Add auto-augment introduced transforms in docs (#3793)

* Add auto-augment introduced transforms in docs.

* Adding examples in Gallery.

* Make posterize more extreme.

docs/source/transforms.rst

@@ -136,6 +136,24 @@ Transforms on PIL Image and torch.\*Tensor
 .. autoclass:: GaussianBlur
     :members:
 
+.. autoclass:: RandomInvert
+    :members:
+
+.. autoclass:: RandomPosterize
+    :members:
+
+.. autoclass:: RandomSolarize
+    :members:
+
+.. autoclass:: RandomAdjustSharpness
+    :members:
+
+.. autoclass:: RandomAutocontrast
+    :members:
+
+.. autoclass:: RandomEqualize
+    :members:
+
 .. _transforms_pil_only:
 
 Transforms on PIL Image only

gallery/plot_transforms.py

@@ -167,6 +167,68 @@ resize_cropper = T.RandomResizedCrop(size=(32, 32))
 resized_crops = [resize_cropper(orig_img) for _ in range(4)]
 plot(resized_crops)
 
+####################################
+# RandomInvert
+# ~~~~~~~~~~~~
+# The :class:`~torchvision.transforms.RandomInvert` transform
+# (see also :func:`~torchvision.transforms.functional.invert`)
+# randomly inverts the colors of the given image.
+inverter = T.RandomInvert()
+invertered_imgs = [inverter(orig_img) for _ in range(4)]
+plot(invertered_imgs)
+
+####################################
+# RandomPosterize
+# ~~~~~~~~~~~~~~~
+# The :class:`~torchvision.transforms.RandomPosterize` transform
+# (see also :func:`~torchvision.transforms.functional.posterize`)
+# randomly posterizes the image by reducing the number of bits
+# of each color channel.
+posterizer = T.RandomPosterize(bits=2)
+posterized_imgs = [posterizer(orig_img) for _ in range(4)]
+plot(posterized_imgs)
+
+####################################
+# RandomSolarize
+# ~~~~~~~~~~~~~~
+# The :class:`~torchvision.transforms.RandomSolarize` transform
+# (see also :func:`~torchvision.transforms.functional.solarize`)
+# randomly solarizes the image by inverting all pixel values above
+# the threshold.
+solarizer = T.RandomSolarize(threshold=192.0)
+solarized_imgs = [solarizer(orig_img) for _ in range(4)]
+plot(solarized_imgs)
+
+####################################
+# RandomAdjustSharpness
+# ~~~~~~~~~~~~~~~~~~~~~
+# The :class:`~torchvision.transforms.RandomAdjustSharpness` transform
+# (see also :func:`~torchvision.transforms.functional.adjust_sharpness`)
+# randomly adjusts the sharpness of the given image.
+sharpness_adjuster = T.RandomAdjustSharpness(sharpness_factor=2)
+sharpened_imgs = [sharpness_adjuster(orig_img) for _ in range(4)]
+plot(sharpened_imgs)
+
+####################################
+# RandomAutocontrast
+# ~~~~~~~~~~~~~~~~~~
+# The :class:`~torchvision.transforms.RandomAutocontrast` transform
+# (see also :func:`~torchvision.transforms.functional.autocontrast`)
+# randomly applies autocontrast to the given image.
+autocontraster = T.RandomAutocontrast()
+autocontrasted_imgs = [autocontraster(orig_img) for _ in range(4)]
+plot(autocontrasted_imgs)
+
+####################################
+# RandomEqualize
+# ~~~~~~~~~~~~~~
+# The :class:`~torchvision.transforms.RandomEqualize` transform
+# (see also :func:`~torchvision.transforms.functional.equalize`)
+# randomly equalizes the histogram of the given image.
+equalizer = T.RandomEqualize()
+equalized_imgs = [equalizer(orig_img) for _ in range(4)]
+plot(equalized_imgs)
+
 ####################################
 # AutoAugment
 # ~~~~~~~~~~~