Merge branch 'main' into aelfjnalefjnaef

.github/scripts/setup-env.sh

@@ -22,17 +22,6 @@ case $(uname) in
     ;;
 esac
 
-if [[ "${OS_TYPE}" == "macos" && $(uname -m) == x86_64 ]]; then
-  echo '::group::Uninstall system JPEG libraries on macOS'
-  # The x86 macOS runners, e.g. the GitHub Actions native "macos-12" runner, has some JPEG and PNG libraries
-  # installed by default that interfere with our build. We uninstall them here and use the one from conda below.
-  IMAGE_LIBS=$(brew list | grep -E "jpeg|png")
-  for lib in $IMAGE_LIBS; do
-    brew uninstall --ignore-dependencies --force "${lib}"
-  done
-  echo '::endgroup::'
-fi
-
 echo '::group::Create build environment'
 # See https://github.com/pytorch/vision/issues/7296 for ffmpeg
 conda create \

.github/workflows/build-cmake.yml

@@ -40,7 +40,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - runner: macos-12
           - runner: macos-m1-stable
       fail-fast: false
     uses: pytorch/test-infra/.github/workflows/macos_job.yml@main

.github/workflows/prototype-tests-linux-gpu.yml

@@ -1,5 +1,8 @@
 name: Prototype tests on Linux
 
+# IMPORTANT: This workflow has been manually disabled from the GitHub interface
+# in June 2024. The file is kept for reference in case we ever put this back.
+
 on:
   pull_request:
 

.github/workflows/tests.yml

@@ -53,16 +53,11 @@ jobs:
           - "3.10"
           - "3.11"
           - "3.12"
-        runner: ["macos-12"]
-        include:
-          - python-version: "3.8"
-            runner: macos-m1-stable
+        runner: ["macos-m1-stable"]
       fail-fast: false
     uses: pytorch/test-infra/.github/workflows/macos_job.yml@main
     with:
       repository: pytorch/vision
-      # We need an increased timeout here, since the macos-12 runner is the free one from GH
-      # and needs roughly 2 hours to just run the test suite
       timeout: 240
       runner: ${{ matrix.runner }}
       test-infra-ref: main

docs/source/transforms.rst

@@ -350,6 +350,7 @@ Color
     v2.RGB
     v2.RandomGrayscale
     v2.GaussianBlur
+    v2.GaussianNoise
     v2.RandomInvert
     v2.RandomPosterize
     v2.RandomSolarize
@@ -368,6 +369,7 @@ Functionals
     v2.functional.grayscale_to_rgb
     v2.functional.to_grayscale
     v2.functional.gaussian_blur
+    v2.functional.gaussian_noise
     v2.functional.invert
     v2.functional.posterize
     v2.functional.solarize

references/classification/transforms.py

@@ -19,9 +19,9 @@ def get_mixup_cutmix(*, mixup_alpha, cutmix_alpha, num_classes, use_v2):
         )
     if cutmix_alpha > 0:
         mixup_cutmix.append(
-            transforms_module.CutMix(alpha=mixup_alpha, num_classes=num_classes)
+            transforms_module.CutMix(alpha=cutmix_alpha, num_classes=num_classes)
             if use_v2
-            else RandomCutMix(num_classes=num_classes, p=1.0, alpha=mixup_alpha)
+            else RandomCutMix(num_classes=num_classes, p=1.0, alpha=cutmix_alpha)
         )
     if not mixup_cutmix:
         return None

setup.py

@@ -554,6 +554,7 @@ def run(self):
         zip_safe=False,
         install_requires=requirements,
         extras_require={
+            "gdown": ["gdown>=4.7.3"],
             "scipy": ["scipy"],
         },
         ext_modules=get_extensions(),

test/test_datasets.py

@@ -2472,28 +2472,68 @@ def inject_fake_data(self, tmpdir, config):
         base_folder = os.path.join(tmpdir, "fer2013")
         os.makedirs(base_folder)
 
