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Fix models documentation

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docs/source/index.rst
View file @ cd20d5f6
torchvision torchvision
=================== ===========
The :mod:`torchvision` package consists of popular datasets, model The :mod:`torchvision` package consists of popular datasets, model
architectures, and common image transformations for computer vision. architectures, and common image transformations for computer vision.
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 2
:caption: Package Reference :caption: Package Reference
datasets datasets
......
docs/source/models.rst
View file @ cd20d5f6
Models Models
====== ======
The models subpackage contains definitions for the following model
architectures:
- `AlexNet`_
- `VGG`_
- `ResNet`_
- `SqueezeNet`_
- `DenseNet`_
- `Inception`_ v3
You can construct a model with random weights by calling its constructor:
.. code:: python
import torchvision.models as models
resnet18 = models.resnet18()
alexnet = models.alexnet()
vgg16 = models.vgg16()
squeezenet = models.squeezenet1_0()
densenet = models.densenet_161()
inception = models.inception_v3()
We provide pre-trained models, using the PyTorch :mod:`torch.utils.model_zoo`.
These can be constructed by passing ``pretrained=True``:
.. code:: python
import torchvision.models as models
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)
squeezenet = models.squeezenet1_0(pretrained=True)
vgg16 = models.vgg16(pretrained=True)
densenet = models.densenet_161(pretrained=True)
inception = models.inception_v3(pretrained=True)
All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape (3 x H x W),
where H and W are expected to be at least 224.
The images have to be loaded in to a range of [0, 1] and then normalized
using ``mean = [0.485, 0.456, 0.406]`` and ``std = [0.229, 0.224, 0.225]``.
You can use the following transform to normalize::
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
An example of such normalization can be found in the imagenet example
`here <https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101>`_
ImageNet 1-crop error rates (224x224)
================================ ============= =============
Network Top-1 error Top-5 error
================================ ============= =============
AlexNet 43.45 20.91
VGG-11 30.98 11.37
VGG-13 30.07 10.75
VGG-16 28.41 9.62
VGG-19 27.62 9.12
VGG-11 with batch normalization 29.62 10.19
VGG-13 with batch normalization 28.45 9.63
VGG-16 with batch normalization 26.63 8.50
VGG-19 with batch normalization 25.76 8.15
ResNet-18 30.24 10.92
ResNet-34 26.70 8.58
ResNet-50 23.85 7.13
ResNet-101 22.63 6.44
ResNet-152 21.69 5.94
SqueezeNet 1.0 41.90 19.58
SqueezeNet 1.1 41.81 19.38
Densenet-121 25.35 7.83
Densenet-169 24.00 7.00
Densenet-201 22.80 6.43
Densenet-161 22.35 6.20
Inception v3 22.55 6.44
================================ ============= =============
.. _AlexNet: https://arxiv.org/abs/1404.5997
.. _VGG: https://arxiv.org/abs/1409.1556
.. _ResNet: https://arxiv.org/abs/1512.03385
.. _SqueezeNet: https://arxiv.org/abs/1602.07360
.. _DenseNet: https://arxiv.org/abs/1608.06993
.. _Inception: https://arxiv.org/abs/1512.00567
.. currentmodule:: torchvision.models .. currentmodule:: torchvision.models
.. automodule:: torchvision.models Alexnet
:members: alexnet, resnet18, resnet34, resnet50, resnet101, resnet152, -------
vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19,
vgg19_bn, inception_v3, squeezenet1_0, squeezenet1_1, densenet121, .. autofunction:: alexnet
densenet169, densenet201, densenet161
:undoc-members: VGG
---
.. autofunction:: vgg11
.. autofunction:: vgg11_bn
.. autofunction:: vgg13
.. autofunction:: vgg13_bn
.. autofunction:: vgg16
.. autofunction:: vgg16_bn
.. autofunction:: vgg19
.. autofunction:: vgg19_bn
ResNet
------
.. autofunction:: resnet18
.. autofunction:: resnet34
.. autofunction:: resnet50
.. autofunction:: resnet101
.. autofunction:: resnet152
SqueezeNet
----------
.. autofunction:: squeezenet1_0
.. autofunction:: squeezenet1_1
DensetNet
---------
.. autofunction:: densenet121
.. autofunction:: densenet169
.. autofunction:: densenet161
.. autofunction:: densenet201
Inception v3
------------
.. autofunction:: inception_v3
torchvision/models/__init__.py
View file @ cd20d5f6
"""The models subpackage contains definitions for the following model
architectures:
- `AlexNet`_
- `VGG`_
- `ResNet`_
- `SqueezeNet`_
- `DenseNet`_
- `Inception`_ v3
You can construct a model with random weights by calling its constructor:
.. code:: python
import torchvision.models as models
resnet18 = models.resnet18()
alexnet = models.alexnet()
vgg16 = models.vgg16()
squeezenet = models.squeezenet1_0()
densenet = models.densenet_161()
inception = models.inception_v3()
We provide pre-trained models, using the PyTorch :mod:`torch.utils.model_zoo`.
