test_transforms_tensor.py

import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F
from PIL import Image

import numpy as np

import unittest


class Tester(unittest.TestCase):
    def _create_data(self, height=3, width=3, channels=3):
        tensor = torch.randint(0, 255, (channels, height, width), dtype=torch.uint8)
        pil_img = Image.fromarray(tensor.permute(1, 2, 0).contiguous().numpy())
        return tensor, pil_img

    def compareTensorToPIL(self, tensor, pil_image):
        pil_tensor = torch.as_tensor(np.array(pil_image).transpose((2, 0, 1)))
        self.assertTrue(tensor.equal(pil_tensor))

    def _test_functional_geom_op(self, func, fn_kwargs):
        if fn_kwargs is None:
            fn_kwargs = {}
        tensor, pil_img = self._create_data(height=10, width=10)
        transformed_tensor = getattr(F, func)(tensor, **fn_kwargs)
        transformed_pil_img = getattr(F, func)(pil_img, **fn_kwargs)
        self.compareTensorToPIL(transformed_tensor, transformed_pil_img)

    def _test_geom_op(self, func, method, fn_kwargs=None, meth_kwargs=None):
        if fn_kwargs is None:
            fn_kwargs = {}
        if meth_kwargs is None:
            meth_kwargs = {}
        tensor, pil_img = self._create_data(height=10, width=10)
        transformed_tensor = getattr(F, func)(tensor, **fn_kwargs)
        transformed_pil_img = getattr(F, func)(pil_img, **fn_kwargs)
        self.compareTensorToPIL(transformed_tensor, transformed_pil_img)

        scripted_fn = torch.jit.script(getattr(F, func))
        transformed_tensor_script = scripted_fn(tensor, **fn_kwargs)
        self.assertTrue(transformed_tensor.equal(transformed_tensor_script))

        # test for class interface
        f = getattr(T, method)(**meth_kwargs)
        scripted_fn = torch.jit.script(f)
        scripted_fn(tensor)

    def test_random_horizontal_flip(self):
        self._test_geom_op('hflip', 'RandomHorizontalFlip')

    def test_random_vertical_flip(self):
        self._test_geom_op('vflip', 'RandomVerticalFlip')

    def test_adjustments(self):
        fns = ['adjust_brightness', 'adjust_contrast', 'adjust_saturation']
        for _ in range(20):
            factor = 3 * torch.rand(1).item()
            tensor, _ = self._create_data()
            pil_img = T.ToPILImage()(tensor)

            for func in fns:
                adjusted_tensor = getattr(F, func)(tensor, factor)
                adjusted_pil_img = getattr(F, func)(pil_img, factor)

                adjusted_pil_tensor = T.ToTensor()(adjusted_pil_img)
                scripted_fn = torch.jit.script(getattr(F, func))
                adjusted_tensor_script = scripted_fn(tensor, factor)

                if not tensor.dtype.is_floating_point:
                    adjusted_tensor = adjusted_tensor.to(torch.float) / 255
                    adjusted_tensor_script = adjusted_tensor_script.to(torch.float) / 255

                # F uses uint8 and F_t uses float, so there is a small
                # difference in values caused by (at most 5) truncations.
                max_diff = (adjusted_tensor - adjusted_pil_tensor).abs().max()
                max_diff_scripted = (adjusted_tensor - adjusted_tensor_script).abs().max()
                self.assertLess(max_diff, 5 / 255 + 1e-5)
                self.assertLess(max_diff_scripted, 5 / 255 + 1e-5)

    def test_pad(self):

        # Test functional.pad (PIL and Tensor) with padding as single int
        self._test_functional_geom_op(
            "pad", fn_kwargs={"padding": 2, "fill": 0, "padding_mode": "constant"}
        )
        # Test functional.pad and transforms.Pad with padding as [int, ]
        fn_kwargs = meth_kwargs = {"padding": [2, ], "fill": 0, "padding_mode": "constant"}
        self._test_geom_op(
            "pad", "Pad", fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
        )
        # Test functional.pad and transforms.Pad with padding as list
        fn_kwargs = meth_kwargs = {"padding": [4, 4], "fill": 0, "padding_mode": "constant"}
        self._test_geom_op(
            "pad", "Pad", fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
        )
        # Test functional.pad and transforms.Pad with padding as tuple
        fn_kwargs = meth_kwargs = {"padding": (2, 2, 2, 2), "fill": 127, "padding_mode": "constant"}
        self._test_geom_op(
            "pad", "Pad", fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
        )


if __name__ == '__main__':
    unittest.main()