+        use_icml = config.pop("use_icml", False)
+        use_fer = config.pop("use_fer", False)
+
         num_samples = 5
-        with open(os.path.join(base_folder, f"{config['split']}.csv"), "w", newline="") as file:
-            writer = csv.DictWriter(
-                file,
-                fieldnames=("emotion", "pixels") if config["split"] == "train" else ("pixels",),
-                quoting=csv.QUOTE_NONNUMERIC,
-                quotechar='"',
-            )
-            writer.writeheader()
-            for _ in range(num_samples):
-                row = dict(
-                    pixels=" ".join(
-                        str(pixel) for pixel in datasets_utils.create_image_or_video_tensor((48, 48)).view(-1).tolist()
-                    )
+
+        if use_icml or use_fer:
+            pixels_key, usage_key = (" pixels", " Usage") if use_icml else ("pixels", "Usage")
+            fieldnames = ("emotion", usage_key, pixels_key) if use_icml else ("emotion", pixels_key, usage_key)
+            filename = "icml_face_data.csv" if use_icml else "fer2013.csv"
+            with open(os.path.join(base_folder, filename), "w", newline="") as file:
+                writer = csv.DictWriter(
+                    file,
+                    fieldnames=fieldnames,
+                    quoting=csv.QUOTE_NONNUMERIC,
+                    quotechar='"',
                 )
-                if config["split"] == "train":
-                    row["emotion"] = str(int(torch.randint(0, 7, ())))
+                writer.writeheader()
+                for i in range(num_samples):
+                    row = {
+                        "emotion": str(int(torch.randint(0, 7, ()))),
+                        usage_key: "Training" if i % 2 else "PublicTest",
+                        pixels_key: " ".join(
+                            str(pixel)
+                            for pixel in datasets_utils.create_image_or_video_tensor((48, 48)).view(-1).tolist()
+                        ),
+                    }
+
+                    writer.writerow(row)
+        else:
+            with open(os.path.join(base_folder, f"{config['split']}.csv"), "w", newline="") as file:
+                writer = csv.DictWriter(
+                    file,
+                    fieldnames=("emotion", "pixels") if config["split"] == "train" else ("pixels",),
+                    quoting=csv.QUOTE_NONNUMERIC,
+                    quotechar='"',
+                )
+                writer.writeheader()
+                for _ in range(num_samples):
+                    row = dict(
+                        pixels=" ".join(
+                            str(pixel)
+                            for pixel in datasets_utils.create_image_or_video_tensor((48, 48)).view(-1).tolist()
+                        )
+                    )
+                    if config["split"] == "train":
+                        row["emotion"] = str(int(torch.randint(0, 7, ())))
 
-                writer.writerow(row)
+                    writer.writerow(row)
 
         return num_samples
 
+    def test_icml_file(self):
+        config = {"split": "test"}
+        with self.create_dataset(config=config) as (dataset, _):
+            assert all(s[1] is None for s in dataset)
+
+        for split in ("train", "test"):
+            for d in ({"use_icml": True}, {"use_fer": True}):
+                config = {"split": split, **d}
+                with self.create_dataset(config=config) as (dataset, _):
+                    assert all(s[1] is not None for s in dataset)
+
 
 class GTSRBTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.GTSRB

test/test_image.py

@@ -551,21 +551,33 @@ def test_pathlib_support(tmpdir):
     write_png(img, write_path)
 
 
-@pytest.mark.parametrize("name", ("gifgrid", "fire", "porsche", "treescap", "treescap-interlaced", "solid2", "x-trans"))
-def test_decode_gif(tmpdir, name):
+@pytest.mark.parametrize(
+    "name", ("gifgrid", "fire", "porsche", "treescap", "treescap-interlaced", "solid2", "x-trans", "earth")
+)
+@pytest.mark.parametrize("scripted", (True, False))
+def test_decode_gif(tmpdir, name, scripted):
     # Using test images from GIFLIB
     # https://sourceforge.net/p/giflib/code/ci/master/tree/pic/, we assert PIL
     # and torchvision decoded outputs are equal.
     # We're not testing against "welcome2" because PIL and GIFLIB disagee on what
     # the background color should be (likely a difference in the way they handle
     # transparency?)
+    # 'earth' image is from wikipedia, licensed under CC BY-SA 3.0
+    # https://creativecommons.org/licenses/by-sa/3.0/
+    # it allows to properly test for transparency, TOP-LEFT offsets, and
+    # disposal modes.
 
     path = tmpdir / f"{name}.gif"
-    url = f"https://sourceforge.net/p/giflib/code/ci/master/tree/pic/{name}.gif?format=raw"
+    if name == "earth":
+        url = "https://upload.wikimedia.org/wikipedia/commons/2/2c/Rotating_earth_%28large%29.gif"
+    else:
+        url = f"https://sourceforge.net/p/giflib/code/ci/master/tree/pic/{name}.gif?format=raw"
     with open(path, "wb") as f:
         f.write(requests.get(url).content)
 
-    tv_out = read_image(path)
+    encoded_bytes = read_file(path)
+    f = torch.jit.script(decode_gif) if scripted else decode_gif
+    tv_out = f(encoded_bytes)
     if tv_out.ndim == 3:
         tv_out = tv_out[None]
 

test/test_ops.py

@@ -14,6 +14,7 @@
 from common_utils import assert_equal, cpu_and_cuda, cpu_and_cuda_and_mps, needs_cuda, needs_mps
 from PIL import Image
 from torch import nn, Tensor
+from torch._dynamo.utils import is_compile_supported
 from torch.autograd import gradcheck
 from torch.nn.modules.utils import _pair
 from torchvision import models, ops
@@ -529,6 +530,10 @@ def test_autocast_cpu(self, aligned, deterministic, x_dtype, rois_dtype):
     def test_backward(self, seed, device, contiguous, deterministic):
         if deterministic and device == "cpu":
             pytest.skip("cpu is always deterministic, don't retest")
+        if deterministic and device == "mps":
+            pytest.skip("no deterministic implementation for mps")
+        if deterministic and not is_compile_supported(device):
+            pytest.skip("deterministic implementation only if torch.compile supported")
         super().test_backward(seed, device, contiguous, deterministic)
 
     def _make_rois(self, img_size, num_imgs, dtype, num_rois=1000):