Rename RandomTranslate to RandomJitter (#4828)

* update

* changelog

* typo

* typo

Author: rusty1s

CHANGELOG.md

@@ -35,6 +35,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for graph-level outputs in `to_hetero` ([#4582](https://github.com/pyg-team/pytorch_geometric/pull/4582))
 - Added `CHANGELOG.md` ([#4581](https://github.com/pyg-team/pytorch_geometric/pull/4581))
 ### Changed
+- Renamed `RandomTranslate` to `RandomJitter` - the usage of `RandomTranslate` is now deprecated ([#4828](https://github.com/pyg-team/pytorch_geometric/pull/4828))
 - Do not allow accessing edge types in `HeteroData` with two node types when there exists multiple relations between these types ([#4782](https://github.com/pyg-team/pytorch_geometric/pull/4782))
 - Allow `edge_type == rev_edge_type` argument in `RandomLinkSplit` ([#4757](https://github.com/pyg-team/pytorch_geometric/pull/4757))
 - Fixed a numerical instability in the `GeneralConv` and `neighbor_sample` tests ([#4754](https://github.com/pyg-team/pytorch_geometric/pull/4754))

docs/source/notes/introduction.rst

@@ -330,7 +330,7 @@ In addition, we can use the :obj:`transform` argument to randomly augment a :cla
 
     dataset = ShapeNet(root='/tmp/ShapeNet', categories=['Airplane'],
                         pre_transform=T.KNNGraph(k=6),
-                        transform=T.RandomTranslate(0.01))
+                        transform=T.RandomJitter(0.01))
 
     dataset[0]
     >>> Data(edge_index=[2, 15108], pos=[2518, 3], y=[2518])

examples/dgcnn_segmentation.py

@@ -13,7 +13,7 @@
 category = 'Airplane'  # Pass in `None` to train on all categories.
 path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'ShapeNet')
 transform = T.Compose([
-    T.RandomTranslate(0.01),
+    T.RandomJitter(0.01),
     T.RandomRotate(15, axis=0),
     T.RandomRotate(15, axis=1),
     T.RandomRotate(15, axis=2)

examples/point_transformer_segmentation.py

@@ -22,7 +22,7 @@
 category = 'Airplane'  # Pass in `None` to train on all categories.
 path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'ShapeNet')
 transform = T.Compose([
-    T.RandomTranslate(0.01),
+    T.RandomJitter(0.01),
     T.RandomRotate(15, axis=0),
     T.RandomRotate(15, axis=1),
     T.RandomRotate(15, axis=2),

examples/pointnet2_segmentation.py

@@ -14,7 +14,7 @@
 category = 'Airplane'  # Pass in `None` to train on all categories.
 path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'ShapeNet')
 transform = T.Compose([
-    T.RandomTranslate(0.01),
+    T.RandomJitter(0.01),
     T.RandomRotate(15, axis=0),
     T.RandomRotate(15, axis=1),
     T.RandomRotate(15, axis=2)

test/transforms/test_random_translate.py test/transforms/test_random_jitter.py

@@ -1,27 +1,27 @@
 import torch
 
 from torch_geometric.data import Data
-from torch_geometric.transforms import RandomTranslate
+from torch_geometric.transforms import RandomJitter
 
 
-def test_random_translate():
-    assert RandomTranslate(0.1).__repr__() == 'RandomTranslate(0.1)'
+def test_random_jitter():
+    assert RandomJitter(0.1).__repr__() == 'RandomJitter(0.1)'
 
     pos = torch.Tensor([[0, 0], [0, 0], [0, 0], [0, 0]])
 
     data = Data(pos=pos)
-    data = RandomTranslate(0)(data)
+    data = RandomJitter(0)(data)
     assert len(data) == 1
     assert data.pos.tolist() == pos.tolist()
 
     data = Data(pos=pos)
-    data = RandomTranslate(0.1)(data)
+    data = RandomJitter(0.1)(data)
     assert len(data) == 1
     assert data.pos.min().item() >= -0.1
     assert data.pos.max().item() <= 0.1
 
     data = Data(pos=pos)
-    data = RandomTranslate([0.1, 1])(data)
+    data = RandomJitter([0.1, 1])(data)
     assert len(data) == 1
     assert data.pos[:, 0].min().item() >= -0.1
     assert data.pos[:, 0].max().item() <= 0.1

torch_geometric/transforms/__init__.py

@@ -16,7 +16,7 @@
 from .center import Center
 from .normalize_rotation import NormalizeRotation
 from .normalize_scale import NormalizeScale
-from .random_translate import RandomTranslate
+from .random_jitter import RandomJitter
 from .random_flip import RandomFlip
 from .linear_transformation import LinearTransformation
 from .random_scale import RandomScale
@@ -70,7 +70,7 @@
     'Center',
     'NormalizeRotation',
     'NormalizeScale',
-    'RandomTranslate',
+    'RandomJitter',
     'RandomFlip',
     'LinearTransformation',
     'RandomScale',
@@ -109,3 +109,8 @@
 ]
 
 classes = __all__
+
+from torch_geometric.deprecation import deprecated  # noqa
+
+RandomTranslate = deprecated("use 'transforms.RandomJitter' instead",
+                             'transforms.RandomTranslate')(RandomJitter)

torch_geometric/transforms/random_translate.py torch_geometric/transforms/random_jitter.py

@@ -7,10 +7,10 @@
 from torch_geometric.transforms import BaseTransform
 
 
-@functional_transform('random_translate')
-class RandomTranslate(BaseTransform):
+@functional_transform('random_jitter')
+class RandomJitter(BaseTransform):
     r"""Translates node positions by randomly sampled translation values
-    within a given interval (functional name: :obj:`random_translate`).
+    within a given interval (functional name: :obj:`random_jitter`).
     In contrast to other random transformations,
     translation is applied separately at each position