +class ROIAlignRotatedTest(unittest.TestCase): + def _box_to_rotated_box(self, box, angle): + return [ + (box[0] + box[2]) / 2.0, + (box[1] + box[3]) / 2.0, + box[2] - box[0], + box[3] - box[1], + angle, + ] + + def _rot90(self, img, num): + num = num % 4 # note: -1 % 4 == 3 + for _ in range(num): + img = img.transpose(0, 1).flip(0) + return img + + def test_forward_output_0_90_180_270(self): + for i in range(4): + # i = 0, 1, 2, 3 corresponding to 0, 90, 180, 270 degrees + img = torch.arange(25, dtype=torch.float32).reshape(5, 5) + """ + 0 1 2 3 4 + 5 6 7 8 9 + 10 11 12 13 14 + 15 16 17 18 19 + 20 21 22 23 24 + """ + box = [1, 1, 3, 3] + rotated_box = self._box_to_rotated_box(box=box, angle=90 * i) + + result = self._simple_roi_align_rotated(img=img, box=rotated_box, resolution=(4, 4)) + + # Here's an explanation for 0 degree case: + # point 0 in the original input lies at [0.5, 0.5] + # (the center of bin [0, 1] x [0, 1]) + # point 1 in the original input lies at [1.5, 0.5], etc. + # since the resolution is (4, 4) that divides [1, 3] x [1, 3] + # into 4 x 4 equal bins, + # the top-left bin is [1, 1.5] x [1, 1.5], and its center + # (1.25, 1.25) lies at the 3/4 position + # between point 0 and point 1, point 5 and point 6, + # point 0 and point 5, point 1 and point 6, so it can be calculated as + # 0.25*(0*0.25+1*0.75)+(5*0.25+6*0.75)*0.75 = 4.5 + result_expected = torch.tensor( + [ + [4.5, 5.0, 5.5, 6.0], + [7.0, 7.5, 8.0, 8.5], + [9.5, 10.0, 10.5, 11.0], + [12.0, 12.5, 13.0, 13.5], + ] + ) + # This is also an upsampled version of [[6, 7], [11, 12]] + + # When the box is rotated by 90 degrees CCW, + # the result would be rotated by 90 degrees CW, thus it's -i here + result_expected = self._rot90(result_expected, -i) + + assert torch.allclose(result, result_expected) + + def test_resize(self): + H, W = 30, 30 + input = torch.rand(H, W) * 100 + box = [10, 10, 20, 20] + rotated_box = self._box_to_rotated_box(box, angle=0) + output = self._simple_roi_align_rotated(img=input, box=rotated_box, resolution=(5, 5)) + + input2x = cv2.resize(input.numpy(), (W // 2, H // 2), interpolation=cv2.INTER_LINEAR) + input2x = torch.from_numpy(input2x) + box2x = [x / 2 for x in box] + rotated_box2x = self._box_to_rotated_box(box2x, angle=0) + output2x = self._simple_roi_align_rotated(img=input2x, box=rotated_box2x, resolution=(5, 5)) + assert torch.allclose(output2x, output) + + def _simple_roi_align_rotated(self, img, box, resolution): + """ + RoiAlignRotated with scale 1.0 and 0 sample ratio. + """ + op = ROIAlignRotated(output_size=resolution, spatial_scale=1.0, sampling_ratio=0) + input = img[None, None, :, :] + + rois = [0] + list(box) + rois = torch.tensor(rois, dtype=torch.float32)[None, :] + result_cpu = op.forward(input, rois) + if torch.cuda.is_available(): + result_cuda = op.forward(input.cuda(), rois.cuda()) + assert torch.allclose(result_cpu, result_cuda.cpu()) + return result_cpu[0, 0] + + def test_empty_box(self): + img = torch.rand(5, 5) + out = self._simple_roi_align_rotated(img, [2, 3, 0, 0, 0], (7, 7)) + self.assertTrue((out == 0).all()) + + def test_roi_align_rotated_gradcheck_cpu(self): + dtype = torch.float64 + device = torch.device("cpu") + roi_align_rotated_op = ROIAlignRotated( + output_size=(5, 5), spatial_scale=0.5, sampling_ratio=1 + ).to(dtype=dtype, device=device) + x = torch.rand(1, 1, 10, 10, dtype=dtype, device=device, requires_grad=True) + # roi format is (batch index, x_center, y_center, width, height, angle) + rois = torch.tensor( + [[0, 4.5, 4.5, 9, 9, 0], [0, 2, 7, 4, 4, 0], [0, 7, 7, 4, 4, 0]], + dtype=dtype, + device=device, + ) + + def func(input): + return roi_align_rotated_op(input, rois) + + assert gradcheck(func, (x,)), "gradcheck failed for RoIAlignRotated CPU" + assert gradcheck(func, (x.transpose(2, 3),)), "gradcheck failed for RoIAlignRotated CPU" + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable") + def test_roi_align_rotated_gradient_cuda(self): + """ + Compute gradients for ROIAlignRotated with multiple bounding boxes on the GPU, + and compare the result with ROIAlign + """ + # torch.manual_seed(123) + dtype = torch.float64 + device = torch.device("cuda") + pool_h, pool_w = (5, 5) + + roi_align = ROIAlign(output_size=(pool_h, pool_w), spatial_scale=1, sampling_ratio=2).to( + device=device + ) + + roi_align_rotated = ROIAlignRotated( + output_size=(pool_h, pool_w), spatial_scale=1, sampling_ratio=2 + ).to(device=device) + + x = torch.rand(1, 1, 10, 10, dtype=dtype, device=device, requires_grad=True) + # x_rotated = x.clone() won't work (will lead to grad_fun=CloneBackward)! + x_rotated = Variable(x.data.clone(), requires_grad=True) + + # roi_rotated format is (batch index, x_center, y_center, width, height, angle) + rois_rotated = torch.tensor( + [[0, 4.5, 4.5, 9, 9, 0], [0, 2, 7, 4, 4, 0], [0, 7, 7, 4, 4, 0]], + dtype=dtype, + device=device, + ) + + y_rotated = roi_align_rotated(x_rotated, rois_rotated) + s_rotated = y_rotated.sum() + s_rotated.backward() + + # roi format is (batch index, x1, y1, x2, y2) + rois = torch.tensor( + [[0, 0, 0, 9, 9], [0, 0, 5, 4, 9], [0, 5, 5, 9, 9]], dtype=dtype, device=device + ) + + y = roi_align(x, rois) + s = y.sum() + s.backward() + + assert torch.allclose( + x.grad, x_rotated.grad + ), "gradients for ROIAlign and ROIAlignRotated mismatch on CUDA" + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_roi_heads.py b/tests/test_roi_heads.py new file mode 100644 index 0000000000..70da179c3a --- /dev/null +++ b/tests/test_roi_heads.py @@ -0,0 +1,108 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import unittest +import torch + +from detectron2.config import get_cfg +from detectron2.modeling.backbone import build_backbone +from detectron2.modeling.proposal_generator.build import build_proposal_generator +from detectron2.modeling.roi_heads import build_roi_heads +from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes +from detectron2.utils.events import EventStorage + +logger = logging.getLogger(__name__) + + +class ROIHeadsTest(unittest.TestCase): + def test_roi_heads(self): + torch.manual_seed(121) + cfg = get_cfg() + cfg.MODEL.ROI_HEADS.NAME = "StandardROIHeads" + cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead" + cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2 + cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2" + cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5) + backbone = build_backbone(cfg) + num_images = 2 + images_tensor = torch.rand(num_images, 20, 30) + image_sizes = [(10, 10), (20, 30)] + images = ImageList(images_tensor, image_sizes) + num_channels = 1024 + features = {"res4": torch.rand(num_images, num_channels, 1, 2)} + + image_shape = (15, 15) + gt_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32) + gt_instance0 = Instances(image_shape) + gt_instance0.gt_boxes = Boxes(gt_boxes0) + gt_instance0.gt_classes = torch.tensor([2, 