Implement all AutoAugment transforms + Policies (#3123)

* Invert Transform (#3104)

* Adding invert operator.

* Make use of the _assert_channels().

* Update upper bound value.

* Remove private doc from invert, create or reuse generic testing methods to avoid duplication of code in the tests. (#3106)

* Create posterize transformation and refactor common methods to assist reuse. (#3108)

* Implement the solarize transform. (#3112)

* Implement the adjust_sharpness transform (#3114)

* Adding functional operator for sharpness.

* Adding transforms for sharpness.

* Handling tiny images and adding a test.

* Implement the autocontrast transform. (#3117)

* Implement the equalize transform (#3119)

* Implement the equalize transform.

* Turn off deterministic for histogram.

* Fixing test. (#3126)

* Force ratio to be float to avoid numeric overflows on blend. (#3127)

* Separate the tests of Adjust Sharpness from ColorJitter. (#3128)

* Add AutoAugment Policies and main Transform (#3142)

* Separate the tests of Adjust Sharpness from ColorJitter.

* Initial implementation, not-jitable.

* AutoAugment passing JIT.

* Adding tests/docs, changing formatting.

* Update test.

* Fix formats

* Fix documentation and imports.

* Apply changes from code review:
- Move the transformations outside of AutoAugment on a separate method.
- Renamed degenerate method for sharpness for better clarity.

* Update torchvision/transforms/functional.py
Co-authored-by: vfdev <vfdev.5@gmail.com>

* Apply more changes from code review:
- Add InterpolationMode parameter.
- Move all declarations away from AutoAugment constructor and into the private method.

* Update documentation.

* Apply suggestions from code review
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Apply changes from code review:
- Refactor code to eliminate as any to() and clamp() as possible.
- Reuse methods where possible.
- Apply speed ups.

* Replacing pad.
Co-authored-by: vfdev <vfdev.5@gmail.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

test/test_functional_tensor.py

@@ -289,13 +289,14 @@ class Tester(TransformsTester):
 
                     self._test_fn_on_batch(batch_tensors, F.pad, padding=script_pad, **kwargs)
 
-    def _test_adjust_fn(self, fn, fn_pil, fn_t, configs, tol=2.0 + 1e-10, agg_method="max"):
+    def _test_adjust_fn(self, fn, fn_pil, fn_t, configs, tol=2.0 + 1e-10, agg_method="max",
+                        dts=(None, torch.float32, torch.float64)):
         script_fn = torch.jit.script(fn)
         torch.manual_seed(15)
         tensor, pil_img = self._create_data(26, 34, 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]:
+        for dt in dts:
 
             if dt is not None:
                 tensor = F.convert_image_dtype(tensor, dt)
@@ -862,6 +863,77 @@ class Tester(TransformsTester):
                                 msg="{}, {}".format(ksize, sigma)
                             )
 
+    def test_invert(self):
+        self._test_adjust_fn(
+            F.invert,
+            F_pil.invert,
+            F_t.invert,
+            [{}],
+            tol=1.0,
+            agg_method="max"
+        )
+
+    def test_posterize(self):
+        self._test_adjust_fn(
+            F.posterize,
+            F_pil.posterize,
+            F_t.posterize,
+            [{"bits": bits} for bits in range(0, 8)],
+            tol=1.0,
+            agg_method="max",
+            dts=(None,)
+        )
+
+    def test_solarize(self):
+        self._test_adjust_fn(
+            F.solarize,
+            F_pil.solarize,
+            F_t.solarize,
+            [{"threshold": threshold} for threshold in [0, 64, 128, 192, 255]],
+            tol=1.0,
+            agg_method="max",
+            dts=(None,)
+        )
+        self._test_adjust_fn(
+            F.solarize,
+            lambda img, threshold: F_pil.solarize(img, 255 * threshold),
+            F_t.solarize,
+            [{"threshold": threshold} for threshold in [0.0, 0.25, 0.5, 0.75, 1.0]],
+            tol=1.0,
+            agg_method="max",
+            dts=(torch.float32, torch.float64)
+        )
+
+    def test_adjust_sharpness(self):
+        self._test_adjust_fn(
+            F.adjust_sharpness,
+            F_pil.adjust_sharpness,
+            F_t.adjust_sharpness,
+            [{"sharpness_factor": f} for f in [0.2, 0.5, 1.0, 1.5, 2.0]]
+        )
+
+    def test_autocontrast(self):
+        self._test_adjust_fn(
+            F.autocontrast,
+            F_pil.autocontrast,
+            F_t.autocontrast,
+            [{}],
+            tol=1.0,
+            agg_method="max"
+        )
+
+    def test_equalize(self):
+        torch.set_deterministic(False)
+        self._test_adjust_fn(
+            F.equalize,
+            F_pil.equalize,
+            F_t.equalize,
+            [{}],
+            tol=1.0,
+            agg_method="max",
+            dts=(None,)
+        )
+
 
 @unittest.skipIf(not torch.cuda.is_available(), reason="Skip if no CUDA device")
 class CUDATester(Tester):

test/test_transforms.py

@@ -1234,6 +1234,48 @@ class Tester(unittest.TestCase):
         y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
         self.assertTrue(np.allclose(y_np, y_ans))
 
