mirror of
https://github.com/open-mmlab/mmsegmentation.git
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4003b8f421
* BiSeNetV2 first commit * BiSeNetV2 unittest * remove pytest * add pytest module * fix ConvModule input name * fix pytest error * fix unittest * refactor * BiSeNetV2 Refactory * fix docstrings and add some small changes * use_sigmoid=False * fix potential bugs about upsampling * Use ConvModule instead * Use ConvModule instead * fix typos * fix typos * fix typos * discard nn.conv2d * discard nn.conv2d * discard nn.conv2d * delete **kwargs * uploading markdown and model * final commit * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * Fix README conflict * Fix unittest problem * Fix unittest problem * BiSeNetV2 * Fixing fps * Fixing typpos * bisenetv2
81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
# model settings
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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=None,
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backbone=dict(
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type='BiSeNetV2',
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detail_channels=(64, 64, 128),
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semantic_channels=(16, 32, 64, 128),
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semantic_expansion_ratio=6,
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bga_channels=128,
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out_indices=(0, 1, 2, 3, 4),
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init_cfg=None,
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align_corners=False),
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decode_head=dict(
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type='FCNHead',
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in_channels=128,
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in_index=0,
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channels=1024,
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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=19,
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norm_cfg=norm_cfg,
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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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auxiliary_head=[
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dict(
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type='FCNHead',
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in_channels=16,
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channels=16,
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num_convs=2,
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num_classes=19,
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in_index=1,
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norm_cfg=norm_cfg,
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concat_input=False,
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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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dict(
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type='FCNHead',
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in_channels=32,
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channels=64,
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num_convs=2,
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num_classes=19,
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in_index=2,
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norm_cfg=norm_cfg,
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concat_input=False,
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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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dict(
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type='FCNHead',
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in_channels=64,
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channels=256,
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num_convs=2,
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num_classes=19,
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in_index=3,
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norm_cfg=norm_cfg,
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concat_input=False,
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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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dict(
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type='FCNHead',
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in_channels=128,
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channels=1024,
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num_convs=2,
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num_classes=19,
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in_index=4,
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norm_cfg=norm_cfg,
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concat_input=False,
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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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],
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# model training and testing settings
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train_cfg=dict(),
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test_cfg=dict(mode='whole'))
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