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

Make a grid of images.

  • 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 (python:int, optional) – Number of images displayed in each row of the grid. The final grid size is (B / nrow, nrow). Default: 8.

  • padding (python:int, optional) – amount of padding. Default: 2.

  • normalize (bool, optional) – If True, shift the image to the range (0, 1), by the min and max values specified by range. Default: False.

  • 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. Default: False.

  • pad_value (python:float, optional) – Value for the padded pixels. Default: 0.


See this notebook here

torchvision.utils.save_image(tensor, fp, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0, format=None)[source]

Save a given Tensor into an image file.

  • 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.

  • - A filename (fp) –

  • format (Optional) – If omitted, the format to use is determined from the filename extension. If a file object was used instead of a filename, this parameter should always be used.

  • **kwargs – Other arguments are documented in make_grid.


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