Hi,
I got an error like in the tittle and it sounds easy (I wonder that I just need to change type of loaded data) but I have a problem with that… I changed types but error still appears.
In my main function:
inputs = load_images(glob.glob(args.input))
outputs = predict(model, inputs)



def load_images(image_files):
    loaded_images = []
    for file in image_files:
        img = Image.open(file)
        new_size = (640, 480)
        img = img.resize(new_size)
        x = np.clip(np.asarray(img) / 255, 0, 1).astype(np.float64)
        loaded_images.append(torch.DoubleTensor(torch.from_numpy(x)))
    return np.stack(loaded_images, axis=0)

def predict(model, images, minDepth=10, maxDepth=1000):
    # Support multiple RGBs, one RGB image, even grayscale
    if len(images.shape) < 3:
        images = np.stack((images, images, images), axis=2)
    if len(images.shape) < 4:
        images = images.reshape((1, images.shape[0], images.shape[1], images.shape[2]))
    # Compute predictions
    images_tensor = []
    for i in images:
        i = torch.DoubleTensor(i)
        i = i.permute(2, 0, 1)
        images_tensor.append(i)
    images_tensor = torch.stack(images_tensor)
    predictions = model(images_tensor.double())
    return (
        np.clip(DepthNorm(predictions, maxDepth=maxDepth), minDepth, maxDepth)
        / maxDepth
    )

Ideas?