张量尺寸的更改导致错误

Question

我正在尝试测试transforms.ize的原理，而我发现一个令人困惑的点。当我运行以下代码：

import numpy as np
import torch
from torchvision import transforms

tim = np.array([[[1, 2, 3],
                 [1, 2, 3],
                 [1, 2, 3]],
                [[1, 2, 3],
                 [1, 2, 3],
                 [1, 2, 3]]]) # (2, 3, 3)

tim = torch.from_numpy(tim)

tf = transforms.Compose([  # Principle?
     transforms.ToPILImage(),
     transforms.Resize((6, 6)), # HW
     transforms.ToTensor()
])

mask = tf(tim)
squ = mask.squeeze()

发生错误时：

Traceback (most recent call last):
  File "C:/Users/Tim/Desktop/U-Net/test.py", line 62, in <module>
    mask = tf(tim)
  File "C:\Users\Tim\.conda\envs\Segment\lib\site-packages\torchvision\transforms\transforms.py", line 95, in __call__
    img = t(img)
  File "C:\Users\Tim\.conda\envs\Segment\lib\site-packages\torchvision\transforms\transforms.py", line 227, in __call__
    return F.to_pil_image(pic, self.mode)
  File "C:\Users\Tim\.conda\envs\Segment\lib\site-packages\torchvision\transforms\functional.py", line 315, in to_pil_image
    raise TypeError(f"Input type {npimg.dtype} is not supported")
TypeError: Input type int32 is not supported

但是，当我更改张量的大小时，问题就解决了：

tim = np.array([[[1, 2, 3],
                 [1, 2, 3],
                 [1, 2, 3]]]) # (1, 3, 3)

我想知道为什么这会发生这种情况，因为错误描述与大小无关，而是类型。如果有人对原因有任何想法，请告诉我，谢谢您的时间！

Answer 1

将数据类型更改为浮动...

tim = np.array([[[1, 2, 3],
                 [1, 2, 3],
                 [1, 2, 3]],
                [[1, 2, 3],
                 [1, 2, 3],
                 [1, 2, 3]]], dtype=np.float32) # (2, 3, 3)

请确保您知道输入数据是什么。