test_functional_tensor.py

import itertools
import os
import unittest
import colorsys
import math

import numpy as np
import pytest

import torch
import torchvision.transforms.functional_tensor as F_t
import torchvision.transforms.functional_pil as F_pil
import torchvision.transforms.functional as F
import torchvision.transforms as T
from torchvision.transforms import InterpolationMode

from common_utils import TransformsTester, cpu_and_gpu

from typing import Dict, List, Sequence, Tuple


NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC


@pytest.fixture(scope='module')
def tester():
    # instanciation of the Tester class used for equality assertions and other utilities
    # TODO: remove this eventually when we don't need the class anymore
    return Tester()


class Tester(TransformsTester):

    def setUp(self):
        self.device = "cpu"

    def _test_fn_on_batch(self, batch_tensors, fn, scripted_fn_atol=1e-8, **fn_kwargs):
        transformed_batch = fn(batch_tensors, **fn_kwargs)
        for i in range(len(batch_tensors)):
            img_tensor = batch_tensors[i, ...]
            transformed_img = fn(img_tensor, **fn_kwargs)
            self.assertTrue(transformed_img.equal(transformed_batch[i, ...]))

        if scripted_fn_atol >= 0:
            scripted_fn = torch.jit.script(fn)
            # scriptable function test
            s_transformed_batch = scripted_fn(batch_tensors, **fn_kwargs)
            self.assertTrue(transformed_batch.allclose(s_transformed_batch, atol=scripted_fn_atol))

    def test_assert_image_tensor(self):
        shape = (100,)
        tensor = torch.rand(*shape, dtype=torch.float, device=self.device)

        list_of_methods = [(F_t._get_image_size, (tensor, )), (F_t.vflip, (tensor, )),
                           (F_t.hflip, (tensor, )), (F_t.crop, (tensor, 1, 2, 4, 5)),
                           (F_t.adjust_brightness, (tensor, 0.)), (F_t.adjust_contrast, (tensor, 1.)),
                           (F_t.adjust_hue, (tensor, -0.5)), (F_t.adjust_saturation, (tensor, 2.)),
                           (F_t.center_crop, (tensor, [10, 11])), (F_t.five_crop, (tensor, [10, 11])),
                           (F_t.ten_crop, (tensor, [10, 11])), (F_t.pad, (tensor, [2, ], 2, "constant")),
                           (F_t.resize, (tensor, [10, 11])), (F_t.perspective, (tensor, [0.2, ])),
                           (F_t.gaussian_blur, (tensor, (2, 2), (0.7, 0.5))),
                           (F_t.invert, (tensor, )), (F_t.posterize, (tensor, 0)),
                           (F_t.solarize, (tensor, 0.3)), (F_t.adjust_sharpness, (tensor, 0.3)),
                           (F_t.autocontrast, (tensor, )), (F_t.equalize, (tensor, ))]

        for func, args in list_of_methods:
            with self.assertRaises(Exception) as context:
                func(*args)

            self.assertTrue('Tensor is not a torch image.' in str(context.exception))

    def test_vflip(self):
        script_vflip = torch.jit.script(F.vflip)

        img_tensor, pil_img = self._create_data(16, 18, device=self.device)
        vflipped_img = F.vflip(img_tensor)
        vflipped_pil_img = F.vflip(pil_img)
        self.compareTensorToPIL(vflipped_img, vflipped_pil_img)

        # scriptable function test
        vflipped_img_script = script_vflip(img_tensor)
        self.assertTrue(vflipped_img.equal(vflipped_img_script))

        batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)
        self._test_fn_on_batch(batch_tensors, F.vflip)

    def test_hflip(self):
        script_hflip = torch.jit.script(F.hflip)

        img_tensor, pil_img = self._create_data(16, 18, device=self.device)
        hflipped_img = F.hflip(img_tensor)
        hflipped_pil_img = F.hflip(pil_img)
        self.compareTensorToPIL(hflipped_img, hflipped_pil_img)

        # scriptable function test
        hflipped_img_script = script_hflip(img_tensor)
        self.assertTrue(hflipped_img.equal(hflipped_img_script))

        batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)
        self._test_fn_on_batch(batch_tensors, F.hflip)

    def test_crop(self):
        script_crop = torch.jit.script(F.crop)

        img_tensor, pil_img = self._create_data(16, 18, device=self.device)

        test_configs = [
            (1, 2, 4, 5),   # crop inside top-left corner
            (2, 12, 3, 4),  # crop inside top-right corner
            (8, 3, 5, 6),   # crop inside bottom-left corner
            (8, 11, 4, 3),  # crop inside bottom-right corner
        ]

        for top, left, height, width in test_configs:
            pil_img_cropped = F.crop(pil_img, top, left, height, width)

            img_tensor_cropped = F.crop(img_tensor, top, left, height, width)
            self.compareTensorToPIL(img_tensor_cropped, pil_img_cropped)

            img_tensor_cropped = script_crop(img_tensor, top, left, height, width)
            self.compareTensorToPIL(img_tensor_cropped, pil_img_cropped)

            batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)
            self._test_fn_on_batch(batch_tensors, F.crop, top=top, left=left, height=height, width=width)

    def test_hsv2rgb(self):
        scripted_fn = torch.jit.script(F_t._hsv2rgb)
        shape = (3, 100, 150)
        for _ in range(10):
            hsv_img = torch.rand(*shape, dtype=torch.float, device=self.device)
            rgb_img = F_t._hsv2rgb(hsv_img)
            ft_img = rgb_img.permute(1, 2, 0).flatten(0, 1)

            h, s, v, = hsv_img.unbind(0)
            h = h.flatten().cpu().numpy()
            s = s.flatten().cpu().numpy()
            v = v.flatten().cpu().numpy()

            rgb = []
            for h1, s1, v1 in zip(h, s, v):
                rgb.append(colorsys.hsv_to_rgb(h1, s1, v1))
            colorsys_img = torch.tensor(rgb, dtype=torch.float32, device=self.device)
            max_diff = (ft_img - colorsys_img).abs().max()
            self.assertLess(max_diff, 1e-5)

            s_rgb_img = scripted_fn(hsv_img)
            self.assertTrue(rgb_img.allclose(s_rgb_img))

        batch_tensors = self._create_data_batch(120, 100, num_samples=4, device=self.device).float()
        self._test_fn_on_batch(batch_tensors, F_t._hsv2rgb)

    def test_rgb2hsv(self):
        scripted_fn = torch.jit.script(F_t._rgb2hsv)
        shape = (3, 150, 100)
        for _ in range(10):
            rgb_img = torch.rand(*shape, dtype=torch.float, device=self.device)
            hsv_img = F_t._rgb2hsv(rgb_img)
            ft_hsv_img = hsv_img.permute(1, 2, 0).flatten(0, 1)

            r, g, b, = rgb_img.unbind(dim=-3)
            r = r.flatten().cpu().numpy()
            g = g.flatten().cpu().numpy()
            b = b.flatten().cpu().numpy()

            hsv = []
            for r1, g1, b1 in zip(r, g, b):
                hsv.append(colorsys.rgb_to_hsv(r1, g1, b1))

            colorsys_img = torch.tensor(hsv, dtype=torch.float32, device=self.device)

            ft_hsv_img_h, ft_hsv_img_sv = torch.split(ft_hsv_img, [1, 2], dim=1)
            colorsys_img_h, colorsys_img_sv = torch.split(colorsys_img, [1, 2], dim=1)

            max_diff_h = ((colorsys_img_h * 2 * math.pi).sin() - (ft_hsv_img_h * 2 * math.pi).sin()).abs().max()
            max_diff_sv = (colorsys_img_sv - ft_hsv_img_sv).abs().max()
            max_diff = max(max_diff_h, max_diff_sv)
            self.assertLess(max_diff, 1e-5)

            s_hsv_img = scripted_fn(rgb_img)
            self.assertTrue(hsv_img.allclose(s_hsv_img, atol=1e-7))

        batch_tensors = self._create_data_batch(120, 100, num_samples=4, device=self.device).float()
        self._test_fn_on_batch(batch_tensors, F_t._rgb2hsv)

    def test_rgb_to_grayscale(self):
        script_rgb_to_grayscale = torch.jit.script(F.rgb_to_grayscale)

        img_tensor, pil_img = self._create_data(32, 34, device=self.device)

        for num_output_channels in (3, 1):
            gray_pil_image = F.rgb_to_grayscale(pil_img, num_output_channels=num_output_channels)
            gray_tensor = F.rgb_to_grayscale(img_tensor, num_output_channels=num_output_channels)

            self.approxEqualTensorToPIL(gray_tensor.float(), gray_pil_image, tol=1.0 + 1e-10, agg_method="max")

            s_gray_tensor = script_rgb_to_grayscale(img_tensor, num_output_channels=num_output_channels)
            self.assertTrue(s_gray_tensor.equal(gray_tensor))

            batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)
            self._test_fn_on_batch(batch_tensors, F.rgb_to_grayscale, num_output_channels=num_output_channels)

    def test_center_crop(self):
        script_center_crop = torch.jit.script(F.center_crop)

        img_tensor, pil_img = self._create_data(32, 34, device=self.device)

        cropped_pil_image = F.center_crop(pil_img, [10, 11])

        cropped_tensor = F.center_crop(img_tensor, [10, 11])
        self.compareTensorToPIL(cropped_tensor, cropped_pil_image)

        cropped_tensor = script_center_crop(img_tensor, [10, 11])
        self.compareTensorToPIL(cropped_tensor, cropped_pil_image)

        batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)
        self._test_fn_on_batch(batch_tensors, F.center_crop, output_size=[10, 11])

    def test_five_crop(self):
        script_five_crop = torch.jit.script(F.five_crop)

        img_tensor, pil_img = self._create_data(32, 34, device=self.device)

        cropped_pil_images = F.five_crop(pil_img, [10, 11])

        cropped_tensors = F.five_crop(img_tensor, [10, 11])
        for i in range(5):
            self.compareTensorToPIL(cropped_tensors[i], cropped_pil_images[i])

        cropped_tensors = script_five_crop(img_tensor, [10, 11])
        for i in range(5):
            self.compareTensorToPIL(cropped_tensors[i], cropped_pil_images[i])

        batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)
        tuple_transformed_batches = F.five_crop(batch_tensors, [10, 11])
        for i in range(len(batch_tensors)):
            img_tensor = batch_tensors[i, ...]
            tuple_transformed_imgs = F.five_crop(img_tensor, [10, 11])
            self.assertEqual(len(tuple_transformed_imgs), len(tuple_transformed_batches))

            for j in range(len(tuple_transformed_imgs)):
                true_transformed_img = tuple_transformed_imgs[j]
                transformed_img = tuple_transformed_batches[j][i, ...]
                self.assertTrue(true_transformed_img.equal(transformed_img))

