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image and video datasets and models for torch deep learning

Project description

torch-vision

This repository consists of:

  • vision.datasets : Data loaders for popular vision datasets
  • vision.models : Definitions for popular model architectures, such as AlexNet, VGG, and ResNet and pre-trained models.
  • 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.

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

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 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.

Models

The models subpackage contains definitions for the following model architectures:

  • AlexNet: AlexNet variant from the “One weird trick” paper.
  • VGG: VGG-11, VGG-13, VGG-16, VGG-19 (with and without batch normalization)
  • ResNet: ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152

You can construct a model with random weights by calling its constructor:

import torchvision.models as models
resnet18 = models.resnet18()
alexnet = models.alexnet()

We provide pre-trained models for the ResNet variants and AlexNet, using the PyTorch model zoo. These can be constructed by passing pretrained=True:

python  import torchvision.models as models  resnet18 = models.resnet18(pretrained=True)  alexnet = models.alexnet(pretrained=True)

Transforms

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

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

Scale(size, interpolation=Image.BILINEAR)

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

CenterCrop(size) - center-crops the image to the given size

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)

RandomCrop(size, padding=0)

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.

RandomHorizontalFlip()

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

RandomSizedCrop(size, interpolation=Image.BILINEAR)

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

Pad(padding, fill=0)

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

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 Transofrms

Lambda(lambda)

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

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

Utils

make_grid(tensor, nrow=8, padding=2)

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

save_image(tensor, filename, nrow=8, padding=2)

Saves a given Tensor into an image file.

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

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