Split transforms.rst

Author: sotte

docs/source/index.rst

@@ -13,6 +13,7 @@ architectures, and common image transformations for computer vision.
    models
    ops
    transforms
+   transforms_functional
    utils
 
 .. automodule:: torchvision

docs/source/transforms.rst

@@ -304,52 +304,3 @@ Generic Transforms
 
 .. autoclass:: Lambda
 
-
-Functional Transforms
----------------------
-
-Functional transforms give you fine-grained control of the transformation pipeline.
-As opposed to the transformations above, functional transforms don't contain a random number
-generator for their parameters.
-That means you have to specify/generate all parameters, but you can reuse the functional transform.
-
-Example:
-you can apply a functional transform with the same parameters to multiple images like this:
-
-.. code:: python
-
-    import torchvision.transforms.functional as TF
-    import random
-
-    def my_segmentation_transforms(image, segmentation):
-        if random.random() > 0.5:
-            angle = random.randint(-30, 30)
-            image = TF.rotate(image, angle)
-            segmentation = TF.rotate(segmentation, angle)
-        # more transforms ...
-        return image, segmentation
-
-
-Example:
-you can use a functional transform to build transform classes with custom behavior:
-
-.. code:: python
-
-    import torchvision.transforms.functional as TF
-    import random
-
-    class MyRotationTransform:
-        """Rotate by one of the given angles."""
-
-        def __init__(self, angles):
-            self.angles = angles
-
-        def __call__(self, x):
-            angle = random.choice(self.angles)
-            return TF.rotate(x, angle)
-
-    rotation_transform = MyRotationTransform(angles=[-30, -15, 0, 15, 30])
-
-
-.. automodule:: torchvision.transforms.functional
-    :members:

docs/source/transforms_functional.rst

@@ -0,0 +1,52 @@
+torchvision.transforms.functional
+=================================
+
+Functional transforms give you fine-grained control of the transformation
+pipeline.
+As opposed to the stateful transformations from :mod:`torchvision.transforms`
+functional transforms don't contain any state or random number generator for
+their parameters.
+That means that you have to specify/generate all parameters,
+but you can reuse a functional transform.
+
+Example:
+you can apply a functional transform with the same parameters to multiple
+images like this:
+
+.. code:: python
+
+    import torchvision.transforms.functional as TF
+    import random
+
+    def my_segmentation_transforms(image, segmentation):
+        if random.random() > 0.5:
+            angle = random.randint(-30, 30)
+            image = TF.rotate(image, angle)
+            segmentation = TF.rotate(segmentation, angle)
+        # more transforms ...
+        return image, segmentation
+
+
+Example:
+you can use a functional transform to build transform classes with custom behavior:
+
+.. code:: python
+
+    import torchvision.transforms.functional as TF
+    import random
+
+    class MyRotationTransform:
+        """Rotate by one of the given angles."""
+
+        def __init__(self, angles):
+            self.angles = angles
+
+        def __call__(self, x):
+            angle = random.choice(self.angles)
+            return TF.rotate(x, angle)
+
+    rotation_transform = MyRotationTransform(angles=[-30, -15, 0, 15, 30])
+
+
+.. automodule:: torchvision.transforms.functional
+    :members: