Add illustrations for AutoAugment in gallery example (#3727)

Co-authored-by: Nicolas Hug <nicolashug@fb.com>

gallery/plot_transforms.py

@@ -178,3 +178,26 @@ plot(random_apply_img, "Random Apply transformed Image")
 # performs gaussianblur transform on an image.
 gaus_blur_img = T.GaussianBlur(kernel_size=(5, 9), sigma=(0.4, 3.0))(orig_img)
 plot(gaus_blur_img, "Gaussian Blurred Image")
+
+
+####################################
+# AutoAugment
+# -----------
+# The :class:`~torchvision.transforms.AutoAugment` transform
+# automatically augments data based on a given auto-augmentation policy.
+# See :class:`~torchvision.transforms.AutoAugmentPolicy` for the available policies.
+policies = [T.AutoAugmentPolicy.CIFAR10, T.AutoAugmentPolicy.IMAGENET, T.AutoAugmentPolicy.SVHN]
+num_cols = 5
+fig, axs = plt.subplots(nrows=len(policies), ncols=num_cols)
+fig.suptitle("Auto-augmented images with different policies")
+
+for pol_idx, policy in enumerate(policies):
+    auto_augmenter = T.AutoAugment(policy)
+    for col in range(num_cols):
+        augmented_img = auto_augmenter(orig_img)
+
+        ax = axs[pol_idx, col]
+        ax.imshow(np.asarray(augmented_img))
+        ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
+        if col == 0:
+            ax.set(ylabel=str(policy).split('.')[-1])

torchvision/transforms/autoaugment.py

@@ -142,16 +142,6 @@ class AutoAugment(torch.nn.Module):
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
         fill (sequence or number, optional): Pixel fill value for the area outside the transformed
             image. If given a number, the value is used for all bands respectively.
-            If input is PIL Image, the options is only available for ``Pillow>=5.0.0``.
-
-    Example:
-        >>> t = transforms.AutoAugment()
-        >>> transformed = t(image)
-
-        >>> transform=transforms.Compose([
-        >>>     transforms.Resize(256),
-        >>>     transforms.AutoAugment(),
-        >>>     transforms.ToTensor()])
     """
 
     def __init__(self, policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET,