Added progress flag to model getters (#875)

* added progress flag to model getters

* flake8

* bug fix

* backward commpability

torchvision/models/alexnet.py

 import torch.nn as nn
-import torch.utils.model_zoo as model_zoo
+from .utils import load_state_dict_from_url
 
 
 __all__ = ['AlexNet', 'alexnet']
@@ -48,14 +48,17 @@ class AlexNet(nn.Module):
         return x
 
 
-def alexnet(pretrained=False, **kwargs):
+def alexnet(pretrained=False, progress=True, **kwargs):
     r"""AlexNet model architecture from the
     `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
     model = AlexNet(**kwargs)
     if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['alexnet']))
+        state_dict = load_state_dict_from_url(model_urls['alexnet'],
+                                              progress=progress)
+        model.load_state_dict(state_dict)
     return model

torchvision/models/densenet.py

@@ -2,12 +2,11 @@ import re
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import torch.utils.model_zoo as model_zoo
+from .utils import load_state_dict_from_url
 from collections import OrderedDict
 
 __all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
 
-
 model_urls = {
     'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
     'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
@@ -22,17 +21,20 @@ class _DenseLayer(nn.Sequential):
         self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
         self.add_module('relu1', nn.ReLU(inplace=True)),
         self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
-                        growth_rate, kernel_size=1, stride=1, bias=False)),
+                                           growth_rate, kernel_size=1, stride=1,
+                                           bias=False)),
         self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
         self.add_module('relu2', nn.ReLU(inplace=True)),
         self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
-                        kernel_size=3, stride=1, padding=1, bias=False)),
+                                           kernel_size=3, stride=1, padding=1,
+                                           bias=False)),
         self.drop_rate = drop_rate
 
     def forward(self, x):
         new_features = super(_DenseLayer, self).forward(x)
         if self.drop_rate > 0:
-            new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
+            new_features = F.dropout(new_features, p=self.drop_rate,
+                                     training=self.training)
         return torch.cat([x, new_features], 1)
 
 
@@ -40,7 +42,8 @@ class _DenseBlock(nn.Sequential):
     def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
         super(_DenseBlock, self).__init__()
         for i in range(num_layers):
-            layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)
+            layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate,
+                                bn_size, drop_rate)
             self.add_module('denselayer%d' % (i + 1), layer)
 
 
@@ -75,7 +78,8 @@ class DenseNet(nn.Module):
 
         # First convolution
         self.features = nn.Sequential(OrderedDict([
-            ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
+            ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2,
+                                padding=3, bias=False)),
             ('norm0', nn.BatchNorm2d(num_init_features)),
             ('relu0', nn.ReLU(inplace=True)),
             ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
@@ -85,11 +89,13 @@ class DenseNet(nn.Module):
         num_features = num_init_features
         for i, num_layers in enumerate(block_config):
             block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
-                                bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
+                                bn_size=bn_size, growth_rate=growth_rate,
+                                drop_rate=drop_rate)
             self.features.add_module('denseblock%d' % (i + 1), block)
             num_features = num_features + num_layers * growth_rate
             if i != len(block_config) - 1:
-                trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)
+                trans = _Transition(num_input_features=num_features,
+                                    num_output_features=num_features // 2)
                 self.features.add_module('transition%d' % (i + 1), trans)
                 num_features = num_features // 2
 
@@ -117,14 +123,15 @@ class DenseNet(nn.Module):
         return out
 
 
-def _load_state_dict(model, model_url):
+def _load_state_dict(model, model_url, progress):
     # '.'s are no longer allowed in module names, but previous _DenseLayer
     # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
     # They are also in the checkpoints in model_urls. This pattern is used
     # to find such keys.
     pattern = re.compile(
         r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
-    state_dict = model_zoo.load_url(model_url)
+
+    state_dict = load_state_dict_from_url(model_url, progress=progress)
     for key in list(state_dict.keys()):
         res = pattern.match(key)
         if res:
@@ -134,57 +141,57 @@ def _load_state_dict(model, model_url):
     model.load_state_dict(state_dict)
 
 
-def densenet121(pretrained=False, **kwargs):
+def _densenet(arch, growth_rate, block_config, num_init_features, pretrained, progress,
+              **kwargs):
+    model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
+    if pretrained:
+        _load_state_dict(model, model_urls[arch], progress)
+    return model
+
+
+def densenet121(pretrained=False, progress=True, **kwargs):
     r"""Densenet-121 model from
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
+    return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress,
                      **kwargs)
-    if pretrained:
-        _load_state_dict(model, model_urls['densenet121'])
-    return model
 
