torchvision 0.2.1

New datasets, transforms and fixes

on Python PyPI

This version introduces several fixes and improvements to the previous version.

Better printing of Datasets and Transforms

• Add descriptions to Transform objects.

# Now T.Compose([T.RandomHorizontalFlip(), T.RandomCrop(224), T.ToTensor()]) prints
Compose(
    RandomHorizontalFlip(p=0.5)
    RandomCrop(size=(224, 224), padding=0)
    ToTensor()
)

• Add descriptions to Datasets

# now torchvision.datasets.MNIST('~') prints
Dataset MNIST
    Number of datapoints: 60000
    Split: train
    Root Location: /private/home/fmassa
    Transforms (if any): None
    Target Transforms (if any): None

New transforms

• Add RandomApply, RandomChoice, RandomOrder transformations #402

  • RandomApply: applies a list of transformation with a probability
  • RandomChoice: choose randomly a single transformation from a list
  • RandomOrder: apply transformations in a random order

• Add random affine transformation #411

• Add reflect, symmetric and edge padding to transforms.pad #460

Performance improvements

• Speedup MNIST preprocessing by a factor of 1000x
• make weight initialization optional to speed VGG construction. This makes loading pre-trained VGG models much faster
• Accelerate transforms.adjust_gamma by using PIL's point function instead of custom numpy-based implementation

New Datasets

• EMNIST - an extension of MNIST for hand-written letters
• OMNIGLOT - a dataset for one-shot learning, with 1623 different handwritten characters from 50 different alphabets
• Add a DatasetFolder class - generalization of ImageFolder

Miscellaneous improvements

• FakeData accepts a seed argument, so having multiple different FakeData instances is now possible
• Use consistent datatypes in Dataset targets. Now all datasets that returns labels will have them as int
• Add probability parameter in RandomHorizontalFlip and RandomHorizontalFlip
• Replace np.random by random in transforms - improves reproducibility in multi-threaded environments with default arguments
• Detect tif images in ImageFolder
• Add pad_if_needed to RandomCrop, so that if the crop size is larger than the image, the image is automatically padded
• Add support in transforms.ToTensor for PIL Images with mode '1'

Bugfixes

• Fix passing list of tensors to utils.save_image
• single images passed to make_grid now are now also normalized
• Fix PIL img close warnings
• Added missing weight initializations to densenet
• Avoid division by zero in make_grid when the image is constant
• Fix ToTensor when PIL Image has mode F
• Fix bug with to_tensor when the input is numpy array of type np.float32.