Merge branch 'main' into patch-1

.github/workflows/docs.yml

@@ -57,7 +57,7 @@ jobs:
         sed -i -e 's/-j auto/-j 1/' Makefile
         make html
 
-        # Below is an imperfect way for us to add "try on collab" links to all of our gallery examples.
+        # Below is an imperfect way for us to add "try on Colab" links to all of our gallery examples.
         # sphinx-gallery will convert all gallery examples to .ipynb notebooks and stores them in
         # build/html/_downloads/<some_hash>/<example_name>.ipynb
         # We copy all those ipynb files in a more convenient folder so that we can more easily link to them.

README.md

@@ -21,6 +21,7 @@ versions.
 | `torch`            | `torchvision`      | Python              |
 | ------------------ | ------------------ | ------------------- |
 | `main` / `nightly` | `main` / `nightly` | `>=3.8`, `<=3.12`   |
+| `2.4`              | `0.19`             | `>=3.8`, `<=3.12`   |
 | `2.3`              | `0.18`             | `>=3.8`, `<=3.12`   |
 | `2.2`              | `0.17`             | `>=3.8`, `<=3.11`   |
 | `2.1`              | `0.16`             | `>=3.8`, `<=3.11`   |

docs/source/conf.py

@@ -59,7 +59,7 @@
 # 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.
+# This is because we also want to add a link to google Colab, so we write our own note in each example.
 from sphinx_gallery import gen_rst
 
 gen_rst.EXAMPLE_HEADER = """

gallery/others/plot_optical_flow.py

@@ -4,7 +4,7 @@
 =====================================================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_optical_flow.ipynb>`_
+    Try on `Colab <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

gallery/others/plot_repurposing_annotations.py

@@ -4,7 +4,7 @@
 =====================================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_repurposing_annotations.ipynb>`_
+    Try on `Colab <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
@@ -149,8 +149,8 @@ def show(imgs):
 model = fasterrcnn_resnet50_fpn(weights=weights, progress=False)
 print(img.size())
 
-tranforms = weights.transforms()
-img = tranforms(img)
+transforms = weights.transforms()
+img = transforms(img)
 target = {}
 target["boxes"] = boxes
 target["labels"] = labels = torch.ones((masks.size(0),), dtype=torch.int64)

gallery/others/plot_scripted_tensor_transforms.py

@@ -4,7 +4,7 @@
 ===================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_scripted_tensor_transforms.ipynb>`_
+    Try on `Colab <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 `torchscript
@@ -26,7 +26,7 @@
 plt.rcParams["savefig.bbox"] = 'tight'
 torch.manual_seed(1)
 
-# If you're trying to run that on collab, you can download the assets and the
+# If you're trying to run that on Colab, you can download the assets and the
 # helpers from https://github.com/pytorch/vision/tree/main/gallery/
 import sys
 sys.path += ["../transforms"]

gallery/others/plot_video_api.py

@@ -4,7 +4,7 @@
 =========
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_video_api.ipynb>`_
+    Try on `Colab <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

gallery/others/plot_visualization_utils.py

@@ -4,7 +4,7 @@
 =======================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_visualization_utils.ipynb>`_
+    Try on `Colab <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

gallery/transforms/plot_custom_transforms.py

@@ -4,7 +4,7 @@
 ===================================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_transforms.ipynb>`_
+    Try on `Colab <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_transforms_plot_custom_transforms.py>` to download the full example code.
 
 This guide explains how to write transforms that are compatible with the

gallery/transforms/plot_custom_tv_tensors.py

@@ -4,7 +4,7 @@
 ====================================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_tv_tensors.ipynb>`_
+    Try on `Colab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_tv_tensors.ipynb>`_
     or :ref:`go to the end <sphx_glr_download_auto_examples_transforms_plot_custom_tv_tensors.py>` to download the full example code.
 
 This guide is intended for advanced users and downstream library maintainers. We explain how to

gallery/transforms/plot_cutmix_mixup.py

@@ -5,7 +5,7 @@
 ===========================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_cutmix_mixup.ipynb>`_
+    Try on `Colab <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_transforms_plot_cutmix_mixup.py>` to download the full example code.
 
