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deeplabv3plus_r101_512x512_face-occlusion-93ec6695.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:0ee938da54fe72f34916ec1456192c81efe40e846c03ad0a78cb460a3420f6a0
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size 251071621
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deeplabv3plus_r101_512x512_face-occlusion.py
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norm_cfg = dict(type="SyncBN", requires_grad=True)
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data_preprocessor = dict(
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bgr_to_rgb=True,
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mean=[123.675, 116.28, 103.53],
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pad_val=0,
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seg_pad_val=255,
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size=(512, 512),
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std=[58.395, 57.12, 57.375],
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type="SegDataPreProcessor",
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)
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test_pipeline = [
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dict(type="LoadImageFromNDArray"),
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dict(type="Resize", scale=(512, 512), keep_ratio=True),
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dict(type="LoadAnnotations", reduce_zero_label=True),
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dict(type="PackSegInputs"),
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]
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img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
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tta_model = dict(type="SegTTAModel")
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tta_pipeline = [
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dict(type="LoadImageFromNDArray", backend_args=None),
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dict(
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type="TestTimeAug",
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transforms=[
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[dict(type="Resize", scale_factor=r, keep_ratio=True) for r in img_ratios],
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[
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dict(type="RandomFlip", prob=0.0, direction="horizontal"),
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dict(type="RandomFlip", prob=1.0, direction="horizontal"),
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],
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[dict(type="LoadAnnotations")],
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[dict(type="PackSegInputs")],
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],
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),
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]
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model = dict(
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type="EncoderDecoder",
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data_preprocessor=data_preprocessor,
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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=norm_cfg,
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norm_eval=False,
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style="pytorch",
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contract_dilation=True,
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),
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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=norm_cfg,
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align_corners=False,
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loss_decode=dict(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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),
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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=norm_cfg,
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align_corners=False,
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loss_decode=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=0.4),
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),
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train_cfg=dict(),
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test_cfg=dict(mode="whole"),
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)
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