Shortcuts

mmdet.models.utils.res_layer 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule, Sequential
from torch import nn as nn


[文档]class ResLayer(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)
[文档]class SimplifiedBasicBlock(BaseModule): """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, init_fg=None): super(SimplifiedBasicBlock, self).__init__(init_fg) 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 @property def norm1(self): """nn.Module: normalization layer after the first convolution layer""" return getattr(self, self.norm1_name) if self.with_norm else None @property def norm2(self): """nn.Module: normalization layer after the second convolution layer""" return getattr(self, self.norm2_name) if self.with_norm else None
[文档] 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
Read the Docs v: dev
Versions
latest
stable
3.x
v2.28.2
v2.28.1
v2.28.0
v2.27.0
v2.26.0
v2.25.3
v2.25.2
v2.25.1
v2.25.0
v2.24.1
v2.24.0
v2.23.0
v2.22.0
v2.21.0
v2.20.0
v2.19.1
v2.19.0
v2.18.1
v2.18.0
v2.17.0
v2.16.0
v2.15.1
v2.15.0
v2.14.0
v2.13.0
dev-3.x
dev
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.