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...@@ -4,18 +4,7 @@ torch-vision ...@@ -4,18 +4,7 @@ torch-vision
.. image:: https://travis-ci.org/pytorch/vision.svg?branch=master .. image:: https://travis-ci.org/pytorch/vision.svg?branch=master
:target: https://travis-ci.org/pytorch/vision :target: https://travis-ci.org/pytorch/vision
This repository consists of: The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
- `vision.datasets <#datasets>`__ : Data loaders for popular vision
datasets
- `vision.models <#models>`__ : Definitions for popular model
architectures, such as AlexNet, VGG, and ResNet and pre-trained
models.
- `vision.transforms <#transforms>`__ : Common image transformations
such as random crop, rotations etc.
- `vision.utils <#utils>`__ : Useful stuff such as saving tensor (3 x H
x W) as image to disk, given a mini-batch creating a grid of images,
etc.
Installation Installation
============ ============
...@@ -38,409 +27,10 @@ From source: ...@@ -38,409 +27,10 @@ From source:
python setup.py install python setup.py install
Datasets Documentation
======== =============
You can find the API doucmentation on the pytorch website: http://pytorch.org/docs/master/torchvision/
The following dataset loaders are available:
- `MNIST and FashionMNIST <#mnist>`__
- `COCO (Captioning and Detection) <#coco>`__
- `LSUN Classification <#lsun>`__
- `ImageFolder <#imagefolder>`__
- `Imagenet-12 <#imagenet-12>`__
- `CIFAR10 and CIFAR100 <#cifar>`__
- `STL10 <#stl10>`__
- `SVHN <#svhn>`__
- `PhotoTour <#phototour>`__
Datasets have the API: - ``__getitem__`` - ``__len__`` They all subclass
from ``torch.utils.data.Dataset`` Hence, they can all be multi-threaded
(python multiprocessing) using standard torch.utils.data.DataLoader.
For example:
``torch.utils.data.DataLoader(coco_cap, batch_size=args.batchSize, shuffle=True, num_workers=args.nThreads)``
In the constructor, each dataset has a slightly different API as needed,
but they all take the keyword args:
- ``transform`` - a function that takes in an image and returns a
transformed version
- common stuff like ``ToTensor``, ``RandomCrop``, etc. These can be
composed together with ``transforms.Compose`` (see transforms section
below)
- ``target_transform`` - a function that takes in the target and
transforms it. For example, take in the caption string and return a
tensor of word indices.
MNIST
~~~~~
``dset.MNIST(root, train=True, transform=None, target_transform=None, download=False)``
``dset.FashionMNIST(root, train=True, transform=None, target_transform=None, download=False)``
``root``: root directory of dataset where ``processed/training.pt`` and ``processed/test.pt`` exist
``train``: ``True`` - use training set, ``False`` - use test set.
``transform``: transform to apply to input images
``target_transform``: transform to apply to targets (class labels)
``download``: whether to download the MNIST data
COCO
~~~~
This requires the `COCO API to be
installed <https://github.com/pdollar/coco/tree/master/PythonAPI>`__
Captions:
^^^^^^^^^
``dset.CocoCaptions(root="dir where images are", annFile="json annotation file", [transform, target_transform])``
Example:
.. code:: python
import torchvision.datasets as dset
import torchvision.transforms as transforms
cap = dset.CocoCaptions(root = 'dir where images are',
annFile = 'json annotation file',
transform=transforms.ToTensor())
print('Number of samples: ', len(cap))
img, target = cap[3] # load 4th sample
print("Image Size: ", img.size())
print(target)
Output:
::
Number of samples: 82783
Image Size: (3L, 427L, 640L)
[u'A plane emitting smoke stream flying over a mountain.',
u'A plane darts across a bright blue sky behind a mountain covered in snow',
u'A plane leaves a contrail above the snowy mountain top.',
u'A mountain that has a plane flying overheard in the distance.',
u'A mountain view with a plume of smoke in the background']
Detection:
^^^^^^^^^^
``dset.CocoDetection(root="dir where images are", annFile="json annotation file", [transform, target_transform])``
LSUN
~~~~
``dset.LSUN(db_path, classes='train', [transform, target_transform])``
- ``db_path`` = root directory for the database files
- ``classes`` =
- ``'train'`` - all categories, training set
- ``'val'`` - all categories, validation set
- ``'test'`` - all categories, test set
- [``'bedroom_train'``, ``'church_train'``, ...] : a list of categories to
load
CIFAR
~~~~~
``dset.CIFAR10(root, train=True, transform=None, target_transform=None, download=False)``
``dset.CIFAR100(root, train=True, transform=None, target_transform=None, download=False)``
- ``root`` : root directory of dataset where there is folder
``cifar-10-batches-py``
- ``train`` : ``True`` = Training set, ``False`` = Test set
- ``download`` : ``True`` = downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, does not do
anything.