+    def test_adjust_sharpness(self):
+        x_shape = [4, 4, 3]
+        x_data = [75, 121, 114, 105, 97, 107, 105, 32, 66, 111, 117, 114, 99, 104, 97, 0,
+                  0, 65, 108, 101, 120, 97, 110, 100, 101, 114, 32, 86, 114, 121, 110, 105,
+                  111, 116, 105, 115, 0, 0, 73, 32, 108, 111, 118, 101, 32, 121, 111, 117]
+        x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
+        x_pil = Image.fromarray(x_np, mode='RGB')
+
+        # test 0
+        y_pil = F.adjust_sharpness(x_pil, 1)
+        y_np = np.array(y_pil)
+        self.assertTrue(np.allclose(y_np, x_np))
+
+        # test 1
+        y_pil = F.adjust_sharpness(x_pil, 0.5)
+        y_np = np.array(y_pil)
+        y_ans = [75, 121, 114, 105, 97, 107, 105, 32, 66, 111, 117, 114, 99, 104, 97, 30,
+                 30, 74, 103, 96, 114, 97, 110, 100, 101, 114, 32, 81, 103, 108, 102, 101,
+                 107, 116, 105, 115, 0, 0, 73, 32, 108, 111, 118, 101, 32, 121, 111, 117]
+        y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
+        self.assertTrue(np.allclose(y_np, y_ans))
+
+        # test 2
+        y_pil = F.adjust_sharpness(x_pil, 2)
+        y_np = np.array(y_pil)
+        y_ans = [75, 121, 114, 105, 97, 107, 105, 32, 66, 111, 117, 114, 99, 104, 97, 0,
+                 0, 46, 118, 111, 132, 97, 110, 100, 101, 114, 32, 95, 135, 146, 126, 112,
+                 119, 116, 105, 115, 0, 0, 73, 32, 108, 111, 118, 101, 32, 121, 111, 117]
+        y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
+        self.assertTrue(np.allclose(y_np, y_ans))
+
+        # test 3
+        x_shape = [2, 2, 3]
+        x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
+        x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
+        x_pil = Image.fromarray(x_np, mode='RGB')
+        x_th = torch.tensor(x_np.transpose(2, 0, 1))
+        y_pil = F.adjust_sharpness(x_pil, 2)
+        y_np = np.array(y_pil).transpose(2, 0, 1)
+        y_th = F.adjust_sharpness(x_th, 2)
+        self.assertTrue(np.allclose(y_np, y_th.numpy()))
+
     def test_adjust_gamma(self):
         x_shape = [2, 2, 3]
         x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
@@ -1270,6 +1312,7 @@ class Tester(unittest.TestCase):
         self.assertEqual(F.adjust_saturation(x_l, 2).mode, 'L')
         self.assertEqual(F.adjust_contrast(x_l, 2).mode, 'L')
         self.assertEqual(F.adjust_hue(x_l, 0.4).mode, 'L')
+        self.assertEqual(F.adjust_sharpness(x_l, 2).mode, 'L')
         self.assertEqual(F.adjust_gamma(x_l, 0.5).mode, 'L')
 
     def test_color_jitter(self):
@@ -1751,6 +1794,86 @@ class Tester(unittest.TestCase):
         with self.assertRaisesRegex(ValueError, r"sigma should be a single number or a list/tuple with length 2"):
             transforms.GaussianBlur(3, "sigma_string")
 
+    def _test_randomness(self, fn, trans, configs):
+        random_state = random.getstate()
+        random.seed(42)
+        img = transforms.ToPILImage()(torch.rand(3, 16, 18))
+
+        for p in [0.5, 0.7]:
+            for config in configs:
+                inv_img = fn(img, **config)
+
+                num_samples = 250
+                counts = 0
+                for _ in range(num_samples):
+                    tranformation = trans(p=p, **config)
+                    tranformation.__repr__()
+                    out = tranformation(img)
+                    if out == inv_img:
+                        counts += 1
+
+                p_value = stats.binom_test(counts, num_samples, p=p)
+                random.setstate(random_state)
+                self.assertGreater(p_value, 0.0001)
+
+    @unittest.skipIf(stats is None, 'scipy.stats not available')
+    def test_random_invert(self):
+        self._test_randomness(
+            F.invert,
+            transforms.RandomInvert,
+            [{}]
+        )
+
+    @unittest.skipIf(stats is None, 'scipy.stats not available')
+    def test_random_posterize(self):
+        self._test_randomness(
+            F.posterize,
+            transforms.RandomPosterize,
+            [{"bits": 4}]
+        )
+
+    @unittest.skipIf(stats is None, 'scipy.stats not available')
+    def test_random_solarize(self):
+        self._test_randomness(
+            F.solarize,
+            transforms.RandomSolarize,
+            [{"threshold": 192}]
+        )
+
+    @unittest.skipIf(stats is None, 'scipy.stats not available')
+    def test_random_adjust_sharpness(self):
+        self._test_randomness(
+            F.adjust_sharpness,
+            transforms.RandomAdjustSharpness,
+            [{"sharpness_factor": 2.0}]
+        )
+
+    @unittest.skipIf(stats is None, 'scipy.stats not available')
+    def test_random_autocontrast(self):
+        self._test_randomness(
+            F.autocontrast,
+            transforms.RandomAutocontrast,
+            [{}]
+        )
+
+    @unittest.skipIf(stats is None, 'scipy.stats not available')
+    def test_random_equalize(self):
+        self._test_randomness(
+            F.equalize,
+            transforms.RandomEqualize,
+            [{}]
+        )
+
+    def test_autoaugment(self):
+        for policy in transforms.AutoAugmentPolicy:
+            for fill in [None, 85, (128, 128, 128)]:
+                random.seed(42)
+                img = Image.open(GRACE_HOPPER)
+                transform = transforms.AutoAugment(policy=policy, fill=fill)
+                for _ in range(100):
+                    img = transform(img)
+                transform.__repr__()
+
 
 if __name__ == '__main__':
     unittest.main()

test/test_transforms_tensor.py

@@ -89,6 +89,34 @@ class Tester(TransformsTester):
     def test_random_vertical_flip(self):
         self._test_op('vflip', 'RandomVerticalFlip')
 
