Pytorch RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 0
I use code from here to train a model to predict printed style number from 0 to 9:

idx_to_class = {0: "0", 1: "1", 2: "2", 3: "3", 4: "4", 5: "5", 6: "6", 7:"7", 8: "8", 9:"9"}
def predict(model, test_image_name):

    transform = image_transforms['test']

    test_image = Image.open(test_image_name)
    plt.imshow(test_image)

    test_image_tensor = transform(test_image)

    if torch.cuda.is_available():
        test_image_tensor = test_image_tensor.view(1, 3, 224, 224).cuda()
    else:
        test_image_tensor = test_image_tensor.view(1, 3, 224, 224)

    with torch.no_grad():
        model.eval()
        # Model outputs log probabilities
        out = model(test_image_tensor)
        ps = torch.exp(out)
        topk, topclass = ps.topk(1, dim=1)
        # print(topclass.cpu().numpy()[0][0])
        print("Image class:  ", idx_to_class[topclass.cpu().numpy()[0][0]])

predict(model, "path_of_test_image")


But I get an error when try to use predict:

Traceback (most recent call last):

  File "&lt;ipython-input-12-f8636d3ba083&gt;", line 26, in &lt;module&gt;
    predict(model, "/home/x/文档/Deep_Learning/pytorch/MNIST/test/2/QQ截图20191022093955.png")

  File "&lt;ipython-input-12-f8636d3ba083&gt;", line 9, in predict
    test_image_tensor = transform(test_image)

  File "/home/x/.local/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 61, in __call__
    img = t(img)

  File "/home/x/.local/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 166, in __call__
    return F.normalize(tensor, self.mean, self.std, self.inplace)

  File "/home/x/.local/lib/python3.6/site-packages/torchvision/transforms/functional.py", line 217, in normalize
    tensor.sub_(mean[:, None, None]).div_(std[:, None, None])

RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 0


How could I fix it? Thanks.

I suspect your test_image has an additional alpha channel per pixel, thus it has 4 channels instead of only three.
Try:
test_image = Image.open(test_image_name).convert('RGB')

