Fix padding logic to avoid negative padding#140
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This PR fixes the padding logic.
The original function breaks in one of my datasets where images are of shape (H_ori, W_ori) = (1190, 1596). This is because target_aspect_ratio is within the expected range, but int(H_ori * target_aspect_ratio) = 1595 so W_new = 1595 and (pad_left, pad_right, pad_top, pad_bottom) = (-1, 0, 0, 0). Negative padding will result in erroneous behavior in "tensor[..., pad1_t : shapes[0] - pad1_b, pad1_l : shapes[1] - pad1_r]" in _postprocess function.
The new code will ensure the padded sizes are not smaller than original, hence making padding sizes non-negative.