+    def test_random_invert(self):
+        self._test_op('invert', 'RandomInvert')
+
+    def test_random_posterize(self):
+        fn_kwargs = meth_kwargs = {"bits": 4}
+        self._test_op(
+            'posterize', 'RandomPosterize', fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+
+    def test_random_solarize(self):
+        fn_kwargs = meth_kwargs = {"threshold": 192.0}
+        self._test_op(
+            'solarize', 'RandomSolarize', fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+
+    def test_random_adjust_sharpness(self):
+        fn_kwargs = meth_kwargs = {"sharpness_factor": 2.0}
+        self._test_op(
+            'adjust_sharpness', 'RandomAdjustSharpness', fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+
+    def test_random_autocontrast(self):
+        self._test_op('autocontrast', 'RandomAutocontrast')
+
+    def test_random_equalize(self):
+        torch.set_deterministic(False)
+        self._test_op('equalize', 'RandomEqualize')
+
     def test_color_jitter(self):
 
         tol = 1.0 + 1e-10
@@ -598,6 +626,22 @@ class Tester(TransformsTester):
         with get_tmp_dir() as tmp_dir:
             scripted_fn.save(os.path.join(tmp_dir, "t_convert_dtype.pt"))
 
+    def test_autoaugment(self):
+        tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=self.device)
+        batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
+
+        for policy in T.AutoAugmentPolicy:
+            for fill in [None, 85, (10, -10, 10), 0.7, [0.0, 0.0, 0.0], [1, ], 1]:
+                for _ in range(100):
+                    transform = T.AutoAugment(policy=policy, fill=fill)
+                    s_transform = torch.jit.script(transform)
+
+                    self._test_transform_vs_scripted(transform, s_transform, tensor)
+                    self._test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
+
+        with get_tmp_dir() as tmp_dir:
+            s_transform.save(os.path.join(tmp_dir, "t_autoaugment.pt"))
+
 
 @unittest.skipIf(not torch.cuda.is_available(), reason="Skip if no CUDA device")
 class CUDATester(Tester):

