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from annotator.mmpkg.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')
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(ResLayer, self).__init__(*layers)
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