| | |
| | from mmcv.cnn import build_conv_layer, build_norm_layer |
| | from mmengine.model import Sequential |
| | from torch import nn as nn |
| |
|
| |
|
| | 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') |
| | multi_grid (int | None): Multi grid dilation rates of last |
| | stage. Default: None |
| | contract_dilation (bool): Whether contract first dilation of each layer |
| | Default: False |
| | """ |
| |
|
| | def __init__(self, |
| | block, |
| | inplanes, |
| | planes, |
| | num_blocks, |
| | stride=1, |
| | dilation=1, |
| | avg_down=False, |
| | conv_cfg=None, |
| | norm_cfg=dict(type='BN'), |
| | multi_grid=None, |
| | contract_dilation=False, |
| | **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 multi_grid is None: |
| | if dilation > 1 and contract_dilation: |
| | first_dilation = dilation // 2 |
| | else: |
| | first_dilation = dilation |
| | else: |
| | first_dilation = multi_grid[0] |
| | layers.append( |
| | block( |
| | inplanes=inplanes, |
| | planes=planes, |
| | stride=stride, |
| | dilation=first_dilation, |
| | downsample=downsample, |
| | conv_cfg=conv_cfg, |
| | norm_cfg=norm_cfg, |
| | **kwargs)) |
| | inplanes = planes * block.expansion |
| | for i in range(1, num_blocks): |
| | layers.append( |
| | block( |
| | inplanes=inplanes, |
| | planes=planes, |
| | stride=1, |
| | dilation=dilation if multi_grid is None else multi_grid[i], |
| | conv_cfg=conv_cfg, |
| | norm_cfg=norm_cfg, |
| | **kwargs)) |
| | super().__init__(*layers) |
| |
|
