Initial commit

models/Old/senet.py

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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()