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torch-vision

This repository consists of:

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

Installation

Binaries:

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

From Source:

pip install -r requirements.txt
pip install .

Datasets

The following dataset loaders are available:

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.

COCO

This requires the COCO API to be installed

Captions:

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']

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

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.

Transforms

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

  • ToTensor() - converts PIL Image to Tensor
  • Normalize(mean, std) - normalizes the image given mean, std (for example: mean = [0.3, 1.2, 2.1])
  • Scale(size, interpolation=Image.BILINEAR) - Scales the smaller image edge to the given size. Interpolation modes are options from PIL
  • CenterCrop(size) - center-crops the image to the given size
  • RandomCrop(size) - Random crops the image to the given size.
  • RandomHorizontalFlip() - hflip the image with probability 0.5
  • RandomSizedCrop(size, interpolation=Image.BILINEAR) - Random crop with size 0.08-1 and aspect ratio 3/4 - 4/3 (Inception-style)

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 ]),
])