Fixes #1797 by adding an init_weights keyword argument to Inception3 (#…

…1832)

torchvision/models/inception.py

@@ -65,7 +65,7 @@ def inception_v3(pretrained=False, progress=True, **kwargs):
 class Inception3(nn.Module):
 
     def __init__(self, num_classes=1000, aux_logits=True, transform_input=False,
-                 inception_blocks=None):
+                 inception_blocks=None, init_weights=True):
         super(Inception3, self).__init__()
         if inception_blocks is None:
             inception_blocks = [
@@ -102,19 +102,19 @@ def __init__(self, num_classes=1000, aux_logits=True, transform_input=False,
         self.Mixed_7b = inception_e(1280)
         self.Mixed_7c = inception_e(2048)
         self.fc = nn.Linear(2048, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
-                import scipy.stats as stats
-                stddev = m.stddev if hasattr(m, 'stddev') else 0.1
-                X = stats.truncnorm(-2, 2, scale=stddev)
-                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)
+        if init_weights:
+            for m in self.modules():
+                if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
+                    import scipy.stats as stats
+                    stddev = m.stddev if hasattr(m, 'stddev') else 0.1
+                    X = stats.truncnorm(-2, 2, scale=stddev)
+                    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)
 
     def _transform_input(self, x):
         if self.transform_input: