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[Feature] Support Delving into High-Quality Synthetic Face Occlusion …
…Segmentation Datasets (#2194) add custom dataset add face occlusion dataset add config file for occlusion face fix format update prepare.md formatting formatting fix typo error for doc update downloading process Update dataset_prepare.md PR fix version to original repository. change to original repository.
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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']) | ||
]) | ||
] | ||
|
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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) | ||
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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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configs/deeplabv3plus/deeplabv3plus_r101_512x512_C-CM+C-WO-NatOcc-SOT.py
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# + | ||
_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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