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# --------------------------------------------------------
# High Resolution Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Rao Fu, RainbowSecret
# --------------------------------------------------------
import os
import copy
import logging
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
class Bottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
with_cp=None,
norm_cfg=dict(type="BN"),
conv_cfg=None,
):
super(Bottleneck, self).__init__()
norm_cfg = copy.deepcopy(norm_cfg)
self.in_channels = inplanes
self.out_channels = planes
self.stride = stride
self.with_cp = with_cp
self.downsample = downsample
self.conv1_stride = 1
self.conv2_stride = stride
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
norm_cfg, planes * self.expansion, postfix=3
)
self.conv1 = build_conv_layer(
conv_cfg,
inplanes,
planes,
kernel_size=1,
stride=self.conv1_stride,
bias=False,
)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
conv_cfg,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=1,
bias=False,
)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
conv_cfg, planes, planes * self.expansion, kernel_size=1, bias=False
)
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
@property
def norm1(self):
"""nn.Module: normalization layer after the first convolution layer"""
return getattr(self, self.norm1_name)
@property
def norm2(self):
"""nn.Module: normalization layer after the second convolution layer"""
return getattr(self, self.norm2_name)
@property
def norm3(self):
"""nn.Module: normalization layer after the third convolution layer"""
return getattr(self, self.norm3_name)
def forward(self, x):
"""Forward function."""
def _inner_forward(x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.norm2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.norm3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out
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