        # scriptable function test
        s_tuple_transformed_batches = script_five_crop(batch_tensors, [10, 11])
        for transformed_batch, s_transformed_batch in zip(tuple_transformed_batches, s_tuple_transformed_batches):
            self.assertTrue(transformed_batch.equal(s_transformed_batch))

    def test_ten_crop(self):
        script_ten_crop = torch.jit.script(F.ten_crop)

        img_tensor, pil_img = self._create_data(32, 34, device=self.device)

        cropped_pil_images = F.ten_crop(pil_img, [10, 11])

        cropped_tensors = F.ten_crop(img_tensor, [10, 11])
        for i in range(10):
            self.compareTensorToPIL(cropped_tensors[i], cropped_pil_images[i])

        cropped_tensors = script_ten_crop(img_tensor, [10, 11])
        for i in range(10):
            self.compareTensorToPIL(cropped_tensors[i], cropped_pil_images[i])

        batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)
        tuple_transformed_batches = F.ten_crop(batch_tensors, [10, 11])
        for i in range(len(batch_tensors)):
            img_tensor = batch_tensors[i, ...]
            tuple_transformed_imgs = F.ten_crop(img_tensor, [10, 11])
            self.assertEqual(len(tuple_transformed_imgs), len(tuple_transformed_batches))

            for j in range(len(tuple_transformed_imgs)):
                true_transformed_img = tuple_transformed_imgs[j]
                transformed_img = tuple_transformed_batches[j][i, ...]
                self.assertTrue(true_transformed_img.equal(transformed_img))

        # scriptable function test
        s_tuple_transformed_batches = script_ten_crop(batch_tensors, [10, 11])
        for transformed_batch, s_transformed_batch in zip(tuple_transformed_batches, s_tuple_transformed_batches):
            self.assertTrue(transformed_batch.equal(s_transformed_batch))

    def test_pad(self):
        script_fn = torch.jit.script(F.pad)
        tensor, pil_img = self._create_data(7, 8, device=self.device)
        batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)

        for dt in [None, torch.float32, torch.float64, torch.float16]:

            if dt == torch.float16 and torch.device(self.device).type == "cpu":
                # skip float16 on CPU case
                continue

            if dt is not None:
                # This is a trivial cast to float of uint8 data to test all cases
                tensor = tensor.to(dt)
                batch_tensors = batch_tensors.to(dt)

            for pad in [2, [3, ], [0, 3], (3, 3), [4, 2, 4, 3]]:
                configs = [
                    {"padding_mode": "constant", "fill": 0},
                    {"padding_mode": "constant", "fill": 10},
                    {"padding_mode": "constant", "fill": 20},
                    {"padding_mode": "edge"},
                    {"padding_mode": "reflect"},
                    {"padding_mode": "symmetric"},
                ]
                for kwargs in configs:
                    pad_tensor = F_t.pad(tensor, pad, **kwargs)
                    pad_pil_img = F_pil.pad(pil_img, pad, **kwargs)

                    pad_tensor_8b = pad_tensor
                    # we need to cast to uint8 to compare with PIL image
                    if pad_tensor_8b.dtype != torch.uint8:
                        pad_tensor_8b = pad_tensor_8b.to(torch.uint8)

                    self.compareTensorToPIL(pad_tensor_8b, pad_pil_img, msg="{}, {}".format(pad, kwargs))

                    if isinstance(pad, int):
                        script_pad = [pad, ]
                    else:
                        script_pad = pad
                    pad_tensor_script = script_fn(tensor, script_pad, **kwargs)
                    self.assertTrue(pad_tensor.equal(pad_tensor_script), msg="{}, {}".format(pad, kwargs))

                    self._test_fn_on_batch(batch_tensors, F.pad, padding=script_pad, **kwargs)

    def test_resize(self):
        script_fn = torch.jit.script(F.resize)
        tensor, pil_img = self._create_data(26, 36, device=self.device)
        batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)

        for dt in [None, torch.float32, torch.float64, torch.float16]:

            if dt == torch.float16 and torch.device(self.device).type == "cpu":
                # skip float16 on CPU case
                continue

            if dt is not None:
                # This is a trivial cast to float of uint8 data to test all cases
                tensor = tensor.to(dt)
                batch_tensors = batch_tensors.to(dt)

            for size in [32, 26, [32, ], [32, 32], (32, 32), [26, 35]]:
                for max_size in (None, 33, 40, 1000):
                    if max_size is not None and isinstance(size, Sequence) and len(size) != 1:
                        continue  # unsupported, see assertRaises below
                    for interpolation in [BILINEAR, BICUBIC, NEAREST]:
                        resized_tensor = F.resize(tensor, size=size, interpolation=interpolation, max_size=max_size)
                        resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation, max_size=max_size)

                        self.assertEqual(
                            resized_tensor.size()[1:], resized_pil_img.size[::-1],
                            msg="{}, {}".format(size, interpolation)
                        )

                        if interpolation not in [NEAREST, ]:
                            # We can not check values if mode = NEAREST, as results are different
                            # E.g. resized_tensor  = [[a, a, b, c, d, d, e, ...]]
                            # E.g. resized_pil_img = [[a, b, c, c, d, e, f, ...]]
                            resized_tensor_f = resized_tensor
                            # we need to cast to uint8 to compare with PIL image
                            if resized_tensor_f.dtype == torch.uint8:
                                resized_tensor_f = resized_tensor_f.to(torch.float)