These can be constructed by passing ``pretrained=True``:
.. code:: python
import torchvision.models as models
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)
squeezenet = models.squeezenet1_0(pretrained=True)
vgg16 = models.vgg16(pretrained=True)
densenet = models.densenet_161(pretrained=True)
inception = models.inception_v3(pretrained=True)
All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape (3 x H x W),
where H and W are expected to be at least 224.
The images have to be loaded in to a range of [0, 1] and then normalized
using ``mean = [0.485, 0.456, 0.406]`` and ``std = [0.229, 0.224, 0.225]``.
You can use the following transform to normalize::
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
An example of such normalization can be found in the imagenet example
`here <https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101>`_
ImageNet 1-crop error rates (224x224)
================================ ============= =============
Network Top-1 error Top-5 error
================================ ============= =============
ResNet-18 30.24 10.92
ResNet-34 26.70 8.58
ResNet-50 23.85 7.13
ResNet-101 22.63 6.44
ResNet-152 21.69 5.94
Inception v3 22.55 6.44
AlexNet 43.45 20.91
VGG-11 30.98 11.37
VGG-13 30.07 10.75
VGG-16 28.41 9.62
VGG-19 27.62 9.12
VGG-11 with batch normalization 29.62 10.19
VGG-13 with batch normalization 28.45 9.63
VGG-16 with batch normalization 26.63 8.50
VGG-19 with batch normalization 25.76 8.15
SqueezeNet 1.0 41.90 19.58
SqueezeNet 1.1 41.81 19.38
Densenet-121 25.35 7.83
Densenet-169 24.00 7.00
Densenet-201 22.80 6.43
Densenet-161 22.35 6.20
================================ ============= =============
.. _AlexNet: https://arxiv.org/abs/1404.5997
.. _VGG: https://arxiv.org/abs/1409.1556
.. _ResNet: https://arxiv.org/abs/1512.03385
.. _SqueezeNet: https://arxiv.org/abs/1602.07360
.. _DenseNet: https://arxiv.org/abs/1608.06993
.. _Inception: https://arxiv.org/abs/1512.00567
"""
from .alexnet import * from .alexnet import *
from .resnet import * from .resnet import *
from .vgg import * from .vgg import *
......
torchvision/models/densenet.py
View file @ cd20d5f6
...@@ -17,7 +17,7 @@ model_urls = { ...@@ -17,7 +17,7 @@ model_urls = {
def densenet121(pretrained=False, **kwargs): def densenet121(pretrained=False, **kwargs):
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
...@@ -31,7 +31,7 @@ def densenet121(pretrained=False, **kwargs): ...@@ -31,7 +31,7 @@ def densenet121(pretrained=False, **kwargs):
def densenet169(pretrained=False, **kwargs): def densenet169(pretrained=False, **kwargs):
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
...@@ -45,7 +45,7 @@ def densenet169(pretrained=False, **kwargs): ...@@ -45,7 +45,7 @@ def densenet169(pretrained=False, **kwargs):
def densenet201(pretrained=False, **kwargs): def densenet201(pretrained=False, **kwargs):
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
...@@ -59,7 +59,7 @@ def densenet201(pretrained=False, **kwargs): ...@@ -59,7 +59,7 @@ def densenet201(pretrained=False, **kwargs):
def densenet161(pretrained=False, **kwargs): def densenet161(pretrained=False, **kwargs):
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
...@@ -111,7 +111,7 @@ class _Transition(nn.Sequential): ...@@ -111,7 +111,7 @@ 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)
......
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