 
-def densenet169(pretrained=False, **kwargs):
-    r"""Densenet-169 model from
+def densenet161(pretrained=False, progress=True, **kwargs):
+    r"""Densenet-161 model from
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32),
+    return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
                      **kwargs)
-    if pretrained:
-        _load_state_dict(model, model_urls['densenet169'])
-    return model
 
 
-def densenet201(pretrained=False, **kwargs):
-    r"""Densenet-201 model from
+def densenet169(pretrained=False, progress=True, **kwargs):
+    r"""Densenet-169 model from
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32),
+    return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress,
                      **kwargs)
-    if pretrained:
-        _load_state_dict(model, model_urls['densenet201'])
-    return model
 
 
-def densenet161(pretrained=False, **kwargs):
-    r"""Densenet-161 model from
+def densenet201(pretrained=False, progress=True, **kwargs):
+    r"""Densenet-201 model from
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24),
+    return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress,
                      **kwargs)
-    if pretrained:
-        _load_state_dict(model, model_urls['densenet161'])
-    return model

torchvision/models/googlenet.py

@@ -3,7 +3,7 @@ from collections import namedtuple
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.utils import model_zoo
+from .utils import load_state_dict_from_url
 
 __all__ = ['GoogLeNet', 'googlenet']
 
@@ -15,12 +15,13 @@ model_urls = {
 _GoogLeNetOuputs = namedtuple('GoogLeNetOuputs', ['logits', 'aux_logits2', 'aux_logits1'])
 
 
-def googlenet(pretrained=False, **kwargs):
+def googlenet(pretrained=False, progress=True, **kwargs):
     r"""GoogLeNet (Inception v1) model architecture from
     `"Going Deeper with Convolutions" <http://arxiv.org/abs/1409.4842>`_.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
         aux_logits (bool): If True, adds two auxiliary branches that can improve training.
             Default: *False* when pretrained is True otherwise *True*
         transform_input (bool): If True, preprocesses the input according to the method with which it
@@ -38,7 +39,9 @@ def googlenet(pretrained=False, **kwargs):
         kwargs['aux_logits'] = True
         kwargs['init_weights'] = False
         model = GoogLeNet(**kwargs)
-        model.load_state_dict(model_zoo.load_url(model_urls['googlenet']))
+        state_dict = load_state_dict_from_url(model_urls['googlenet'],
+                                              progress=progress)
+        model.load_state_dict(state_dict)
         if not original_aux_logits:
             model.aux_logits = False
             del model.aux1, model.aux2

torchvision/models/inception.py

@@ -2,7 +2,7 @@ from collections import namedtuple
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import torch.utils.model_zoo as model_zoo
+from .utils import load_state_dict_from_url
 
 
 __all__ = ['Inception3', 'inception_v3']
@@ -16,7 +16,7 @@ model_urls = {
 _InceptionOuputs = namedtuple('InceptionOuputs', ['logits', 'aux_logits'])
 
 
-def inception_v3(pretrained=False, **kwargs):
+def inception_v3(pretrained=False, progress=True, **kwargs):
     r"""Inception v3 model architecture from
     `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
 
@@ -26,6 +26,7 @@ def inception_v3(pretrained=False, **kwargs):
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
         aux_logits (bool): If True, add an auxiliary branch that can improve training.
             Default: *True*
         transform_input (bool): If True, preprocesses the input according to the method with which it
@@ -40,7 +41,9 @@ def inception_v3(pretrained=False, **kwargs):
         else:
             original_aux_logits = True
         model = Inception3(**kwargs)
-        model.load_state_dict(model_zoo.load_url(model_urls['inception_v3_google']))
+        state_dict = load_state_dict_from_url(model_urls['inception_v3_google'],
+                                              progress=progress)
+        model.load_state_dict(state_dict)
         if not original_aux_logits:
             model.aux_logits = False
             del model.AuxLogits

torchvision/models/resnet.py

 import torch.nn as nn
-import torch.utils.model_zoo as model_zoo
+from .utils import load_state_dict_from_url
 
