如何像这样重塑二维数组？ （通过使用张量）

Question

I want to resize my image from 32 * 32 to 16 * 16. (By using torch.tensor) Like decreasing the resolution? Can anyone help me?

Answer 1

If you have an image (stored in a tensor) and you want to decrease it's resolution , then you are not reshaping it, but rather resizing it.
To that end, you can use pytorch's interpolate :

import torch
from torch.nn import functional as nnf

y = nnf.interpolate(x[None, None, ...], size=(16, 16), mode='bicubic', align_corners=False, antialias=True)

Notes:

1. nnf.interpolate operates on batches of multi-channel images, that is, it expects its input x to have 4 dimensions: batch - channels - height - width . So, if your x is a single image with a single channel (eg, an MNIST digit) you'll have to create a singleton batch dimension and a singleton channel dimension.
2. Pay close attention to align_corners and antialias -- make sure you are using the right configuration for your needs.

For more information regarding aliasing and alignment when resizing images you can look at ResizeRight .

Answer 2

you can use resize from torchvision transforms:

import torchvision.transform as T
T.Resize(16)(your_image)

you can also chain it to other transforms

preprocess = T.Compose([
   T.Resize(N),
   T.CenterCrop(N),
   T.ToTensor(),
   T.Normalize(
       mean=mean,
       std=std
   )
])
preprocess(your_image)