Add links to collab

docs/source/conf.py

@@ -59,6 +59,26 @@
     "beta_status",
 ]
 
+# We override sphinx-gallery's example header to prevent sphinx-gallery from
+# creating a note at the top of the renderred notebook.
+# https://github.com/sphinx-gallery/sphinx-gallery/blob/451ccba1007cc523f39cbcc960ebc21ca39f7b75/sphinx_gallery/gen_rst.py#L1267-L1271
+# This is because we also want to add a link to google collab, so we write our own note in each example.
+from sphinx_gallery import gen_rst
+
+gen_rst.EXAMPLE_HEADER = """
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "{0}"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_{1}:
+
+"""
+
+
 sphinx_gallery_conf = {
     "examples_dirs": "../../gallery/",  # path to your example scripts
     "gallery_dirs": "auto_examples",  # path to where to save gallery generated output

gallery/others/plot_optical_flow.py

@@ -3,6 +3,10 @@
 Optical Flow: Predicting movement with the RAFT model
 =====================================================
 
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_optical_flow.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_others_plot_optical_flow.py>` to download the full example code.
+
 Optical flow is the task of predicting movement between two images, usually two
 consecutive frames of a video. Optical flow models take two images as input, and
 predict a flow: the flow indicates the displacement of every single pixel in the

gallery/others/plot_repurposing_annotations.py

@@ -3,6 +3,10 @@
 Repurposing masks into bounding boxes
 =====================================
 
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_repurposing_annotations.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_others_plot_repurposing_annotations.py>` to download the full example code.
+
 The following example illustrates the operations available
 the :ref:`torchvision.ops <ops>` module for repurposing
 segmentation masks into object localization annotations for different tasks

gallery/others/plot_scripted_tensor_transforms.py

@@ -3,6 +3,10 @@
 Tensor transforms and JIT
 =========================
 
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_scripted_tensor_transforms.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_others_plot_scripted_tensor_transforms.py>` to download the full example code.
+
 This example illustrates various features that are now supported by the
 :ref:`image transformations <transforms>` on Tensor images. In particular, we
 show how image transforms can be performed on GPU, and how one can also script

gallery/others/plot_transforms.py

@@ -3,6 +3,10 @@
 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.
+
 This example illustrates the various transforms available in :ref:`the
 torchvision.transforms module <transforms>`.
 """

gallery/others/plot_video_api.py

@@ -1,7 +1,11 @@
 """
-=======================
+=========
 Video API
-=======================
+=========
+
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_video_api.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_others_plot_video_api.py>` to download the full example code.
 
 This example illustrates some of the APIs that torchvision offers for
 videos, together with the examples on how to build datasets and more.

gallery/others/plot_visualization_utils.py

@@ -3,6 +3,10 @@
 Visualization utilities
 =======================
 
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_visualization_utils.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_others_plot_visualization_utils.py>` to download the full example code.
+
 This example illustrates some of the utilities that torchvision offers for
 visualizing images, bounding boxes, segmentation masks and keypoints.
 """

gallery/v2_transforms/plot_custom_datapoints.py

@@ -3,6 +3,10 @@
 How to write your own Datapoint class
 =====================================
 
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_datapoints.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_v2_transforms_plot_custom_datapoints.py>` to download the full example code.
+
 This guide is intended for advanced users and downstream library maintainers. We explain how to
 write your own datapoint class, and how to make it compatible with the built-in
 Torchvision v2 transforms. Before continuing, make sure you have read

gallery/v2_transforms/plot_custom_transforms.py

@@ -3,6 +3,10 @@
 How to write your own v2 transforms
 ===================================
 
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_transforms.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_v2_transforms_plot_custom_transforms.py>` to download the full example code.
+
 This guide explains how to write transforms that are compatible with the
 torchvision transforms V2 API.
 """

gallery/v2_transforms/plot_cutmix_mixup.py

@@ -4,6 +4,10 @@
 How to use CutMix and MixUp
 ===========================
 
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_cutmix_mixup.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_v2_transforms_plot_cutmix_mixup.py>` to download the full example code.
+
 :class:`~torchvision.transforms.v2.CutMix` and
 :class:`~torchvision.transforms.v2.MixUp` are popular augmentation strategies
 that can improve classification accuracy.

gallery/v2_transforms/plot_datapoints.py

@@ -3,7 +3,10 @@
 Datapoints FAQ
 ==============
 
-https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/_generated_ipynb_notebooks/plot_datapoints.ipynb
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_datapoints.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_v2_transforms_plot_datapoints.py>` to download the full example code.
+
 
 Datapoints are Tensor subclasses introduced together with
 ``torchvision.transforms.v2``. This example showcases what these datapoints are

gallery/v2_transforms/plot_transforms_v2.py

@@ -3,6 +3,10 @@
 Getting started with transforms v2
 ==================================
 
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_transforms_v2.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_v2_transforms_plot_transforms_v2.py>` to download the full example code.
+
 Most computer vision tasks are not supported out of the box by ``torchvision.transforms`` v1, since it only supports
 images. ``torchvision.transforms.v2`` enables jointly transforming images, videos, bounding boxes, and masks. This
 example showcases the core functionality of the new ``torchvision.transforms.v2`` API.

gallery/v2_transforms/plot_transforms_v2_e2e.py

@@ -3,6 +3,10 @@
 Transforms v2: End-to-end object detection example
 ==================================================
 
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_transforms_v2_e2e.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_v2_transforms_plot_transforms_v2_e2e.py>` to download the full example code.
+
 Object detection is not supported out of the box by ``torchvision.transforms`` v1, since it only supports images.
 ``torchvision.transforms.v2`` enables jointly transforming images, videos, bounding boxes, and masks. This example
 showcases an end-to-end object detection training using the stable ``torchvision.datasets`` and ``torchvision.models``