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- '''SENet in PyTorch.
-
- SENet is the winner of ImageNet-2017. The paper is not released yet.
- '''
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
-
- class BasicBlock(nn.Module):
- def __init__(self, in_planes, planes, stride=1):
- super(BasicBlock, self).__init__()
- self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
-
- self.shortcut = nn.Sequential()
- if stride != 1 or in_planes != planes:
- self.shortcut = nn.Sequential(
- nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes)
- )
-
- # SE layers
- self.fc1 = nn.Conv2d(planes, planes//16, kernel_size=1) # Use nn.Conv2d instead of nn.Linear
- self.fc2 = nn.Conv2d(planes//16, planes, kernel_size=1)
-
- def forward(self, x):
- out = F.relu(self.bn1(self.conv1(x)))
- out = self.bn2(self.conv2(out))
-
- # Squeeze
- w = F.avg_pool2d(out, out.size(2))
- w = F.relu(self.fc1(w))
- w = F.sigmoid(self.fc2(w))
- # Excitation
- out = out * w # New broadcasting feature from v0.2!
-
- out += self.shortcut(x)
- out = F.relu(out)
- return out
-
-
- class PreActBlock(nn.Module):
- def __init__(self, in_planes, planes, stride=1):
- super(PreActBlock, self).__init__()
- self.bn1 = nn.BatchNorm2d(in_planes)
- self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
-
- if stride != 1 or in_planes != planes:
- self.shortcut = nn.Sequential(
- nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False)
- )
-
- # SE layers
- self.fc1 = nn.Conv2d(planes, planes//16, kernel_size=1)
- self.fc2 = nn.Conv2d(planes//16, planes, kernel_size=1)
-
- def forward(self, x):
- out = F.relu(self.bn1(x))
- shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
- out = self.conv1(out)
- out = self.conv2(F.relu(self.bn2(out)))
-
- # Squeeze
- w = F.avg_pool2d(out, out.size(2))
- w = F.relu(self.fc1(w))
- w = F.sigmoid(self.fc2(w))
- # Excitation
- out = out * w
-
- out += shortcut
- return out
-
-
- class SENet(nn.Module):
- def __init__(self, block, num_blocks, num_classes=10):
- super(SENet, self).__init__()
- self.in_planes = 64
-
- self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
- self.bn1 = nn.BatchNorm2d(64)
- self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
- self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
- self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
- self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
- self.linear = nn.Linear(512, num_classes)
-
- def _make_layer(self, block, planes, num_blocks, stride):
- strides = [stride] + [1]*(num_blocks-1)
- layers = []
- for stride in strides:
- layers.append(block(self.in_planes, planes, stride))
- self.in_planes = planes
- return nn.Sequential(*layers)
-
- def forward(self, x):
- out = F.relu(self.bn1(self.conv1(x)))
- out = self.layer1(out)
- out = self.layer2(out)
- out = self.layer3(out)
- out = self.layer4(out)
- out = F.avg_pool2d(out, 4)
- out = out.view(out.size(0), -1)
- out = self.linear(out)
- return out
-
-
- def SENet18():
- return SENet(PreActBlock, [2,2,2,2])
-
-
- def test():
- net = SENet18()
- y = net(torch.randn(1,3,32,32))
- print(y.size())
-
- # test()
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