Adding RandAugment implementation (#4348)

* Adding randaugment implementation

* Refactoring.

* Adding num_magnitude_bins.

* Adding FIXME.

references/classification/presets.py

@@ -9,6 +9,9 @@ class ClassificationPresetTrain:
         if hflip_prob > 0:
             trans.append(transforms.RandomHorizontalFlip(hflip_prob))
         if auto_augment_policy is not None:
+            if auto_augment_policy == "ra":
+                trans.append(autoaugment.RandAugment())
+            else:
                 aa_policy = autoaugment.AutoAugmentPolicy(auto_augment_policy)
                 trans.append(autoaugment.AutoAugment(policy=aa_policy))
         trans.extend([

test/test_transforms.py

@@ -1490,6 +1490,18 @@ def test_autoaugment(policy, fill):
     transform.__repr__()
 
 
+@pytest.mark.parametrize('num_ops', [1, 2, 3])
+@pytest.mark.parametrize('magnitude', [7, 9, 11])
+@pytest.mark.parametrize('fill', [None, 85, (128, 128, 128)])
+def test_randaugment(num_ops, magnitude, fill):
+    random.seed(42)
+    img = Image.open(GRACE_HOPPER)
+    transform = transforms.RandAugment(num_ops=num_ops, magnitude=magnitude, fill=fill)
+    for _ in range(100):
+        img = transform(img)
+    transform.__repr__()
+
+
 def test_random_crop():
     height = random.randint(10, 32) * 2
     width = random.randint(10, 32) * 2

test/test_transforms_tensor.py

@@ -525,7 +525,6 @@ def test_autoaugment(device, policy, fill):
     tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
     batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)
 
-    s_transform = None
     transform = T.AutoAugment(policy=policy, fill=fill)
     s_transform = torch.jit.script(transform)
     for _ in range(25):
@@ -533,8 +532,24 @@ def test_autoaugment(device, policy, fill):
         _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
 
 
-def test_autoaugment_save(tmpdir):
-    transform = T.AutoAugment()
+@pytest.mark.parametrize('device', cpu_and_gpu())
+@pytest.mark.parametrize('num_ops', [1, 2, 3])
+@pytest.mark.parametrize('magnitude', [7, 9, 11])
+@pytest.mark.parametrize('fill', [None, 85, (10, -10, 10), 0.7, [0.0, 0.0, 0.0], [1, ], 1])
+def test_randaugment(device, num_ops, magnitude, fill):
+    tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
+    batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)
+
+    transform = T.RandAugment(num_ops=num_ops, magnitude=magnitude, fill=fill)
+    s_transform = torch.jit.script(transform)
+    for _ in range(25):
+        _test_transform_vs_scripted(transform, s_transform, tensor)
+        _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
+
+
+@pytest.mark.parametrize('augmentation', [T.AutoAugment, T.RandAugment])
+def test_autoaugment_save(augmentation, tmpdir):
+    transform = augmentation()
     s_transform = torch.jit.script(transform)
     s_transform.save(os.path.join(tmpdir, "t_autoaugment.pt"))
 

torchvision/transforms/autoaugment.py

@@ -7,7 +7,7 @@ from typing import List, Tuple, Optional, Dict
 
 from . import functional as F, InterpolationMode
 
-__all__ = ["AutoAugmentPolicy", "AutoAugment"]
+__all__ = ["AutoAugmentPolicy", "AutoAugment", "RandAugment"]
 
 
 def _apply_op(img: Tensor, op_name: str, magnitude: float,
@@ -58,6 +58,7 @@ class AutoAugmentPolicy(Enum):
     SVHN = "svhn"
 
 
+# FIXME: Eliminate copy-pasted code for fill standardization and _augmentation_space() by moving stuff on a base class
 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>`_.
@@ -85,9 +86,9 @@ class AutoAugment(torch.nn.Module):
         self.policy = policy
         self.interpolation = interpolation
         self.fill = fill
-        self.transforms = self._get_transforms(policy)
+        self.policies = self._get_policies(policy)
 
-    def _get_transforms(
+    def _get_policies(
         self,
         policy: AutoAugmentPolicy
     ) -> List[Tuple[Tuple[str, float, Optional[int]], Tuple[str, float, Optional[int]]]]:
@@ -178,9 +179,9 @@ class AutoAugment(torch.nn.Module):
         else:
             raise ValueError("The provided policy {} is not recognized.".format(policy))
 
