Added progress flag to model getters

torchvision/models/alexnet.py

@@ -1,5 +1,5 @@
 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 @@ def forward(self, x):
         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 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',
@@ -22,25 +21,29 @@ def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
         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)
 
 
 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 @@ def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
 
         # 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 @@ def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
         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 @@ def forward(self, x):
         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 @@
 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 @@
 _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 @@
 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 @@
 _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

@@ -1,5 +1,5 @@
 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',
@@ -185,75 +185,79 @@ def forward(self, x):
         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)