1]) + gt_boxes1 = torch.tensor([[1, 5, 2, 8], [7, 3, 10, 5]], dtype=torch.float32) + gt_instance1 = Instances(image_shape) + gt_instance1.gt_boxes = Boxes(gt_boxes1) + gt_instance1.gt_classes = torch.tensor([1, 2]) + gt_instances = [gt_instance0, gt_instance1] + + proposal_generator = build_proposal_generator(cfg, backbone.output_shape()) + roi_heads = build_roi_heads(cfg, backbone.output_shape()) + + with EventStorage(): # capture events in a new storage to discard them + proposals, proposal_losses = proposal_generator(images, features, gt_instances) + _, detector_losses = roi_heads(images, features, proposals, gt_instances) + + expected_losses = { + "loss_cls": torch.tensor(4.4236516953), + "loss_box_reg": torch.tensor(0.0091214813), + } + for name in expected_losses.keys(): + assert torch.allclose(detector_losses[name], expected_losses[name]) + + def test_rroi_heads(self): + torch.manual_seed(121) + cfg = get_cfg() + cfg.MODEL.PROPOSAL_GENERATOR.NAME = "RRPN" + cfg.MODEL.ANCHOR_GENERATOR.NAME = "RotatedAnchorGenerator" + cfg.MODEL.ROI_HEADS.NAME = "RROIHeads" + cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead" + cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2 + cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1) + cfg.MODEL.RPN.HEAD_NAME = "StandardRPNHead" + cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignRotated" + cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5, 1) + backbone = build_backbone(cfg) + num_images = 2 + images_tensor = torch.rand(num_images, 20, 30) + image_sizes = [(10, 10), (20, 30)] + images = ImageList(images_tensor, image_sizes) + num_channels = 1024 + features = {"res4": torch.rand(num_images, num_channels, 1, 2)} + + image_shape = (15, 15) + gt_boxes0 = torch.tensor([[2, 2, 2, 2, 30], [4, 4, 4, 4, 0]], dtype=torch.float32) + gt_instance0 = Instances(image_shape) + gt_instance0.gt_boxes = RotatedBoxes(gt_boxes0) + gt_instance0.gt_classes = torch.tensor([2, 1]) + gt_boxes1 = torch.tensor([[1.5, 5.5, 1, 3, 0], [8.5, 4, 3, 2, -50]], dtype=torch.float32) + gt_instance1 = Instances(image_shape) + gt_instance1.gt_boxes = RotatedBoxes(gt_boxes1) + gt_instance1.gt_classes = torch.tensor([1, 2]) + gt_instances = [gt_instance0, gt_instance1] + + proposal_generator = build_proposal_generator(cfg, backbone.output_shape()) + roi_heads = build_roi_heads(cfg, backbone.output_shape()) + + with EventStorage(): # capture events in a new storage to discard them + proposals, proposal_losses = proposal_generator(images, features, gt_instances) + _, detector_losses = roi_heads(images, features, proposals, gt_instances) + + expected_losses = { + "loss_cls": torch.tensor(4.381443977355957), + "loss_box_reg": torch.tensor(0.0011560433777049184), + } + for name in expected_losses.keys(): + err_msg = "detector_losses[{}] = {}, expected losses = {}".format( + name, detector_losses[name], expected_losses[name] + ) + assert torch.allclose(detector_losses[name], expected_losses[name]), err_msg + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_roi_pooler.py b/tests/test_roi_pooler.py new file mode 100644 index 0000000000..127498f487 --- /dev/null +++ b/tests/test_roi_pooler.py @@ -0,0 +1,85 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import unittest +import torch + +from detectron2.modeling.poolers import ROIPooler +from detectron2.structures import Boxes, RotatedBoxes + +logger = logging.getLogger(__name__) + + +class TestROIPooler(unittest.TestCase): + def _rand_boxes(self, num_boxes, x_max, y_max): + coords = torch.rand(num_boxes, 4) + coords[:, 0] *= x_max + coords[:, 1] *= y_max + coords[:, 2] *= x_max + coords[:, 3] *= y_max + boxes = torch.zeros(num_boxes, 4) + boxes[:, 0] = torch.min(coords[:, 0], coords[:, 2]) + boxes[:, 1] = torch.min(coords[:, 1], coords[:, 3]) + boxes[:, 2] = torch.max(coords[:, 0], coords[:, 2]) + boxes[:, 3] = torch.max(coords[:, 1], coords[:, 3]) + return boxes + + def _test_roialignv2_roialignrotated_match(self, device): + pooler_resolution = 14 + canonical_level = 4 + canonical_scale_factor = 2 ** canonical_level + pooler_scales = (1.0 / canonical_scale_factor,) + sampling_ratio = 0 + + N, C, H, W = 2, 4, 10, 8 + N_rois = 10 + std = 11 + mean = 0 + feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean + + features = [feature.to(device)] + + rois = [] + rois_rotated = [] + for _ in range(N): + boxes = self._rand_boxes( + num_boxes=N_rois, x_max=W * canonical_scale_factor, y_max=H * canonical_scale_factor + ) + + rotated_boxes = torch.zeros(N_rois, 5) + rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 + rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 + rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] + rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] + rois.append(Boxes(boxes).to(device)) + rois_rotated.append(RotatedBoxes(rotated_boxes).to(device)) + + roialignv2_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type="ROIAlignV2", + ) + + roialignv2_out = roialignv2_pooler(features, rois) + + roialignrotated_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type="ROIAlignRotated", + ) + + roialignrotated_out = roialignrotated_pooler(features, rois_rotated) + + assert torch.allclose(roialignv2_out, roialignrotated_out, atol=1e-4) + + def test_roialignv2_roialignrotated_match_cpu(self): + self._test_roialignv2_roialignrotated_match(device="cpu") + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable") + def test_roialignv2_roialignrotated_match_cuda(self): + self._test_roialignv2_roialignrotated_match(device="cuda") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_rotated_boxes.py b/tests/test_rotated_boxes.py new file mode 100644 index 0000000000..052ad8b193 --- /dev/null +++ b/tests/test_rotated_boxes.py @@ -0,0 +1,578 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from __future__ import absolute_import, division, print_function, unicode_literals +import logging +import math +import random +import unittest +import torch +from fvcore.common.benchmark import benchmark + +from detectron2.layers.rotated_boxes import pairwise_iou_rotated +from detectron2.structures.boxes import Boxes +from detectron2.structures.rotated_boxes import RotatedBoxes, pairwise_iou + +logger = logging.getLogger(__name__) + + +class TestRotatedBoxesLayer(unittest.TestCase): + def test_iou_0_dim_cpu(self): + boxes1 = torch.rand(0, 5, dtype=torch.float32) + boxes2 = torch.rand(10, 5, dtype=torch.float32) + expected_ious = torch.zeros(0, 10, dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + assert torch.allclose(ious, expected_ious) + + boxes1 = torch.rand(10, 5, dtype=torch.float32) + boxes2 = torch.rand(0, 5, dtype=torch.float32) + expected_ious = torch.zeros(10, 0, dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_iou_0_dim_cuda(self): + boxes1 = torch.rand(0, 5, dtype=torch.float32) + boxes2 = torch.rand(10, 5, dtype=torch.float32) + expected_ious = torch.zeros(0, 10, dtype=torch.float32) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + assert torch.allclose(ious_cuda.cpu(), expected_ious) + + boxes1 = torch.rand(10, 5, dtype=torch.float32) + boxes2 = torch.rand(0, 5, dtype=torch.float32) + expected_ious = torch.zeros(10, 0, dtype=torch.float32) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + assert torch.allclose(ious_cuda.cpu(), expected_ious) + + def test_iou_half_overlap_cpu(self): + boxes1 = torch.tensor([[0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32) + boxes2 = torch.tensor([[0.25, 0.5, 0.5, 1.0, 0.0]], dtype=torch.float32) + expected_ious = torch.tensor([[0.5]], dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_iou_half_overlap_cuda(self): + boxes1 = torch.tensor([[0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32) + boxes2 = torch.tensor([[0.25, 0.5, 0.5, 1.0, 0.0]], dtype=torch.float32) + expected_ious = torch.tensor([[0.5]], dtype=torch.float32) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + assert torch.allclose(ious_cuda.cpu(), expected_ious) + + def test_iou_0_degree_cpu(self): + boxes1 = torch.tensor( + [[0.5, 0.5, 1.0, 1.0, 0.0], [0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32 + ) + boxes2 = torch.tensor( + [ + [0.5, 0.5, 1.0, 1.0, 0.0], + [0.25, 0.5, 0.5, 1.0, 0.0], + [0.5, 0.25, 1.0, 0.5, 0.0], + [0.25, 0.25, 0.5, 0.5, 0.0], + [0.75, 0.75, 0.5, 0.5, 0.0], + [1.0, 1.0, 1.0, 1.0, 0.0], + ], + dtype=torch.float32, + ) + expected_ious = torch.tensor( + [ + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + ], + dtype=torch.float32, + ) + ious = pairwise_iou_rotated(boxes1, boxes2) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_iou_0_degree_cuda(self): + boxes1 = torch.tensor( + [[0.5, 0.5, 1.0, 1.0, 0.0], [0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32 + ) + boxes2 = torch.tensor( + [ + [0.5, 0.5, 1.0, 1.0, 0.0], + [0.25, 0.5, 0.5, 1.0, 0.0], + [0.5, 0.25, 1.0, 0.5, 0.0], + [0.25, 0.25, 0.5, 0.5, 0.0], + [0.75, 0.75, 0.5, 0.5, 0.0], + [1.0, 1.0, 1.0, 1.0, 0.0], + ], + dtype=torch.float32, + ) + expected_ious = torch.tensor( + [ + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + ], + dtype=torch.float32, + ) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + assert torch.allclose(ious_cuda.cpu(), expected_ious) + + def test_iou_45_degrees_cpu(self): + boxes1 = torch.tensor( + [ + [1, 1, math.sqrt(2), math.sqrt(2), 45], + [1, 1, 2 * math.sqrt(2), 2 * math.sqrt(2), -45], + ], + dtype=torch.float32, + ) + boxes2 = torch.tensor([[1, 1, 2, 2, 0]], dtype=torch.float32) + expected_ious = torch.tensor([[0.5], [0.5]], dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_iou_45_degrees_cuda(self): + boxes1 = torch.tensor( + [ + [1, 1, math.sqrt(2), math.sqrt(2), 45], + [1, 1, 2 * math.sqrt(2), 2 * math.sqrt(2), -45], + ], + dtype=torch.float32, + ) + boxes2 = torch.tensor([[1, 1, 2, 2, 0]], dtype=torch.float32) + expected_ious = torch.tensor([[0.5], [0.5]], dtype=torch.float32) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + assert torch.allclose(ious_cuda.cpu(), expected_ious) + + def test_iou_perpendicular_cpu(self): + boxes1 = torch.tensor([[5, 5, 10.0, 6, 55]], dtype=torch.float32) + boxes2 = torch.tensor([[5, 5, 10.0, 6, -35]], dtype=torch.float32) + iou = (6.0 * 6.0) / (6.0 * 6.0 + 4.0 * 6.0 + 4.0 * 6.0) + expected_ious = torch.tensor([[iou]], dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable") + def test_iou_perpendicular_cuda(self): + boxes1 = torch.tensor([[5, 5, 10.0, 6, 55]], dtype=torch.float32) + boxes2 = torch.tensor([[5, 5, 10.0, 6, -35]], dtype=torch.float32) + iou = (6.0 * 6.0) / (6.0 * 6.0 + 4.0 * 6.0 + 4.0 * 6.0) + expected_ious = torch.tensor([[iou]], dtype=torch.float32) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + assert torch.allclose(ious_cuda.cpu(), expected_ious) + + def test_iou_large_close_boxes_cpu(self): + boxes1 = torch.tensor( + [[299.500000, 417.370422, 600.000000, 364.259186, 27.1828]], dtype=torch.float32 + ) + boxes2 = torch.tensor( + [[299.500000, 417.370422, 600.000000, 364.259155, 27.1828]], dtype=torch.float32 + ) + iou = 364.259155 / 364.259186 + expected_ious = torch.tensor([[iou]], dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_iou_large_close_boxes_cuda(self): + boxes1 = torch.tensor( + [[299.500000, 417.370422, 600.000000, 364.259186, 27.1828]], dtype=torch.float32 + ) + boxes2 = torch.tensor( + [[299.500000, 417.370422, 600.000000, 364.259155, 27.1828]], dtype=torch.float32 + ) + iou = 364.259155 / 364.259186 + expected_ious = torch.tensor([[iou]], dtype=torch.float32) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + assert torch.allclose(ious_cuda.cpu(), expected_ious) + + def test_iou_precision_cpu(self): + boxes1 = torch.tensor([[565, 565, 10, 10, 0]], dtype=torch.float32) + boxes2 = torch.tensor([[565, 565, 10, 8.3, 0]], dtype=torch.float32) + iou = 8.3 / 10.0 + expected_ious = torch.tensor([[iou]], dtype=torch.float32) + ious = pairwise_iou_rotated(boxes1, boxes2) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_iou_precision_cuda(self): + boxes1 = torch.tensor([[565, 565, 10, 10, 0]], dtype=torch.float32) + boxes2 = torch.tensor([[565, 565, 10, 8.3, 0]], dtype=torch.float32) + iou = 8.3 / 10.0 + expected_ious = torch.tensor([[iou]], dtype=torch.float32) + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + assert torch.allclose(ious_cuda.cpu(), expected_ious) + + def test_iou_many_boxes_cpu(self): + num_boxes1 = 100 + num_boxes2 = 200 + boxes1 = torch.stack( + [ + torch.tensor([5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32) + for i in range(num_boxes1) + ] + ) + boxes2 = torch.stack( + [ + torch.tensor( + [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], dtype=torch.float32 + ) + for i in range(num_boxes2) + ] + ) + expected_ious = torch.zeros(num_boxes1, num_boxes2, dtype=torch.float32) + for i in range(min(num_boxes1, num_boxes2)): + expected_ious[i][i] = (1 + 9 * i / num_boxes2) / 10.0 + ious = pairwise_iou_rotated(boxes1, boxes2) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_iou_many_boxes_cuda(self): + num_boxes1 = 100 + num_boxes2 = 200 + boxes1 = torch.stack( + [ + torch.tensor([5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32) + for i in range(num_boxes1) + ] + ) + boxes2 = torch.stack( + [ + torch.tensor( + [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], dtype=torch.float32 + ) + for i in range(num_boxes2) + ] + ) + expected_ious = torch.zeros(num_boxes1, num_boxes2, dtype=torch.float32) + for i in range(min(num_boxes1, num_boxes2)): + expected_ious[i][i] = (1 + 9 * i / num_boxes2) / 10.0 + ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) + assert torch.allclose(ious_cuda.cpu(), expected_ious) + + +class TestRotatedBoxesStructure(unittest.TestCase): + def test_clip_area_0_degree(self): + for _ in range(50): + num_boxes = 100 + boxes_5d = torch.zeros(num_boxes, 5) + boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) + boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) + # Convert from (x_ctr, y_ctr, w, h, 0) to (x1, y1, x2, y2) + boxes_4d = torch.zeros(num_boxes, 4) + boxes_4d[:, 0] = boxes_5d[:, 0] - boxes_5d[:, 2] / 2.0 + boxes_4d[:, 1] = boxes_5d[:, 1] - boxes_5d[:, 3] / 2.0 + boxes_4d[:, 2] = boxes_5d[:, 0] + boxes_5d[:, 2] / 2.0 + boxes_4d[:, 3] = boxes_5d[:, 1] + boxes_5d[:, 3] / 2.0 + + image_size = (500, 600) + test_boxes_4d = Boxes(boxes_4d) + test_boxes_5d = RotatedBoxes(boxes_5d) + # Before clip + areas_4d = test_boxes_4d.area() + areas_5d = test_boxes_5d.area() + assert torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5) + # After clip + test_boxes_4d.clip(image_size) + test_boxes_5d.clip(image_size) + areas_4d = test_boxes_4d.area() + areas_5d = test_boxes_5d.area() + assert torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5) + + def test_clip_area_arbitrary_angle(self): + num_boxes = 100 + boxes_5d = torch.zeros(num_boxes, 5) + boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) + boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) + boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) + clip_angle_threshold = random.uniform(0, 180) + + image_size = (500, 600) + test_boxes_5d = RotatedBoxes(boxes_5d) + # Before clip + areas_before = test_boxes_5d.area() + # After clip + test_boxes_5d.clip(image_size, clip_angle_threshold) + areas_diff = test_boxes_5d.area() - areas_before + + # the areas should only decrease after clipping + assert torch.all(areas_diff <= 0) + # whenever the box is clipped (thus the area shrinks), + # the angle for the box must be within the clip_angle_threshold + # Note that the clip function will normalize the angle range + # to be within (-180, 180] + assert torch.all( + torch.abs(boxes_5d[:, 4][torch.where(areas_diff < 0)]) < clip_angle_threshold + ) + + def test_normalize_angles(self): + # torch.manual_seed(0) + for _ in range(50): + num_boxes = 100 + boxes_5d = torch.zeros(num_boxes, 5) + boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) + boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) + boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) + boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) + rotated_boxes = RotatedBoxes(boxes_5d) + normalized_boxes = rotated_boxes.clone() + normalized_boxes.normalize_angles() + assert torch.all(normalized_boxes.tensor[:, 4] > -180) + assert torch.all(normalized_boxes.tensor[:, 4] <= 180) + # x, y, w, h should not change + assert torch.allclose(boxes_5d[:, :4], normalized_boxes.tensor[:, :4]) + # the cos/sin values of the angles should stay the same + + assert torch.allclose( + torch.cos(boxes_5d[:, 4] * math.pi / 180), + torch.cos(normalized_boxes.tensor[:, 4] * math.pi / 180), + atol=1e-5, + ) + + assert torch.allclose( + torch.sin(boxes_5d[:, 4] * math.pi / 180), + torch.sin(normalized_boxes.tensor[:, 4] * math.pi / 180), + atol=1e-5, + ) + + def test_pairwise_iou_0_degree_cpu(self): + device = torch.device("cpu") + boxes1 = torch.tensor( + [[0.5, 0.5, 1.0, 1.0, 0.0], [0.5, 0.5, 1.0, 1.0, 0.0]], + dtype=torch.float32, + device=device, + ) + boxes2 = torch.tensor( + [ + [0.5, 0.5, 1.0, 1.0, 0.0], + [0.25, 0.5, 0.5, 1.0, 0.0], + [0.5, 0.25, 1.0, 0.5, 0.0], + [0.25, 0.25, 0.5, 0.5, 0.0], + [0.75, 0.75, 0.5, 0.5, 0.0], + [1.0, 1.0, 1.0, 1.0, 0.0], + ], + dtype=torch.float32, + device=device, + ) + expected_ious = torch.tensor( + [ + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + ], + dtype=torch.float32, + device=device, + ) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_pairwise_iou_0_degree_cuda(self): + device = torch.device("cuda") + boxes1 = torch.tensor( + [[0.5, 0.5, 1.0, 1.0, 0.0], [0.5, 0.5, 1.0, 1.0, 0.0]], + dtype=torch.float32, + device=device, + ) + boxes2 = torch.tensor( + [ + [0.5, 0.5, 1.0, 1.0, 0.0], + [0.25, 0.5, 0.5, 1.0, 0.0], + [0.5, 0.25, 1.0, 0.5, 0.0], + [0.25, 0.25, 0.5, 0.5, 0.0], + [0.75, 0.75, 0.5, 0.5, 0.0], + [1.0, 1.0, 1.0, 1.0, 0.0], + ], + dtype=torch.float32, + device=device, + ) + expected_ious = torch.tensor( + [ + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], + ], + dtype=torch.float32, + device=device, + ) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + assert torch.allclose(ious, expected_ious) + + def test_pairwise_iou_45_degrees_cpu(self): + device = torch.device("cpu") + boxes1 = torch.tensor( + [ + [1, 1, math.sqrt(2), math.sqrt(2), 45], + [1, 1, 2 * math.sqrt(2), 2 * math.sqrt(2), -45], + ], + dtype=torch.float32, + device=device, + ) + boxes2 = torch.tensor([[1, 1, 2, 2, 0]], dtype=torch.float32, device=device) + expected_ious = torch.tensor([[0.5], [0.5]], dtype=torch.float32, device=device) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_pairwise_iou_45_degrees_cuda(self): + device = torch.device("cuda") + boxes1 = torch.tensor( + [ + [1, 1, math.sqrt(2), math.sqrt(2), 45], + [1, 1, 2 * math.sqrt(2), 2 * math.sqrt(2), -45], + ], + dtype=torch.float32, + device=device, + ) + boxes2 = torch.tensor([[1, 1, 2, 2, 0]], dtype=torch.float32, device=device) + expected_ious = torch.tensor([[0.5], [0.5]], dtype=torch.float32, device=device) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + assert torch.allclose(ious, expected_ious) + + def test_pairwise_iou_orthogonal_cpu(self): + device = torch.device("cpu") + boxes1 = torch.tensor([[5, 5, 10, 6, 55]], dtype=torch.float32, device=device) + boxes2 = torch.tensor([[5, 5, 10, 6, -35]], dtype=torch.float32, device=device) + iou = (6.0 * 6.0) / (6.0 * 6.0 + 4.0 * 6.0 + 4.0 * 6.0) + expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_pairwise_iou_orthogonal_cuda(self): + device = torch.device("cuda") + boxes1 = torch.tensor([[5, 5, 10, 6, 55]], dtype=torch.float32, device=device) + boxes2 = torch.tensor([[5, 5, 10, 6, -35]], dtype=torch.float32, device=device) + iou = (6.0 * 6.0) / (6.0 * 6.0 + 4.0 * 6.0 + 4.0 * 6.0) + expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + assert torch.allclose(ious, expected_ious) + + def test_pairwise_iou_large_close_boxes_cpu(self): + device = torch.device("cpu") + boxes1 = torch.tensor( + [[299.500000, 417.370422, 600.000000, 364.259186, 27.1828]], + dtype=torch.float32, + device=device, + ) + boxes2 = torch.tensor( + [[299.500000, 417.370422, 600.000000, 364.259155, 27.1828]], + dtype=torch.float32, + device=device, + ) + iou = 364.259155 / 364.259186 + expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_pairwise_iou_large_close_boxes_cuda(self): + device = torch.device("cuda") + boxes1 = torch.tensor( + [[299.500000, 417.370422, 600.000000, 364.259186, 27.1828]], + dtype=torch.float32, + device=device, + ) + boxes2 = torch.tensor( + [[299.500000, 417.370422, 600.000000, 364.259155, 27.1828]], + dtype=torch.float32, + device=device, + ) + iou = 364.259155 / 364.259186 + expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + assert