torchvision/transforms/__init__.py

 from .transforms import *
+from .autoaugment import *

torchvision/transforms/autoaugment.py

+import math
+import torch
+
+from enum import Enum
+from torch import Tensor
+from torch.jit.annotations import List, Tuple
+from typing import Optional
+
+from . import functional as F, InterpolationMode
+
+
+class AutoAugmentPolicy(Enum):
+    """AutoAugment policies learned on different datasets.
+    """
+    IMAGENET = "imagenet"
+    CIFAR10 = "cifar10"
+    SVHN = "svhn"
+
+
+def _get_transforms(policy: AutoAugmentPolicy):
+    if policy == AutoAugmentPolicy.IMAGENET:
+        return [
+            (("Posterize", 0.4, 8), ("Rotate", 0.6, 9)),
+            (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)),
+            (("Equalize", 0.8, None), ("Equalize", 0.6, None)),
+            (("Posterize", 0.6, 7), ("Posterize", 0.6, 6)),
+            (("Equalize", 0.4, None), ("Solarize", 0.2, 4)),
+            (("Equalize", 0.4, None), ("Rotate", 0.8, 8)),
+            (("Solarize", 0.6, 3), ("Equalize", 0.6, None)),
+            (("Posterize", 0.8, 5), ("Equalize", 1.0, None)),
+            (("Rotate", 0.2, 3), ("Solarize", 0.6, 8)),
+            (("Equalize", 0.6, None), ("Posterize", 0.4, 6)),
+            (("Rotate", 0.8, 8), ("Color", 0.4, 0)),
+            (("Rotate", 0.4, 9), ("Equalize", 0.6, None)),
+            (("Equalize", 0.0, None), ("Equalize", 0.8, None)),
+            (("Invert", 0.6, None), ("Equalize", 1.0, None)),
+            (("Color", 0.6, 4), ("Contrast", 1.0, 8)),
+            (("Rotate", 0.8, 8), ("Color", 1.0, 2)),
+            (("Color", 0.8, 8), ("Solarize", 0.8, 7)),
+            (("Sharpness", 0.4, 7), ("Invert", 0.6, None)),
+            (("ShearX", 0.6, 5), ("Equalize", 1.0, None)),
+            (("Color", 0.4, 0), ("Equalize", 0.6, None)),
+            (("Equalize", 0.4, None), ("Solarize", 0.2, 4)),
+            (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)),
+            (("Invert", 0.6, None), ("Equalize", 1.0, None)),
+            (("Color", 0.6, 4), ("Contrast", 1.0, 8)),
+            (("Equalize", 0.8, None), ("Equalize", 0.6, None)),
+        ]
+    elif policy == AutoAugmentPolicy.CIFAR10:
+        return [
+            (("Invert", 0.1, None), ("Contrast", 0.2, 6)),
+            (("Rotate", 0.7, 2), ("TranslateX", 0.3, 9)),
+            (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)),
+            (("ShearY", 0.5, 8), ("TranslateY", 0.7, 9)),
+            (("AutoContrast", 0.5, None), ("Equalize", 0.9, None)),
+            (("ShearY", 0.2, 7), ("Posterize", 0.3, 7)),
+            (("Color", 0.4, 3), ("Brightness", 0.6, 7)),
+            (("Sharpness", 0.3, 9), ("Brightness", 0.7, 9)),
+            (("Equalize", 0.6, None), ("Equalize", 0.5, None)),
+            (("Contrast", 0.6, 7), ("Sharpness", 0.6, 5)),
+            (("Color", 0.7, 7), ("TranslateX", 0.5, 8)),
+            (("Equalize", 0.3, None), ("AutoContrast", 0.4, None)),
+            (("TranslateY", 0.4, 3), ("Sharpness", 0.2, 6)),
+            (("Brightness", 0.9, 6), ("Color", 0.2, 8)),
+            (("Solarize", 0.5, 2), ("Invert", 0.0, None)),
+            (("Equalize", 0.2, None), ("AutoContrast", 0.6, None)),
+            (("Equalize", 0.2, None), ("Equalize", 0.6, None)),
+            (("Color", 0.9, 9), ("Equalize", 0.6, None)),
+            (("AutoContrast", 0.8, None), ("Solarize", 0.2, 8)),
+            (("Brightness", 0.1, 3), ("Color", 0.7, 0)),
+            (("Solarize", 0.4, 5), ("AutoContrast", 0.9, None)),
+            (("TranslateY", 0.9, 9), ("TranslateY", 0.7, 9)),
+            (("AutoContrast", 0.9, None), ("Solarize", 0.8, 3)),
+            (("Equalize", 0.8, None), ("Invert", 0.1, None)),
+            (("TranslateY", 0.7, 9), ("AutoContrast", 0.9, None)),
+        ]
+    elif policy == AutoAugmentPolicy.SVHN:
+        return [
+            (("ShearX", 0.9, 4), ("Invert", 0.2, None)),
+            (("ShearY", 0.9, 8), ("Invert", 0.7, None)),
+            (("Equalize", 0.6, None), ("Solarize", 0.6, 6)),
+            (("Invert", 0.9, None), ("Equalize", 0.6, None)),
+            (("Equalize", 0.6, None), ("Rotate", 0.9, 3)),
+            (("ShearX", 0.9, 4), ("AutoContrast", 0.8, None)),
+            (("ShearY", 0.9, 8), ("Invert", 0.4, None)),
+            (("ShearY", 0.9, 5), ("Solarize", 0.2, 6)),
+            (("Invert", 0.9, None), ("AutoContrast", 0.8, None)),
+            (("Equalize", 0.6, None), ("Rotate", 0.9, 3)),
+            (("ShearX", 0.9, 4), ("Solarize", 0.3, 3)),
+            (("ShearY", 0.8, 8), ("Invert", 0.7, None)),
+            (("Equalize", 0.9, None), ("TranslateY", 0.6, 6)),
+            (("Invert", 0.9, None), ("Equalize", 0.6, None)),
+            (("Contrast", 0.3, 3), ("Rotate", 0.8, 4)),
+            (("Invert", 0.8, None), ("TranslateY", 0.0, 2)),
+            (("ShearY", 0.7, 6), ("Solarize", 0.4, 8)),
+            (("Invert", 0.6, None), ("Rotate", 0.8, 4)),
+            (("ShearY", 0.3, 7), ("TranslateX", 0.9, 3)),
+            (("ShearX", 0.1, 6), ("Invert", 0.6, None)),
+            (("Solarize", 0.7, 2), ("TranslateY", 0.6, 7)),
+            (("ShearY", 0.8, 4), ("Invert", 0.8, None)),
+            (("ShearX", 0.7, 9), ("TranslateY", 0.8, 3)),
+            (("ShearY", 0.8, 5), ("AutoContrast", 0.7, None)),
+            (("ShearX", 0.7, 2), ("Invert", 0.1, None)),
+        ]
+
+
+def _get_magnitudes():
+    _BINS = 10
+    return {
+        # name: (magnitudes, signed)
+        "ShearX": (torch.linspace(0.0, 0.3, _BINS), True),
+        "ShearY": (torch.linspace(0.0, 0.3, _BINS), True),
+        "TranslateX": (torch.linspace(0.0, 150.0 / 331.0, _BINS), True),
+        "TranslateY": (torch.linspace(0.0, 150.0 / 331.0, _BINS), True),
+        "Rotate": (torch.linspace(0.0, 30.0, _BINS), True),
+        "Brightness": (torch.linspace(0.0, 0.9, _BINS), True),
+        "Color": (torch.linspace(0.0, 0.9, _BINS), True),
+        "Contrast": (torch.linspace(0.0, 0.9, _BINS), True),
