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Merge pull request #1 from jinwonkim93/face_occlusion
add config file for occlusion face
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configs/_base_/datasets/occlude_face.py
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82
configs/_base_/datasets/occlude_face.py
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dataset_type = 'FaceOccluded'
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data_root = 'data/occlusion-aware-dataset'
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crop_size = (512, 512)
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(512, 512)),
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dict(type='RandomFlip', prob=0.5),
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dict(type='RandomRotate', degree=(-30, 30), prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(512, 512),
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img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=True,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='ResizeToMultiple', size_divisor=32),
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dict(type='RandomFlip'),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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])
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]
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dataset_train_A = dict(
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type='FaceOccluded',
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data_root=data_root,
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img_dir='CelebAMask-HQ-original/image',
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ann_dir='CelebAMask-HQ-original/mask_edited',
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split='CelebAMask-HQ-original/split/train_ori.txt',
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pipeline=train_pipeline)
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dataset_train_B = dict(
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type='FaceOccluded',
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data_root=data_root,
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img_dir='NatOcc-SOT/image',
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ann_dir='NatOcc-SOT/mask',
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split='NatOcc-SOT/split/train.txt',
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pipeline=train_pipeline)
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dataset_valid = dict(
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type='FaceOccluded',
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data_root=data_root,
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img_dir='occlusion-aware-dataset/HQ-FO-dataset/RealOcc/image',
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ann_dir='occlusion-aware-dataset/HQ-FO-dataset/RealOcc/mask',
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split='occlusion-aware-dataset/HQ-FO-dataset/RealOcc/split/val.txt',
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pipeline=test_pipeline)
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dataset_test = dict(
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type='FaceOccluded',
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data_root=data_root,
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img_dir='occlusion-aware-dataset/HQ-FO-dataset/RealOcc/image',
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ann_dir='occlusion-aware-dataset/HQ-FO-dataset/RealOcc/mask',
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split='occlusion-aware-dataset/HQ-FO-dataset/RealOcc/test.txt',
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pipeline=test_pipeline)
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=[
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dataset_train_A,dataset_train_B,
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],
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val= dataset_valid,
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test=dataset_test)
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# +
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_base_ = '../_base_/dataset/occlude_face.py'
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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type='EncoderDecoder',
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pretrained='open-mmlab://resnet101_v1c',
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backbone=dict(
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type='ResNetV1c',
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depth=101,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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dilations=(1, 1, 2, 4),
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strides=(1, 2, 1, 1),
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norm_cfg=dict(type='SyncBN', requires_grad=True),
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norm_eval=False,
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style='pytorch',
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contract_dilation=True),
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decode_head=dict(
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type='DepthwiseSeparableASPPHead',
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in_channels=2048,
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in_index=3,
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channels=512,
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dilations=(1, 12, 24, 36),
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c1_in_channels=256,
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c1_channels=48,
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dropout_ratio=0.1,
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num_classes=2,
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norm_cfg=dict(type='SyncBN', requires_grad=True),
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
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sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)),
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auxiliary_head=dict(
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type='FCNHead',
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in_channels=1024,
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in_index=2,
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channels=256,
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num_convs=1,
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concat_input=False,
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dropout_ratio=0.1,
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num_classes=2,
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norm_cfg=dict(type='SyncBN', requires_grad=True),
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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train_cfg=dict(),
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test_cfg=dict(mode='whole'))
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log_config = dict(
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interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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cudnn_benchmark = True
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optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
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optimizer_config = dict()
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lr_config = dict(policy='poly', power=0.9, min_lr=0.0001, by_epoch=False)
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runner = dict(type='IterBasedRunner', max_iters=30000)
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checkpoint_config = dict(by_epoch=False, interval=400)
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evaluation = dict(
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interval=400, metric=['mIoU', 'mDice', 'mFscore'], pre_eval=True)
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work_dir = './work_dirs/deeplabv3plus_r101_512x512_C-CM+C-WO-NatOcc-SOT'
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gpu_ids = range(0, 2)
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auto_resume = False
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