我正在嘗試測驗transforms.Resize當我發現一個令人困惑的點時的原理。當我運行以下代碼時：
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()
出現錯誤：
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，當我改變張量的大小時，問題就解決了：
tim = np.array([[[1, 2, 3],
[1, 2, 3],
[1, 2, 3]]]) # (1, 3, 3)
我想知道為什么會發生這種情況，因為錯誤描述與大小無關，而與型別無關。如果有人對原因有任何想法，請告訴我，謝謝您的時間！
uj5u.com熱心網友回復：
將資料型別更改為浮動...
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)
確保您知道輸入資料是什么。
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