[Feature] Support Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets #2194
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|---|---|---|
| @@ -0,0 +1,78 @@ | ||
| dataset_type = 'FaceOccludedDataset' | ||
| data_root = 'data/occlusion-aware-face-dataset' | ||
| crop_size = (512, 512) | ||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations'), | ||
| dict(type='Resize', img_scale=(512, 512)), | ||
| dict(type='RandomFlip', prob=0.5), | ||
| dict(type='RandomRotate', degree=(-30, 30), prob=0.5), | ||
| dict(type='PhotoMetricDistortion'), | ||
| dict( | ||
| type='Normalize', | ||
| mean=[123.675, 116.28, 103.53], | ||
| std=[58.395, 57.12, 57.375], | ||
| to_rgb=True), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_semantic_seg']) | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(512, 512), | ||
| img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], | ||
| flip=True, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='ResizeToMultiple', size_divisor=32), | ||
| dict(type='RandomFlip'), | ||
| dict( | ||
| type='Normalize', | ||
| mean=[123.675, 116.28, 103.53], | ||
| std=[58.395, 57.12, 57.375], | ||
| to_rgb=True), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']) | ||
| ]) | ||
| ] | ||
|
|
||
| dataset_train_A = dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| img_dir='NatOcc_hand_sot/img', | ||
| ann_dir='NatOcc_hand_sot/mask', | ||
| split='train.txt', | ||
| pipeline=train_pipeline) | ||
|
|
||
| dataset_train_B = dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| img_dir='NatOcc_object/img', | ||
| ann_dir='NatOcc_object/mask', | ||
| split='train.txt', | ||
| pipeline=train_pipeline) | ||
|
|
||
| dataset_train_C = dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| img_dir='RandOcc/img', | ||
| ann_dir='RandOcc/mask', | ||
| split='train.txt', | ||
| pipeline=train_pipeline) | ||
|
|
||
| dataset_valid = dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| img_dir='RealOcc/image', | ||
| ann_dir='RealOcc/mask', | ||
| split='RealOcc/split/val.txt', | ||
| pipeline=test_pipeline) | ||
|
|
||
| data = dict( | ||
| samples_per_gpu=2, | ||
| workers_per_gpu=2, | ||
| train=[dataset_train_A, dataset_train_B, dataset_train_C], | ||
| val=dataset_valid) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,63 @@ | ||
| # + | ||
| _base_ = '../_base_/datasets/occlude_face.py' | ||
| norm_cfg = dict(type='SyncBN', requires_grad=True) | ||
| model = dict( | ||
| type='EncoderDecoder', | ||
| pretrained='open-mmlab:https://resnet101_v1c', | ||
| backbone=dict( | ||
| type='ResNetV1c', | ||
| depth=101, | ||
| num_stages=4, | ||
| out_indices=(0, 1, 2, 3), | ||
| dilations=(1, 1, 2, 4), | ||
| strides=(1, 2, 1, 1), | ||
| norm_cfg=dict(type='SyncBN', requires_grad=True), | ||
| norm_eval=False, | ||
| style='pytorch', | ||
| contract_dilation=True), | ||
| decode_head=dict( | ||
| type='DepthwiseSeparableASPPHead', | ||
| in_channels=2048, | ||
| in_index=3, | ||
| channels=512, | ||
| dilations=(1, 12, 24, 36), | ||
| c1_in_channels=256, | ||
| c1_channels=48, | ||
| dropout_ratio=0.1, | ||
| num_classes=2, | ||
| norm_cfg=dict(type='SyncBN', requires_grad=True), | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | ||
| sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)), | ||
| auxiliary_head=dict( | ||
| type='FCNHead', | ||
| in_channels=1024, | ||
| in_index=2, | ||
| channels=256, | ||
| num_convs=1, | ||
| concat_input=False, | ||
| dropout_ratio=0.1, | ||
| num_classes=2, | ||
| norm_cfg=dict(type='SyncBN', requires_grad=True), | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| train_cfg=dict(), | ||
| test_cfg=dict(mode='whole')) | ||
| log_config = dict( | ||
| interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] | ||
| cudnn_benchmark = True | ||
| optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) | ||
| optimizer_config = dict() | ||
| lr_config = dict(policy='poly', power=0.9, min_lr=0.0001, by_epoch=False) | ||
| runner = dict(type='IterBasedRunner', max_iters=30000) | ||
| checkpoint_config = dict(by_epoch=False, interval=400) | ||
| evaluation = dict( | ||
| interval=400, metric=['mIoU', 'mDice', 'mFscore'], pre_eval=True) | ||
| auto_resume = False |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,23 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| import os.path as osp | ||
|
|
||
| from .builder import DATASETS | ||
| from .custom import CustomDataset | ||
|
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|
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| @DATASETS.register_module() | ||
| class FaceOccludedDataset(CustomDataset): | ||
| """Face Occluded dataset. | ||
|
|
||
| Args: | ||
| split (str): Split txt file for Pascal VOC. | ||
| """ | ||
|
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| CLASSES = ('background', 'face') | ||
|
|
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| PALETTE = [[0, 0, 0], [128, 0, 0]] | ||
|
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| def __init__(self, split, **kwargs): | ||
| super(FaceOccludedDataset, self).__init__( | ||
| img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs) | ||
| assert osp.exists(self.img_dir) and self.split is not None |
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Is it the
Hands.zip?There was a problem hiding this comment.
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No. the datas can be found in the drive. https://github.com/jinwonkim93/mmsegmentation/blob/c222684c292f1f7edbb40ef761e7ff48a3b73602/docs/en/dataset_prepare.md?plain=1#L403
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Perhaps I missed some information, I only found this link to download hand images at https://sites.google.com/view/11khands.
It is
Hands.zipbut not11k-hands_image.7z, am I right?There was a problem hiding this comment.
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yes. https://sites.google.com/view/11khands visit this site and download Hand images
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i will redefine the steps to download materials.
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i rewrote the procedure of downloading materials