How to convert PyTorch Tensor to Numpy ndarray

Question

I have this function that rotates the MNIST images. The function returns a pytorch Tensor. I am more familiar with Tensorflow and I want to convert the pytorch tensor to a numpy ndarray that I can use. Is there a function that will allow me to do that? I tried to modify the function a little bit by adding .numpy() after tensor(img.rotate(rotation)).view(784) and save it in an empty ndarray, but that didn't work. Parameter d is MNIST data saved in .pt (pytensor, I think). Thanks! (Would love to know if there is a tensorflow function that can rotate the data.)

t = 1
min_rot = 1.0 * t / 20 * (180 - 0) + \
        0
max_rot = 1.0 * (t + 1) / 20 * \
    (180 - 0) + 0
rot = random.random() * (max_rot - min_rot) + min_rot
rotate_dataset(x_tr, rot)

def rotate_dataset(d, rotation):
    result = torch.FloatTensor(d.size(0), 784)
    tensor = transforms.ToTensor()

    for i in range(d.size(0)):
        img = Image.fromarray(d[i].numpy(), mode='L')
        result[i] = tensor(img.rotate(rotation)).view(784)
    return result

Answer 1

首先不转换为tensor怎么样：

result[i] = np.array(img.rotate(rotation)).flatten()