torch.allclose(ious, expected_ious) + + def test_pairwise_iou_many_boxes_cpu(self): + device = torch.device("cpu") + num_boxes1 = 100 + num_boxes2 = 200 + boxes1 = torch.stack( + [ + torch.tensor( + [5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32, device=device + ) + for i in range(num_boxes1) + ] + ) + boxes2 = torch.stack( + [ + torch.tensor( + [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], + dtype=torch.float32, + device=device, + ) + for i in range(num_boxes2) + ] + ) + expected_ious = torch.zeros(num_boxes1, num_boxes2, dtype=torch.float32, device=device) + for i in range(min(num_boxes1, num_boxes2)): + expected_ious[i][i] = (1 + 9 * i / num_boxes2) / 10.0 + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + assert torch.allclose(ious, expected_ious) + + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_pairwise_iou_many_boxes_cuda(self): + device = torch.device("cuda") + num_boxes1 = 100 + num_boxes2 = 200 + boxes1 = torch.stack( + [ + torch.tensor( + [5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32, device=device + ) + for i in range(num_boxes1) + ] + ) + boxes2 = torch.stack( + [ + torch.tensor( + [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], + dtype=torch.float32, + device=device, + ) + for i in range(num_boxes2) + ] + ) + expected_ious = torch.zeros(num_boxes1, num_boxes2, dtype=torch.float32, device=device) + for i in range(min(num_boxes1, num_boxes2)): + expected_ious[i][i] = (1 + 9 * i / num_boxes2) / 10.0 + ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) + assert torch.allclose(ious, expected_ious) + + +def benchmark_rotated_iou(): + num_boxes1 = 200 + num_boxes2 = 500 + boxes1 = torch.stack( + [ + torch.tensor([5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32) + for i in range(num_boxes1) + ] + ) + boxes2 = torch.stack( + [ + torch.tensor( + [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], dtype=torch.float32 + ) + for i in range(num_boxes2) + ] + ) + + def func(dev, n=1): + b1 = boxes1.to(device=dev) + b2 = boxes2.to(device=dev) + + def bench(): + for _ in range(n): + pairwise_iou_rotated(b1, b2) + if dev.type == "cuda": + torch.cuda.synchronize() + + return bench + + # only run it once per timed loop, since it's slow + args = [{"dev": torch.device("cpu"), "n": 1}] + if torch.cuda.is_available(): + args.append({"dev": torch.device("cuda"), "n": 10}) + + benchmark(func, "rotated_iou", args, warmup_iters=3) + + +if __name__ == "__main__": + unittest.main() + benchmark_rotated_iou() diff --git a/tests/test_rpn.py b/tests/test_rpn.py new file mode 100644 index 0000000000..e014100d92 --- /dev/null +++ b/tests/test_rpn.py @@ -0,0 +1,206 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import logging +import unittest +import torch + +from detectron2.config import get_cfg +from detectron2.modeling.backbone import build_backbone +from detectron2.modeling.proposal_generator.build import build_proposal_generator +from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes +from detectron2.utils.events import EventStorage + +logger = logging.getLogger(__name__) + + +class RPNTest(unittest.TestCase): + def test_rpn(self): + torch.manual_seed(121) + cfg = get_cfg() + cfg.MODEL.PROPOSAL_GENERATOR.NAME = "RPN" + cfg.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator" + cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1) + backbone = build_backbone(cfg) + proposal_generator = build_proposal_generator(cfg, backbone.output_shape()) + num_images = 2 + images_tensor = torch.rand(num_images, 20, 30) + image_sizes = [(10, 10), (20, 30)] + images = ImageList(images_tensor, image_sizes) + image_shape = (15, 15) + num_channels = 1024 + features = {"res4": torch.rand(num_images, num_channels, 1, 2)} + gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32) + gt_instances = Instances(image_shape) + gt_instances.gt_boxes = Boxes(gt_boxes) + with EventStorage(): # capture events in a new storage to discard them + proposals, proposal_losses = proposal_generator(images, features, gt_instances) + + expected_losses = { + "loss_rpn_cls": torch.tensor(0.0804563984), + "loss_rpn_loc": torch.tensor(0.0990132466), + } + for name in expected_losses.keys(): + assert torch.allclose(proposal_losses[name], expected_losses[name]) + + expected_proposal_boxes = [ + Boxes(torch.tensor([[0, 0, 10, 10], [7.3365392685, 0, 10, 10]])), + Boxes( + torch.tensor( + [ + [0, 0, 30, 20], + [0, 0, 16.7862777710, 13.1362524033], + [0, 0, 30, 13.3173446655], + [0, 0, 10.8602609634, 20], + [7.7165775299, 0, 27.3875980377, 20], + ] + ) + ), + ] + + expected_objectness_logits = [ + torch.tensor([0.1225359365, -0.0133192837]), + torch.tensor([0.1415634006, 0.0989848152, 0.0565387346, -0.0072308783, -0.0428492837]), + ] + + for i in range(len(image_sizes)): + assert len(proposals[i]) == len(expected_proposal_boxes[i]) + assert proposals[i].image_size == (image_sizes[i][0], image_sizes[i][1]) + assert torch.allclose( + proposals[i].proposal_boxes.tensor, expected_proposal_boxes[i].tensor + ) + assert torch.allclose(proposals[i].objectness_logits, expected_objectness_logits[i]) + + def test_rrpn(self): + torch.manual_seed(121) + cfg = get_cfg() + cfg.MODEL.PROPOSAL_GENERATOR.NAME = "RRPN" + cfg.MODEL.ANCHOR_GENERATOR.NAME = "RotatedAnchorGenerator" + cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]] + cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1]] + cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [[0, 60]] + cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1) + cfg.MODEL.RPN.HEAD_NAME = "StandardRPNHead" + backbone = build_backbone(cfg) + proposal_generator = build_proposal_generator(cfg, backbone.output_shape()) + num_images = 2 + images_tensor = torch.rand(num_images, 20, 30) + image_sizes = [(10, 10), (20, 30)] + images = ImageList(images_tensor, image_sizes) + image_shape = (15, 15) + num_channels = 1024 + features = {"res4": torch.rand(num_images, num_channels, 1, 2)} + gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32) + gt_instances = Instances(image_shape) + gt_instances.gt_boxes = RotatedBoxes(gt_boxes) + with EventStorage(): # capture events in a new storage to discard them + proposals, proposal_losses = proposal_generator(images, features, gt_instances) + + expected_losses = { + "loss_rpn_cls": torch.tensor(0.0432923734), + "loss_rpn_loc": torch.tensor(0.1552739739), + } + for name in expected_losses.keys(): + assert torch.allclose(proposal_losses[name], expected_losses[name]) + + expected_proposal_boxes = [ + RotatedBoxes( + torch.tensor( + [ + [0.60189795, 1.24095452, 61.98131943, 18.03621292, -4.07244873], + [15.64940453, 1.69624567, 59.59749603, 16.34339333, 2.62692475], + [-3.02982378, -2.69752932, 67.90952301, 59.62455750, 59.97010040], + [16.71863365, 1.98309708, 35.61507797, 32.81484985, 62.92267227], + [0.49432933, -7.92979717, 67.77606201, 62.93098450, -1.85656738], + [8.00880814, 