 :class:`~torchvision.transforms.v2.CutMix` and

gallery/transforms/plot_transforms_e2e.py

@@ -4,7 +4,7 @@
 ===============================================================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_transforms_e2e.ipynb>`_
+    Try on `Colab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_transforms_e2e.ipynb>`_
     or :ref:`go to the end <sphx_glr_download_auto_examples_transforms_plot_transforms_e2e.py>` to download the full example code.
 
 Object detection and segmentation tasks are natively supported:
@@ -30,7 +30,7 @@
 
 # This loads fake data for illustration purposes of this example. In practice, you'll have
 # to replace this with the proper data.
-# If you're trying to run that on collab, you can download the assets and the
+# If you're trying to run that on Colab, you can download the assets and the
 # helpers from https://github.com/pytorch/vision/tree/main/gallery/
 ROOT = pathlib.Path("../assets") / "coco"
 IMAGES_PATH = str(ROOT / "images")

gallery/transforms/plot_transforms_getting_started.py

@@ -4,7 +4,7 @@
 ==================================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_transforms_getting_started.ipynb>`_
+    Try on `Colab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_transforms_getting_started.ipynb>`_
     or :ref:`go to the end <sphx_glr_download_auto_examples_transforms_plot_transforms_getting_started.py>` to download the full example code.
 
 This example illustrates all of what you need to know to get started with the
@@ -25,7 +25,7 @@
 
 torch.manual_seed(1)
 
-# If you're trying to run that on collab, you can download the assets and the
+# If you're trying to run that on Colab, you can download the assets and the
 # helpers from https://github.com/pytorch/vision/tree/main/gallery/
 from helpers import plot
 img = read_image(str(Path('../assets') / 'astronaut.jpg'))

gallery/transforms/plot_transforms_illustrations.py

@@ -4,7 +4,7 @@
 ==========================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_transforms_illustrations.ipynb>`_
+    Try on `Colab <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 some of the various transforms available in :ref:`the
@@ -27,7 +27,7 @@
 # properly show that the image can be both transformed and *not* transformed!
 torch.manual_seed(0)
 
-# If you're trying to run that on collab, you can download the assets and the
+# If you're trying to run that on Colab, 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')

gallery/transforms/plot_tv_tensors.py

@@ -4,7 +4,7 @@
 =============
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_tv_tensors.ipynb>`_
+    Try on `Colab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_tv_tensors.ipynb>`_
     or :ref:`go to the end <sphx_glr_download_auto_examples_transforms_plot_tv_tensors.py>` to download the full example code.
 
 

packaging/pre_build_script.sh

@@ -29,21 +29,8 @@ else
     conda install -yq ffmpeg=4.2 libjpeg-turbo -c pytorch-nightly
   fi
 
-  # Install native CentOS libJPEG, freetype and GnuTLS
   yum install -y libjpeg-turbo-devel freetype gnutls
-
-  # Download all the dependencies required to compile image and video_reader
-  # extensions
-  mkdir -p ext_libraries
-  pushd ext_libraries
-  popd
-  export PATH="$(pwd)/ext_libraries/bin:$PATH"
   pip install auditwheel
-
-  # Point to custom libraries
-  export LD_LIBRARY_PATH=$(pwd)/ext_libraries/lib:$LD_LIBRARY_PATH
-  export TORCHVISION_INCLUDE=$(pwd)/ext_libraries/include
-  export TORCHVISION_LIBRARY=$(pwd)/ext_libraries/lib
 fi
 
 pip install numpy pyyaml future ninja

references/classification/README.md

@@ -135,7 +135,7 @@ torchrun --nproc_per_node=8 train.py\
      --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\
      --lr-warmup-epochs=5 --lr-warmup-decay=0.1
 ```
-Here `$MODEL` is one of `regnet_x_400mf`, `regnet_x_800mf`, `regnet_x_1_6gf`, `regnet_y_400mf`, `regnet_y_800mf` and `regnet_y_1_6gf`. Please note we used learning rate 0.4 for `regent_y_400mf` to get the same Acc@1 as [the paper)(https://arxiv.org/abs/2003.13678).
+Here `$MODEL` is one of `regnet_x_400mf`, `regnet_x_800mf`, `regnet_x_1_6gf`, `regnet_y_400mf`, `regnet_y_800mf` and `regnet_y_1_6gf`. Please note we used learning rate 0.4 for `regent_y_400mf` to get the same Acc@1 as [the paper](https://arxiv.org/abs/2003.13678).
 
 #### Medium models
 ```