image and video datasets and models for torch deep learning
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.
conda install torchvision -c https://conda.anaconda.org/t/6N-MsQ4WZ7jo/soumith
pip install -r requirements.txt pip install .
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.
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.
This requires the COCO API to be installed
dset.CocoDetection(root="dir where images are", annFile="json annotation file", [transform, target_transform])
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
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.
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
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 are common image transforms. They can be chained together using 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
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)
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.
Randomly horizontally flips the given PIL.Image with a probability of 0.5
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
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
Given mean: (R, G, B) and std: (R, G, B), will normalize each channel of the torch.*Tensor, i.e. channel = (channel - mean) / std
- 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]
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)
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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