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Unverified Commit 8e878f0f authored by Milos's avatar Milos Committed by GitHub
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Fixes incorrectly rendered html MNASNet documentation due to docstring formatting (#3005) 

* Fix MNASNet docstrings so they are rendered correctly

* Add dot after url link in models docstrings for consistency
parent 4fcf11d8
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Showing with 35 additions and 31 deletions
torchvision/models/densenet.py
View file @ 8e878f0f
...@@ -141,7 +141,7 @@ class _Transition(nn.Sequential): ...@@ -141,7 +141,7 @@ class _Transition(nn.Sequential):
class DenseNet(nn.Module): class DenseNet(nn.Module):
r"""Densenet-BC model class, based on r"""Densenet-BC model class, based on
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
Args: Args:
growth_rate (int) - how many filters to add each layer (`k` in paper) growth_rate (int) - how many filters to add each layer (`k` in paper)
...@@ -152,7 +152,7 @@ class DenseNet(nn.Module): ...@@ -152,7 +152,7 @@ class DenseNet(nn.Module):
drop_rate (float) - dropout rate after each dense layer drop_rate (float) - dropout rate after each dense layer
num_classes (int) - number of classification classes num_classes (int) - number of classification classes
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_ but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
""" """
def __init__( def __init__(
...@@ -256,13 +256,13 @@ def _densenet( ...@@ -256,13 +256,13 @@ def _densenet(
def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
r"""Densenet-121 model from r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_ but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
""" """
return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress, return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress,
**kwargs) **kwargs)
...@@ -270,13 +270,13 @@ def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) ...@@ -270,13 +270,13 @@ def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any)
def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
r"""Densenet-161 model from r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_ but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
""" """
return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress, return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
**kwargs) **kwargs)
...@@ -284,13 +284,13 @@ def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) ...@@ -284,13 +284,13 @@ def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any)
def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
r"""Densenet-169 model from r"""Densenet-169 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_ but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
""" """
return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress, return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress,
**kwargs) **kwargs)
...@@ -298,13 +298,13 @@ def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) ...@@ -298,13 +298,13 @@ def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any)
def densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet: def densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
r"""Densenet-201 model from r"""Densenet-201 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_ but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
""" """
return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress, return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress,
**kwargs) **kwargs)
torchvision/models/mnasnet.py
View file @ 8e878f0f
...@@ -216,9 +216,10 @@ def _load_pretrained(model_name: str, model: nn.Module, progress: bool) -> None: ...@@ -216,9 +216,10 @@ def _load_pretrained(model_name: str, model: nn.Module, progress: bool) -> None:
def mnasnet0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet: def mnasnet0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
"""MNASNet with depth multiplier of 0.5 from r"""MNASNet with depth multiplier of 0.5 from
`"MnasNet: Platform-Aware Neural Architecture Search for Mobile" `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
<https://arxiv.org/pdf/1807.11626.pdf>`_. <https://arxiv.org/pdf/1807.11626.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr progress (bool): If True, displays a progress bar of the download to stderr
...@@ -230,9 +231,10 @@ def mnasnet0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) - ...@@ -230,9 +231,10 @@ def mnasnet0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -
def mnasnet0_75(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet: def mnasnet0_75(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
"""MNASNet with depth multiplier of 0.75 from r"""MNASNet with depth multiplier of 0.75 from
`"MnasNet: Platform-Aware Neural Architecture Search for Mobile" `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
<https://arxiv.org/pdf/1807.11626.pdf>`_. <https://arxiv.org/pdf/1807.11626.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr progress (bool): If True, displays a progress bar of the download to stderr
...@@ -244,9 +246,10 @@ def mnasnet0_75(pretrained: bool = False, progress: bool = True, **kwargs: Any) ...@@ -244,9 +246,10 @@ def mnasnet0_75(pretrained: bool = False, progress: bool = True, **kwargs: Any)
def mnasnet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet: def mnasnet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
"""MNASNet with depth multiplier of 1.0 from r"""MNASNet with depth multiplier of 1.0 from
`"MnasNet: Platform-Aware Neural Architecture Search for Mobile" `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
<https://arxiv.org/pdf/1807.11626.pdf>`_. <https://arxiv.org/pdf/1807.11626.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr progress (bool): If True, displays a progress bar of the download to stderr
...@@ -258,9 +261,10 @@ def mnasnet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) - ...@@ -258,9 +261,10 @@ def mnasnet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -
def mnasnet1_3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet: def mnasnet1_3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
"""MNASNet with depth multiplier of 1.3 from r"""MNASNet with depth multiplier of 1.3 from
`"MnasNet: Platform-Aware Neural Architecture Search for Mobile" `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
<https://arxiv.org/pdf/1807.11626.pdf>`_. <https://arxiv.org/pdf/1807.11626.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr progress (bool): If True, displays a progress bar of the download to stderr
......
