注意：以下这段代码是将一张图片的tensor形式转为PIL.Image图片格式，拿到了img后就可以保存了，导入PIL中的Image，img.save('xx.png')就行。
def transform_invert(self,img, show=False):
# Tensor -> PIL.Image
# 注意：img.shape = [3,32,32] cifar10中的一张图片，经过transform后的tensor格式
if img.dim() == 3: # single image # 3,32,32
img = img.unsqueeze(0) #在第0维增加一个维度 1,3,32,32
low = float(img.min())
high = float(img.max())
# img.clamp_(min=low, max=high)
img.sub_(low).div_(max(high - low, 1e-5)) # (img - low)/(high-low)
grid = img.squeeze(0) #去除维度为1的维度
ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
img = Image.fromarray(ndarr)
if show:
img.show()
return img以下代码是将一个batch的tensor存储为一张网格图片。
def transform_invert(self,imgs,path):
def norm_ip(img, low, high):
img.clamp_(min=low, max=high)
img.sub_(low).div_(max(high - low, 1e-5))
def norm_range(t, value_range):
if value_range is not None:
norm_ip(t, value_range[0], value_range[1])
else:
norm_ip(t, float(t.min()), float(t.max()))
nrow = 4
tensor = imgs
import math
padding: int = 2
pad_value = 8
norm_range(imgs, None)
# 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)
num_channels = tensor.size(1)
grid = tensor.new_full((num_channels, height * ymaps + padding, width * xmaps + padding), pad_value)
k = 0
for y in range(ymaps):
for x in range(xmaps):
if k >= nmaps:
break
# Tensor.copy_() is a valid method but seems to be missing from the stubs
# https://pytorch.org/docs/stable/tensors.html#torch.Tensor.copy_
grid.narrow(1, y * height + padding, height - padding).narrow( # type: ignore[attr-defined]
2, x * width + padding, width - padding
).copy_(tensor[k])
k = k + 1
# 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(path, format=format)