STL10
~~~~~
``dset.STL10(root, split='train', transform=None, target_transform=None, download=False)``
- ``root`` : root directory of dataset where there is folder ``stl10_binary``
- ``split`` : ``'train'`` = Training set, ``'test'`` = Test set, ``'unlabeled'`` = Unlabeled set,
``'train+unlabeled'`` = Training + Unlabeled set (missing label marked as ``-1``)
- ``download`` : ``True`` = downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, does not do
anything.
SVHN
~~~~
Note: The SVHN dataset assigns the label `10` to the digit `0`. However, in this Dataset,
we assign the label `0` to the digit `0` to be compatible with PyTorch loss functions which
expect the class labels to be in the range `[0, C-1]`
``dset.SVHN(root, split='train', transform=None, target_transform=None, download=False)``
- ``root`` : root directory of dataset where there is folder ``SVHN``
- ``split`` : ``'train'`` = Training set, ``'test'`` = Test set, ``'extra'`` = Extra training set
- ``download`` : ``True`` = downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, does not do
anything.
ImageFolder
~~~~~~~~~~~
A generic data loader where the images are arranged in this way:
::
root/dog/xxx.png
root/dog/xxy.png
root/dog/xxz.png
root/cat/123.png
root/cat/nsdf3.png
root/cat/asd932_.png
``dset.ImageFolder(root="root folder path", [transform, target_transform])``
It has the members:
- ``self.classes`` - The class names as a list
- ``self.class_to_idx`` - Corresponding class indices
- ``self.imgs`` - The list of (image path, class-index) tuples
Imagenet-12
~~~~~~~~~~~
This is simply implemented with an ImageFolder dataset.
The data is preprocessed `as described
here <https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md#download-the-imagenet-dataset>`__
`Here is an
example <https://github.com/pytorch/examples/blob/27e2a46c1d1505324032b1d94fc6ce24d5b67e97/imagenet/main.py#L48-L62>`__.
PhotoTour
~~~~~~~~~
**Learning Local Image Descriptors Data**
http://phototour.cs.washington.edu/patches/default.htm
.. code:: python
import torchvision.datasets as dset
import torchvision.transforms as transforms
dataset = dset.PhotoTour(root = 'dir where images are',
name = 'name of the dataset to load',
transform=transforms.ToTensor())
print('Loaded PhotoTour: {} with {} images.'
.format(dataset.name, len(dataset.data)))
Models Contributing
====== ============
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
The models subpackage contains definitions for the following model
architectures:
- `AlexNet <https://arxiv.org/abs/1404.5997>`__: AlexNet variant from
the "One weird trick" paper.
- `VGG <https://arxiv.org/abs/1409.1556>`__: VGG-11, VGG-13, VGG-16,
VGG-19 (with and without batch normalization)
- `ResNet <https://arxiv.org/abs/1512.03385>`__: ResNet-18, ResNet-34,
ResNet-50, ResNet-101, ResNet-152
- `SqueezeNet <https://arxiv.org/abs/1602.07360>`__: SqueezeNet 1.0, and
SqueezeNet 1.1
- `DenseNet <https://arxiv.org/pdf/1608.06993.pdf>`__: DenseNet-128, DenseNet-169, DenseNet-201 and DenseNet-161
- `Inception v3 <https://arxiv.org/abs/1512.00567>`__ : 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.densenet161()
inception = models.inception_v3()
We provide pre-trained models for the ResNet variants, SqueezeNet 1.0 and 1.1,
AlexNet, VGG, Inception v3 and DenseNet using the PyTorch `model zoo <http://pytorch.org/docs/model_zoo.html>`__.
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.densenet161(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]`
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>`__
Transforms
==========
Transforms are common image transforms. They can be chained together
using ``transforms.Compose``
``transforms.Compose``
~~~~~~~~~~~~~~~~~~~~~~
One can compose several transforms together. For example.