 
 __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
@@ -204,75 +204,79 @@ class ResNet(nn.Module):
         return x
 
 
-def resnet18(pretrained=False, **kwargs):
+def _resnet(arch, inplanes, planes, pretrained, progress, **kwargs):
+    model = ResNet(inplanes, planes, **kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(model_urls[arch],
+                                              progress=progress)
+        model.load_state_dict(state_dict)
+    return model
+
+
+def resnet18(pretrained=False, progress=True, **kwargs):
     """Constructs a ResNet-18 model.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
-    return model
+    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
+                   **kwargs)
 
 
-def resnet34(pretrained=False, **kwargs):
+def resnet34(pretrained=False, progress=True, **kwargs):
     """Constructs a ResNet-34 model.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
-    return model
+    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
+                   **kwargs)
 
 
-def resnet50(pretrained=False, **kwargs):
+def resnet50(pretrained=False, progress=True, **kwargs):
     """Constructs a ResNet-50 model.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
-    return model
+    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
+                   **kwargs)
 
 
-def resnet101(pretrained=False, **kwargs):
+def resnet101(pretrained=False, progress=True, **kwargs):
     """Constructs a ResNet-101 model.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
-    return model
+    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
+                   **kwargs)
 
 
-def resnet152(pretrained=False, **kwargs):
+def resnet152(pretrained=False, progress=True, **kwargs):
     """Constructs a ResNet-152 model.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
-    return model
+    return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
+                   **kwargs)
 
 
-def resnext50_32x4d(pretrained=False, **kwargs):
-    model = ResNet(Bottleneck, [3, 4, 6, 3], groups=32, width_per_group=4, **kwargs)
-    # if pretrained:
-    #     model.load_state_dict(model_zoo.load_url(model_urls['resnext50_32x4d']))
-    return model
+def resnext50_32x4d(**kwargs):
+    kwargs['groups'] = 32
+    kwargs['width_per_group'] = 4
+    return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
+                   pretrained=False, progress=True, **kwargs)
 
 
-def resnext101_32x8d(pretrained=False, **kwargs):
-    model = ResNet(Bottleneck, [3, 4, 23, 3], groups=32, width_per_group=8, **kwargs)
-    # if pretrained:
-    #     model.load_state_dict(model_zoo.load_url(model_urls['resnext101_32x8d']))
-    return model
+def resnext101_32x8d(**kwargs):
+    kwargs['groups'] = 32
+    kwargs['width_per_group'] = 8
+    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
+                   pretrained=False, progress=True, **kwargs)

torchvision/models/squeezenet.py

 import torch
 import torch.nn as nn
 import torch.nn.init as init
-import torch.utils.model_zoo as model_zoo
-
+from .utils import load_state_dict_from_url
 
 __all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']
 
-
 model_urls = {
     'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
     'squeezenet1_1': 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth',
@@ -38,13 +36,10 @@ class Fire(nn.Module):
 
 class SqueezeNet(nn.Module):
 
-    def __init__(self, version=1.0, num_classes=1000):
+    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:
+        if version == '1_0':
             self.features = nn.Sequential(
                 nn.Conv2d(3, 96, kernel_size=7, stride=2),
                 nn.ReLU(inplace=True),
@@ -60,7 +55,7 @@ class SqueezeNet(nn.Module):
                 nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
                 Fire(512, 64, 256, 256),
             )
-        else:
+        elif version == '1_1':
             self.features = nn.Sequential(
                 nn.Conv2d(3, 64, kernel_size=3, stride=2),
                 nn.ReLU(inplace=True),
@@ -76,6 +71,13 @@ class SqueezeNet(nn.Module):
                 Fire(384, 64, 256, 256),
                 Fire(512, 64, 256, 256),
             )
+        else:
+            # FIXME: Is this needed? SqueezeNet should only be called from the
+            # FIXME: squeezenet1_x() functions
+            # FIXME: This checking is not done for the other models
+            raise ValueError("Unsupported SqueezeNet version {version}:"
+                             "1_0 or 1_1 expected".format(version=version))
+
         # Final convolution is initialized differently from the rest
         final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
         self.classifier = nn.Sequential(
@@ -100,21 +102,29 @@ class SqueezeNet(nn.Module):
         return x.view(x.size(0), self.num_classes)
 