1.36017394, 121.81007385, 32.74150467, 50.44297409], + [16.44299889, -4.82221127, 63.39775848, 61.22503662, 54.12270737], + [5.00000000, 5.00000000, 10.00000000, 10.00000000, -0.76943970], + [17.64130402, -0.98095351, 61.40377808, 16.28918839, 55.53118134], + [0.13016054, 4.60568953, 35.80157471, 32.30180359, 62.52872086], + [-4.26460743, 0.39604485, 124.30079651, 31.84611320, -1.58203125], + [7.52815342, -0.91636634, 62.39784622, 15.45565224, 60.79549789], + ] + ) + ), + RotatedBoxes( + torch.tensor( + [ + [0.07734215, 0.81635046, 65.33510590, 17.34688377, -1.51821899], + [-3.41833067, -3.11320257, 64.17595673, 60.55617905, 58.27033234], + [20.67383385, -6.16561556, 63.60531998, 62.52315903, 54.85546494], + [15.00000000, 10.00000000, 30.00000000, 20.00000000, -0.18218994], + [9.22646523, -6.84775209, 62.09895706, 65.46472931, -2.74307251], + [15.00000000, 4.93451595, 30.00000000, 9.86903191, -0.60272217], + [8.88342094, 2.65560246, 120.95362854, 32.45022202, 55.75970078], + [16.39088631, 2.33887148, 34.78761292, 35.61492920, 60.81977463], + [9.78298569, 10.00000000, 19.56597137, 20.00000000, -0.86660767], + [1.28576660, 5.49873352, 34.93610382, 33.22600174, 60.51599884], + [17.58912468, -1.63270092, 62.96052551, 16.45713997, 52.91245270], + [5.64749718, -1.90428460, 62.37649155, 16.19474792, 61.09543991], + [0.82255805, 2.34931135, 118.83985901, 32.83671188, 56.50753784], + [-5.33874989, 1.64404404, 125.28501892, 33.35424042, -2.80731201], + ] + ) + ), + ] + + expected_objectness_logits = [ + torch.tensor( + [ + 0.10111768, + 0.09112845, + 0.08466332, + 0.07589971, + 0.06650183, + 0.06350251, + 0.04299347, + 0.01864817, + 0.00986163, + 0.00078543, + -0.04573630, + -0.04799230, + ] + ), + torch.tensor( + [ + 0.11373727, + 0.09377633, + 0.05281663, + 0.05143715, + 0.04040275, + 0.03250912, + 0.01307789, + 0.01177734, + 0.00038105, + -0.00540255, + -0.01194804, + -0.01461012, + -0.03061717, + -0.03599222, + ] + ), + ] + + torch.set_printoptions(precision=8, sci_mode=False) + + for i in range(len(image_sizes)): + assert len(proposals[i]) == len(expected_proposal_boxes[i]) + assert proposals[i].image_size == (image_sizes[i][0], image_sizes[i][1]) + # It seems that there's some randomness in the result across different machines: + # This test can be run on a local machine for 100 times with exactly the same result, + # However, a different machine might produce slightly different results, + # thus the atol here. + err_msg = "computed proposal boxes = {}, expected {}".format( + proposals[i].proposal_boxes.tensor, expected_proposal_boxes[i].tensor + ) + assert torch.allclose( + proposals[i].proposal_boxes.tensor, expected_proposal_boxes[i].tensor, atol=1e-5 + ), err_msg + err_msg = "computed objectness logits = {}, expected {}".format( + proposals[i].objectness_logits, expected_objectness_logits[i] + ) + assert torch.allclose( + proposals[i].objectness_logits, expected_objectness_logits[i], atol=1e-5 + ), err_msg + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_visualizer.py b/tests/test_visualizer.py new file mode 100644 index 0000000000..0aa35b5628 --- /dev/null +++ b/tests/test_visualizer.py @@ -0,0 +1,75 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# File: + +import numpy as np +import unittest +import torch + +from detectron2.data import MetadataCatalog +from detectron2.structures import Instances +from detectron2.utils.visualizer import Visualizer + + +class TestVisualizer(unittest.TestCase): + def _random_data(self): + H, W = 100, 100 + N = 10 + img = np.random.rand(H, W, 3) * 255 + boxxy = np.random.rand(N, 2) * (H // 2) + boxes = np.concatenate((boxxy, boxxy + H // 2), axis=1) + + def _rand_poly(): + return np.random.rand(3, 2).flatten() * H + + polygons = [[_rand_poly() for _ in range(np.random.randint(1, 5))] for _ in range(N)] + + mask = np.zeros_like(img[:, :, 0], dtype=np.bool) + mask[:10, 10:20] = 1 + + labels = [str(i) for i in range(N)] + return img, boxes, labels, polygons, [mask] * N + + @property + def metadata(self): + return MetadataCatalog.get("coco_2017_train") + + def test_overlay_instances(self): + img, boxes, labels, polygons, masks = self._random_data() + + v = Visualizer(img, self.metadata) + output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image() + self.assertEqual(output.shape, img.shape) + + # Test 2x scaling + v = Visualizer(img, self.metadata, scale=2.0) + output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image() + self.assertEqual(output.shape[0], img.shape[0] * 2) + + # Test overlay masks + v = Visualizer(img, self.metadata) + output = v.overlay_instances(masks=masks, boxes=boxes, labels=labels).get_image() + self.assertEqual(output.shape, img.shape) + + def test_overlay_instances_no_boxes(self): + img, boxes, labels, polygons, _ = self._random_data() + v = Visualizer(img, self.metadata) + v.overlay_instances(masks=polygons, boxes=None, labels=labels).get_image() + + def test_draw_instance_predictions(self): + img, boxes, _, _, masks = self._random_data() + num_inst = len(boxes) + inst = Instances((img.shape[0], img.shape[1])) + inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) + inst.scores = torch.rand(num_inst) + inst.pred_boxes = torch.from_numpy(boxes) + inst.pred_masks = torch.from_numpy(np.asarray(masks)) + + v = Visualizer(img, self.metadata) + v.draw_instance_predictions(inst) + + def test_correct_output_shape(self): + img = np.random.rand(928, 928, 3) * 255 + v = Visualizer(img, self.metadata) + out = v.output.get_image() + self.assertEqual(out.shape, img.shape) diff --git a/tools/benchmark.py b/tools/benchmark.py new file mode 100755 index 0000000000..7310833a21 --- /dev/null +++ b/tools/benchmark.py @@ -0,0 +1,138 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +A script to benchmark builtin models. +""" + +import itertools +import logging +import torch +import tqdm +from fvcore.common.timer import Timer +from torch.nn.parallel import DistributedDataParallel + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.data import ( + DatasetFromList, + build_detection_test_loader, + build_detection_train_loader, +) +from detectron2.engine import SimpleTrainer, default_argument_parser, hooks, launch +from detectron2.modeling import build_model +from detectron2.solver import build_optimizer +from detectron2.utils import comm +from detectron2.utils.events import CommonMetricPrinter +from detectron2.utils.logger import setup_logger + +logger = logging.getLogger("detectron2") + + +def setup(args): + cfg = get_cfg() + cfg.merge_from_file(args.config_file) + cfg.SOLVER.BASE_LR = 0.001 # Avoid NaNs. Not useful in this script anyway. + cfg.merge_from_list(args.opts) + cfg.freeze() + setup_logger(distributed_rank=comm.get_rank()) + return cfg + + +def benchmark_data(args): + cfg = setup(args) + + dataloader = build_detection_train_loader(cfg) + + itr = iter(dataloader) + for _ in