torchvision/models/resnet.py
View file @ 8e878f0f
...@@ -267,7 +267,7 @@ def _resnet( ...@@ -267,7 +267,7 @@ def _resnet(
def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNet-18 model from r"""ResNet-18 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -279,7 +279,7 @@ def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ...@@ -279,7 +279,7 @@ def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNet-34 model from r"""ResNet-34 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -291,7 +291,7 @@ def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ...@@ -291,7 +291,7 @@ def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNet-50 model from r"""ResNet-50 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -303,7 +303,7 @@ def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ...@@ -303,7 +303,7 @@ def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNet-101 model from r"""ResNet-101 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -315,7 +315,7 @@ def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ...@@ -315,7 +315,7 @@ def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNet-152 model from r"""ResNet-152 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -327,7 +327,7 @@ def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ...@@ -327,7 +327,7 @@ def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNeXt-50 32x4d model from r"""ResNeXt-50 32x4d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -341,7 +341,7 @@ def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: A ...@@ -341,7 +341,7 @@ def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: A
def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNeXt-101 32x8d model from r"""ResNeXt-101 32x8d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -355,7 +355,7 @@ def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: ...@@ -355,7 +355,7 @@ def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs:
def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""Wide ResNet-50-2 model from r"""Wide ResNet-50-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
The model is the same as ResNet except for the bottleneck number of channels The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1 which is twice larger in every block. The number of channels in outer 1x1
...@@ -373,7 +373,7 @@ def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: A ...@@ -373,7 +373,7 @@ def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: A
def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""Wide ResNet-101-2 model from r"""Wide ResNet-101-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
The model is the same as ResNet except for the bottleneck number of channels The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1 which is twice larger in every block. The number of channels in outer 1x1
......
torchvision/models/vgg.py
View file @ 8e878f0f
...@@ -104,7 +104,7 @@ def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool ...@@ -104,7 +104,7 @@ def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool
def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
r"""VGG 11-layer model (configuration "A") from r"""VGG 11-layer model (configuration "A") from
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_ `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -115,7 +115,7 @@ def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG ...@@ -115,7 +115,7 @@ def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG
def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
r"""VGG 11-layer model (configuration "A") with batch normalization r"""VGG 11-layer model (configuration "A") with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_ `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -126,7 +126,7 @@ def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ...@@ -126,7 +126,7 @@ def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
r"""VGG 13-layer model (configuration "B") r"""VGG 13-layer model (configuration "B")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_ `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -137,7 +137,7 @@ def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG ...@@ -137,7 +137,7 @@ def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG
def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
r"""VGG 13-layer model (configuration "B") with batch normalization r"""VGG 13-layer model (configuration "B") with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_ `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -148,7 +148,7 @@ def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ...@@ -148,7 +148,7 @@ def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
r"""VGG 16-layer model (configuration "D") r"""VGG 16-layer model (configuration "D")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_ `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -159,7 +159,7 @@ def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG ...@@ -159,7 +159,7 @@ def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG
def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
r"""VGG 16-layer model (configuration "D") with batch normalization r"""VGG 16-layer model (configuration "D") with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_ `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -170,7 +170,7 @@ def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ...@@ -170,7 +170,7 @@ def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
r"""VGG 19-layer model (configuration "E") r"""VGG 19-layer model (configuration "E")
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_ `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
...@@ -181,7 +181,7 @@ def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG ...@@ -181,7 +181,7 @@ def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG
def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
r"""VGG 19-layer model (configuration 'E') with batch normalization r"""VGG 19-layer model (configuration 'E') with batch normalization
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_ `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
Args: Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet pretrained (bool): If True, returns a model pre-trained on ImageNet
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