helper.py

import torch
import math
from PIL import Image
irange = range


def make_grid(tensor, nrow=8, padding=2,
              normalize=False, range=None, scale_each=False, pad_value=0):


    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.view(1, tensor.size(0), tensor.size(1))
    if tensor.dim() == 3:  # single image
        if tensor.size(0) == 1:  # if single-channel, convert to 3-channel
            tensor = torch.cat((tensor, tensor, tensor), 0)
        tensor = tensor.view(1, tensor.size(0), tensor.size(1), tensor.size(2))

    if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images
        tensor = torch.cat((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


def norm_ip(img, min, max):
    img.clamp_(min=min, max=max)
    img.add_(-min).div_(max - min + 1e-5)
    return img

def save_image(tensor, filename, nrow=8, padding=2,
               normalize=False, range=None, scale_each=False, pad_value=0):

    
    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_single_image(tensor,filename):
    img = norm_ip(tensor, float(tensor.min()), float(tensor.max()))
    # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
    ndarr = img.mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
    im = Image.fromarray(ndarr)
    im.save(filename)