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from torch import nn
class Bottleneck(nn.Module):
"""
(b,c_in,y,x) -> (b,4*c_out,y,x)
"""
expansion = 4
def __init__(self, inplanes, planes, downsample=None, bn_momentum=.1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes,
kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)
self.conv3 = nn.Conv2d(planes, planes * self.expansion,
kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion,
momentum=bn_momentum)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
def forward(self, x):
residual = x
out = self.relu(self.bn1(self.conv1(x)))
out = self.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
if self.downsample is not None:
residual = self.downsample(x)
out += residual
return self.relu(out)
if __name__ == '__main__':
import torch
downsample = nn.Sequential(
nn.Conv2d(64, 256, kernel_size=1, stride=1, bias=False),
nn.BatchNorm2d(256),
)
model = Bottleneck(64, 64, downsample=downsample)
x = torch.randn(1, 64, 128, 128)
print(model(x).size()) # torch.Size([1,256,128,128])
model = Bottleneck(256,64)
x = torch.randn(1,256,128,128)
print(model(x).size()) # torch.Size([2,256,128,128])
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