                            # Pay attention to high tolerance for MAE
                            self.approxEqualTensorToPIL(
                                resized_tensor_f, resized_pil_img, tol=8.0, msg="{}, {}".format(size, interpolation)
                            )

                        if isinstance(size, int):
                            script_size = [size, ]
                        else:
                            script_size = size

                        resize_result = script_fn(tensor, size=script_size, interpolation=interpolation,
                                                  max_size=max_size)
                        self.assertTrue(resized_tensor.equal(resize_result), msg="{}, {}".format(size, interpolation))

                        self._test_fn_on_batch(
                            batch_tensors, F.resize, size=script_size, interpolation=interpolation, max_size=max_size
                        )

        # assert changed type warning
        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
            res1 = F.resize(tensor, size=32, interpolation=2)
            res2 = F.resize(tensor, size=32, interpolation=BILINEAR)
            self.assertTrue(res1.equal(res2))

        for img in (tensor, pil_img):
            exp_msg = "max_size should only be passed if size specifies the length of the smaller edge"
            with self.assertRaisesRegex(ValueError, exp_msg):
                F.resize(img, size=(32, 34), max_size=35)
            with self.assertRaisesRegex(ValueError, "max_size = 32 must be strictly greater"):
                F.resize(img, size=32, max_size=32)

    def test_resized_crop(self):
        # test values of F.resized_crop in several cases:
        # 1) resize to the same size, crop to the same size => should be identity
        tensor, _ = self._create_data(26, 36, device=self.device)

        for mode in [NEAREST, BILINEAR, BICUBIC]:
            out_tensor = F.resized_crop(tensor, top=0, left=0, height=26, width=36, size=[26, 36], interpolation=mode)
            self.assertTrue(tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5]))

        # 2) resize by half and crop a TL corner
        tensor, _ = self._create_data(26, 36, device=self.device)
        out_tensor = F.resized_crop(tensor, top=0, left=0, height=20, width=30, size=[10, 15], interpolation=NEAREST)
        expected_out_tensor = tensor[:, :20:2, :30:2]
        self.assertTrue(
            expected_out_tensor.equal(out_tensor),
            msg="{} vs {}".format(expected_out_tensor[0, :10, :10], out_tensor[0, :10, :10])
        )

        batch_tensors = self._create_data_batch(26, 36, num_samples=4, device=self.device)
        self._test_fn_on_batch(
            batch_tensors, F.resized_crop, top=1, left=2, height=20, width=30, size=[10, 15], interpolation=NEAREST
        )

    def _test_affine_identity_map(self, tensor, scripted_affine):
        # 1) identity map
        out_tensor = F.affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)

        self.assertTrue(
            tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5])
        )
        out_tensor = scripted_affine(
            tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
        )
        self.assertTrue(
            tensor.equal(out_tensor), msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5])
        )

    def _test_affine_square_rotations(self, tensor, pil_img, scripted_affine):
        # 2) Test rotation
        test_configs = [
            (90, torch.rot90(tensor, k=1, dims=(-1, -2))),
            (45, None),
            (30, None),
            (-30, None),
            (-45, None),
            (-90, torch.rot90(tensor, k=-1, dims=(-1, -2))),
            (180, torch.rot90(tensor, k=2, dims=(-1, -2))),
        ]
        for a, true_tensor in test_configs:
            out_pil_img = F.affine(
                pil_img, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
            )
            out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1))).to(self.device)

            for fn in [F.affine, scripted_affine]:
                out_tensor = fn(
                    tensor, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
                )
                if true_tensor is not None:
                    self.assertTrue(
                        true_tensor.equal(out_tensor),
                        msg="{}\n{} vs \n{}".format(a, out_tensor[0, :5, :5], true_tensor[0, :5, :5])
                    )

                if out_tensor.dtype != torch.uint8:
                    out_tensor = out_tensor.to(torch.uint8)

                num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
                ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
                # Tolerance : less than 6% of different pixels
                self.assertLess(
                    ratio_diff_pixels,
                    0.06,
                    msg="{}\n{} vs \n{}".format(
                        ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
                    )
                )

    def _test_affine_rect_rotations(self, tensor, pil_img, scripted_affine):
        test_configs = [
            90, 45, 15, -30, -60, -120
        ]
        for a in test_configs:

            out_pil_img = F.affine(
                pil_img, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
            )
            out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))

            for fn in [F.affine, scripted_affine]:
                out_tensor = fn(
                    tensor, angle=a, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
                ).cpu()

                if out_tensor.dtype != torch.uint8:
                    out_tensor = out_tensor.to(torch.uint8)

                num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
                ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
                # Tolerance : less than 3% of different pixels
                self.assertLess(
                    ratio_diff_pixels,
                    0.03,
                    msg="{}: {}\n{} vs \n{}".format(
                        a, ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
                    )
                )

    def _test_affine_translations(self, tensor, pil_img, scripted_affine):
        # 3) Test translation
        test_configs = [
            [10, 12], (-12, -13)
        ]
        for t in test_configs:

            out_pil_img = F.affine(pil_img, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)

            for fn in [F.affine, scripted_affine]:
                out_tensor = fn(tensor, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)

                if out_tensor.dtype != torch.uint8:
                    out_tensor = out_tensor.to(torch.uint8)

                self.compareTensorToPIL(out_tensor, out_pil_img)

