Hi,
while using libtorch 1.71 to resize feature, I found that F::interpolate would get the wrong result which is different with pytorch 1.71.
Here is my libtorch code.
cv::Mat image = cv::imread("/home/lll/Pictures/test.jpg");


torch::Tensor image_tensor = torch::from_blob(image.data, {image.rows, image.cols, 3}, torch::kByte);

image_tensor = image_tensor.permute({2, 0, 1}).toType(torch::kFloat).div_(255);
image_tensor.sub_(0.5).div_(0.5);
image_tensor = image_tensor.unsqueeze(0);
image_tensor = image_tensor.to(torch::kCUDA);

namespace F = torch::nn::functional;
image_tensor = F::interpolate(
        image_tensor,
        F::InterpolateFuncOptions()
                .mode(torch::kBilinear)
                .size(std::vector<int64_t>({256, 256}))
                .align_corners(true)
);
image_tensor = image_tensor.mul(0.5).add(0.5).mul(255);
image_tensor = image_tensor.squeeze(0).permute({1, 2, 0}).toType(torch::kByte).to(torch::kCPU);

cv::Mat test_mat(256, 256, CV_8UC3);
std::memcpy((void *) test_mat.data, image_tensor.data_ptr(), sizeof(torch::kU8) * image_tensor.numel());
cv::imshow("test", test_mat);
cv::waitKey(0);

Here is my pytorch code.
_transforms = transforms.Compose([
transforms.Normalize((0.5, 0.5, 0.5), (128 / 255., 128 / 255., 128 / 255.))])
image = cv2.imread("/home/lll/Pictures/test.jpg")

image = torch.from_numpy(image)
image = image.permute(2, 0, 1)
image = image.unsqueeze(0)
image = image.float()

image = _transforms(image)

image = F.interpolate(image, size=(256, 256), mode='bilinear', align_corners=True)
image = image.squeeze(0).permute(1, 2, 0)

image = image * 0.5 + 0.5
image = image.byte()
cv2.imshow("test", image.numpy())
cv2.waitKey(0)

I using opencv to visualize the result.
This is the original image

and this is the worng result.It seems to split the channels and merge them into a single image

This is the correct result witch I want

Strangely enough, when I use Libtorch 1.4.0 with the same code,I could get the right results.
If you could help me I will appreciate it.