torchvision/utils.py
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)
    im.save(filename)
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)
    im.save(filename)
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``.