range(10): # warmup + next(itr) + timer = Timer() + max_iter = 1000 + for _ in tqdm.trange(max_iter): + next(itr) + logger.info( + "{} iters ({} images) in {} seconds.".format( + max_iter, max_iter * cfg.SOLVER.IMS_PER_BATCH, timer.seconds() + ) + ) + + +def benchmark_train(args): + cfg = setup(args) + model = build_model(cfg) + logger.info("Model:\n{}".format(model)) + if comm.get_world_size() > 1: + model = DistributedDataParallel( + model, device_ids=[comm.get_local_rank()], broadcast_buffers=False + ) + optimizer = build_optimizer(cfg, model) + checkpointer = DetectionCheckpointer(model, optimizer=optimizer) + checkpointer.load(cfg.MODEL.WEIGHTS) + + cfg.defrost() + cfg.DATALOADER.NUM_WORKERS = 0 + data_loader = build_detection_train_loader(cfg) + dummy_data = list(itertools.islice(data_loader, 100)) + + def f(): + while True: + yield from DatasetFromList(dummy_data, copy=False) + + max_iter = 400 + trainer = SimpleTrainer(model, f(), optimizer) + trainer.register_hooks( + [hooks.IterationTimer(), hooks.PeriodicWriter([CommonMetricPrinter(max_iter)])] + ) + trainer.train(1, max_iter) + + +@torch.no_grad() +def benchmark_eval(args): + cfg = setup(args) + model = build_model(cfg) + model.eval() + logger.info("Model:\n{}".format(model)) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + + cfg.defrost() + cfg.DATALOADER.NUM_WORKERS = 0 + data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0]) + dummy_data = list(itertools.islice(data_loader, 100)) + + def f(): + while True: + yield from DatasetFromList(dummy_data, copy=False) + + for _ in range(5): # warmup + model(dummy_data[0]) + + max_iter = 400 + timer = Timer() + with tqdm.tqdm(total=max_iter) as pbar: + for idx, d in enumerate(f()): + if idx == max_iter: + break + model(d) + pbar.update() + logger.info("{} iters in {} seconds.".format(max_iter, timer.seconds())) + + +if __name__ == "__main__": + parser = default_argument_parser() + parser.add_argument("--task", choices=["train", "eval", "data"], required=True) + args = parser.parse_args() + assert not args.eval_only and not args.resume + + if args.task == "data": + f = benchmark_data + elif args.task == "train": + """ + Note: training speed may not be representative. + The training cost of a R-CNN model varies with the content of the data + and the quality of the model. + """ + f = benchmark_train + elif args.task == "eval": + f = benchmark_eval + # only benchmark single-GPU inference. + assert args.num_gpus == 1 and args.num_machines == 1 + launch(f, args.num_gpus, args.num_machines, args.machine_rank, args.dist_url, args=(args,)) diff --git a/tools/train_net.py b/tools/train_net.py new file mode 100755 index 0000000000..642cbc4888 --- /dev/null +++ b/tools/train_net.py @@ -0,0 +1,155 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Detection Training Script. + +This scripts reads a given config file and runs the training. +It is an entry point that is made to train standard models in detectron2. + +In order to let one script support training of many models, +this script contains logic that are specific to these built-in models and therefore +may not be suitable for your own project. +For example, your research project perhaps only needs a single "evaluator". + +Therefore, we recommend you to use detectron2 as an library and take +this file as an example of how to use the library. +You may want to write your own script with your datasets and other customizations. +""" + +import logging +import os +from collections import OrderedDict +import torch + +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.data import MetadataCatalog +from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch +from detectron2.evaluation import ( + CityscapesEvaluator, + COCOEvaluator, + COCOPanopticEvaluator, + DatasetEvaluators, + LVISEvaluator, + PascalVOCDetectionEvaluator, + SemSegEvaluator, + verify_results, +) +from detectron2.modeling import GeneralizedRCNNWithTTA + + +class Trainer(DefaultTrainer): + @classmethod + def build_evaluator(cls, cfg, dataset_name, output_folder=None): + """ + Create evaluator(s) for a given dataset. + This uses the special metadata "evaluator_type" associated with each builtin dataset. + For your own dataset, you can simply create an evaluator manually in your + script and do not have to worry about the hacky if-else logic here. + """ + if output_folder is None: + output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") + evaluator_list = [] + evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type + if evaluator_type in ["sem_seg", "coco_panoptic_seg"]: + evaluator_list.append( + SemSegEvaluator( + dataset_name, + distributed=True, + num_classes=cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, + ignore_label=cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + output_dir=output_folder, + ) + ) + if evaluator_type in ["coco", "coco_panoptic_seg"]: + evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder)) + if evaluator_type == "coco_panoptic_seg": + evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder)) + if evaluator_type == "cityscapes": + assert ( + torch.cuda.device_count() >= comm.get_rank() + ), "CityscapesEvaluator currently do not work with multiple machines." + return CityscapesEvaluator(dataset_name) + if evaluator_type == "pascal_voc": + return PascalVOCDetectionEvaluator(dataset_name) + if evaluator_type == "lvis": + return LVISEvaluator(dataset_name, cfg, True, output_folder) + if len(evaluator_list) == 0: + raise NotImplementedError( + "no Evaluator for the dataset {} with the type {}".format( + dataset_name, evaluator_type + ) + ) + if len(evaluator_list) == 1: + return evaluator_list[0] + return DatasetEvaluators(evaluator_list) + + @classmethod + def test_with_TTA(cls, cfg, model): + logger = logging.getLogger("detectron2.trainer") + # In the end of training, run an evaluation with TTA + # Only support some R-CNN models. + logger.info("Running inference with test-time augmentation ...") + model = GeneralizedRCNNWithTTA(cfg, model) + evaluators = [ + cls.build_evaluator( + cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA") + ) + for name in cfg.DATASETS.TEST + ] + res = cls.test(cfg, model, evaluators) + res = OrderedDict({k + "_TTA": v for k, v in res.items()}) + return res + + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg() + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + default_setup(cfg, args) + return cfg + + +def main(args): + cfg = setup(args) + + if args.eval_only: + model = Trainer.build_model(cfg) + DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( + cfg.MODEL.WEIGHTS, resume=args.resume + ) + res = Trainer.test(cfg, model) + if comm.is_main_process(): + verify_results(cfg, res) + if cfg.TEST.AUG.ENABLED: + res.update(Trainer.test_with_TTA(cfg, model)) + return res + + """ + If you'd like to do anything fancier than the standard training logic, + consider writing your own training loop or subclassing the trainer. + """ + trainer = Trainer(cfg) + trainer.resume_or_load(resume=args.resume) + if cfg.TEST.AUG.ENABLED: + trainer.register_hooks( + [hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))] + ) + return trainer.train() + + +if __name__ == "__main__": + args = default_argument_parser().parse_args() + print("Command Line Args:", args) + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + ) diff --git a/tools/visualize_coco_results.py b/tools/visualize_coco_results.py new file mode 100755 index 0000000000..6101081589 --- /dev/null +++ b/tools/visualize_coco_results.