Source code for torchvision.utils

import torch
import math
irange = range

[docs]def make_grid(tensor, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0): """Make a grid of images. Args: 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: ``8``. padding (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 :attr:`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 (float, optional): Value for the padded pixels. Default: ``0``. Example: See this notebook `here <>`_ """ if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))): raise TypeError('tensor or list of tensors expected, got {}'.format(type(tensor))) # if list of tensors, convert to a 4D mini-batch Tensor if isinstance(tensor, list): tensor = torch.stack(tensor, dim=0) if tensor.dim() == 2: # single image H x W tensor = tensor.unsqueeze(0) if tensor.dim() == 3: # single image if tensor.size(0) == 1: # if single-channel, convert to 3-channel tensor =, tensor, tensor), 0) tensor = tensor.unsqueeze(0) if tensor.dim() == 4 and tensor.size(1) == 1: # single-channel images tensor =, tensor, tensor), 1) if normalize is True: tensor = tensor.clone() # avoid modifying tensor in-place if range is not None: assert isinstance(range, tuple), \ "range has to be a tuple (min, max) if specified. min and max are numbers" def norm_ip(img, min, max): img.clamp_(min=min, max=max) img.add_(-min).div_(max - min + 1e-5) def norm_range(t, range): if range is not None: norm_ip(t, range[0], range[1]) else: norm_ip(t, float(t.min()), float(t.max())) if scale_each is True: for t in tensor: # loop over mini-batch dimension norm_range(t, range) else: norm_range(tensor, range) if tensor.size(0) == 1: return tensor.squeeze() # make the mini-batch of images into a grid nmaps = tensor.size(0) xmaps = min(nrow, nmaps) ymaps = int(math.ceil(float(nmaps) / xmaps)) height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding) grid = tensor.new_full((3, height * ymaps + padding, width * xmaps + padding), pad_value) k = 0 for y in irange(ymaps): for x in irange(xmaps): if k >= nmaps: break grid.narrow(1, y * height + padding, height - padding)\ .narrow(2, x * width + padding, width - padding)\ .copy_(tensor[k]) k = k + 1 return grid
[docs]def 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. Args: 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``. """ from PIL import Image grid = make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value, normalize=normalize, range=range, scale_each=scale_each) # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer ndarr = grid.mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy() im = Image.fromarray(ndarr)


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