64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
# +
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_base_ = '../_base_/datasets/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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auto_resume = False
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