.. code:: python
transform = transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
std = [ 0.229, 0.224, 0.225 ]),
])
Transforms on PIL.Image
~~~~~~~~~~~~~~~~~~~~~~~
``Scale(size, interpolation=Image.BILINEAR)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Rescales the input PIL.Image to the given 'size'.
If 'size' is a 2-element tuple or list in the order of (width, height), it will be the exactly size to scale.
If 'size' is a number, it will indicate the size of the smaller edge.
For example, if height > width, then image will be rescaled to (size \*
height / width, size) - size: size of the smaller edge - interpolation:
Default: PIL.Image.BILINEAR
``CenterCrop(size)`` - center-crops the image to the given size
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Crops the given PIL.Image at the center to have a region of the given
size. size can be a tuple (target\_height, target\_width) or an integer,
in which case the target will be of a square shape (size, size)
``RandomCrop(size, padding=0)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Crops the given PIL.Image at a random location to have a region of the
given size. size can be a tuple (target\_height, target\_width) or an
integer, in which case the target will be of a square shape (size, size)
If ``padding`` is non-zero, then the image is first zero-padded on each
side with ``padding`` pixels.
``RandomHorizontalFlip()``
^^^^^^^^^^^^^^^^^^^^^^^^^^
Randomly horizontally flips the given PIL.Image with a probability of
0.5
``RandomSizedCrop(size, interpolation=Image.BILINEAR)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Random crop the given PIL.Image to a random size of (0.08 to 1.0) of the
original size and and a random aspect ratio of 3/4 to 4/3 of the
original aspect ratio
This is popularly used to train the Inception networks - size: size of
the smaller edge - interpolation: Default: PIL.Image.BILINEAR
``Pad(padding, fill=0)``
^^^^^^^^^^^^^^^^^^^^^^^^
Pads the given image on each side with ``padding`` number of pixels, and
the padding pixels are filled with pixel value ``fill``. If a ``5x5``
image is padded with ``padding=1`` then it becomes ``7x7``
Transforms on torch.\*Tensor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
``Normalize(mean, std)``
^^^^^^^^^^^^^^^^^^^^^^^^
Given mean: (R, G, B) and std: (R, G, B), will normalize each channel of
the torch.\*Tensor, i.e. channel = (channel - mean) / std
``LinearTransformation(transformation_matrix)``
^^^^^^^^^^^^^^^^^^^^^^^^
Given ``transformation_matrix`` (D x D), where D = (C x H x W), will compute its
dot product with the flattened torch.\*Tensor and then reshape it to its
original dimensions.
Applications:
- whitening: zero-center the data, compute the data covariance matrix [D x D] with
np.dot(X.T, X), perform SVD on this matrix and pass the principal components as
transformation_matrix.
Conversion Transforms
~~~~~~~~~~~~~~~~~~~~~
- ``ToTensor()`` - Converts a PIL.Image (RGB) or numpy.ndarray (H x W x
C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W)
in the range [0.0, 1.0]
- ``ToPILImage()`` - Converts a torch.\*Tensor of range [0, 1] and
shape C x H x W or numpy ndarray of dtype=uint8, range[0, 255] and
shape H x W x C to a PIL.Image of range [0, 255]
Generic Transforms
~~~~~~~~~~~~~~~~~~
``Lambda(lambda)``
^^^^^^^^^^^^^^^^^^
Given a Python lambda, applies it to the input ``img`` and returns it.
For example:
.. code:: python
transforms.Lambda(lambda x: x.add(10))
Utils
=====
``make_grid(tensor, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0)``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Given a 4D mini-batch Tensor of shape (B x C x H x W),
or a list of images all of the same size,
makes a grid of images
``normalize=True`` will shift the image to the range (0, 1),
by subtracting the minimum and dividing by the maximum pixel value.
if ``range=(min, max)`` where ``min`` and ``max`` are numbers, then these numbers are used to
normalize the image.
``scale_each=True`` will scale each image in the batch of images separately rather than
computing the ``(min, max)`` over all images.
``pad_value=<float>`` sets the value for the padded pixels.
Example usage is given in this `notebook <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`__
``save_image(tensor, filename, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0)``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Saves a given Tensor into an image file.
If given a mini-batch tensor, will save the tensor as a grid of images.
All options after ``filename`` are passed through to ``make_grid``. Refer to it's documentation for
more details
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