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| from mmcv.cnn import build_conv_layer, build_norm_layer | |
| from torch import nn as nn | |
| class ResLayer(nn.Sequential): | |
| """ResLayer to build ResNet style backbone. | |
| Args: | |
| block (nn.Module): block used to build ResLayer. | |
| inplanes (int): inplanes of block. | |
| planes (int): planes of block. | |
| num_blocks (int): number of blocks. | |
| stride (int): stride of the first block. Default: 1 | |
| avg_down (bool): Use AvgPool instead of stride conv when | |
| downsampling in the bottleneck. Default: False | |
| conv_cfg (dict): dictionary to construct and config conv layer. | |
| Default: None | |
| norm_cfg (dict): dictionary to construct and config norm layer. | |
| Default: dict(type='BN') | |
| downsample_first (bool): Downsample at the first block or last block. | |
| False for Hourglass, True for ResNet. Default: True | |
| """ | |
| def __init__(self, | |
| block, | |
| inplanes, | |
| planes, | |
| num_blocks, | |
| stride=1, | |
| avg_down=False, | |
| conv_cfg=None, | |
| norm_cfg=dict(type='BN'), | |
| downsample_first=True, | |
| **kwargs): | |
| self.block = block | |
| downsample = None | |
| if stride != 1 or inplanes != planes * block.expansion: | |
| downsample = [] | |
| conv_stride = stride | |
| if avg_down: | |
| conv_stride = 1 | |
| downsample.append( | |
| nn.AvgPool2d( | |
| kernel_size=stride, | |
| stride=stride, | |
| ceil_mode=True, | |
| count_include_pad=False)) | |
| downsample.extend([ | |
| build_conv_layer( | |
| conv_cfg, | |
| inplanes, | |
| planes * block.expansion, | |
| kernel_size=1, | |
| stride=conv_stride, | |
| bias=False), | |
| build_norm_layer(norm_cfg, planes * block.expansion)[1] | |
| ]) | |
| downsample = nn.Sequential(*downsample) | |
| layers = [] | |
| if downsample_first: | |
| layers.append( | |
| block( | |
| inplanes=inplanes, | |
| planes=planes, | |
| stride=stride, | |
| downsample=downsample, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| **kwargs)) | |
| inplanes = planes * block.expansion | |
| for _ in range(1, num_blocks): | |
| layers.append( | |
| block( | |
| inplanes=inplanes, | |
| planes=planes, | |
| stride=1, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| **kwargs)) | |
| else: # downsample_first=False is for HourglassModule | |
| for _ in range(num_blocks - 1): | |
| layers.append( | |
| block( | |
| inplanes=inplanes, | |
| planes=inplanes, | |
| stride=1, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| **kwargs)) | |
| layers.append( | |
| block( | |
| inplanes=inplanes, | |
| planes=planes, | |
| stride=stride, | |
| downsample=downsample, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| **kwargs)) | |
| super(ResLayer, self).__init__(*layers) | |
| class SimplifiedBasicBlock(nn.Module): | |
| """Simplified version of original basic residual block. This is used in | |
| `SCNet <https://arxiv.org/abs/2012.10150>`_. | |
| - Norm layer is now optional | |
| - Last ReLU in forward function is removed | |
| """ | |
| expansion = 1 | |
| def __init__(self, | |
| inplanes, | |
| planes, | |
| stride=1, | |
| dilation=1, | |
| downsample=None, | |
| style='pytorch', | |
| with_cp=False, | |
| conv_cfg=None, | |
| norm_cfg=dict(type='BN'), | |
| dcn=None, | |
| plugins=None): | |
| super(SimplifiedBasicBlock, self).__init__() | |
| assert dcn is None, 'Not implemented yet.' | |
| assert plugins is None, 'Not implemented yet.' | |
| assert not with_cp, 'Not implemented yet.' | |
| self.with_norm = norm_cfg is not None | |
| with_bias = True if norm_cfg is None else False | |
| self.conv1 = build_conv_layer( | |
| conv_cfg, | |
| inplanes, | |
| planes, | |
| 3, | |
| stride=stride, | |
| padding=dilation, | |
| dilation=dilation, | |
| bias=with_bias) | |
| if self.with_norm: | |
| self.norm1_name, norm1 = build_norm_layer( | |
| norm_cfg, planes, postfix=1) | |
| self.add_module(self.norm1_name, norm1) | |
| self.conv2 = build_conv_layer( | |
| conv_cfg, planes, planes, 3, padding=1, bias=with_bias) | |
| if self.with_norm: | |
| self.norm2_name, norm2 = build_norm_layer( | |
| norm_cfg, planes, postfix=2) | |
| self.add_module(self.norm2_name, norm2) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = downsample | |
| self.stride = stride | |
| self.dilation = dilation | |
| self.with_cp = with_cp | |
| def norm1(self): | |
| """nn.Module: normalization layer after the first convolution layer""" | |
| return getattr(self, self.norm1_name) if self.with_norm else None | |
| def norm2(self): | |
| """nn.Module: normalization layer after the second convolution layer""" | |
| return getattr(self, self.norm2_name) if self.with_norm else None | |
| def forward(self, x): | |
| """Forward function.""" | |
| identity = x | |
| out = self.conv1(x) | |
| if self.with_norm: | |
| out = self.norm1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| if self.with_norm: | |
| out = self.norm2(out) | |
| if self.downsample is not None: | |
| identity = self.downsample(x) | |
| out += identity | |
| return out | |