    def _test_affine_all_ops(self, tensor, pil_img, scripted_affine):
        # 4) Test rotation + translation + scale + share
        test_configs = [
            (45.5, [5, 6], 1.0, [0.0, 0.0], None),
            (33, (5, -4), 1.0, [0.0, 0.0], [0, 0, 0]),
            (45, [-5, 4], 1.2, [0.0, 0.0], (1, 2, 3)),
            (33, (-4, -8), 2.0, [0.0, 0.0], [255, 255, 255]),
            (85, (10, -10), 0.7, [0.0, 0.0], [1, ]),
            (0, [0, 0], 1.0, [35.0, ], (2.0, )),
            (-25, [0, 0], 1.2, [0.0, 15.0], None),
            (-45, [-10, 0], 0.7, [2.0, 5.0], None),
            (-45, [-10, -10], 1.2, [4.0, 5.0], None),
            (-90, [0, 0], 1.0, [0.0, 0.0], None),
        ]
        for r in [NEAREST, ]:
            for a, t, s, sh, f in test_configs:
                f_pil = int(f[0]) if f is not None and len(f) == 1 else f
                out_pil_img = F.affine(pil_img, angle=a, translate=t, scale=s, shear=sh, interpolation=r, fill=f_pil)
                out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))

                for fn in [F.affine, scripted_affine]:
                    out_tensor = fn(tensor, angle=a, translate=t, scale=s, shear=sh, interpolation=r, fill=f).cpu()

                    if out_tensor.dtype != torch.uint8:
                        out_tensor = out_tensor.to(torch.uint8)

                    num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
                    ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
                    # Tolerance : less than 5% (cpu), 6% (cuda) of different pixels
                    tol = 0.06 if self.device == "cuda" else 0.05
                    self.assertLess(
                        ratio_diff_pixels,
                        tol,
                        msg="{}: {}\n{} vs \n{}".format(
                            (r, a, t, s, sh, f), ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
                        )
                    )

    def test_affine(self):
        # Tests on square and rectangular images
        scripted_affine = torch.jit.script(F.affine)

        data = [self._create_data(26, 26, device=self.device), self._create_data(32, 26, device=self.device)]
        for tensor, pil_img in data:

            for dt in [None, torch.float32, torch.float64, torch.float16]:

                if dt == torch.float16 and torch.device(self.device).type == "cpu":
                    # skip float16 on CPU case
                    continue

                if dt is not None:
                    tensor = tensor.to(dtype=dt)

                self._test_affine_identity_map(tensor, scripted_affine)
                if pil_img.size[0] == pil_img.size[1]:
                    self._test_affine_square_rotations(tensor, pil_img, scripted_affine)
                else:
                    self._test_affine_rect_rotations(tensor, pil_img, scripted_affine)
                self._test_affine_translations(tensor, pil_img, scripted_affine)
                self._test_affine_all_ops(tensor, pil_img, scripted_affine)

                batch_tensors = self._create_data_batch(26, 36, num_samples=4, device=self.device)
                if dt is not None:
                    batch_tensors = batch_tensors.to(dtype=dt)

                self._test_fn_on_batch(
                    batch_tensors, F.affine, angle=-43, translate=[-3, 4], scale=1.2, shear=[4.0, 5.0]
                )

        tensor, pil_img = data[0]
        # assert deprecation warning and non-BC
        with self.assertWarnsRegex(UserWarning, r"Argument resample is deprecated and will be removed"):
            res1 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=2)
            res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
            self.assertTrue(res1.equal(res2))

        # assert changed type warning
        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
            res1 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=2)
            res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
            self.assertTrue(res1.equal(res2))

        with self.assertWarnsRegex(UserWarning, r"Argument fillcolor is deprecated and will be removed"):
            res1 = F.affine(pil_img, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], fillcolor=10)
            res2 = F.affine(pil_img, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], fill=10)
            self.assertEqual(res1, res2)

    def _test_rotate_all_options(self, tensor, pil_img, scripted_rotate, centers):
        img_size = pil_img.size
        dt = tensor.dtype
        for r in [NEAREST, ]:
            for a in range(-180, 180, 17):
                for e in [True, False]:
                    for c in centers:
                        for f in [None, [0, 0, 0], (1, 2, 3), [255, 255, 255], [1, ], (2.0, )]:
                            f_pil = int(f[0]) if f is not None and len(f) == 1 else f
                            out_pil_img = F.rotate(pil_img, angle=a, interpolation=r, expand=e, center=c, fill=f_pil)
                            out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
                            for fn in [F.rotate, scripted_rotate]:
                                out_tensor = fn(tensor, angle=a, interpolation=r, expand=e, center=c, fill=f).cpu()

                                if out_tensor.dtype != torch.uint8:
                                    out_tensor = out_tensor.to(torch.uint8)

                                self.assertEqual(
                                    out_tensor.shape,
                                    out_pil_tensor.shape,
                                    msg="{}: {} vs {}".format(
                                        (img_size, r, dt, a, e, c), out_tensor.shape, out_pil_tensor.shape
                                    ))