+        "Sharpness": (torch.linspace(0.0, 0.9, _BINS), True),
+        "Posterize": (torch.tensor([8, 8, 7, 7, 6, 6, 5, 5, 4, 4]), False),
+        "Solarize": (torch.linspace(256.0, 0.0, _BINS), False),
+        "AutoContrast": (None, None),
+        "Equalize": (None, None),
+        "Invert": (None, None),
+    }
+
+
+class AutoAugment(torch.nn.Module):
+    r"""AutoAugment data augmentation method based on
+    `"AutoAugment: Learning Augmentation Strategies from Data" <https://arxiv.org/pdf/1805.09501.pdf>`_.
+    The image can be a PIL Image or a Tensor, in which case it is expected
+    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
+
+    Args:
+        policy (AutoAugmentPolicy): Desired policy enum defined by
+            :class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``.
+        interpolation (InterpolationMode): Desired interpolation enum defined by
+            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
+            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+        fill (sequence or int or float, optional): Pixel fill value for the area outside the transformed
+            image. If int or float, the value is used for all bands respectively.
+            This option is supported for PIL image and Tensor inputs.
+            If input is PIL Image, the options is only available for ``Pillow>=5.0.0``.
+
+    Example:
+        >>> t = transforms.AutoAugment()
+        >>> transformed = t(image)
+
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     transforms.AutoAugment(),
+        >>>     transforms.ToTensor()])
+    """
+
+    def __init__(self, policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET,
+                 interpolation: InterpolationMode = InterpolationMode.NEAREST, fill: Optional[List[float]] = None):
+        super().__init__()
+        self.policy = policy
+        self.interpolation = interpolation
+        self.fill = fill
+
+        self.transforms = _get_transforms(policy)
+        if self.transforms is None:
+            raise ValueError("The provided policy {} is not recognized.".format(policy))
+        self._op_meta = _get_magnitudes()
+
+    @staticmethod
+    def get_params(transform_num: int) -> Tuple[int, Tensor, Tensor]:
+        """Get parameters for autoaugment transformation
+
+        Returns:
+            params required by the autoaugment transformation
+        """
+        policy_id = torch.randint(transform_num, (1,)).item()
+        probs = torch.rand((2,))
+        signs = torch.randint(2, (2,))
+
+        return policy_id, probs, signs
+
+    def _get_op_meta(self, name: str) -> Tuple[Optional[Tensor], Optional[bool]]:
+        return self._op_meta[name]
+
+    def forward(self, img: Tensor):
+        """
+            img (PIL Image or Tensor): Image to be transformed.
+
+        Returns:
+            PIL Image or Tensor: AutoAugmented image.
+        """
+        fill = self.fill
+        if isinstance(img, Tensor):
+            if isinstance(fill, (int, float)):
+                fill = [float(fill)] * F._get_image_num_channels(img)
+            elif fill is not None:
+                fill = [float(f) for f in fill]
+
+        transform_id, probs, signs = self.get_params(len(self.transforms))
+
+        for i, (op_name, p, magnitude_id) in enumerate(self.transforms[transform_id]):
+            if probs[i] <= p:
+                magnitudes, signed = self._get_op_meta(op_name)
+                magnitude = float(magnitudes[magnitude_id].item()) \
+                    if magnitudes is not None and magnitude_id is not None else 0.0
+                if signed is not None and signed and signs[i] == 0:
+                    magnitude *= -1.0
+
+                if op_name == "ShearX":
+                    img = F.affine(img, angle=0.0, translate=[0, 0], scale=1.0, shear=[math.degrees(magnitude), 0.0],
+                                   interpolation=self.interpolation, fill=fill)
+                elif op_name == "ShearY":
+                    img = F.affine(img, angle=0.0, translate=[0, 0], scale=1.0, shear=[0.0, math.degrees(magnitude)],
+                                   interpolation=self.interpolation, fill=fill)
+                elif op_name == "TranslateX":
+                    img = F.affine(img, angle=0.0, translate=[int(F._get_image_size(img)[0] * magnitude), 0], scale=1.0,
+                                   interpolation=self.interpolation, shear=[0.0, 0.0], fill=fill)
+                elif op_name == "TranslateY":
+                    img = F.affine(img, angle=0.0, translate=[0, int(F._get_image_size(img)[1] * magnitude)], scale=1.0,
+                                   interpolation=self.interpolation, shear=[0.0, 0.0], fill=fill)
+                elif op_name == "Rotate":
+                    img = F.rotate(img, magnitude, interpolation=self.interpolation, fill=fill)
+                elif op_name == "Brightness":
+                    img = F.adjust_brightness(img, 1.0 + magnitude)
+                elif op_name == "Color":
+                    img = F.adjust_saturation(img, 1.0 + magnitude)
+                elif op_name == "Contrast":
+                    img = F.adjust_contrast(img, 1.0 + magnitude)
+                elif op_name == "Sharpness":
+                    img = F.adjust_sharpness(img, 1.0 + magnitude)
+                elif op_name == "Posterize":
+                    img = F.posterize(img, int(magnitude))
+                elif op_name == "Solarize":
+                    img = F.solarize(img, magnitude)
+                elif op_name == "AutoContrast":
+                    img = F.autocontrast(img)
+                elif op_name == "Equalize":
+                    img = F.equalize(img)
+                elif op_name == "Invert":
+                    img = F.invert(img)
+                else:
+                    raise ValueError("The provided operator {} is not recognized.".format(op_name))
+
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(policy={}, fill={})'.format(self.policy, self.fill)

