Merge branch 'pytorch:main' into main

Author: jdsgomes

scripts/README.rst

@@ -0,0 +1,23 @@
+Utility scripts
+===============
+
+* `fbcode_to_main_sync.sh`
+
+This shell script is used to synchronise internal changes with the main repository.
+
+To run this script:
+
+.. code:: bash
+
+    chmod +x fbcode_to_main_sync.sh
+    ./fbcode_to_main_sync.sh <commit_hash> <fork_name> <fork_main_branch>
+
+where
+
+``commit_hash`` represents the commit hash in fbsync branch from where we should start the sync.
+
+``fork_name`` is the name of the remote corresponding to your fork, you can check it by doing `"git remote -v"`.
+
+``fork_main_branch`` (optional) is the name of the main branch on your fork(default="main").
+
+This script will create PRs corresponding to the commits in fbsync. Please review these, add the [FBCode->GH] prefix on the title and publish them. Most importantly, add the [FBCode->GH] prefix at the beginning of the merge message as well.

scripts/fbcode_to_main_sync.sh

@@ -0,0 +1,47 @@
+#!/bin/bash
+
+if [ -z $1 ]
+then
+    echo "Commit hash is required to be passed when running this script."
+    echo "./fbcode_to_main_sync.sh <commit_hash> <fork_name> <fork_main_branch>"
+    exit 1
+fi
+commit_hash=$1
+
+if [ -z $2 ]
+then
+    echo "Fork name is required to be passed when running this script."
+    echo "./fbcode_to_main_sync.sh <commit_hash> <fork_name> <fork_main_branch>"
+    exit 1
+fi
+fork_name=$2
+
+if [ -z $3 ]
+then
+    fork_main_branch="main"
+else
+    fork_main_branch=$3
+fi
+
+from_branch="fbsync"
+git stash
+git checkout $from_branch
+git pull
+# Add random prefix in the new branch name to keep it unique per run
+prefix=$RANDOM
+IFS='
+'
+for line in $(git log --pretty=oneline "$commit_hash"..HEAD)
+do
+    if [[ $line != *\[fbsync\]* ]]
+    then
+        echo "Parsing $line"
+        hash=$(echo $line | cut -f1 -d' ')
+        git checkout $fork_main_branch
+        git checkout -B cherrypick_${prefix}_${hash}
+        git cherry-pick -x "$hash"
+        git push $fork_name cherrypick_${prefix}_${hash}
+        git checkout $from_branch
+    fi
+done
+echo "Please review the PRs, add [FBCode->GH] prefix in the title and publish them."

torchvision/prototype/datasets/__init__.py

@@ -11,5 +11,5 @@
 from ._home import home
 
 # Load this last, since some parts depend on the above being loaded first
-from ._api import register, _list as list, info, load  # usort: skip
+from ._api import register, _list as list, info, load, find  # usort: skip
 from ._folder import from_data_folder, from_image_folder

torchvision/prototype/models/__init__.py

@@ -4,7 +4,9 @@
 from .vgg import *
 from .efficientnet import *
 from .mobilenetv3 import *
+from .mobilenetv2 import *
 from .mnasnet import *
+from .regnet import *
 from . import detection
 from . import quantization
 from . import segmentation

torchvision/prototype/models/mobilenetv2.py

@@ -0,0 +1,46 @@
+import warnings
+from functools import partial
+from typing import Any, Optional
+
+from torchvision.transforms.functional import InterpolationMode
+
+from ...models.mobilenetv2 import MobileNetV2
+from ..transforms.presets import ImageNetEval
+from ._api import Weights, WeightEntry
+from ._meta import _IMAGENET_CATEGORIES
+
+
+__all__ = ["MobileNetV2", "MobileNetV2Weights", "mobilenet_v2"]
+
+
+_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+
+
+class MobileNetV2Weights(Weights):
+    ImageNet1K_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
+            "acc@1": 71.878,
+            "acc@5": 90.286,
+        },
+    )
+
+
+def mobilenet_v2(weights: Optional[MobileNetV2Weights] = None, progress: bool = True, **kwargs: Any) -> MobileNetV2:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = MobileNetV2Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+    weights = MobileNetV2Weights.verify(weights)
+
+    if weights is not None:
+        kwargs["num_classes"] = len(weights.meta["categories"])
+
+    model = MobileNetV2(**kwargs)
+
+    if weights is not None:
+        model.load_state_dict(weights.state_dict(progress=progress))
+
+    return model