Merge branch 'main' into malfet/build-on-m1

Author: malfet

android/gradle.properties

@@ -1,6 +1,6 @@
 ABI_FILTERS=armeabi-v7a,arm64-v8a,x86,x86_64
 
-VERSION_NAME=0.13.0-SNAPSHOT
+VERSION_NAME=0.14.0-SNAPSHOT
 GROUP=org.pytorch
 MAVEN_GROUP=org.pytorch
 SONATYPE_STAGING_PROFILE=orgpytorch

docs/source/models.rst

@@ -471,7 +471,7 @@ Here is an example of how to use the pre-trained video classification models:
     from torchvision.io.video import read_video
     from torchvision.models.video import r3d_18, R3D_18_Weights
 
-    vid, _, _ = read_video("test/assets/videos/v_SoccerJuggling_g23_c01.avi")
+    vid, _, _ = read_video("test/assets/videos/v_SoccerJuggling_g23_c01.avi", output_format="TCHW")
     vid = vid[:32]  # optionally shorten duration
 
     # Step 1: Initialize model with the best available weights

gallery/plot_optical_flow.py

@@ -72,8 +72,7 @@ def plot(imgs, **imshow_kwargs):
 # single model input.
 
 from torchvision.io import read_video
-frames, _, _ = read_video(str(video_path))
-frames = frames.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
+frames, _, _ = read_video(str(video_path), output_format="TCHW")
 
 img1_batch = torch.stack([frames[100], frames[150]])
 img2_batch = torch.stack([frames[101], frames[151]])

packaging/build_cmake.sh

@@ -15,7 +15,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 . "$script_dir/pkg_helpers.bash"
 
 export BUILD_TYPE=conda
-setup_env 0.13.0
+setup_env 0.14.0
 export SOURCE_ROOT_DIR="$PWD"
 setup_conda_pytorch_constraint
 setup_conda_cudatoolkit_plain_constraint

packaging/build_conda.sh

@@ -5,7 +5,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 . "$script_dir/pkg_helpers.bash"
 
 export BUILD_TYPE=conda
-setup_env 0.13.0
+setup_env 0.14.0
 export SOURCE_ROOT_DIR="$PWD"
 setup_conda_pytorch_constraint
 setup_conda_cudatoolkit_constraint

packaging/build_wheel.sh

@@ -5,7 +5,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 . "$script_dir/pkg_helpers.bash"
 
 export BUILD_TYPE=wheel
-setup_env 0.13.0
+setup_env 0.14.0
 setup_wheel_python
 pip_install numpy pyyaml future ninja
 pip_install --upgrade setuptools

references/video_classification/train.py

@@ -157,6 +157,7 @@ def main(args):
                 "avi",
                 "mp4",
             ),
+            output_format="TCHW",
         )
         if args.cache_dataset:
             print(f"Saving dataset_train to {cache_path}")
@@ -193,6 +194,7 @@ def main(args):
                 "avi",
                 "mp4",
             ),
+            output_format="TCHW",
         )
         if args.cache_dataset:
             print(f"Saving dataset_test to {cache_path}")