Add examples to all transform classes

Author: sotte

docs/source/transforms.rst

@@ -9,51 +9,274 @@ Functional transforms give fine-grained control over the transformations.
 This is useful if you have to build a more complex transformation pipeline
 (e.g. in the case of segmentation tasks).
 
+.. Note::
+    Most transform classese have an equivalent in :mod:`torchvision.transforms.functional`.
+
+
 .. autoclass:: Compose
 
 Transforms on PIL Image
 -----------------------
 
 .. autoclass:: CenterCrop
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import CenterCrop
+    from torchvision.utils import _plot_images, _sample_image
+
+    _plot_images(
+        _sample_image(),
+        CenterCrop(256)(_sample_image()),
+        CenterCrop((200, 300))(_sample_image()),
+    )
+
+
 .. autoclass:: ColorJitter
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import ColorJitter
+    from torchvision.utils import _plot_images, _sample_image
+
+    transform = ColorJitter(brightness=0.5)
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+    transform = ColorJitter(contrast=0.5)
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+    transform = ColorJitter(saturation=0.5)
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+    transform = ColorJitter(hue=0.1)
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+
 .. autoclass:: FiveCrop
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import FiveCrop
+    from torchvision.utils import _plot_images, _sample_image
+
+    transform = FiveCrop(256)
+    _plot_images(*transform(_sample_image()))
+
+
 .. autoclass:: Grayscale
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import Grayscale
+    from torchvision.utils import _plot_images, _sample_image
+
+    transform = Grayscale(3)
+    _plot_images(transform(_sample_image()))
+
+
 .. autoclass:: Pad
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import Pad
+    from torchvision.utils import _plot_images, _sample_image
+
+    transform = Pad(100)
+    _plot_images(
+        Pad(100, padding_mode="constant", fill=0)(_sample_image()),
+        Pad(100, padding_mode="constant", fill=(128, 128, 128))(_sample_image()),
+        Pad(100, padding_mode="constant", fill=(255, 255, 255))(_sample_image()),
+        Pad(100, padding_mode="edge")(_sample_image()),
+        Pad(100, padding_mode="reflect")(_sample_image()),
+        Pad(100, padding_mode="symmetric")(_sample_image()),
+    )
+
+
 .. autoclass:: RandomAffine
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import RandomAffine
+    from torchvision.utils import _plot_images, _sample_image
+
+    transform = RandomAffine(degrees=30)
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+    transform = RandomAffine(degrees=0, translate=(.4, .2))
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+    transform = RandomAffine(degrees=0, scale=(.5, 1.5))
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+    transform = RandomAffine(degrees=0, shear=30)
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+
+
 .. autoclass:: RandomApply
 
 .. autoclass:: RandomChoice
 
 .. autoclass:: RandomCrop
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import RandomCrop
+    from torchvision.utils import _plot_images, _sample_image
+
+    transform = RandomCrop(150)
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+
 .. autoclass:: RandomGrayscale
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import RandomGrayscale
+    from torchvision.utils import _plot_images, _sample_image
+
+    _plot_images(
+        RandomGrayscale(p=1.0)(_sample_image()),
+        RandomGrayscale(p=0.0)(_sample_image()),
+    )
+
+
 .. autoclass:: RandomHorizontalFlip
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import RandomHorizontalFlip
+    from torchvision.utils import _plot_images, _sample_image
+
+    _plot_images(
+        RandomHorizontalFlip(p=1.0)(_sample_image()),
+        RandomHorizontalFlip(p=0.0)(_sample_image()),
+    )
+
+
 .. autoclass:: RandomOrder
 
 .. autoclass:: RandomPerspective
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import RandomPerspective
+    from torchvision.utils import _plot_images, _sample_image
+
+    transform = RandomPerspective(p=1.0)
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+
 .. autoclass:: RandomResizedCrop
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import RandomResizedCrop
+    from torchvision.utils import _plot_images, _sample_image
+
+    transform = RandomResizedCrop(250)
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+
 .. autoclass:: RandomRotation
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import RandomRotation
+    from torchvision.utils import _plot_images, _sample_image
+
+    transform = RandomRotation(30)
+    _plot_images(*[transform(_sample_image()) for _ in range(5)])
+
+
 .. autoclass:: RandomSizedCrop
 
+
 .. autoclass:: RandomVerticalFlip
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import RandomVerticalFlip
+    from torchvision.utils import _plot_images, _sample_image
+
+    transform = RandomVerticalFlip()
+    _plot_images(
+        RandomVerticalFlip(p=0.0)(_sample_image()),
+        RandomVerticalFlip(p=1.0)(_sample_image()),
+    )
+
+
 .. autoclass:: Resize
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import Resize
+    from torchvision.utils import _plot_images, _sample_image
+
+    _plot_images(
+        Resize(200)(_sample_image()),
+        Resize((200, 200))(_sample_image()),
+        Resize(32)(_sample_image()),
+    )
+
+
 .. autoclass:: Scale
 
 .. autoclass:: TenCrop
 
+**Example**
+
+.. plot::
+   :include-source:
+
+    from torchvision.transforms import TenCrop
+    from torchvision.utils import _plot_images, _sample_image
+
+    _plot_images(*TenCrop(200)(_sample_image()))
+
+
 Transforms on torch.\*Tensor
 ----------------------------