torchvision package designed to prepare visual images for learning process.
torchvision package in turn has additional subpackages.
datasets models transforms utils
torchvision.datasets subpackage contains most important datasets. At the current moment these are:
cifar cityscapes coco fakedata flickr folder lsun mnist omniglot phototour sbu semeion stl10 svhn utils voc
torchvision.models subpackage contains these models at the current moment:
alexnet densenet inception resnet squeezenet vgg
torchvision.utils help us save Tensors to a file. These tensors are of shape:
BxCxHxW : number of mini batches, channels, height, width
and create grids of images.
But the most interesting sub-package today is the
This package has exactly two sub pakages
torchvision.transforms.transforms that holds the classes behind the
torchvision.transforms.functional package depends on
PIL.Image functionality. Contains methods to detect the image type:
_is_numpy_image _is_pil_image _is_tensor_image
Methods to adjust the image:
adjust_brightness adjust_contrast adjust_gamma adjust_hue adjust_saturation
Methods to transform the image
affine (keeps the center in place) center_crop crop five_crop hflip pad resize resized_crop rotate scale ten_crop vflip
Some handy methods to convert the image:
to_grayscale to_pil_image to_tensor
And also the method to normalize the image.