Make transforms illutration example use v2 instead of v1 (#7886)

docs/source/conf.py

@@ -84,6 +84,7 @@ class CustomGalleryExampleSortKey:
 
     transforms_subsection_order = [
         "plot_transforms_getting_started.py",
+        "plot_transforms_illustrations.py",
         "plot_transforms_e2e.py",
         "plot_cutmix_mixup.py",
         "plot_custom_transforms.py",

gallery/others/plot_scripted_tensor_transforms.py

@@ -62,7 +62,7 @@ show([dog1, dog2])
 # --------------------------
 # Most transforms natively support tensors on top of PIL images (to visualize
 # the effect of the transforms, you may refer to see
-# :ref:`sphx_glr_auto_examples_others_plot_transforms.py`).
+# :ref:`sphx_glr_auto_examples_transforms_plot_transforms_illustrations.py`).
 # Using tensor images, we can run the transforms on GPUs if cuda is available!
 
 import torch.nn as nn

gallery/transforms/helpers.py

@@ -5,7 +5,7 @@ from torchvision import datapoints
 from torchvision.transforms.v2 import functional as F
 
 
-def plot(imgs):
+def plot(imgs, row_title=None, **imshow_kwargs):
     if not isinstance(imgs[0], list):
         # Make a 2d grid even if there's just 1 row
         imgs = [imgs]
@@ -40,7 +40,11 @@ def plot(imgs):
                 img = draw_segmentation_masks(img, masks.to(torch.bool), colors=["green"] * masks.shape[0], alpha=.65)
 
             ax = axs[row_idx, col_idx]
-            ax.imshow(img.permute(1, 2, 0).numpy())
+            ax.imshow(img.permute(1, 2, 0).numpy(), **imshow_kwargs)
             ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
 
+    if row_title is not None:
+        for row_idx in range(num_rows):
+            axs[row_idx, 0].set(ylabel=row_title[row_idx])
+
     plt.tight_layout()

gallery/others/plot_transforms.py → gallery/transforms/plot_transforms_illustrations.py

@@ -4,55 +4,33 @@ Illustration of transforms
 ==========================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_transforms.ipynb>`_
-    or :ref:`go to the end <sphx_glr_download_auto_examples_others_plot_transforms.py>` to download the full example code.
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_transforms_illustrations.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_transforms_plot_transforms_illustrations.py>` to download the full example code.
 
-This example illustrates the various transforms available in :ref:`the
-torchvision.transforms module <transforms>`.
+This example illustrates some of the various transforms available in :ref:`the
+torchvision.transforms.v2 module <transforms>`.
 """
+# %%
 
 # sphinx_gallery_thumbnail_path = "../../gallery/assets/transforms_thumbnail.png"
 
 from PIL import Image
 from pathlib import Path
 import matplotlib.pyplot as plt
-import numpy as np
 
 import torch
-import torchvision.transforms as T
-
+from torchvision.transforms import v2
 
 plt.rcParams["savefig.bbox"] = 'tight'
-orig_img = Image.open(Path('../assets') / 'astronaut.jpg')
+
 # if you change the seed, make sure that the randomly-applied transforms
 # properly show that the image can be both transformed and *not* transformed!
 torch.manual_seed(0)
 
-
-def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
-    if not isinstance(imgs[0], list):
-        # Make a 2d grid even if there's just 1 row
-        imgs = [imgs]
-
-    num_rows = len(imgs)
-    num_cols = len(imgs[0]) + with_orig
-    fig, axs = plt.subplots(nrows=num_rows, ncols=num_cols, squeeze=False)
-    for row_idx, row in enumerate(imgs):
-        row = [orig_img] + row if with_orig else row
-        for col_idx, img in enumerate(row):
-            ax = axs[row_idx, col_idx]
-            ax.imshow(np.asarray(img), **imshow_kwargs)
-            ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
-
-    if with_orig:
-        axs[0, 0].set(title='Original image')
-        axs[0, 0].title.set_size(8)
-    if row_title is not None:
-        for row_idx in range(num_rows):
-            axs[row_idx, 0].set(ylabel=row_title[row_idx])
-
-    plt.tight_layout()
-
+# If you're trying to run that on collab, you can download the assets and the
+# helpers from https://github.com/pytorch/vision/tree/main/gallery/
+from helpers import plot
+orig_img = Image.open(Path('../assets') / 'astronaut.jpg')
 