torchvision/transforms/functional.py

@@ -1173,3 +1173,118 @@ def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[floa
     if not isinstance(img, torch.Tensor):
         output = to_pil_image(output)
     return output
+
+
+def invert(img: Tensor) -> Tensor:
+    """Invert the colors of an RGB/grayscale PIL Image or torch Tensor.
+
+    Args:
+        img (PIL Image or Tensor): Image to have its colors inverted.
+            If img is a Tensor, it is expected to be in [..., H, W] format,
+            where ... means it can have an arbitrary number of trailing
+            dimensions.
+
+    Returns:
+        PIL Image or Tensor: Color inverted image.
+    """
+    if not isinstance(img, torch.Tensor):
+        return F_pil.invert(img)
+
+    return F_t.invert(img)
+
+
+def posterize(img: Tensor, bits: int) -> Tensor:
+    """Posterize a PIL Image or torch Tensor by reducing the number of bits for each color channel.
+
+    Args:
+        img (PIL Image or Tensor): Image to have its colors posterized.
+            If img is a Tensor, it should be of type torch.uint8 and
+            it is expected to be in [..., H, W] format, where ... means
+            it can have an arbitrary number of trailing dimensions.
+        bits (int): The number of bits to keep for each channel (0-8).
+    Returns:
+        PIL Image or Tensor: Posterized image.
+    """
+    if not (0 <= bits <= 8):
+        raise ValueError('The number if bits should be between 0 and 8. Got {}'.format(bits))
+
+    if not isinstance(img, torch.Tensor):
+        return F_pil.posterize(img, bits)
+
+    return F_t.posterize(img, bits)
+
+
+def solarize(img: Tensor, threshold: float) -> Tensor:
+    """Solarize a PIL Image or torch Tensor by inverting all pixel values above a threshold.
+
+    Args:
+        img (PIL Image or Tensor): Image to have its colors inverted.
+            If img is a Tensor, it is expected to be in [..., H, W] format,
+            where ... means it can have an arbitrary number of trailing
+            dimensions.
+        threshold (float): All pixels equal or above this value are inverted.
+    Returns:
+        PIL Image or Tensor: Solarized image.
+    """
+    if not isinstance(img, torch.Tensor):
+        return F_pil.solarize(img, threshold)
+
+    return F_t.solarize(img, threshold)
+
+
+def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor:
+    """Adjust the sharpness of an Image.
+
+    Args:
+        img (PIL Image or Tensor): Image to be adjusted.
+        sharpness_factor (float):  How much to adjust the sharpness. Can be
+            any non negative number. 0 gives a blurred image, 1 gives the
+            original image while 2 increases the sharpness by a factor of 2.
+
+    Returns:
+        PIL Image or Tensor: Sharpness adjusted image.
+    """
+    if not isinstance(img, torch.Tensor):
+        return F_pil.adjust_sharpness(img, sharpness_factor)
+
+    return F_t.adjust_sharpness(img, sharpness_factor)
+
+
+def autocontrast(img: Tensor) -> Tensor:
+    """Maximize contrast of a PIL Image or torch Tensor by remapping its
+    pixels per channel so that the lowest becomes black and the lightest
+    becomes white.
+
+    Args:
+        img (PIL Image or Tensor): Image on which autocontrast is applied.
+            If img is a Tensor, it is expected to be in [..., H, W] format,
+            where ... means it can have an arbitrary number of trailing
+            dimensions.
+
+    Returns:
+        PIL Image or Tensor: An image that was autocontrasted.
+    """
+    if not isinstance(img, torch.Tensor):
+        return F_pil.autocontrast(img)
+
+    return F_t.autocontrast(img)
+
+
+def equalize(img: Tensor) -> Tensor:
+    """Equalize the histogram of a PIL Image or torch Tensor by applying
+    a non-linear mapping to the input in order to create a uniform
+    distribution of grayscale values in the output.
+
+    Args:
+        img (PIL Image or Tensor): Image on which equalize is applied.
+            If img is a Tensor, it is expected to be in [..., H, W] format,
+            where ... means it can have an arbitrary number of trailing
+            dimensions.
+
+    Returns:
+        PIL Image or Tensor: An image that was equalized.
+    """
+    if not isinstance(img, torch.Tensor):
+        return F_pil.equalize(img)
+
+    return F_t.equalize(img)

torchvision/transforms/functional_pil.py

@@ -606,3 +606,48 @@ def to_grayscale(img, num_output_channels):
         raise ValueError('num_output_channels should be either 1 or 3')
 
     return img
+
+
+@torch.jit.unused
+def invert(img):
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+    return ImageOps.invert(img)
+
+
+@torch.jit.unused
+def posterize(img, bits):
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+    return ImageOps.posterize(img, bits)
+
+
+@torch.jit.unused
+def solarize(img, threshold):
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+    return ImageOps.solarize(img, threshold)
+
+
+@torch.jit.unused
+def adjust_sharpness(img, sharpness_factor):
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+
+    enhancer = ImageEnhance.Sharpness(img)
+    img = enhancer.enhance(sharpness_factor)
+    return img
+
+
+@torch.jit.unused
+def autocontrast(img):
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+    return ImageOps.autocontrast(img)
+
+
+@torch.jit.unused
+def equalize(img):
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+    return ImageOps.equalize(img)

torchvision/transforms/functional_tensor.py

@@ -570,6 +570,7 @@ def ten_crop(img: Tensor, size: BroadcastingList2[int], vertical_flip: bool = Fa
 
 
 def _blend(img1: Tensor, img2: Tensor, ratio: float) -> Tensor:
+    ratio = float(ratio)
     bound = 1.0 if img1.is_floating_point() else 255.0
     return (ratio * img1 + (1.0 - ratio) * img2).clamp(0, bound).to(img1.dtype)
 
@@ -1180,3 +1181,133 @@ def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: List[float]) -> Te
 
