Add config and data convert function for occlusion-aware-face-dataset

.pre-commit-config.yaml

@@ -1,5 +1,5 @@
 repos:
-  - repo: https://gitlab.com/pycqa/flake8.git
+  - repo: https://github.com/PYCQA/flake8.git
     rev: 5.0.4
     hooks:
       - id: flake8

configs/_base_/datasets/occlude_face.py

@@ -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)

configs/deeplabv3plus/deeplabv3plus_r101_512x512_C-CM+C-WO-NatOcc-SOT.py

@@ -0,0 +1,137 @@
+# +
+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='CelebAMask-HQ/img',
+    ann_dir='CelebAMask-HQ/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)
+
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    pretrained='open-mmlab://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

configs/deeplabv3plus/deeplabv3plus_r101_512x512_C-WO-NatOcc-SOT.py

@@ -0,0 +1,62 @@
+_base_ = '../_base_/datasets/occlude_face.py'
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    pretrained='open-mmlab://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