Initial commit

models/Old/mobilenet.py

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+'''MobileNet in PyTorch.
+
+See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
+for more details.
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Block(nn.Module):
+    '''Depthwise conv + Pointwise conv'''
+    def __init__(self, in_planes, out_planes, stride=1):
+        super(Block, self).__init__()
+        self.conv1 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=in_planes, bias=False)
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_planes)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = F.relu(self.bn2(self.conv2(out)))
+        return out
+
+
+class MobileNet(nn.Module):
+    # (128,2) means conv planes=128, conv stride=2, by default conv stride=1
+    cfg = [64, (128,2), 128, (256,2), 256, (512,2), 512, 512, 512, 512, 512, (1024,2), 1024]
+
+    def __init__(self, num_classes=10):
+        super(MobileNet, self).__init__()
+        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_planes=32)
+        self.linear = nn.Linear(1024, num_classes)
+
+    def _make_layers(self, in_planes):
+        layers = []
+        for x in self.cfg:
+            out_planes = x if isinstance(x, int) else x[0]
+            stride = 1 if isinstance(x, int) else x[1]
+            layers.append(Block(in_planes, out_planes, stride))
+            in_planes = out_planes
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.layers(out)
+        out = F.avg_pool2d(out, 2)
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+        return out
+
+
+def test():
+    net = MobileNet()
+    x = torch.randn(1,3,32,32)
+    y = net(x)
+    print(y.size())
+
+# test()