This repository consists of:

• vision.datasets : Data loaders for popular vision datasets
• vision.transforms : Common image transformations such as random crop, rotations etc.
• vision.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.
• [WIP] vision.models : Model definitions and Pre-trained models for popular models such as AlexNet, VGG, ResNet etc.

Binaries:

conda install torchvision -c https://conda.anaconda.org/t/6N-MsQ4WZ7jo/soumith

From Source:

pip install -r requirements.txt
pip install .

The following dataset loaders are available:

• COCO (Captioning and Detection)
• LSUN Classification
• ImageFolder
• Imagenet-12
• CIFAR10 and CIFAR100

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.

This requires the COCO API to be installed

dset.CocoCaptions(root="dir where images are", annFile="json annotation file", [transform, target_transform])

Example:

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']

dset.CocoDetection(root="dir where images are", annFile="json annotation file", [transform, target_transform])

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

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 already downloaded, does not do anything.

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

This is simply implemented with an ImageFolder dataset.

The data is preprocessed as described here

Here is an example.

Transforms are common image transforms. They can be chained together using transforms.Compose

One can compose several transforms together. For example.

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 ]),
])

Rescales the input PIL.Image to the given 'size'. 'size' will be 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

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)

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.

Randomly horizontally flips the given PIL.Image with a probability of 0.5

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

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

Given mean: (R, G, B) and std: (R, G, B), will normalize each channel of the torch.*Tensor, i.e. channel = (channel - mean) / std

• 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]

Given a Python lambda, applies it to the input img and returns it. For example:

transforms.Lambda(lambda x: x.add(10))

Given a 4D mini-batch Tensor of shape (B x C x H x W), makes a grid of images

Saves a given Tensor into an image file.

If given a mini-batch tensor, will save the tensor as a grid of images.