                                num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
                                ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
                                # Tolerance : less than 3% of different pixels
                                self.assertLess(
                                    ratio_diff_pixels,
                                    0.03,
                                    msg="{}: {}\n{} vs \n{}".format(
                                        (img_size, r, dt, a, e, c, f),
                                        ratio_diff_pixels,
                                        out_tensor[0, :7, :7],
                                        out_pil_tensor[0, :7, :7]
                                    )
                                )

    def test_rotate(self):
        # Tests on square image
        scripted_rotate = torch.jit.script(F.rotate)

        data = [self._create_data(26, 26, device=self.device), self._create_data(32, 26, device=self.device)]
        for tensor, pil_img in data:

            img_size = pil_img.size
            centers = [
                None,
                (int(img_size[0] * 0.3), int(img_size[0] * 0.4)),
                [int(img_size[0] * 0.5), int(img_size[0] * 0.6)]
            ]

            for dt in [None, torch.float32, torch.float64, torch.float16]:

                if dt == torch.float16 and torch.device(self.device).type == "cpu":
                    # skip float16 on CPU case
                    continue

                if dt is not None:
                    tensor = tensor.to(dtype=dt)

                self._test_rotate_all_options(tensor, pil_img, scripted_rotate, centers)

                batch_tensors = self._create_data_batch(26, 36, num_samples=4, device=self.device)
                if dt is not None:
                    batch_tensors = batch_tensors.to(dtype=dt)

                center = (20, 22)
                self._test_fn_on_batch(
                    batch_tensors, F.rotate, angle=32, interpolation=NEAREST, expand=True, center=center
                )
        tensor, pil_img = data[0]
        # assert deprecation warning and non-BC
        with self.assertWarnsRegex(UserWarning, r"Argument resample is deprecated and will be removed"):
            res1 = F.rotate(tensor, 45, resample=2)
            res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
            self.assertTrue(res1.equal(res2))

        # assert changed type warning
        with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
            res1 = F.rotate(tensor, 45, interpolation=2)
            res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
            self.assertTrue(res1.equal(res2))

    def test_gaussian_blur(self):
        small_image_tensor = torch.from_numpy(
            np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
        ).permute(2, 0, 1).to(self.device)

        large_image_tensor = torch.from_numpy(
            np.arange(26 * 28, dtype="uint8").reshape((1, 26, 28))
        ).to(self.device)

        scripted_transform = torch.jit.script(F.gaussian_blur)

        # true_cv2_results = {
        #     # np_img = np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
        #     # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.8)
        #     "3_3_0.8": ...
        #     # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.5)
        #     "3_3_0.5": ...
        #     # cv2.GaussianBlur(np_img, ksize=(3, 5), sigmaX=0.8)
        #     "3_5_0.8": ...
        #     # cv2.GaussianBlur(np_img, ksize=(3, 5), sigmaX=0.5)
        #     "3_5_0.5": ...
        #     # np_img2 = np.arange(26 * 28, dtype="uint8").reshape((26, 28))
        #     # cv2.GaussianBlur(np_img2, ksize=(23, 23), sigmaX=1.7)
        #     "23_23_1.7": ...
        # }
        p = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'assets', 'gaussian_blur_opencv_results.pt')
        true_cv2_results = torch.load(p)

        for tensor in [small_image_tensor, large_image_tensor]:

            for dt in [None, torch.float32, torch.float64, torch.float16]:
                if dt == torch.float16 and torch.device(self.device).type == "cpu":
                    # skip float16 on CPU case
                    continue

                if dt is not None:
                    tensor = tensor.to(dtype=dt)

                for ksize in [(3, 3), [3, 5], (23, 23)]:
                    for sigma in [[0.5, 0.5], (0.5, 0.5), (0.8, 0.8), (1.7, 1.7)]:

                        _ksize = (ksize, ksize) if isinstance(ksize, int) else ksize
                        _sigma = sigma[0] if sigma is not None else None
                        shape = tensor.shape
                        gt_key = "{}_{}_{}__{}_{}_{}".format(
                            shape[-2], shape[-1], shape[-3],
                            _ksize[0], _ksize[1], _sigma
                        )
                        if gt_key not in true_cv2_results:
                            continue

                        true_out = torch.tensor(
                            true_cv2_results[gt_key]
                        ).reshape(shape[-2], shape[-1], shape[-3]).permute(2, 0, 1).to(tensor)

                        for fn in [F.gaussian_blur, scripted_transform]:
                            out = fn(tensor, kernel_size=ksize, sigma=sigma)
                            self.assertEqual(true_out.shape, out.shape, msg="{}, {}".format(ksize, sigma))
                            self.assertLessEqual(
                                torch.max(true_out.float() - out.float()),
                                1.0,
                                msg="{}, {}".format(ksize, sigma)
                            )


@unittest.skipIf(not torch.cuda.is_available(), reason="Skip if no CUDA device")
class CUDATester(Tester):

    def setUp(self):
        self.device = "cuda"

    def test_scale_channel(self):
        """Make sure that _scale_channel gives the same results on CPU and GPU as
        histc or bincount are used depending on the device.
        """
        # TODO: when # https://github.com/pytorch/pytorch/issues/53194 is fixed,
        # only use bincount and remove that test.
        size = (1_000,)
        img_chan = torch.randint(0, 256, size=size).to('cpu')
        scaled_cpu = F_t._scale_channel(img_chan)
        scaled_cuda = F_t._scale_channel(img_chan.to('cuda'))
        self.assertTrue(scaled_cpu.equal(scaled_cuda.to('cpu')))


def _get_data_dims_and_points_for_perspective():
    # Ideally we would parametrize independently over data dims and points, but
    # we want to tests on some points that also depend on the data dims.
    # Pytest doesn't support covariant parametrization, so we do it somewhat manually here.

    data_dims = [(26, 34), (26, 26)]
    points = [
        [[[0, 0], [33, 0], [33, 25], [0, 25]], [[3, 2], [32, 3], [30, 24], [2, 25]]],
        [[[3, 2], [32, 3], [30, 24], [2, 25]], [[0, 0], [33, 0], [33, 25], [0, 25]]],
        [[[3, 2], [32, 3], [30, 24], [2, 25]], [[5, 5], [30, 3], [33, 19], [4, 25]]],
    ]

    dims_and_points = list(itertools.product(data_dims, points))

