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from torch import nn
class BasicBlock(nn.Module):
"""
(b,c,y,x) -> (b,c,y,x)
"""
expansion = 1
def __init__(self, planes, bn_momentum=.1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(planes, planes, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)
def forward(self, x):
residual = x
out = self.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += residual
return self.relu(out)
if __name__ == '__main__':
import torch
model = BasicBlock(256)
x = torch.randn(1, 256, 128, 128)
print(model(x).size()) # torch.Size([1,256,128,128])
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