 # %%
 # Geometric Transforms
@@ -66,8 +44,8 @@ def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
 # The :class:`~torchvision.transforms.Pad` transform
 # (see also :func:`~torchvision.transforms.functional.pad`)
 # pads all image borders with some pixel values.
-padded_imgs = [T.Pad(padding=padding)(orig_img) for padding in (3, 10, 30, 50)]
-plot(padded_imgs)
+padded_imgs = [v2.Pad(padding=padding)(orig_img) for padding in (3, 10, 30, 50)]
+plot([orig_img] + padded_imgs)
 
 # %%
 # Resize
@@ -75,8 +53,8 @@ plot(padded_imgs)
 # The :class:`~torchvision.transforms.Resize` transform
 # (see also :func:`~torchvision.transforms.functional.resize`)
 # resizes an image.
-resized_imgs = [T.Resize(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
-plot(resized_imgs)
+resized_imgs = [v2.Resize(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
+plot([orig_img] + resized_imgs)
 
 # %%
 # CenterCrop
@@ -84,8 +62,8 @@ plot(resized_imgs)
 # The :class:`~torchvision.transforms.CenterCrop` transform
 # (see also :func:`~torchvision.transforms.functional.center_crop`)
 # crops the given image at the center.
-center_crops = [T.CenterCrop(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
-plot(center_crops)
+center_crops = [v2.CenterCrop(size=size)(orig_img) for size in (30, 50, 100, orig_img.size)]
+plot([orig_img] + center_crops)
 
 # %%
 # FiveCrop
@@ -93,8 +71,8 @@ plot(center_crops)
 # The :class:`~torchvision.transforms.FiveCrop` transform
 # (see also :func:`~torchvision.transforms.functional.five_crop`)
 # crops the given image into four corners and the central crop.
-(top_left, top_right, bottom_left, bottom_right, center) = T.FiveCrop(size=(100, 100))(orig_img)
-plot([top_left, top_right, bottom_left, bottom_right, center])
+(top_left, top_right, bottom_left, bottom_right, center) = v2.FiveCrop(size=(100, 100))(orig_img)
+plot([orig_img] + [top_left, top_right, bottom_left, bottom_right, center])
 
 # %%
 # RandomPerspective
@@ -102,9 +80,9 @@ plot([top_left, top_right, bottom_left, bottom_right, center])
 # The :class:`~torchvision.transforms.RandomPerspective` transform
 # (see also :func:`~torchvision.transforms.functional.perspective`)
 # performs random perspective transform on an image.
-perspective_transformer = T.RandomPerspective(distortion_scale=0.6, p=1.0)
+perspective_transformer = v2.RandomPerspective(distortion_scale=0.6, p=1.0)
 perspective_imgs = [perspective_transformer(orig_img) for _ in range(4)]
-plot(perspective_imgs)
+plot([orig_img] + perspective_imgs)
 
 # %%
 # RandomRotation
@@ -112,9 +90,9 @@ plot(perspective_imgs)
 # The :class:`~torchvision.transforms.RandomRotation` transform
 # (see also :func:`~torchvision.transforms.functional.rotate`)
 # rotates an image with random angle.
-rotater = T.RandomRotation(degrees=(0, 180))
+rotater = v2.RandomRotation(degrees=(0, 180))
 rotated_imgs = [rotater(orig_img) for _ in range(4)]
-plot(rotated_imgs)
+plot([orig_img] + rotated_imgs)
 
 # %%
 # RandomAffine
@@ -122,9 +100,9 @@ plot(rotated_imgs)
 # The :class:`~torchvision.transforms.RandomAffine` transform
 # (see also :func:`~torchvision.transforms.functional.affine`)
 # performs random affine transform on an image.
-affine_transfomer = T.RandomAffine(degrees=(30, 70), translate=(0.1, 0.3), scale=(0.5, 0.75))
+affine_transfomer = v2.RandomAffine(degrees=(30, 70), translate=(0.1, 0.3), scale=(0.5, 0.75))
 affine_imgs = [affine_transfomer(orig_img) for _ in range(4)]
-plot(affine_imgs)
+plot([orig_img] + affine_imgs)
 
 # %%
 # ElasticTransform
@@ -133,9 +111,9 @@ plot(affine_imgs)
 # (see also :func:`~torchvision.transforms.functional.elastic_transform`)
 # Randomly transforms the morphology of objects in images and produces a
 # see-through-water-like effect.
-elastic_transformer = T.ElasticTransform(alpha=250.0)
+elastic_transformer = v2.ElasticTransform(alpha=250.0)
 transformed_imgs = [elastic_transformer(orig_img) for _ in range(2)]
-plot(transformed_imgs)
+plot([orig_img] + transformed_imgs)
 
