Cutmix -> CutMix (#7784)

Author: NicolasHug

docs/source/transforms.rst

@@ -274,8 +274,8 @@ are combining pairs of images together. These can be used after the dataloader
     :toctree: generated/
     :template: class.rst
 
-    v2.Cutmix
-    v2.Mixup
+    v2.CutMix
+    v2.MixUp
 
 .. _functional_transforms:
 

references/classification/transforms.py

@@ -13,24 +13,24 @@ def get_mixup_cutmix(*, mixup_alpha, cutmix_alpha, num_categories, use_v2):
     mixup_cutmix = []
     if mixup_alpha > 0:
         mixup_cutmix.append(
-            transforms_module.Mixup(alpha=mixup_alpha, num_categories=num_categories)
+            transforms_module.MixUp(alpha=mixup_alpha, num_categories=num_categories)
             if use_v2
-            else RandomMixup(num_classes=num_categories, p=1.0, alpha=mixup_alpha)
+            else RandomMixUp(num_classes=num_categories, p=1.0, alpha=mixup_alpha)
         )
     if cutmix_alpha > 0:
         mixup_cutmix.append(
-            transforms_module.Cutmix(alpha=mixup_alpha, num_categories=num_categories)
+            transforms_module.CutMix(alpha=mixup_alpha, num_categories=num_categories)
             if use_v2
-            else RandomCutmix(num_classes=num_categories, p=1.0, alpha=mixup_alpha)
+            else RandomCutMix(num_classes=num_categories, p=1.0, alpha=mixup_alpha)
         )
     if not mixup_cutmix:
         return None
 
     return transforms_module.RandomChoice(mixup_cutmix)
 
 
-class RandomMixup(torch.nn.Module):
-    """Randomly apply Mixup to the provided batch and targets.
+class RandomMixUp(torch.nn.Module):
+    """Randomly apply MixUp to the provided batch and targets.
     The class implements the data augmentations as described in the paper
     `"mixup: Beyond Empirical Risk Minimization" <https://arxiv.org/abs/1710.09412>`_.
 
@@ -112,8 +112,8 @@ def __repr__(self) -> str:
         return s
 
 
-class RandomCutmix(torch.nn.Module):
-    """Randomly apply Cutmix to the provided batch and targets.
+class RandomCutMix(torch.nn.Module):
+    """Randomly apply CutMix to the provided batch and targets.
     The class implements the data augmentations as described in the paper
     `"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features"
     <https://arxiv.org/abs/1905.04899>`_.

test/test_prototype_transforms.py

@@ -60,8 +60,8 @@ def parametrize(transforms_with_inputs):
             ],
         )
         for transform in [
-            transforms.RandomMixup(alpha=1.0),
-            transforms.RandomCutmix(alpha=1.0),
+            transforms.RandomMixUp(alpha=1.0),
+            transforms.RandomCutMix(alpha=1.0),
         ]
     ]
 )

test/test_transforms_v2_refactored.py

@@ -1914,7 +1914,7 @@ def __getitem__(self, idx):
         def __len__(self):
             return self.size
 
-    @pytest.mark.parametrize("T", [transforms.Cutmix, transforms.Mixup])
+    @pytest.mark.parametrize("T", [transforms.CutMix, transforms.MixUp])
     def test_supported_input_structure(self, T):
 
         batch_size = 32
@@ -1964,7 +1964,7 @@ def collate_fn_2(batch):
             check_output(img, target)
 
     @needs_cuda
-    @pytest.mark.parametrize("T", [transforms.Cutmix, transforms.Mixup])
+    @pytest.mark.parametrize("T", [transforms.CutMix, transforms.MixUp])
     def test_cpu_vs_gpu(self, T):
         num_classes = 10
         batch_size = 3
@@ -1976,7 +1976,7 @@ def test_cpu_vs_gpu(self, T):
 
         _check_kernel_cuda_vs_cpu(cutmix_mixup, imgs, labels, rtol=None, atol=None)
 
-    @pytest.mark.parametrize("T", [transforms.Cutmix, transforms.Mixup])
+    @pytest.mark.parametrize("T", [transforms.CutMix, transforms.MixUp])
     def test_error(self, T):
 
         num_classes = 10

torchvision/prototype/transforms/__init__.py

@@ -1,6 +1,6 @@
 from ._presets import StereoMatching  # usort: skip
 
-from ._augment import RandomCutmix, RandomMixup, SimpleCopyPaste
+from ._augment import RandomCutMix, RandomMixUp, SimpleCopyPaste
 from ._geometry import FixedSizeCrop
 from ._misc import PermuteDimensions, TransposeDimensions
 from ._type_conversion import LabelToOneHot

torchvision/prototype/transforms/_augment.py

@@ -14,7 +14,7 @@
 from torchvision.transforms.v2.utils import has_any, is_simple_tensor, query_size
 