 
-def squeezenet1_0(pretrained=False, **kwargs):
+def _squeezenet(version, pretrained, progress, **kwargs):
+    model = SqueezeNet(version, **kwargs)
+    if pretrained:
+        arch = 'squeezenet' + version
+        state_dict = load_state_dict_from_url(model_urls[arch],
+                                              progress=progress)
+        model.load_state_dict(state_dict)
+    return model
+
+
+def squeezenet1_0(pretrained=False, progress=True, **kwargs):
     r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
     accuracy with 50x fewer parameters and <0.5MB model size"
     <https://arxiv.org/abs/1602.07360>`_ paper.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = SqueezeNet(version=1.0, **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['squeezenet1_0']))
-    return model
+    return _squeezenet('1_0', pretrained, progress, **kwargs)
 
 
-def squeezenet1_1(pretrained=False, **kwargs):
+def squeezenet1_1(pretrained=False, progress=True, **kwargs):
     r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
     <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
     SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
@@ -122,8 +132,6 @@ def squeezenet1_1(pretrained=False, **kwargs):
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    model = SqueezeNet(version=1.1, **kwargs)
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(model_urls['squeezenet1_1']))
-    return model
+    return _squeezenet('1_1', pretrained, progress, **kwargs)

torchvision/models/utils.py

+try:
+    from torch.hub import load_state_dict_from_url
+except ImportError:
+    from torch.utils.model_zoo import load_url as load_state_dict_from_url

torchvision/models/vgg.py

 import torch.nn as nn
-import torch.utils.model_zoo as model_zoo
+from .utils import load_state_dict_from_url
 
 
 __all__ = [
@@ -75,7 +75,7 @@ def make_layers(cfg, batch_norm=False):
     return nn.Sequential(*layers)
 
 
-cfg = {
+cfgs = {
     'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
     'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
     'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
@@ -83,113 +83,92 @@ cfg = {
 }
 
 
-def vgg11(pretrained=False, **kwargs):
+def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs):
+    if pretrained:
+        kwargs['init_weights'] = False
+    model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(model_urls[arch],
+                                              progress=progress)
+        model.load_state_dict(state_dict)
+    return model
+
+
+def vgg11(pretrained=False, progress=True, **kwargs):
     """VGG 11-layer model (configuration "A")
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    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']))
-    return model
+    return _vgg('vgg11', 'A', False, pretrained, progress, **kwargs)
 
 
-def vgg11_bn(pretrained=False, **kwargs):
+def vgg11_bn(pretrained=False, progress=True, **kwargs):
     """VGG 11-layer model (configuration "A") with batch normalization
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    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']))
-    return model
+    return _vgg('vgg11_bn', 'A', True, pretrained, progress, **kwargs)
 
 
-def vgg13(pretrained=False, **kwargs):
+def vgg13(pretrained=False, progress=True, **kwargs):
     """VGG 13-layer model (configuration "B")
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    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']))
-    return model
+    return _vgg('vgg13', 'B', False, pretrained, progress, **kwargs)
 
 
-def vgg13_bn(pretrained=False, **kwargs):
+def vgg13_bn(pretrained=False, progress=True, **kwargs):
     """VGG 13-layer model (configuration "B") with batch normalization
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    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']))
-    return model
+    return _vgg('vgg13_bn', 'B', True, pretrained, progress, **kwargs)
 
 
-def vgg16(pretrained=False, **kwargs):
+def vgg16(pretrained=False, progress=True, **kwargs):
     """VGG 16-layer model (configuration "D")
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    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']))
-    return model
+    return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs)
 
 
-def vgg16_bn(pretrained=False, **kwargs):
+def vgg16_bn(pretrained=False, progress=True, **kwargs):
     """VGG 16-layer model (configuration "D") with batch normalization
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    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']))
-    return model
+    return _vgg('vgg16_bn', 'D', True, pretrained, progress, **kwargs)
 
 
-def vgg19(pretrained=False, **kwargs):
+def vgg19(pretrained=False, progress=True, **kwargs):
     """VGG 19-layer model (configuration "E")
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    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']))
-    return model
+    return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs)
 
 
-def vgg19_bn(pretrained=False, **kwargs):
+def vgg19_bn(pretrained=False, progress=True, **kwargs):
     """VGG 19-layer model (configuration 'E') with batch normalization
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
     """
-    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']))
-    return model
+    return _vgg('vgg19_bn', 'E', True, pretrained, progress, **kwargs)