97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from mmcv.cnn import build_conv_layer, build_norm_layer
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from mmengine.model import Sequential
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from torch import nn as nn
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class ResLayer(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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multi_grid (int | None): Multi grid dilation rates of last
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stage. Default: None
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contract_dilation (bool): Whether contract first dilation of each layer
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Default: False
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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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dilation=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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multi_grid=None,
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contract_dilation=False,
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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 multi_grid is None:
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if dilation > 1 and contract_dilation:
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first_dilation = dilation // 2
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else:
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first_dilation = dilation
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else:
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first_dilation = multi_grid[0]
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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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dilation=first_dilation,
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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 i 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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dilation=dilation if multi_grid is None else multi_grid[i],
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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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