Uses DisableTorchFunction again

Merge branch 'main' of github.com:pytorch/vision into proto-perf-feature-improvements

Author: vfdev-5

.circleci/config.yml

@@ -377,7 +377,12 @@ jobs:
     steps:
       - checkout_merge
       - designate_upload_channel
-      - run: packaging/build_wheel.sh
+      - run:
+          name: Build conda packages
+          no_output_timeout: 30m
+          command: |
+              set -ex
+              packaging/build_wheel.sh
       - store_artifacts:
           path: dist
       - persist_to_workspace:
@@ -393,7 +398,12 @@ jobs:
     steps:
       - checkout_merge
       - designate_upload_channel
-      - run: packaging/build_conda.sh
+      - run:
+          name: Build conda packages
+          no_output_timeout: 30m
+          command: |
+              set -ex
+              packaging/build_conda.sh
       - store_artifacts:
           path: /opt/conda/conda-bld/linux-64
       - persist_to_workspace:
@@ -411,7 +421,7 @@ jobs:
       - designate_upload_channel
       - run:
           name: Build conda packages
-          no_output_timeout: 20m
+          no_output_timeout: 30m
           command: |
             set -ex
             source packaging/windows/internal/vc_install_helper.sh
@@ -438,6 +448,7 @@ jobs:
       - designate_upload_channel
       - run:
           name: Build wheel packages
+          no_output_timeout: 30m
           command: |
             set -ex
             source packaging/windows/internal/vc_install_helper.sh

.circleci/config.yml.in

@@ -210,7 +210,7 @@ def cache(fn):
     """
     sentinel = object()
     out_cache = {}
-    exc_cache = {}
+    exc_tb_cache = {}
 
     @functools.wraps(fn)
     def wrapper(*args, **kwargs):
@@ -220,14 +220,17 @@ def wrapper(*args, **kwargs):
         if out is not sentinel:
             return out
 
-        exc = exc_cache.get(key, sentinel)
-        if exc is not sentinel:
-            raise exc
+        exc_tb = exc_tb_cache.get(key, sentinel)
+        if exc_tb is not sentinel:
+            raise exc_tb[0].with_traceback(exc_tb[1])
 
         try:
             out = fn(*args, **kwargs)
         except Exception as exc:
-            exc_cache[key] = exc
+            # We need to cache the traceback here as well. Otherwise, each re-raise will add the internal pytest
+            # traceback frames anew, but they will only be removed once. Thus, the traceback will be ginormous hiding
+            # the actual information in the noise. See https://github.com/pytest-dev/pytest/issues/10363 for details.
+            exc_tb_cache[key] = exc, exc.__traceback__
             raise exc
 
         out_cache[key] = out

test/common_utils.py

@@ -184,13 +184,18 @@ def load(self, device="cpu"):
         return args, kwargs
 
 
-DEFAULT_SQUARE_IMAGE_SIZE = 15
-DEFAULT_LANDSCAPE_IMAGE_SIZE = (7, 33)
-DEFAULT_PORTRAIT_IMAGE_SIZE = (31, 9)
-DEFAULT_IMAGE_SIZES = (DEFAULT_LANDSCAPE_IMAGE_SIZE, DEFAULT_PORTRAIT_IMAGE_SIZE, DEFAULT_SQUARE_IMAGE_SIZE, "random")
+DEFAULT_SQUARE_SPATIAL_SIZE = 15
+DEFAULT_LANDSCAPE_SPATIAL_SIZE = (7, 33)
+DEFAULT_PORTRAIT_SPATIAL_SIZE = (31, 9)
+DEFAULT_SPATIAL_SIZES = (
+    DEFAULT_LANDSCAPE_SPATIAL_SIZE,
+    DEFAULT_PORTRAIT_SPATIAL_SIZE,
+    DEFAULT_SQUARE_SPATIAL_SIZE,
+    "random",
+)
 
 
-def _parse_image_size(size, *, name="size"):
+def _parse_spatial_size(size, *, name="size"):
     if size == "random":
         return tuple(torch.randint(15, 33, (2,)).tolist())
     elif isinstance(size, int) and size > 0:
@@ -246,11 +251,11 @@ def load(self, device):
 @dataclasses.dataclass
 class ImageLoader(TensorLoader):
     color_space: features.ColorSpace
-    image_size: Tuple[int, int] = dataclasses.field(init=False)
+    spatial_size: Tuple[int, int] = dataclasses.field(init=False)
     num_channels: int = dataclasses.field(init=False)
 
     def __post_init__(self):
-        self.image_size = self.shape[-2:]
+        self.spatial_size = self.shape[-2:]
         self.num_channels = self.shape[-3]
 
 
@@ -277,7 +282,7 @@ def make_image_loader(
     dtype=torch.float32,
     constant_alpha=True,
 ):
-    size = _parse_image_size(size)
+    size = _parse_spatial_size(size)
     num_channels = get_num_channels(color_space)
 
     def fn(shape, dtype, device):
@@ -295,7 +300,7 @@ def fn(shape, dtype, device):
 
 def make_image_loaders(
     *,
-    sizes=DEFAULT_IMAGE_SIZES,
+    sizes=DEFAULT_SPATIAL_SIZES,
     color_spaces=(
         features.ColorSpace.GRAY,
         features.ColorSpace.GRAY_ALPHA,
@@ -316,7 +321,7 @@ def make_image_loaders(
 @dataclasses.dataclass
 class BoundingBoxLoader(TensorLoader):
     format: features.BoundingBoxFormat
-    image_size: Tuple[int, int]
+    spatial_size: Tuple[int, int]
 
 
 def randint_with_tensor_bounds(arg1, arg2=None, **kwargs):
@@ -331,7 +336,7 @@ def randint_with_tensor_bounds(arg1, arg2=None, **kwargs):
     ).reshape(low.shape)
 