     img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype)
     return img
+
+
+def invert(img: Tensor) -> Tensor:
+    if not _is_tensor_a_torch_image(img):
+        raise TypeError('tensor is not a torch image.')
+
+    if img.ndim < 3:
+        raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim))
+
+    _assert_channels(img, [1, 3])
+
+    bound = torch.tensor(1 if img.is_floating_point() else 255, dtype=img.dtype, device=img.device)
+    return bound - img
+
+
+def posterize(img: Tensor, bits: int) -> Tensor:
+    if not _is_tensor_a_torch_image(img):
+        raise TypeError('tensor is not a torch image.')
+
+    if img.ndim < 3:
+        raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim))
+    if img.dtype != torch.uint8:
+        raise TypeError("Only torch.uint8 image tensors are supported, but found {}".format(img.dtype))
+
+    _assert_channels(img, [1, 3])
+    mask = -int(2**(8 - bits))  # JIT-friendly for: ~(2 ** (8 - bits) - 1)
+    return img & mask
+
+
+def solarize(img: Tensor, threshold: float) -> Tensor:
+    if not _is_tensor_a_torch_image(img):
+        raise TypeError('tensor is not a torch image.')
+
+    if img.ndim < 3:
+        raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim))
+
+    _assert_channels(img, [1, 3])
+
+    inverted_img = invert(img)
+    return torch.where(img >= threshold, inverted_img, img)
+
+
+def _blurred_degenerate_image(img: Tensor) -> Tensor:
+    dtype = img.dtype if torch.is_floating_point(img) else torch.float32
+
+    kernel = torch.ones((3, 3), dtype=dtype, device=img.device)
+    kernel[1, 1] = 5.0
+    kernel /= kernel.sum()
+    kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1])
+
+    result_tmp, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype, ])
+    result_tmp = conv2d(result_tmp, kernel, groups=result_tmp.shape[-3])
+    result_tmp = _cast_squeeze_out(result_tmp, need_cast, need_squeeze, out_dtype)
+
+    result = img.clone()
+    result[..., 1:-1, 1:-1] = result_tmp
+
+    return result
+
+
+def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor:
+    if sharpness_factor < 0:
+        raise ValueError('sharpness_factor ({}) is not non-negative.'.format(sharpness_factor))
+
+    if not _is_tensor_a_torch_image(img):
+        raise TypeError('tensor is not a torch image.')
+
+    _assert_channels(img, [1, 3])
+
+    if img.size(-1) <= 2 or img.size(-2) <= 2:
+        return img
+
+    return _blend(img, _blurred_degenerate_image(img), sharpness_factor)
+
+
+def autocontrast(img: Tensor) -> Tensor:
+    if not _is_tensor_a_torch_image(img):
+        raise TypeError('tensor is not a torch image.')
+
+    if img.ndim < 3:
+        raise TypeError("Input image tensor should have at least 3 dimensions, but found {}".format(img.ndim))
+
+    _assert_channels(img, [1, 3])
+
+    bound = 1.0 if img.is_floating_point() else 255.0
+    dtype = img.dtype if torch.is_floating_point(img) else torch.float32
+
+    minimum = img.amin(dim=(-2, -1), keepdim=True).to(dtype)
+    maximum = img.amax(dim=(-2, -1), keepdim=True).to(dtype)
+    eq_idxs = torch.where(minimum == maximum)[0]
+    minimum[eq_idxs] = 0
+    maximum[eq_idxs] = bound
+    scale = bound / (maximum - minimum)
+
+    return ((img - minimum) * scale).clamp(0, bound).to(img.dtype)
+
+
+def _scale_channel(img_chan):
+    hist = torch.histc(img_chan.to(torch.float32), bins=256, min=0, max=255)
+
+    nonzero_hist = hist[hist != 0]
+    step = nonzero_hist[:-1].sum() // 255
+    if step == 0:
+        return img_chan
+
+    lut = (torch.cumsum(hist, 0) + (step // 2)) // step
+    lut = torch.nn.functional.pad(lut, [1, 0])[:-1].clamp(0, 255)
+
+    return lut[img_chan.to(torch.int64)].to(torch.uint8)
+
+
+def _equalize_single_image(img: Tensor) -> Tensor:
+    return torch.stack([_scale_channel(img[c]) for c in range(img.size(0))])
+
+
+def equalize(img: Tensor) -> Tensor:
+    if not _is_tensor_a_torch_image(img):
+        raise TypeError('tensor is not a torch image.')
+
+    if not (3 <= img.ndim <= 4):
+        raise TypeError("Input image tensor should have 3 or 4 dimensions, but found {}".format(img.ndim))
+    if img.dtype != torch.uint8:
+        raise TypeError("Only torch.uint8 image tensors are supported, but found {}".format(img.dtype))
+
+    _assert_channels(img, [1, 3])
+
+    if img.ndim == 3:
+        return _equalize_single_image(img)
+
+    return torch.stack([_equalize_single_image(x) for x in img])

torchvision/transforms/transforms.py

@@ -21,7 +21,8 @@ __all__ = ["Compose", "ToTensor", "PILToTensor", "ConvertImageDtype", "ToPILImag
            "CenterCrop", "Pad", "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop",
            "RandomHorizontalFlip", "RandomVerticalFlip", "RandomResizedCrop", "RandomSizedCrop", "FiveCrop", "TenCrop",
            "LinearTransformation", "ColorJitter", "RandomRotation", "RandomAffine", "Grayscale", "RandomGrayscale",
-           "RandomPerspective", "RandomErasing", "GaussianBlur", "InterpolationMode"]
+           "RandomPerspective", "RandomErasing", "GaussianBlur", "InterpolationMode", "RandomInvert", "RandomPosterize",
+           "RandomSolarize", "RandomAdjustSharpness", "RandomAutocontrast", "RandomEqualize"]
 
 
 class Compose:
@@ -1038,7 +1039,7 @@ class LinearTransformation(torch.nn.Module):
 
 
 class ColorJitter(torch.nn.Module):
-    """Randomly change the brightness, contrast and saturation of an image.
+    """Randomly change the brightness, contrast, saturation and hue of an image.
 
     Args:
         brightness (float or tuple of float (min, max)): How much to jitter brightness.
@@ -1699,3 +1700,190 @@ def _setup_angle(x, name, req_sizes=(2, )):
         _check_sequence_input(x, name, req_sizes)
 
     return [float(d) for d in x]
+
+
+class RandomInvert(torch.nn.Module):
+    """Inverts the colors of the given image randomly with a given probability.
+    The image can be a PIL Image or a torch Tensor, in which case it is expected
+    to have [..., H, W] shape, where ... means an arbitrary number of leading
+    dimensions.
+
+    Args:
+        p (float): probability of the image being color inverted. Default value is 0.5
+    """
+
+    def __init__(self, p=0.5):
+        super().__init__()
+        self.p = p
+
+    def forward(self, img):
+        """
+        Args:
+            img (PIL Image or Tensor): Image to be inverted.
+
+        Returns:
+            PIL Image or Tensor: Randomly color inverted image.
+        """
+        if torch.rand(1).item() < self.p:
+            return F.invert(img)
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(p={})'.format(self.p)
+
+
+class RandomPosterize(torch.nn.Module):
+    """Posterize the image randomly with a given probability by reducing the
+    number of bits for each color channel. The image can be a PIL Image or a torch
+    Tensor, in which case it is expected to have [..., H, W] shape, where ... means
+    an arbitrary number of leading dimensions.
+
+    Args:
+        bits (int): number of bits to keep for each channel (0-8)
+        p (float): probability of the image being color inverted. Default value is 0.5
+    """
+
+    def __init__(self, bits, p=0.5):
+        super().__init__()
+        self.bits = bits
+        self.p = p
+
+    def forward(self, img):
+        """
+        Args:
+            img (PIL Image or Tensor): Image to be posterized.
+
+        Returns:
+            PIL Image or Tensor: Randomly posterized image.
+        """
+        if torch.rand(1).item() < self.p:
+            return F.posterize(img, self.bits)
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(bits={},p={})'.format(self.bits, self.p)
+
+
+class RandomSolarize(torch.nn.Module):
+    """Solarize the image randomly with a given probability by inverting all pixel
+    values above a threshold. The image can be a PIL Image or a torch Tensor, in
+    which case it is expected to have [..., H, W] shape, where ... means an arbitrary
+    number of leading dimensions.
+
+    Args:
+        threshold (float): all pixels equal or above this value are inverted.
+        p (float): probability of the image being color inverted. Default value is 0.5
+    """
+
+    def __init__(self, threshold, p=0.5):
+        super().__init__()
+        self.threshold = threshold
+        self.p = p
+
+    def forward(self, img):
+        """
+        Args:
+            img (PIL Image or Tensor): Image to be solarized.
+
+        Returns:
+            PIL Image or Tensor: Randomly solarized image.
+        """
+        if torch.rand(1).item() < self.p:
+            return F.solarize(img, self.threshold)
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(threshold={},p={})'.format(self.threshold, self.p)
+
+
+class RandomAdjustSharpness(torch.nn.Module):
+    """Adjust the sharpness of the image randomly with a given probability. The image
+    can be a PIL Image or a torch Tensor, in which case it is expected to have [..., H, W]
+    shape, where ... means an arbitrary number of leading dimensions.
+
+    Args:
+        sharpness_factor (float):  How much to adjust the sharpness. Can be
+            any non negative number. 0 gives a blurred image, 1 gives the
+            original image while 2 increases the sharpness by a factor of 2.
+        p (float): probability of the image being color inverted. Default value is 0.5
+    """
+
+    def __init__(self, sharpness_factor, p=0.5):
+        super().__init__()
+        self.sharpness_factor = sharpness_factor
+        self.p = p
+
+    def forward(self, img):
+        """
+        Args:
+            img (PIL Image or Tensor): Image to be sharpened.
+
+        Returns:
+            PIL Image or Tensor: Randomly sharpened image.
+        """
+        if torch.rand(1).item() < self.p:
+            return F.adjust_sharpness(img, self.sharpness_factor)
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(sharpness_factor={},p={})'.format(self.sharpness_factor, self.p)
+
+
+class RandomAutocontrast(torch.nn.Module):
+    """Autocontrast the pixels of the given image randomly with a given probability.
+    The image can be a PIL Image or a torch Tensor, in which case it is expected
+    to have [..., H, W] shape, where ... means an arbitrary number of leading
+    dimensions.
+
+    Args:
+        p (float): probability of the image being autocontrasted. Default value is 0.5
+    """
+
+    def __init__(self, p=0.5):
+        super().__init__()
+        self.p = p
+
+    def forward(self, img):
+        """
+        Args:
+            img (PIL Image or Tensor): Image to be autocontrasted.
+
+        Returns:
+            PIL Image or Tensor: Randomly autocontrasted image.
+        """
+        if torch.rand(1).item() < self.p:
+            return F.autocontrast(img)
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(p={})'.format(self.p)
+
+
+class RandomEqualize(torch.nn.Module):
+    """Equalize the histogram of the given image randomly with a given probability.
+    The image can be a PIL Image or a torch Tensor, in which case it is expected
+    to have [..., H, W] shape, where ... means an arbitrary number of leading
+    dimensions.
+
+    Args:
+        p (float): probability of the image being equalized. Default value is 0.5
+    """
+
+    def __init__(self, p=0.5):
+        super().__init__()
+        self.p = p
+
+    def forward(self, img):
+        """
+        Args:
+            img (PIL Image or Tensor): Image to be equalized.
+
+        Returns:
+            PIL Image or Tensor: Randomly equalized image.
+        """
+        if torch.rand(1).item() < self.p:
+            return F.equalize(img)
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(p={})'.format(self.p)