Update squeezenet test.

Author: gmalivenko

tests/squeezenet.py

@@ -11,18 +11,112 @@
 sys.path.append('../pytorch2keras')
 from converter import pytorch_to_keras
 
+# The code from torchvision
+import math
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+
+
+class Fire(nn.Module):
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.squeeze_activation(self.squeeze(x))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(x)),
+            self.expand3x3_activation(self.expand3x3(x))
+        ], 1)
+
+
+class SqueezeNet(nn.Module):
+
+    def __init__(self, version=1.0, num_classes=1000):
+        super(SqueezeNet, self).__init__()
+        if version not in [1.0, 1.1]:
+            raise ValueError("Unsupported SqueezeNet version {version}:"
+                             "1.0 or 1.1 expected".format(version=version))
+        self.num_classes = num_classes
+        if version == 1.0:
+            self.features = nn.Sequential(
+                nn.Conv2d(3, 96, kernel_size=7, stride=2),
+                nn.ReLU(inplace=True),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
+                Fire(96, 16, 64, 64),
+                Fire(128, 16, 64, 64),
+                Fire(128, 32, 128, 128),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
+                Fire(256, 32, 128, 128),
+                Fire(256, 48, 192, 192),
+                Fire(384, 48, 192, 192),
+                Fire(384, 64, 256, 256),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
+                Fire(512, 64, 256, 256),
+            )
+        else:
+            self.features = nn.Sequential(
+                nn.Conv2d(3, 64, kernel_size=3, stride=2),
+                nn.ReLU(inplace=True),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
+                Fire(64, 16, 64, 64),
+                Fire(128, 16, 64, 64),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
+                Fire(128, 32, 128, 128),
+                Fire(256, 32, 128, 128),
+                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=False),
+                Fire(256, 48, 192, 192),
+                Fire(384, 48, 192, 192),
+                Fire(384, 64, 256, 256),
+                Fire(512, 64, 256, 256),
+            )
+        # Final convolution is initialized differently form the rest
+        final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
+        self.classifier = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            nn.ReLU(inplace=True),
+            nn.AvgPool2d(13, stride=1)
+        )
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if m is final_conv:
+                    init.normal(m.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.classifier(x)
+        return x.view(x.size(0), self.num_classes)
+
+
 if __name__ == '__main__':
     max_error = 0
     for i in range(10):
-        model = torchvision.models.SqueezeNet()
+        model = SqueezeNet(version=1.1)
         for m in model.modules():
             m.training = False
 
-        input_np = np.random.uniform(0, 1, (1, 3, 299, 299))
+        input_np = np.random.uniform(0, 1, (1, 3, 224, 224))
         input_var = Variable(torch.FloatTensor(input_np))
         output = model(input_var)
 
-        k_model = pytorch_to_keras(model, input_var, (3, 299, 299,), verbose=True)
+        k_model = pytorch_to_keras(model, input_var, (3, 224, 224,), verbose=True)
 
         pytorch_output = output.data.numpy()
         keras_output = k_model.predict(input_np)