 # %%
 # RandomCrop
@@ -143,9 +121,9 @@ plot(transformed_imgs)
 # The :class:`~torchvision.transforms.RandomCrop` transform
 # (see also :func:`~torchvision.transforms.functional.crop`)
 # crops an image at a random location.
-cropper = T.RandomCrop(size=(128, 128))
+cropper = v2.RandomCrop(size=(128, 128))
 crops = [cropper(orig_img) for _ in range(4)]
-plot(crops)
+plot([orig_img] + crops)
 
 # %%
 # RandomResizedCrop
@@ -154,9 +132,9 @@ plot(crops)
 # (see also :func:`~torchvision.transforms.functional.resized_crop`)
 # crops an image at a random location, and then resizes the crop to a given
 # size.
-resize_cropper = T.RandomResizedCrop(size=(32, 32))
+resize_cropper = v2.RandomResizedCrop(size=(32, 32))
 resized_crops = [resize_cropper(orig_img) for _ in range(4)]
-plot(resized_crops)
+plot([orig_img] + resized_crops)
 
 # %%
 # Photometric Transforms
@@ -175,17 +153,17 @@ plot(resized_crops)
 # The :class:`~torchvision.transforms.Grayscale` transform
 # (see also :func:`~torchvision.transforms.functional.to_grayscale`)
 # converts an image to grayscale
-gray_img = T.Grayscale()(orig_img)
-plot([gray_img], cmap='gray')
+gray_img = v2.Grayscale()(orig_img)
+plot([orig_img, gray_img], cmap='gray')
 
 # %%
 # ColorJitter
 # ~~~~~~~~~~~
 # The :class:`~torchvision.transforms.ColorJitter` transform
 # randomly changes the brightness, contrast, saturation, hue, and other properties of an image.
-jitter = T.ColorJitter(brightness=.5, hue=.3)
-jitted_imgs = [jitter(orig_img) for _ in range(4)]
-plot(jitted_imgs)
+jitter = v2.ColorJitter(brightness=.5, hue=.3)
+jittered_imgs = [jitter(orig_img) for _ in range(4)]
+plot([orig_img] + jittered_imgs)
 
 # %%
 # GaussianBlur
@@ -193,9 +171,9 @@ plot(jitted_imgs)
 # The :class:`~torchvision.transforms.GaussianBlur` transform
 # (see also :func:`~torchvision.transforms.functional.gaussian_blur`)
 # performs gaussian blur transform on an image.
-blurrer = T.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5))
+blurrer = v2.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5.))
 blurred_imgs = [blurrer(orig_img) for _ in range(4)]
-plot(blurred_imgs)
+plot([orig_img] + blurred_imgs)
 
 # %%
 # RandomInvert
@@ -203,9 +181,9 @@ plot(blurred_imgs)
 # The :class:`~torchvision.transforms.RandomInvert` transform
 # (see also :func:`~torchvision.transforms.functional.invert`)
 # randomly inverts the colors of the given image.
-inverter = T.RandomInvert()
+inverter = v2.RandomInvert()
 invertered_imgs = [inverter(orig_img) for _ in range(4)]
-plot(invertered_imgs)
+plot([orig_img] + invertered_imgs)
 
 # %%
 # RandomPosterize
@@ -214,9 +192,9 @@ plot(invertered_imgs)
 # (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)
+posterizer = v2.RandomPosterize(bits=2)
 posterized_imgs = [posterizer(orig_img) for _ in range(4)]
-plot(posterized_imgs)
+plot([orig_img] + posterized_imgs)
 
 # %%
 # RandomSolarize
@@ -225,9 +203,9 @@ plot(posterized_imgs)
 # (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)
+solarizer = v2.RandomSolarize(threshold=192.0)
 solarized_imgs = [solarizer(orig_img) for _ in range(4)]
-plot(solarized_imgs)
+plot([orig_img] + solarized_imgs)
 
 # %%
 # RandomAdjustSharpness
@@ -235,9 +213,9 @@ plot(solarized_imgs)
 # 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)
+sharpness_adjuster = v2.RandomAdjustSharpness(sharpness_factor=2)
 sharpened_imgs = [sharpness_adjuster(orig_img) for _ in range(4)]
-plot(sharpened_imgs)
+plot([orig_img] + sharpened_imgs)
 
 # %%
 # RandomAutocontrast
@@ -245,9 +223,9 @@ plot(sharpened_imgs)
 # The :class:`~torchvision.transforms.RandomAutocontrast` transform
 # (see also :func:`~torchvision.transforms.functional.autocontrast`)
 # randomly applies autocontrast to the given image.
-autocontraster = T.RandomAutocontrast()
+autocontraster = v2.RandomAutocontrast()
 autocontrasted_imgs = [autocontraster(orig_img) for _ in range(4)]
-plot(autocontrasted_imgs)
+plot([orig_img] + autocontrasted_imgs)
 