 
-def make_bounding_box_loader(*, extra_dims=(), format, image_size="random", dtype=torch.float32):
+def make_bounding_box_loader(*, extra_dims=(), format, spatial_size="random", dtype=torch.float32):
     if isinstance(format, str):
         format = features.BoundingBoxFormat[format]
     if format not in {
@@ -341,7 +346,7 @@ def make_bounding_box_loader(*, extra_dims=(), format, image_size="random", dtyp
     }:
         raise pytest.UsageError(f"Can't make bounding box in format {format}")
 
-    image_size = _parse_image_size(image_size, name="image_size")
+    spatial_size = _parse_spatial_size(spatial_size, name="spatial_size")
 
     def fn(shape, dtype, device):
         *extra_dims, num_coordinates = shape
@@ -350,10 +355,10 @@ def fn(shape, dtype, device):
 
         if any(dim == 0 for dim in extra_dims):
             return features.BoundingBox(
-                torch.empty(*extra_dims, 4, dtype=dtype, device=device), format=format, image_size=image_size
+                torch.empty(*extra_dims, 4, dtype=dtype, device=device), format=format, spatial_size=spatial_size
             )
 
-        height, width = image_size
+        height, width = spatial_size
 
         if format == features.BoundingBoxFormat.XYXY:
             x1 = torch.randint(0, width // 2, extra_dims)
@@ -375,10 +380,10 @@ def fn(shape, dtype, device):
             parts = (cx, cy, w, h)
 
         return features.BoundingBox(
-            torch.stack(parts, dim=-1).to(dtype=dtype, device=device), format=format, image_size=image_size
+            torch.stack(parts, dim=-1).to(dtype=dtype, device=device), format=format, spatial_size=spatial_size
         )
 
-    return BoundingBoxLoader(fn, shape=(*extra_dims, 4), dtype=dtype, format=format, image_size=image_size)
+    return BoundingBoxLoader(fn, shape=(*extra_dims, 4), dtype=dtype, format=format, spatial_size=spatial_size)
 
 
 make_bounding_box = from_loader(make_bounding_box_loader)
@@ -388,11 +393,11 @@ def make_bounding_box_loaders(
     *,
     extra_dims=DEFAULT_EXTRA_DIMS,
     formats=tuple(features.BoundingBoxFormat),
-    image_size="random",
+    spatial_size="random",
     dtypes=(torch.float32, torch.int64),
 ):
     for params in combinations_grid(extra_dims=extra_dims, format=formats, dtype=dtypes):
-        yield make_bounding_box_loader(**params, image_size=image_size)
+        yield make_bounding_box_loader(**params, spatial_size=spatial_size)
 
 
 make_bounding_boxes = from_loaders(make_bounding_box_loaders)
@@ -475,7 +480,7 @@ class MaskLoader(TensorLoader):
 
 def make_detection_mask_loader(size="random", *, num_objects="random", extra_dims=(), dtype=torch.uint8):
     # This produces "detection" masks, i.e. `(*, N, H, W)`, where `N` denotes the number of objects
-    size = _parse_image_size(size)
+    size = _parse_spatial_size(size)
     num_objects = int(torch.randint(1, 11, ())) if num_objects == "random" else num_objects
 
     def fn(shape, dtype, device):
@@ -489,7 +494,7 @@ def fn(shape, dtype, device):
 
 
 def make_detection_mask_loaders(
-    sizes=DEFAULT_IMAGE_SIZES,
+    sizes=DEFAULT_SPATIAL_SIZES,
     num_objects=(1, 0, "random"),
     extra_dims=DEFAULT_EXTRA_DIMS,
     dtypes=(torch.uint8,),
@@ -503,7 +508,7 @@ def make_detection_mask_loaders(
 
 def make_segmentation_mask_loader(size="random", *, num_categories="random", extra_dims=(), dtype=torch.uint8):
     # This produces "segmentation" masks, i.e. `(*, H, W)`, where the category is encoded in the values
-    size = _parse_image_size(size)
+    size = _parse_spatial_size(size)
     num_categories = int(torch.randint(1, 11, ())) if num_categories == "random" else num_categories
 
     def fn(shape, dtype, device):
@@ -518,7 +523,7 @@ def fn(shape, dtype, device):
 
 def make_segmentation_mask_loaders(
     *,
-    sizes=DEFAULT_IMAGE_SIZES,
+    sizes=DEFAULT_SPATIAL_SIZES,
     num_categories=(1, 2, "random"),
     extra_dims=DEFAULT_EXTRA_DIMS,
     dtypes=(torch.uint8,),
@@ -532,7 +537,7 @@ def make_segmentation_mask_loaders(
 
 def make_mask_loaders(
     *,
-    sizes=DEFAULT_IMAGE_SIZES,
+    sizes=DEFAULT_SPATIAL_SIZES,
     num_objects=(1, 0, "random"),
     num_categories=(1, 2, "random"),
     extra_dims=DEFAULT_EXTRA_DIMS,
@@ -559,7 +564,7 @@ def make_video_loader(
     extra_dims=(),
     dtype=torch.uint8,
 ):
-    size = _parse_image_size(size)
+    size = _parse_spatial_size(size)
     num_frames = int(torch.randint(1, 5, ())) if num_frames == "random" else num_frames
 
     def fn(shape, dtype, device):
@@ -576,7 +581,7 @@ def fn(shape, dtype, device):
 
 def make_video_loaders(
     *,
-    sizes=DEFAULT_IMAGE_SIZES,
+    sizes=DEFAULT_SPATIAL_SIZES,
     color_spaces=(
         features.ColorSpace.GRAY,
         features.ColorSpace.RGB,