    # up to here, we could just have used 2 @parametrized.
    # Down below is the covarariant part as the points depend on the data dims.

    n = 10
    for dim in data_dims:
        points += [
            (dim, T.RandomPerspective.get_params(dim[1], dim[0], i / n))
            for i in range(n)
        ]
    return dims_and_points


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dims_and_points', _get_data_dims_and_points_for_perspective())
@pytest.mark.parametrize('dt', [None, torch.float32, torch.float64, torch.float16])
@pytest.mark.parametrize('fill', (None, [0, 0, 0], [1, 2, 3], [255, 255, 255], [1, ], (2.0, )))
@pytest.mark.parametrize('fn', [F.perspective, torch.jit.script(F.perspective)])
def test_perspective_pil_vs_tensor(device, dims_and_points, dt, fill, fn, tester):

    if dt == torch.float16 and device == "cpu":
        # skip float16 on CPU case
        return

    data_dims, (spoints, epoints) = dims_and_points

    tensor, pil_img = tester._create_data(*data_dims, device=device)
    if dt is not None:
        tensor = tensor.to(dtype=dt)

    interpolation = NEAREST
    fill_pil = int(fill[0]) if fill is not None and len(fill) == 1 else fill
    out_pil_img = F.perspective(pil_img, startpoints=spoints, endpoints=epoints, interpolation=interpolation,
                                fill=fill_pil)
    out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
    out_tensor = fn(tensor, startpoints=spoints, endpoints=epoints, interpolation=interpolation, fill=fill).cpu()

    if out_tensor.dtype != torch.uint8:
        out_tensor = out_tensor.to(torch.uint8)

    num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
    ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
    # Tolerance : less than 5% of different pixels
    assert ratio_diff_pixels < 0.05


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dims_and_points', _get_data_dims_and_points_for_perspective())
@pytest.mark.parametrize('dt', [None, torch.float32, torch.float64, torch.float16])
def test_perspective_batch(device, dims_and_points, dt, tester):

    if dt == torch.float16 and device == "cpu":
        # skip float16 on CPU case
        return

    data_dims, (spoints, epoints) = dims_and_points

    batch_tensors = tester._create_data_batch(*data_dims, num_samples=4, device=device)
    if dt is not None:
        batch_tensors = batch_tensors.to(dtype=dt)

    # Ignore the equivalence between scripted and regular function on float16 cuda. The pixels at
    # the border may be entirely different due to small rounding errors.
    scripted_fn_atol = -1 if (dt == torch.float16 and device == "cuda") else 1e-8
    tester._test_fn_on_batch(
        batch_tensors, F.perspective, scripted_fn_atol=scripted_fn_atol,
        startpoints=spoints, endpoints=epoints, interpolation=NEAREST
    )


def test_perspective_interpolation_warning(tester):
    # assert changed type warning
    spoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
    epoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
    tensor = torch.randint(0, 256, (3, 26, 26))
    with tester.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
        res1 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=2)
        res2 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=BILINEAR)
        tester.assertTrue(res1.equal(res2))


@pytest.mark.parametrize('device', ["cpu", ])
@pytest.mark.parametrize('dt', [None, torch.float32, torch.float64, torch.float16])
@pytest.mark.parametrize('size', [[96, 72], [96, 420], [420, 72]])
@pytest.mark.parametrize('interpolation', [BILINEAR, BICUBIC])
def test_resize_antialias(device, dt, size, interpolation, tester):

    if dt == torch.float16 and device == "cpu":
        # skip float16 on CPU case
        return

    script_fn = torch.jit.script(F.resize)
    tensor, pil_img = tester._create_data(320, 290, device=device)

    if dt is not None:
        # This is a trivial cast to float of uint8 data to test all cases
        tensor = tensor.to(dt)

    resized_tensor = F.resize(tensor, size=size, interpolation=interpolation, antialias=True)
    resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation)

    tester.assertEqual(
        resized_tensor.size()[1:], resized_pil_img.size[::-1],
        msg=f"{size}, {interpolation}, {dt}"
    )

    resized_tensor_f = resized_tensor
    # we need to cast to uint8 to compare with PIL image
    if resized_tensor_f.dtype == torch.uint8:
        resized_tensor_f = resized_tensor_f.to(torch.float)

    tester.approxEqualTensorToPIL(
        resized_tensor_f, resized_pil_img, tol=0.5, msg=f"{size}, {interpolation}, {dt}"
    )

    accepted_tol = 1.0 + 1e-5
    if interpolation == BICUBIC:
        # this overall mean value to make the tests pass
        # High value is mostly required for test cases with
        # downsampling and upsampling where we can not exactly
        # match PIL implementation.
        accepted_tol = 15.0

    tester.approxEqualTensorToPIL(
        resized_tensor_f, resized_pil_img, tol=accepted_tol, agg_method="max",
        msg=f"{size}, {interpolation}, {dt}"
    )

    if isinstance(size, int):
        script_size = [size, ]
    else:
        script_size = size

    resize_result = script_fn(tensor, size=script_size, interpolation=interpolation, antialias=True)
    tester.assertTrue(resized_tensor.equal(resize_result), msg=f"{size}, {interpolation}, {dt}")


def check_functional_vs_PIL_vs_scripted(fn, fn_pil, fn_t, config, device, dtype, tol=2.0 + 1e-10, agg_method="max"):

    tester = Tester()

    script_fn = torch.jit.script(fn)
    torch.manual_seed(15)
    tensor, pil_img = tester._create_data(26, 34, device=device)
    batch_tensors = tester._create_data_batch(16, 18, num_samples=4, device=device)

    if dtype is not None:
        tensor = F.convert_image_dtype(tensor, dtype)
        batch_tensors = F.convert_image_dtype(batch_tensors, dtype)

    out_fn_t = fn_t(tensor, **config)
    out_pil = fn_pil(pil_img, **config)
    out_scripted = script_fn(tensor, **config)
    assert out_fn_t.dtype == out_scripted.dtype
    assert out_fn_t.size()[1:] == out_pil.size[::-1]

    rbg_tensor = out_fn_t

    if out_fn_t.dtype != torch.uint8:
        rbg_tensor = F.convert_image_dtype(out_fn_t, torch.uint8)

