Split tests for transforms v2 and prototype (#7278)

.circleci/unittest/linux/scripts/run_test.sh

@@ -6,4 +6,17 @@ eval "$(./conda/bin/conda shell.bash hook)"
 conda activate ./env
 
 python -m torch.utils.collect_env
-pytest --junitxml=test-results/junit.xml -v --durations 20
+
+case "$(uname -s)" in
+  Darwin*)
+    # The largest macOS runner is not able to handle the regular test suite plus the transforms v2 tests at the same
+    # time due to insufficient resources. Thus, we ignore the transforms v2 tests at first and run them in a separate
+    # step afterwards.
+    GLOB='test/test_transforms_v2*'
+    pytest --junitxml=test-results/junit.xml -v --durations 20 --ignore-glob="${GLOB}"
+    eval "pytest --junitxml=test-results/junit-transforms-v2.xml -v --durations 20 ${GLOB}"
+    ;;
+  *)
+    pytest --junitxml=test-results/junit.xml -v --durations 20
+    ;;
+esac

test/builtin_dataset_mocks.py

@@ -18,7 +18,8 @@ from collections import Counter, defaultdict
 import numpy as np
 import pytest
 import torch
-from datasets_utils import combinations_grid, create_image_file, create_image_folder, make_tar, make_zip
+from common_utils import combinations_grid
+from datasets_utils import create_image_file, create_image_folder, make_tar, make_zip
 from torch.nn.functional import one_hot
 from torch.testing import make_tensor as _make_tensor
 from torchvision.prototype import datasets

test/datasets_utils.py

@@ -170,23 +170,6 @@ def test_all_configs(test):
     return wrapper
 
 
-def combinations_grid(**kwargs):
-    """Creates a grid of input combinations.
-
-    Each element in the returned sequence is a dictionary containing one possible combination as values.
-
-    Example:
-        >>> combinations_grid(foo=("bar", "baz"), spam=("eggs", "ham"))
-        [
-            {'foo': 'bar', 'spam': 'eggs'},
-            {'foo': 'bar', 'spam': 'ham'},
-            {'foo': 'baz', 'spam': 'eggs'},
-            {'foo': 'baz', 'spam': 'ham'}
-        ]
-    """
-    return [dict(zip(kwargs.keys(), values)) for values in itertools.product(*kwargs.values())]
-
-
 class DatasetTestCase(unittest.TestCase):
     """Abstract base class for all dataset testcases.
 

test/test_datapoints.py

+import pytest
+import torch
+from PIL import Image
+
+from torchvision import datapoints
+
+
+@pytest.mark.parametrize("data", [torch.rand(3, 32, 32), Image.new("RGB", (32, 32), color=123)])
+def test_image_instance(data):
+    image = datapoints.Image(data)
+    assert isinstance(image, torch.Tensor)
+    assert image.ndim == 3 and image.shape[0] == 3
+
+
+@pytest.mark.parametrize("data", [torch.randint(0, 10, size=(1, 32, 32)), Image.new("L", (32, 32), color=2)])
+def test_mask_instance(data):
+    mask = datapoints.Mask(data)
+    assert isinstance(mask, torch.Tensor)
+    assert mask.ndim == 3 and mask.shape[0] == 1
+
+
+@pytest.mark.parametrize("data", [torch.randint(0, 32, size=(5, 4)), [[0, 0, 5, 5], [2, 2, 7, 7]]])
+@pytest.mark.parametrize(
+    "format", ["XYXY", "CXCYWH", datapoints.BoundingBoxFormat.XYXY, datapoints.BoundingBoxFormat.XYWH]
+)
+def test_bbox_instance(data, format):
+    bboxes = datapoints.BoundingBox(data, format=format, spatial_size=(32, 32))
+    assert isinstance(bboxes, torch.Tensor)
+    assert bboxes.ndim == 2 and bboxes.shape[1] == 4
+    if isinstance(format, str):
+        format = datapoints.BoundingBoxFormat.from_str(format.upper())
+    assert bboxes.format == format

test/test_prototype_datapoints.py

 import pytest
 import torch
 
-from PIL import Image
-
-from torchvision import datapoints
 from torchvision.prototype import datapoints as proto_datapoints
 
 
@@ -134,30 +131,3 @@ def test_wrap_like():
     assert type(label_new) is proto_datapoints.Label
     assert label_new.data_ptr() == output.data_ptr()
     assert label_new.categories is label.categories
-
-
-@pytest.mark.parametrize("data", [torch.rand(3, 32, 32), Image.new("RGB", (32, 32), color=123)])
-def test_image_instance(data):
-    image = datapoints.Image(data)
-    assert isinstance(image, torch.Tensor)
-    assert image.ndim == 3 and image.shape[0] == 3
-
-
-@pytest.mark.parametrize("data", [torch.randint(0, 10, size=(1, 32, 32)), Image.new("L", (32, 32), color=2)])
-def test_mask_instance(data):
-    mask = datapoints.Mask(data)
-    assert isinstance(mask, torch.Tensor)
-    assert mask.ndim == 3 and mask.shape[0] == 1
-
-
-@pytest.mark.parametrize("data", [torch.randint(0, 32, size=(5, 4)), [[0, 0, 5, 5], [2, 2, 7, 7]]])
-@pytest.mark.parametrize(
-    "format", ["XYXY", "CXCYWH", datapoints.BoundingBoxFormat.XYXY, datapoints.BoundingBoxFormat.XYWH]
-)
-def test_bbox_instance(data, format):
-    bboxes = datapoints.BoundingBox(data, format=format, spatial_size=(32, 32))
-    assert isinstance(bboxes, torch.Tensor)
-    assert bboxes.ndim == 2 and bboxes.shape[1] == 4
-    if isinstance(format, str):
-        format = datapoints.BoundingBoxFormat.from_str(format.upper())
-    assert bboxes.format == format

test/test_prototype_transforms_consistency.py → test/test_transforms_v2_consistency.py

