Match Tensorflows implementation of GoogLeNet more closely (#821)

* Match Tensorflows implementation of GoogLeNet

* just disable the branch when pretrained is true

* don't use legacy code

torchvision/models/googlenet.py

@@ -17,12 +17,16 @@ def googlenet(pretrained=False, **kwargs):
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        aux_logits (bool): If True, adds two auxiliary branches that can improve training.
+            Automatically set to False if 'pretrained' is True. Default: *True*
         transform_input (bool): If True, preprocesses the input according to the method with which it
             was trained on ImageNet. Default: *False*
     """
     if pretrained:
         if 'transform_input' not in kwargs:
             kwargs['transform_input'] = True
+        if 'aux_logits' not in kwargs:
+            kwargs['aux_logits'] = False
         kwargs['init_weights'] = False
         model = GoogLeNet(**kwargs)
         model.load_state_dict(model_zoo.load_url(model_urls['googlenet']))
@@ -57,11 +61,12 @@ class GoogLeNet(nn.Module):
 
         self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
         self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
-        if aux_logits:
-            self.aux1 = InceptionAux(512, num_classes)
-            self.aux2 = InceptionAux(528, num_classes)
+
+        self.aux1 = InceptionAux(512, num_classes)
+        self.aux2 = InceptionAux(528, num_classes)
+
         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-        self.dropout = nn.Dropout(0.4)
+        self.dropout = nn.Dropout(0.2)
         self.fc = nn.Linear(1024, num_classes)
 
         if init_weights:
@@ -69,13 +74,13 @@ class GoogLeNet(nn.Module):
 
     def _initialize_weights(self):
         for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.xavier_uniform_(m.weight)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0.2)
-            elif isinstance(m, nn.Linear):
-                nn.init.xavier_uniform_(m.weight)
-                nn.init.constant_(m.bias, 0)
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
+                import scipy.stats as stats
+                X = stats.truncnorm(-2, 2, scale=0.01)
+                values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype)
+                values = values.view(m.weight.size())
+                with torch.no_grad():
+                    m.weight.copy_(values)
             elif isinstance(m, nn.BatchNorm2d):
                 nn.init.constant_(m.weight, 1)
                 nn.init.constant_(m.bias, 0)