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import argparse +import json +import numpy as np +import os +from collections import defaultdict +import cv2 +import tqdm +from fvcore.common.file_io import PathManager + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.structures import Boxes, BoxMode, Instances +from detectron2.utils.logger import setup_logger +from detectron2.utils.visualizer import Visualizer + + +def create_instances(predictions, image_size): + ret = Instances(image_size) + + score = np.asarray([x["score"] for x in predictions]) + chosen = (score > args.conf_threshold).nonzero()[0] + score = score[chosen] + bbox = np.asarray([predictions[i]["bbox"] for i in chosen]) + bbox = BoxMode.convert(bbox, BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + + labels = np.asarray([dataset_id_map(predictions[i]["category_id"]) for i in chosen]) + + ret.scores = score + ret.pred_boxes = Boxes(bbox) + ret.pred_classes = labels + + try: + ret.pred_masks = [predictions[i]["segmentation"] for i in chosen] + except KeyError: + pass + return ret + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--input", required=True, help="JSON file produced by the model") + parser.add_argument("--output", required=True, help="output directory") + parser.add_argument("--dataset", help="name of the dataset", default="coco_2017_val") + parser.add_argument("--conf-threshold", default=0.5, type=float, help="confidence threshold") + args = parser.parse_args() + + logger = setup_logger() + + with PathManager.open(args.input, "r") as f: + predictions = json.load(f) + + pred_by_image = defaultdict(list) + for p in predictions: + pred_by_image[p["image_id"]].append(p) + + dicts = list(DatasetCatalog.get(args.dataset)) + metadata = MetadataCatalog.get(args.dataset) + if hasattr(metadata, "thing_dataset_id_to_contiguous_id"): + + def dataset_id_map(ds_id): + return metadata.thing_dataset_id_to_contiguous_id[ds_id] + + elif "lvis" in args.dataset: + # LVIS results are in the same format as COCO results, but have a different + # mapping from dataset category id to contiguous category id in [0, #categories - 1] + def dataset_id_map(ds_id): + return ds_id - 1 + + else: + raise ValueError("Unsupported dataset: {}".format(args.dataset)) + + os.makedirs(args.output, exist_ok=True) + + for dic in tqdm.tqdm(dicts): + img = cv2.imread(dic["file_name"], cv2.IMREAD_COLOR)[:, :, ::-1] + basename = os.path.basename(dic["file_name"]) + + predictions = create_instances(pred_by_image[dic["image_id"]], img.shape[:2]) + vis = Visualizer(img, metadata) + vis_pred = vis.draw_instance_predictions(predictions).get_image() + + vis = Visualizer(img, metadata) + vis_gt = vis.draw_dataset_dict(dic).get_image() + + concat = np.concatenate((vis_pred, vis_gt), axis=1) + cv2.imwrite(os.path.join(args.output, basename), concat[:, :, ::-1]) diff --git a/tools/visualize_data.py b/tools/visualize_data.py new file mode 100755 index 0000000000..8ae8253e23 --- /dev/null +++ b/tools/visualize_data.py @@ -0,0 +1,99 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import argparse +import numpy as np +import os +from itertools import chain +import cv2 +from PIL import Image + +from detectron2.config import get_cfg +from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_train_loader +from detectron2.data import detection_utils as utils +from detectron2.data.build import filter_images_with_few_keypoints +from detectron2.utils.logger import setup_logger +from detectron2.utils.visualizer import Visualizer + + +def setup(args): + cfg = get_cfg() + if args.config_file: + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + return cfg + + +def parse_args(in_args=None): + parser = argparse.ArgumentParser(description="Visualizes Ground-truth Dataset") + parser.add_argument( + "--source", + choices=["annotation", "dataloader"], + required=True, + help="visualize the annotations or the data loader (with pre-processing)", + ) + parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") + parser.add_argument("--output-dir", default="./", help="path to output directory") + parser.add_argument("--show", action="store_true", help="show output in a window") + parser.add_argument( + "opts", + help="Modify config options using the command-line", + default=None, + nargs=argparse.REMAINDER, + ) + return parser.parse_args(in_args) + + +if __name__ == "__main__": + """ + General utility to visualize ground truth dataset. + """ + args = parse_args() + logger = setup_logger() + logger.info("Arguments: " + str(args)) + cfg = setup(args) + + dirname = args.output_dir + os.makedirs(dirname, exist_ok=True) + metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0]) + + def output(vis, fname): + if args.show: + print(fname) + cv2.imshow("window", vis.get_image()[:, :, ::-1]) + cv2.waitKey() + else: + filepath = os.path.join(dirname, fname) + print("Saving to {} ...".format(filepath)) + vis.save(filepath) + + scale = 2.0 if args.show else 1.0 + if args.source == "dataloader": + train_data_loader = build_detection_train_loader(cfg) + for batch in train_data_loader: + for per_image in batch: + # Pytorch tensor is in (C, H, W) format + img = per_image["image"].permute(1, 2, 0) + if cfg.INPUT.FORMAT == "BGR": + img = img[:, :, [2, 1, 0]] + else: + img = np.asarray(Image.fromarray(img, mode=cfg.INPUT.FORMAT).convert("RGB")) + + visualizer = Visualizer(img, metadata=metadata, scale=scale) + target_fields = per_image["instances"].get_fields() + labels = [metadata.thing_classes[i] for i in target_fields["gt_classes"]] + vis = visualizer.overlay_instances( + labels=labels, + boxes=target_fields.get("gt_boxes", None), + masks=target_fields.get("gt_masks", None), + keypoints=target_fields.get("gt_keypoints", None), + ) + output(vis, str(per_image["image_id"]) + ".jpg") + else: + dicts = list(chain.from_iterable([DatasetCatalog.get(k) for k in cfg.DATASETS.TRAIN])) + if cfg.MODEL.KEYPOINT_ON: + dicts = filter_images_with_few_keypoints(dicts, 1) + for dic in dicts: + img = utils.read_image(dic["file_name"], "RGB") + visualizer = Visualizer(img, metadata=metadata, scale=scale) + vis = visualizer.draw_dataset_dict(dic) + output(vis, os.path.basename(dic["file_name"]))

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