@@ -12,9 +12,8 @@ import PIL.Image
 import pytest
 
 import torch
-import torchvision.prototype.transforms as prototype_transforms
 import torchvision.transforms.v2 as v2_transforms
-from prototype_common_utils import (
+from common_utils import (
     ArgsKwargs,
     assert_close,
     assert_equal,
@@ -22,7 +21,6 @@ from prototype_common_utils import (
     make_detection_mask,
     make_image,
     make_images,
-    make_label,
     make_segmentation_mask,
 )
 from torch import nn
@@ -1056,6 +1054,9 @@ class TestRefDetTransforms:
         size = (600, 800)
         num_objects = 22
 
+        def make_label(extra_dims, categories):
+            return torch.randint(categories, extra_dims, dtype=torch.int64)
+
         pil_image = to_image_pil(make_image(size=size, color_space="RGB"))
         target = {
             "boxes": make_bounding_box(spatial_size=size, format="XYXY", extra_dims=(num_objects,), dtype=torch.float),
@@ -1102,11 +1103,6 @@ class TestRefDetTransforms:
             ),
             (det_transforms.RandomZoomOut(), v2_transforms.RandomZoomOut(), {"with_mask": False}),
             (det_transforms.ScaleJitter((1024, 1024)), v2_transforms.ScaleJitter((1024, 1024)), {}),
-            (
-                det_transforms.FixedSizeCrop((1024, 1024), fill=0),
-                prototype_transforms.FixedSizeCrop((1024, 1024), fill=0),
-                {},
-            ),
             (
                 det_transforms.RandomShortestSize(
                     min_size=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), max_size=1333

test/test_prototype_transforms_functional.py → test/test_transforms_v2_functional.py

@@ -11,15 +11,16 @@ import pytest
 
 import torch
 
-from common_utils import cache, cpu_and_gpu, needs_cuda, set_rng_seed
-from prototype_common_utils import (
+from common_utils import (
     assert_close,
+    cache,
+    cpu_and_gpu,
     DEFAULT_SQUARE_SPATIAL_SIZE,
     make_bounding_boxes,
+    needs_cuda,
     parametrized_error_message,
+    set_rng_seed,
 )
-from prototype_transforms_dispatcher_infos import DISPATCHER_INFOS
-from prototype_transforms_kernel_infos import KERNEL_INFOS
 from torch.utils._pytree import tree_map
 from torchvision import datapoints
 from torchvision.transforms.functional import _get_perspective_coeffs
@@ -27,6 +28,8 @@ from torchvision.transforms.v2 import functional as F
 from torchvision.transforms.v2.functional._geometry import _center_crop_compute_padding
 from torchvision.transforms.v2.functional._meta import clamp_bounding_box, convert_format_bounding_box
 from torchvision.transforms.v2.utils import is_simple_tensor
+from transforms_v2_dispatcher_infos import DISPATCHER_INFOS
+from transforms_v2_kernel_infos import KERNEL_INFOS
 
 
 KERNEL_INFOS_MAP = {info.kernel: info for info in KERNEL_INFOS}
@@ -635,7 +638,7 @@ class TestConvertFormatBoundingBox:
 
 
 # TODO: All correctness checks below this line should be ported to be references on a `KernelInfo` in
-#  `prototype_transforms_kernel_infos.py`
+#  `transforms_v2_kernel_infos.py`
 
 
 def _compute_affine_matrix(angle_, translate_, scale_, shear_, center_):

test/test_prototype_transforms_utils.py → test/test_transforms_v2_utils.py

@@ -4,7 +4,7 @@ import pytest
 import torch
 
 import torchvision.transforms.v2.utils
-from prototype_common_utils import make_bounding_box, make_detection_mask, make_image
+from common_utils import make_bounding_box, make_detection_mask, make_image
 
 from torchvision import datapoints
 from torchvision.transforms.v2.functional import to_image_pil

test/prototype_transforms_dispatcher_infos.py → test/transforms_v2_dispatcher_infos.py

@@ -2,9 +2,9 @@ import collections.abc
 
 import pytest
 import torchvision.transforms.v2.functional as F
-from prototype_common_utils import InfoBase, TestMark
-from prototype_transforms_kernel_infos import KERNEL_INFOS, pad_xfail_jit_fill_condition
+from common_utils import InfoBase, TestMark
 from torchvision import datapoints
+from transforms_v2_kernel_infos import KERNEL_INFOS, pad_xfail_jit_fill_condition
 
 __all__ = ["DispatcherInfo", "DISPATCHER_INFOS"]
 
@@ -49,7 +49,7 @@ class DispatcherInfo(InfoBase):
             if not kernel_info:
                 raise pytest.UsageError(
                     f"Can't register {kernel.__name__} for type {datapoint_type} since there is no `KernelInfo` for it. "
-                    f"Please add a `KernelInfo` for it in `prototype_transforms_kernel_infos.py`."
+                    f"Please add a `KernelInfo` for it in `transforms_v2_kernel_infos.py`."
                 )
             kernel_infos[datapoint_type] = kernel_info
         self.kernel_infos = kernel_infos

test/prototype_transforms_kernel_infos.py → test/transforms_v2_kernel_infos.py

@@ -9,9 +9,9 @@ import pytest
 import torch.testing
 import torchvision.ops
 import torchvision.transforms.v2.functional as F
-from datasets_utils import combinations_grid
-from prototype_common_utils import (
+from common_utils import (
     ArgsKwargs,
+    combinations_grid,
     get_num_channels,
     ImageLoader,
     InfoBase,