-    def _get_magnitudes(self, num_bins: int, image_size: List[int]) -> Dict[str, Tuple[Tensor, bool]]:
+    def _augmentation_space(self, num_bins: int, image_size: List[int]) -> Dict[str, Tuple[Tensor, bool]]:
         return {
-            # name: (magnitudes, signed)
+            # op_name: (magnitudes, signed)
             "ShearX": (torch.linspace(0.0, 0.3, num_bins), True),
             "ShearY": (torch.linspace(0.0, 0.3, num_bins), True),
             "TranslateX": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], num_bins), True),
@@ -224,11 +225,11 @@ class AutoAugment(torch.nn.Module):
             elif fill is not None:
                 fill = [float(f) for f in fill]
 
-        transform_id, probs, signs = self.get_params(len(self.transforms))
+        transform_id, probs, signs = self.get_params(len(self.policies))
 
-        for i, (op_name, p, magnitude_id) in enumerate(self.transforms[transform_id]):
+        for i, (op_name, p, magnitude_id) in enumerate(self.policies[transform_id]):
             if probs[i] <= p:
-                op_meta = self._get_magnitudes(10, F.get_image_size(img))
+                op_meta = self._augmentation_space(10, F.get_image_size(img))
                 magnitudes, signed = op_meta[op_name]
                 magnitude = float(magnitudes[magnitude_id].item()) if magnitude_id is not None else 0.0
                 if signed and signs[i] == 0:
@@ -239,3 +240,87 @@ class AutoAugment(torch.nn.Module):
 
     def __repr__(self) -> str:
         return self.__class__.__name__ + '(policy={}, fill={})'.format(self.policy, self.fill)
+
+
+class RandAugment(torch.nn.Module):
+    r"""RandAugment data augmentation method based on
+    `"RandAugment: Practical automated data augmentation with a reduced search space"
+    <https://arxiv.org/abs/1909.13719>`.
+    If the image is torch Tensor, it should be of type torch.uint8, and it is expected
+    to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+    If img is PIL Image, it is expected to be in mode "L" or "RGB".
+
+    Args:
+        num_ops (int): Number of augmentation transformations to apply sequentially.
+        magnitude (int): Magnitude for all the transformations.
+        num_magnitude_bins (int): The number of different magnitude values.
+        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 number, optional): Pixel fill value for the area outside the transformed
+            image. If given a number, the value is used for all bands respectively.
+        """
+
+    def __init__(self, num_ops: int = 2, magnitude: int = 9, num_magnitude_bins: int = 30,
+                 interpolation: InterpolationMode = InterpolationMode.NEAREST,
+                 fill: Optional[List[float]] = None) -> None:
+        super().__init__()
+        self.num_ops = num_ops
+        self.magnitude = magnitude
+        self.num_magnitude_bins = num_magnitude_bins
+        self.interpolation = interpolation
+        self.fill = fill
+
+    def _augmentation_space(self, num_bins: int, image_size: List[int]) -> Dict[str, Tuple[Tensor, bool]]:
+        return {
+            # op_name: (magnitudes, signed)
+            "ShearX": (torch.linspace(0.0, 0.3, num_bins), True),
+            "ShearY": (torch.linspace(0.0, 0.3, num_bins), True),
+            "TranslateX": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], num_bins), True),
+            "TranslateY": (torch.linspace(0.0, 150.0 / 331.0 * image_size[1], num_bins), True),
+            "Rotate": (torch.linspace(0.0, 30.0, num_bins), True),
+            "Brightness": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Color": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Contrast": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().int(), False),
+            "Solarize": (torch.linspace(256.0, 0.0, num_bins), False),
+            "AutoContrast": (torch.tensor(0.0), False),
+            "Equalize": (torch.tensor(0.0), False),
+            "Invert": (torch.tensor(0.0), False),
+        }
+
+    def forward(self, img: Tensor) -> Tensor:
+        """
+            img (PIL Image or Tensor): Image to be transformed.
+        Returns:
+            PIL Image or Tensor: Transformed 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]
+
+        for _ in range(self.num_ops):
+            op_meta = self._augmentation_space(self.num_magnitude_bins, F.get_image_size(img))
+            op_index = int(torch.randint(len(op_meta), (1,)).item())
+            op_name = list(op_meta.keys())[op_index]
+            magnitudes, signed = op_meta[op_name]
+            magnitude = float(magnitudes[self.magnitude].item()) if magnitudes.ndim > 0 else 0.0
+            if signed and torch.randint(2, (1,)):
+                magnitude *= -1.0
+            img = _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill)
+
+        return img
+
+    def __repr__(self) -> str:
+        s = self.__class__.__name__ + '('
+        s += 'num_ops={num_ops}'
+        s += ', magnitude={magnitude}'
+        s += ', num_magnitude_bins={num_magnitude_bins}'
+        s += ', interpolation={interpolation}'
+        s += ', fill={fill}'
+        s += ')'
+        return s.format(**self.__dict__)