Utils

torchvision.utils.make_grid(tensor, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0)

Make a grid of images.

Parameters:

• tensor (Tensor or list) – 4D mini-batch Tensor of shape (B x C x H x W) or a list of images all of the same size.
• nrow (int, optional) – Number of images displayed in each row of the grid. The Final grid size is (B / nrow, nrow). Default is 8.
• padding (int, optional) – amount of padding. Default is 2.
• normalize (bool, optional) – If True, shift the image to the range (0, 1), by subtracting the minimum and dividing by the maximum pixel value.
• range (tuple, optional) – tuple (min, max) where min and max are numbers, then these numbers are used to normalize the image. By default, min and max are computed from the tensor.
• scale_each (bool, optional) – If True, scale each image in the batch of images separately rather than the (min, max) over all images.
• pad_value (float, optional) – Value for the padded pixels.

Example

See this notebook here

torchvision.utils.save_image(tensor, filename, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0)

Save a given Tensor into an image file.

Parameters:

• tensor (Tensor or list) – Image to be saved. If given a mini-batch tensor, saves the tensor as a grid of images by calling make_grid.
• **kwargs – Other arguments are documented in make_grid.

torchvision.get_image_backend()

Gets the name of the package used to load images

torchvision.set_image_backend(backend)

Specifies the package used to load images.

Parameters:backend (string) – Name of the image backend. one of {‘PIL’, ‘accimage’}. The accimage package uses the Intel IPP library. It is generally faster than PIL, but does not support as many operations.