Refactor config (#367)

* update readme

* refactor config

* fix unit tests

* fix unit tests

* fix unit tests

* fix unit tests

* fix unit tests

.github/workflows/workflow.yml

@@ -34,12 +34,12 @@ jobs:
  runs-on: ubuntu-latest
  strategy:
  matrix:
- torch: [1.7.0, 1.8.0, 1.9.0]
+ torch: [1.7.0, 1.8.1, 1.9.0]
  include:
  - torch: 1.7.0
  torchvision: 0.8.1
- - torch: 1.8.0
- torchvision: 0.9.0
+ - torch: 1.8.1
+ torchvision: 0.9.1
  - torch: 1.9.0
  torchvision: 0.10.0
  steps:

README.md

@@ -1,10 +1,9 @@
 <div align="center">
 
-# NanoDet-Plus
-
 <img src="docs/imgs/Title.jpg" />
 
-### Super fast and high accuracy lightweight anchor-free object detection model. Real-time on mobile devices.
+# NanoDet-Plus
+**Super fast and high accuracy lightweight anchor-free object detection model. Real-time on mobile devices.**
 
 
 [![CI testing](https://img.shields.io/github/checks-status/RangiLyu/nanodet/main?label=CI&style=flat)](https://img.shields.io/github/checks-status/RangiLyu/nanodet/main?label=CI&style=flat)

config/nanodet-plus-m_320.yml

@@ -88,7 +88,7 @@ data:
  pipeline:
  normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
 device:
- gpu_ids: [0]
+ gpu_ids: [0] # Set like [0, 1, 2, 3] if you have multi-GPUs
  workers_per_gpu: 10
  batchsize_per_gpu: 96
 schedule:

config/nanodet_custom_xml_dataset.yml

@@ -1,29 +1,34 @@
 #Config File example
 save_dir: workspace/nanodet_m
 model:
+ weight_averager:
+ name: ExpMovingAverager
+ decay: 0.9998
  arch:
- name: OneStageDetector
+ name: NanoDetPlus
+ detach_epoch: 10
  backbone:
  name: ShuffleNetV2
  model_size: 1.0x
  out_stages: [2,3,4]
  activation: LeakyReLU
  fpn:
- name: PAN
+ name: GhostPAN
  in_channels: [116, 232, 464]
  out_channels: 96
- start_level: 0
- num_outs: 3
+ kernel_size: 5
+ num_extra_level: 1
+ use_depthwise: True
+ activation: LeakyReLU
  head:
- name: NanoDetHead
- num_classes: 80 #Please fill in the number of categories (not include background category)
+ name: NanoDetPlusHead
+ num_classes: 80
  input_channel: 96
  feat_channels: 96
  stacked_convs: 2
- share_cls_reg: True
- octave_base_scale: 5
- scales_per_octave: 1
- strides: [8, 16, 32]
+ kernel_size: 5
+ strides: [8, 16, 32, 64]
+ activation: LeakyReLU
  reg_max: 7
  norm_cfg:
  type: BN
@@ -39,6 +44,16 @@ model:
  loss_bbox:
  name: GIoULoss
  loss_weight: 2.0
+ # Auxiliary head, only use in training time.
+ aux_head:
+ name: SimpleConvHead
+ num_classes: 80
+ input_channel: 192
+ feat_channels: 192
+ stacked_convs: 4
+ strides: [8, 16, 32, 64]
+ activation: LeakyReLU
+ reg_max: 7
 
 class_names: &class_names ['NAME1', 'NAME2', 'NAME3', 'NAME4', '...'] #Please fill in the category names (not include background category)
 data:
@@ -71,27 +86,27 @@ data:
  pipeline:
  normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]]
 device:
- gpu_ids: [0]
- workers_per_gpu: 12
- batchsize_per_gpu: 160
+ gpu_ids: [0] # Set like [0, 1, 2, 3] if you have multi-GPUs
+ workers_per_gpu: 8
+ batchsize_per_gpu: 96
 schedule:
 # resume:
 # load_model: YOUR_MODEL_PATH
  optimizer:
- name: SGD
- lr: 0.14
- momentum: 0.9
- weight_decay: 0.0001
+ name: AdamW
+ lr: 0.001
+ weight_decay: 0.05
  warmup:
  name: linear
- steps: 300
- ratio: 0.1
- total_epochs: 190
+ steps: 500
+ ratio: 0.0001
+ total_epochs: 300
  lr_schedule:
- name: MultiStepLR
- milestones: [130,160,175,185]
- gamma: 0.1
+ name: CosineAnnealingLR
+ T_max: 300
+ eta_min: 0.00005
  val_intervals: 10
+grad_clip: 35
 evaluator:
  name: CocoDetectionEvaluator
  save_key: mAP

tests/test_trainer/test_lightning_task.py

@@ -72,7 +72,7 @@ def func(*args, **kwargs):
 
 
 def test_lightning_training_task():
- load_config(cfg, "./config/nanodet-m.yml")
+ load_config(cfg, "./config/legacy_v0.x_configs/nanodet-m.yml")
  task = TrainingTask(cfg)
  runner = DummyRunner(task)
  runner.test()

tests/test_utils/test_flops.py

@@ -3,7 +3,7 @@
 
 
 def test_flops():
- load_config(cfg, "./config/nanodet-m.yml")
+ load_config(cfg, "./config/legacy_v0.x_configs/nanodet-m.yml")
 
  model = build_model(cfg.model)
  input_shape = (3, 320, 320)

tests/test_utils/test_logger.py

@@ -18,7 +18,7 @@ def test_logger():
 
  logger.log_metrics({"mAP": 30.1}, 1)
 
- load_config(cfg, "./config/nanodet-m.yml")
+ load_config(cfg, "./config/legacy_v0.x_configs/nanodet-m.yml")
  logger.dump_cfg(cfg)
 
  logger.finalize(None)