    # Check that max difference does not exceed 2 in [0, 255] range
    # Exact matching is not possible due to incompatibility convert_image_dtype and PIL results
    tester.approxEqualTensorToPIL(rbg_tensor.float(), out_pil, tol=tol, agg_method=agg_method)

    atol = 1e-6
    if out_fn_t.dtype == torch.uint8 and "cuda" in torch.device(device).type:
        atol = 1.0
    assert out_fn_t.allclose(out_scripted, atol=atol)

    # FIXME: fn will be scripted again in _test_fn_on_batch. We could avoid that.
    tester._test_fn_on_batch(batch_tensors, fn, scripted_fn_atol=atol, **config)


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"brightness_factor": f} for f in (0.1, 0.5, 1.0, 1.34, 2.5)])
def test_adjust_brightness(device, dtype, config):
    check_functional_vs_PIL_vs_scripted(
        F.adjust_brightness,
        F_pil.adjust_brightness,
        F_t.adjust_brightness,
        config,
        device,
        dtype,
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
def test_invert(device, dtype):
    check_functional_vs_PIL_vs_scripted(
        F.invert,
        F_pil.invert,
        F_t.invert,
        {},
        device,
        dtype,
        tol=1.0,
        agg_method="max"
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('config', [{"bits": bits} for bits in range(0, 8)])
def test_posterize(device, config):
    check_functional_vs_PIL_vs_scripted(
        F.posterize,
        F_pil.posterize,
        F_t.posterize,
        config,
        device,
        dtype=None,
        tol=1.0,
        agg_method="max",
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('config', [{"threshold": threshold} for threshold in [0, 64, 128, 192, 255]])
def test_solarize1(device, config):
    check_functional_vs_PIL_vs_scripted(
        F.solarize,
        F_pil.solarize,
        F_t.solarize,
        config,
        device,
        dtype=None,
        tol=1.0,
        agg_method="max",
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"threshold": threshold} for threshold in [0.0, 0.25, 0.5, 0.75, 1.0]])
def test_solarize2(device, dtype, config):
    check_functional_vs_PIL_vs_scripted(
        F.solarize,
        lambda img, threshold: F_pil.solarize(img, 255 * threshold),
        F_t.solarize,
        config,
        device,
        dtype,
        tol=1.0,
        agg_method="max",
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"sharpness_factor": f} for f in [0.2, 0.5, 1.0, 1.5, 2.0]])
def test_adjust_sharpness(device, dtype, config):
    check_functional_vs_PIL_vs_scripted(
        F.adjust_sharpness,
        F_pil.adjust_sharpness,
        F_t.adjust_sharpness,
        config,
        device,
        dtype,
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
def test_autocontrast(device, dtype):
    check_functional_vs_PIL_vs_scripted(
        F.autocontrast,
        F_pil.autocontrast,
        F_t.autocontrast,
        {},
        device,
        dtype,
        tol=1.0,
        agg_method="max"
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
def test_equalize(device):
    torch.set_deterministic(False)
    check_functional_vs_PIL_vs_scripted(
        F.equalize,
        F_pil.equalize,
        F_t.equalize,
        {},
        device,
        dtype=None,
        tol=1.0,
        agg_method="max",
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"contrast_factor": f} for f in [0.2, 0.5, 1.0, 1.5, 2.0]])
def test_adjust_contrast(device, dtype, config):
    check_functional_vs_PIL_vs_scripted(
        F.adjust_contrast,
        F_pil.adjust_contrast,
        F_t.adjust_contrast,
        config,
        device,
        dtype
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"saturation_factor": f} for f in [0.5, 0.75, 1.0, 1.5, 2.0]])
def test_adjust_saturation(device, dtype, config):
    check_functional_vs_PIL_vs_scripted(
        F.adjust_saturation,
        F_pil.adjust_saturation,
        F_t.adjust_saturation,
        config,
        device,
        dtype
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"hue_factor": f} for f in [-0.45, -0.25, 0.0, 0.25, 0.45]])
def test_adjust_hue(device, dtype, config):
    check_functional_vs_PIL_vs_scripted(
        F.adjust_hue,
        F_pil.adjust_hue,
        F_t.adjust_hue,
        config,
        device,
        dtype,
        tol=16.1,
        agg_method="max"
    )


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
@pytest.mark.parametrize('config', [{"gamma": g1, "gain": g2} for g1, g2 in zip([0.8, 1.0, 1.2], [0.7, 1.0, 1.3])])
def test_adjust_gamma(device, dtype, config):
    check_functional_vs_PIL_vs_scripted(
        F.adjust_gamma,
        F_pil.adjust_gamma,
        F_t.adjust_gamma,
        config,
        device,
        dtype,
    )


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