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103 lines
3.6 KiB
Python
Executable File
103 lines
3.6 KiB
Python
Executable File
from mmcv.cnn import build_conv_layer, build_norm_layer
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from torch import nn as nn
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class ResLayer(nn.Sequential):
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"""ResLayer to build ResNet style backbone.
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Args:
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block (nn.Module): block used to build ResLayer.
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inplanes (int): inplanes of block.
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planes (int): planes of block.
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num_blocks (int): number of blocks.
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stride (int): stride of the first block. Default: 1
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avg_down (bool): Use AvgPool instead of stride conv when
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downsampling in the bottleneck. Default: False
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conv_cfg (dict): dictionary to construct and config conv layer.
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Default: None
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norm_cfg (dict): dictionary to construct and config norm layer.
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Default: dict(type='BN')
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downsample_first (bool): Downsample at the first block or last block.
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False for Hourglass, True for ResNet. Default: True
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"""
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def __init__(self,
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block,
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inplanes,
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planes,
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num_blocks,
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stride=1,
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avg_down=False,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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downsample_first=True,
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**kwargs):
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self.block = block
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downsample = None
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if stride != 1 or inplanes != planes * block.expansion:
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downsample = []
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conv_stride = stride
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if avg_down:
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conv_stride = 1
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downsample.append(
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nn.AvgPool2d(
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kernel_size=stride,
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stride=stride,
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ceil_mode=True,
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count_include_pad=False))
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downsample.extend([
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build_conv_layer(
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conv_cfg,
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inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=conv_stride,
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bias=False),
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build_norm_layer(norm_cfg, planes * block.expansion)[1]
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])
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downsample = nn.Sequential(*downsample)
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layers = []
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if downsample_first:
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layers.append(
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block(
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inplanes=inplanes,
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planes=planes,
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stride=stride,
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downsample=downsample,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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**kwargs))
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inplanes = planes * block.expansion
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for _ in range(1, num_blocks):
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layers.append(
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block(
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inplanes=inplanes,
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planes=planes,
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stride=1,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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**kwargs))
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else: # downsample_first=False is for HourglassModule
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for _ in range(num_blocks - 1):
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layers.append(
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block(
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inplanes=inplanes,
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planes=inplanes,
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stride=1,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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**kwargs))
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layers.append(
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block(
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inplanes=inplanes,
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planes=planes,
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stride=stride,
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downsample=downsample,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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**kwargs))
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super(ResLayer, self).__init__(*layers)
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