 # %%
 # RandomEqualize
@@ -255,9 +233,9 @@ plot(autocontrasted_imgs)
 # The :class:`~torchvision.transforms.RandomEqualize` transform
 # (see also :func:`~torchvision.transforms.functional.equalize`)
 # randomly equalizes the histogram of the given image.
-equalizer = T.RandomEqualize()
+equalizer = v2.RandomEqualize()
 equalized_imgs = [equalizer(orig_img) for _ in range(4)]
-plot(equalized_imgs)
+plot([orig_img] + equalized_imgs)
 
 # %%
 # Augmentation Transforms
@@ -270,22 +248,22 @@ plot(equalized_imgs)
 # 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]
-augmenters = [T.AutoAugment(policy) for policy in policies]
+policies = [v2.AutoAugmentPolicy.CIFAR10, v2.AutoAugmentPolicy.IMAGENET, v2.AutoAugmentPolicy.SVHN]
+augmenters = [v2.AutoAugment(policy) for policy in policies]
 imgs = [
     [augmenter(orig_img) for _ in range(4)]
     for augmenter in augmenters
 ]
 row_title = [str(policy).split('.')[-1] for policy in policies]
-plot(imgs, row_title=row_title)
+plot([[orig_img] + row for row in imgs], row_title=row_title)
 
 # %%
 # RandAugment
 # ~~~~~~~~~~~
 # The :class:`~torchvision.transforms.RandAugment` is an alternate version of AutoAugment.
-augmenter = T.RandAugment()
+augmenter = v2.RandAugment()
 imgs = [augmenter(orig_img) for _ in range(4)]
-plot(imgs)
+plot([orig_img] + imgs)
 
 # %%
 # TrivialAugmentWide
@@ -293,17 +271,17 @@ plot(imgs)
 # The :class:`~torchvision.transforms.TrivialAugmentWide` is an alternate implementation of AutoAugment.
 # However, instead of transforming an image multiple times, it transforms an image only once
 # using a random transform from a given list with a random strength number.
-augmenter = T.TrivialAugmentWide()
+augmenter = v2.TrivialAugmentWide()
 imgs = [augmenter(orig_img) for _ in range(4)]
-plot(imgs)
+plot([orig_img] + imgs)
 
 # %%
 # AugMix
 # ~~~~~~
 # The :class:`~torchvision.transforms.AugMix` transform interpolates between augmented versions of an image.
-augmenter = T.AugMix()
+augmenter = v2.AugMix()
 imgs = [augmenter(orig_img) for _ in range(4)]
-plot(imgs)
+plot([orig_img] + imgs)
 
 # %%
 # Randomly-applied Transforms
@@ -318,9 +296,9 @@ plot(imgs)
 # The :class:`~torchvision.transforms.RandomHorizontalFlip` transform
 # (see also :func:`~torchvision.transforms.functional.hflip`)
 # performs horizontal flip of an image, with a given probability.
-hflipper = T.RandomHorizontalFlip(p=0.5)
+hflipper = v2.RandomHorizontalFlip(p=0.5)
 transformed_imgs = [hflipper(orig_img) for _ in range(4)]
-plot(transformed_imgs)
+plot([orig_img] + transformed_imgs)
 
 # %%
 # RandomVerticalFlip
@@ -328,15 +306,15 @@ plot(transformed_imgs)
 # The :class:`~torchvision.transforms.RandomVerticalFlip` transform
 # (see also :func:`~torchvision.transforms.functional.vflip`)
 # performs vertical flip of an image, with a given probability.
-vflipper = T.RandomVerticalFlip(p=0.5)
+vflipper = v2.RandomVerticalFlip(p=0.5)
 transformed_imgs = [vflipper(orig_img) for _ in range(4)]
-plot(transformed_imgs)
+plot([orig_img] + transformed_imgs)
 
 # %%
 # RandomApply
 # ~~~~~~~~~~~
 # The :class:`~torchvision.transforms.RandomApply` transform
 # randomly applies a list of transforms, with a given probability.
-applier = T.RandomApply(transforms=[T.RandomCrop(size=(64, 64))], p=0.5)
+applier = v2.RandomApply(transforms=[v2.RandomCrop(size=(64, 64))], p=0.5)
 transformed_imgs = [applier(orig_img) for _ in range(4)]
-plot(transformed_imgs)
+plot([orig_img] + transformed_imgs)