 
-class _BaseMixupCutmix(_RandomApplyTransform):
+class _BaseMixUpCutMix(_RandomApplyTransform):
     def __init__(self, alpha: float, p: float = 0.5) -> None:
         super().__init__(p=p)
         self.alpha = alpha
@@ -38,7 +38,7 @@ def _mixup_onehotlabel(self, inpt: proto_datapoints.OneHotLabel, lam: float) ->
         return proto_datapoints.OneHotLabel.wrap_like(inpt, output)
 
 
-class RandomMixup(_BaseMixupCutmix):
+class RandomMixUp(_BaseMixUpCutMix):
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
         return dict(lam=float(self._dist.sample(())))  # type: ignore[arg-type]
 
@@ -60,7 +60,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
             return inpt
 
 
-class RandomCutmix(_BaseMixupCutmix):
+class RandomCutMix(_BaseMixUpCutMix):
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
         lam = float(self._dist.sample(()))  # type: ignore[arg-type]
 

torchvision/transforms/v2/__init__.py

@@ -4,7 +4,7 @@
 
 from ._transform import Transform  # usort: skip
 
-from ._augment import Cutmix, Mixup, RandomErasing
+from ._augment import CutMix, MixUp, RandomErasing
 from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
 from ._color import (
     ColorJitter,

torchvision/transforms/v2/_augment.py

@@ -140,7 +140,7 @@ def _transform(
         return inpt
 
 
-class _BaseMixupCutmix(Transform):
+class _BaseMixUpCutMix(Transform):
     def __init__(self, *, alpha: float = 1.0, num_classes: int, labels_getter="default") -> None:
         super().__init__()
         self.alpha = float(alpha)
@@ -203,10 +203,10 @@ def _mixup_label(self, label: torch.Tensor, *, lam: float) -> torch.Tensor:
         return label.roll(1, 0).mul_(1.0 - lam).add_(label.mul(lam))
 
 
-class Mixup(_BaseMixupCutmix):
+class MixUp(_BaseMixUpCutMix):
     """[BETA] Apply MixUp to the provided batch of images and labels.
 
-    .. v2betastatus:: Mixup transform
+    .. v2betastatus:: MixUp transform
 
     Paper: `mixup: Beyond Empirical Risk Minimization <https://arxiv.org/abs/1710.09412>`_.
 
@@ -227,7 +227,7 @@ class Mixup(_BaseMixupCutmix):
         num_classes (int): number of classes in the batch. Used for one-hot-encoding.
         labels_getter (callable or "default", optional): indicates how to identify the labels in the input.
             By default, this will pick the second parameter a the labels if it's a tensor. This covers the most
-            common scenario where this transform is called as ``Mixup()(imgs_batch, labels_batch)``.
+            common scenario where this transform is called as ``MixUp()(imgs_batch, labels_batch)``.
             It can also be a callable that takes the same input as the transform, and returns the labels.
     """
 
@@ -252,10 +252,10 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
             return inpt
 
 
-class Cutmix(_BaseMixupCutmix):
+class CutMix(_BaseMixUpCutMix):
     """[BETA] Apply CutMix to the provided batch of images and labels.
 
-    .. v2betastatus:: Cutmix transform
+    .. v2betastatus:: CutMix transform
 
     Paper: `CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
     <https://arxiv.org/abs/1905.04899>`_.
@@ -277,7 +277,7 @@ class Cutmix(_BaseMixupCutmix):
         num_classes (int): number of classes in the batch. Used for one-hot-encoding.
         labels_getter (callable or "default", optional): indicates how to identify the labels in the input.
             By default, this will pick the second parameter a the labels if it's a tensor. This covers the most
-            common scenario where this transform is called as ``Cutmix()(imgs_batch, labels_batch)``.
+            common scenario where this transform is called as ``CutMix()(imgs_batch, labels_batch)``.
             It can also be a callable that takes the same input as the transform, and returns the labels.
     """
 

torchvision/transforms/v2/_utils.py

@@ -89,7 +89,7 @@ def _find_labels_default_heuristic(inputs: Any) -> torch.Tensor:
     This heuristic covers three cases:
 
     1. The input is tuple or list whose second item is a labels tensor. This happens for already batched
-       classification inputs for Mixup and Cutmix (typically after the Dataloder).
+       classification inputs for MixUp and CutMix (typically after the Dataloder).
     2. The input is a tuple or list whose second item is a dictionary that contains the labels tensor
        under a label-like (see below) key. This happens for the inputs of detection models.
     3. The input is a dictionary that is structured as the one from 2.
@@ -103,7 +103,7 @@ def _find_labels_default_heuristic(inputs: Any) -> torch.Tensor:
     if isinstance(inputs, (tuple, list)):
         inputs = inputs[1]
 
-    # Mixup, Cutmix
+    # MixUp, CutMix
     if isinstance(inputs, torch.Tensor):
         return inputs