torchvision 0.1.8

MNIST

dset.MNIST(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

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)

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']

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)

• downloadTrue = downloads the dataset from the internet and

  puts it in root directory. If dataset is already downloaded, does not do anything.

SVHN

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

• downloadTrue = 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

Here is an example.

PhotoTour

Learning Local Image Descriptors Data http://phototour.cs.washington.edu/patches/default.htm

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)))

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 ]),
])

Transforms on PIL.Image

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

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:

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

make_grid(tensor, nrow=8, padding=2, normalize=False, range=None, scale_each=False)

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.

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)

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