make weight initialization optional to speed vgg-construction (#377)

torchvision/models/vgg.py

@@ -23,7 +23,7 @@ model_urls = {
 
 class VGG(nn.Module):
 
-    def __init__(self, features, num_classes=1000):
+    def __init__(self, features, num_classes=1000, init_weights=True):
         super(VGG, self).__init__()
         self.features = features
         self.classifier = nn.Sequential(
@@ -35,7 +35,8 @@ class VGG(nn.Module):
             nn.Dropout(),
             nn.Linear(4096, num_classes),
         )
-        self._initialize_weights()
+        if init_weights:
+            self._initialize_weights()
 
     def forward(self, x):
         x = self.features(x)
@@ -88,6 +89,8 @@ def vgg11(pretrained=False, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
     """
+    if pretrained:
+        kwargs['init_weights'] = False
     model = VGG(make_layers(cfg['A']), **kwargs)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['vgg11']))
@@ -100,6 +103,8 @@ def vgg11_bn(pretrained=False, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
     """
+    if pretrained:
+        kwargs['init_weights'] = False
     model = VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['vgg11_bn']))
@@ -112,6 +117,8 @@ def vgg13(pretrained=False, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
     """
+    if pretrained:
+        kwargs['init_weights'] = False
     model = VGG(make_layers(cfg['B']), **kwargs)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['vgg13']))
@@ -124,6 +131,8 @@ def vgg13_bn(pretrained=False, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
     """
+    if pretrained:
+        kwargs['init_weights'] = False
     model = VGG(make_layers(cfg['B'], batch_norm=True), **kwargs)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['vgg13_bn']))
@@ -136,6 +145,8 @@ def vgg16(pretrained=False, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
     """
+    if pretrained:
+        kwargs['init_weights'] = False
     model = VGG(make_layers(cfg['D']), **kwargs)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['vgg16']))
@@ -148,6 +159,8 @@ def vgg16_bn(pretrained=False, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
     """
+    if pretrained:
+        kwargs['init_weights'] = False
     model = VGG(make_layers(cfg['D'], batch_norm=True), **kwargs)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['vgg16_bn']))
@@ -160,6 +173,8 @@ def vgg19(pretrained=False, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
     """
+    if pretrained:
+        kwargs['init_weights'] = False
     model = VGG(make_layers(cfg['E']), **kwargs)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['vgg19']))
@@ -172,6 +187,8 @@ def vgg19_bn(pretrained=False, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
     """
+    if pretrained:
+        kwargs['init_weights'] = False
     model = VGG(make_layers(cfg['E'], batch_norm=True), **kwargs)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['vgg19_bn']))