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import torch.nn as nn
class Bottleneck(nn.Module):
expansion = 4
def __init__(
self, inplanes, planes, stride=1, dilation=1, downsample=None, BatchNorm=None
):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = BatchNorm(planes)
self.conv2 = nn.Conv2d(
planes,
planes,
kernel_size=3,
stride=stride,
dilation=dilation,
padding=dilation,
bias=False,
)
self.bn2 = BatchNorm(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = BatchNorm(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(
self, block, layers, output_stride, BatchNorm, verbose=0, no_init=False
):
self.inplanes = 64
self.verbose = verbose
super(ResNet, self).__init__()
blocks = [1, 2, 4]
if output_stride == 16:
strides = [1, 2, 2, 1]
dilations = [1, 1, 1, 2]
elif output_stride == 8:
strides = [1, 2, 1, 1]
dilations = [1, 1, 2, 4]
else:
raise NotImplementedError
# Modules
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = BatchNorm(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(
block,
64,
layers[0],
stride=strides[0],
dilation=dilations[0],
BatchNorm=BatchNorm,
)
self.layer2 = self._make_layer(
block,
128,
layers[1],
stride=strides[1],
dilation=dilations[1],
BatchNorm=BatchNorm,
)
self.layer3 = self._make_layer(
block,
256,
layers[2],
stride=strides[2],
dilation=dilations[2],
BatchNorm=BatchNorm,
)
self.layer4 = self._make_MG_unit(
block,
512,
blocks=blocks,
stride=strides[3],
dilation=dilations[3],
BatchNorm=BatchNorm,
)
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False,
),
BatchNorm(planes * block.expansion),
)
layers = []
layers.append(
block(self.inplanes, planes, stride, dilation, downsample, BatchNorm)
)
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(self.inplanes, planes, dilation=dilation, BatchNorm=BatchNorm)
)
return nn.Sequential(*layers)
def _make_MG_unit(
self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None
):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False,
),
BatchNorm(planes * block.expansion),
)
layers = []
layers.append(
block(
self.inplanes,
planes,
stride,
dilation=blocks[0] * dilation,
downsample=downsample,
BatchNorm=BatchNorm,
)
)
self.inplanes = planes * block.expansion
for i in range(1, len(blocks)):
layers.append(
block(
self.inplanes,
planes,
stride=1,
dilation=blocks[i] * dilation,
BatchNorm=BatchNorm,
)
)
return nn.Sequential(*layers)
def forward(self, input):
x = self.conv1(input)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
low_level_feat = x
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x, low_level_feat
def ResNet101(output_stride=8, BatchNorm=nn.BatchNorm2d, verbose=0, no_init=False):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(
Bottleneck,
[3, 4, 23, 3],
output_stride,
BatchNorm,
verbose=verbose,
no_init=no_init,
)
return model
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