构建MobileNetv2

Author: Kedreamix

CIFAR10_code/nets/MobileNetv2.py

@@ -0,0 +1,104 @@
+'''
+MobileNetV2 in PyTorch.
+
+See the paper "Inverted Residuals and Linear Bottlenecks:
+Mobile Networks for Classification, Detection and Segmentation" for more details.
+'''
+import torch
+import torch.nn as nn
+
+
+class Block(nn.Module):
+    # 使用了inverted residuals
+    '''expand + depthwise + pointwise'''
+    def __init__(self, in_channels, out_channels, expansion, stride):
+        super(Block, self).__init__()
+        self.stride = stride
+        channels        = expansion * in_channels  # 倒残差结构先升维 再降维
+        self.conv1      = nn.Conv2d(in_channels, channels, kernel_size = 1, stride=1, padding=0, bias=False)
+        self.bn1        = nn.BatchNorm2d(channels)
+        self.conv2      = nn.Conv2d(channels,channels,kernel_size=3,stride=stride,padding=1, groups=channels, bias=False)
+        self.bn2        = nn.BatchNorm2d(channels)
+        self.conv3      = nn.Conv2d(channels, out_channels, kernel_size=1,stride=1, padding = 0, bias=False)
+        self.bn3        = nn.BatchNorm2d(out_channels)
+        
+        self.shortcut   = nn.Sequential()
+        if stride == 1 and in_channels != out_channels:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),
+                nn.BatchNorm2d(out_channels)
+            )
+        self.relu6      = nn.ReLU6()    
+    def forward(self, x):
+        out = self.relu6(self.bn1(self.conv1(x)))
+        out = self.relu6(self.bn2(self.conv2(out)))
+        out = self.bn3(self.conv3(out))
+        out = out + self.shortcut(x) if self.stride == 1 else out
+        
+        return out
+    
+class MobileNetV2(nn.Module):
+    # (expansion, out_channels, num_blocks, stride)
+    cfg = [(1,  16, 1, 1),
+           (6,  24, 2, 1),  # NOTE: change stride 2 -> 1 for CIFAR10
+           (6,  32, 3, 2),
+           (6,  64, 4, 2),
+           (6,  96, 3, 1),
+           (6, 160, 3, 2),
+           (6, 320, 1, 1)]
+    
+    def __init__(self, num_classes=10):
+        super(MobileNetV2, self).__init__()
+        # NOTE: change conv1 stride 2 -> 1 for CIFAR10
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.layers = self._make_layers(in_channels=32)
+        self.conv2 = nn.Conv2d(320, 1280, kernel_size=1, stride=1, padding=0, bias=False)
+        self.bn2 = nn.BatchNorm2d(1280)
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+        self.linear = nn.Linear(1280, num_classes)
+        self.relu6 = nn.ReLU6()
+        
+    def _make_layers(self, in_channels):
+        layers = []
+        for expansion, out_channels, num_block, stride in self.cfg:
+            strides = [stride] + [1]*(num_block-1)
+            for stride in strides:
+                layers.append(Block(in_channels, out_channels, expansion, stride))
+                in_channels = out_channels
+        return nn.Sequential(*layers)
+    
+    def init_weight(self):
+        for w in self.modules():
+            if isinstance(w, nn.Conv2d):
+                nn.init.kaiming_normal_(w.weight, mode='fan_out')
+                if w.bias is not None:
+                    nn.init.zeros_(w.bias)
+            elif isinstance(w, nn.BatchNorm2d):
+                nn.init.ones_(w.weight)
+                nn.init.zeros_(w.bias)
+            elif isinstance(w, nn.Linear):
+                nn.init.normal_(w.weight, 0, 0.01)
+                nn.init.zeros_(w.bias)    
+    
+    def forward(self, x):
+        out = self.relu6(self.bn1(self.conv1(x)))
+        out = self.layers(out)
+        out = self.relu6(self.bn2(self.conv2(out)))
+        out = self.avgpool(out)
+        out = out.view(out.size(0),-1)
+        out = self.linear(out)
+        return out
+
+    
+def test():
+    net = MobileNetV2()
+    x = torch.randn(2,3,32,32)
+    y = net(x)
+    print(y.size())
+    from torchinfo import summary
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    net = net.to(device)
+    summary(net,(32,3,32,32))
+    
+test()