use pytest markers instead of custom solution for prototype transforms functional tests (#6653)

* use pytest markers instead of custom solution for prototype transforms functional tests

* cleanup

* cleanup

* trigger CI

Author: pmeier

test/prototype_transforms_dispatcher_infos.py

@@ -2,12 +2,12 @@
 import dataclasses
 
 from collections import defaultdict
+
 from typing import Callable, Dict, List, Optional, Sequence, Type
 
 import pytest
 import torchvision.prototype.transforms.functional as F
-from prototype_common_utils import BoundingBoxLoader
-from prototype_transforms_kernel_infos import KERNEL_INFOS, KernelInfo, Skip
+from prototype_transforms_kernel_infos import KERNEL_INFOS, TestMark
 from torchvision.prototype import features
 
 __all__ = ["DispatcherInfo", "DISPATCHER_INFOS"]
@@ -24,35 +24,27 @@ def __post_init__(self):
         self.kernel_name = self.kernel_name or self.kernel.__name__
 
 
-def skip_python_scalar_arg_jit(name, *, reason="Python scalar int or float is not supported when scripting"):
-    return Skip(
-        "test_scripted_smoke",
-        condition=lambda args_kwargs, device: isinstance(args_kwargs.kwargs[name], (int, float)),
-        reason=reason,
-    )
-
-
-def skip_integer_size_jit(name="size"):
-    return skip_python_scalar_arg_jit(name, reason="Integer size is not supported when scripting.")
-
-
 @dataclasses.dataclass
 class DispatcherInfo:
     dispatcher: Callable
     kernels: Dict[Type, Callable]
-    kernel_infos: Dict[Type, KernelInfo] = dataclasses.field(default=None)
     pil_kernel_info: Optional[PILKernelInfo] = None
     method_name: str = dataclasses.field(default=None)
-    skips: Sequence[Skip] = dataclasses.field(default_factory=list)
-    _skips_map: Dict[str, List[Skip]] = dataclasses.field(default=None, init=False)
+    test_marks: Sequence[TestMark] = dataclasses.field(default_factory=list)
+    _test_marks_map: Dict[str, List[TestMark]] = dataclasses.field(default=None, init=False)
 
     def __post_init__(self):
         self.kernel_infos = {feature_type: KERNEL_INFO_MAP[kernel] for feature_type, kernel in self.kernels.items()}
         self.method_name = self.method_name or self.dispatcher.__name__
-        skips_map = defaultdict(list)
-        for skip in self.skips:
-            skips_map[skip.test_name].append(skip)
-        self._skips_map = dict(skips_map)
+        test_marks_map = defaultdict(list)
+        for test_mark in self.test_marks:
+            test_marks_map[test_mark.test_id].append(test_mark)
+        self._test_marks_map = dict(test_marks_map)
+
+    def get_marks(self, test_id, args_kwargs):
+        return [
+            test_mark.mark for test_mark in self._test_marks_map.get(test_id, []) if test_mark.condition(args_kwargs)
+        ]
 
     def sample_inputs(self, *feature_types, filter_metadata=True):
         for feature_type in feature_types or self.kernels.keys():
@@ -70,17 +62,27 @@ def sample_inputs(self, *feature_types, filter_metadata=True):
 
                     yield args_kwargs
 
-    def maybe_skip(self, *, test_name, args_kwargs, device):
-        skips = self._skips_map.get(test_name)
-        if not skips:
-            return
 
-        for skip in skips:
-            if skip.condition(args_kwargs, device):
-                pytest.skip(skip.reason)
+def xfail_python_scalar_arg_jit(name, *, reason=None):
+    reason = reason or f"Python scalar int or float for `{name}` is not supported when scripting"
+    return TestMark(
+        ("TestDispatchers", "test_scripted_smoke"),
+        pytest.mark.xfail(reason=reason),
+        condition=lambda args_kwargs: isinstance(args_kwargs.kwargs[name], (int, float)),
+    )
+
 
+def xfail_integer_size_jit(name="size"):
+    return xfail_python_scalar_arg_jit(name, reason=f"Integer `{name}` is not supported when scripting.")
 
-def fill_sequence_needs_broadcast(args_kwargs, device):
+
+skip_dispatch_feature = TestMark(
+    ("TestDispatchers", "test_dispatch_feature"),
+    pytest.mark.skip(reason="Dispatcher doesn't support arbitrary feature dispatch."),
+)
+
+
+def fill_sequence_needs_broadcast(args_kwargs):
     (image_loader, *_), kwargs = args_kwargs
     try:
         fill = kwargs["fill"]
@@ -93,15 +95,12 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
     return image_loader.num_channels > 1
 
 
-skip_dispatch_pil_if_fill_sequence_needs_broadcast = Skip(
-    "test_dispatch_pil",
+xfail_dispatch_pil_if_fill_sequence_needs_broadcast = TestMark(
+    ("TestDispatchers", "test_dispatch_pil"),
+    pytest.mark.xfail(
+        reason="PIL kernel doesn't support sequences of length 1 for `fill` if the number of color channels is larger."
+    ),
     condition=fill_sequence_needs_broadcast,
-    reason="PIL kernel doesn't support sequences of length 1 if the number of channels is larger.",
-)
-
-skip_dispatch_feature = Skip(
-    "test_dispatch_feature",
-    reason="Dispatcher doesn't support arbitrary feature dispatch.",
 )
 
 
@@ -123,8 +122,8 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
             features.Mask: F.resize_mask,
         },
         pil_kernel_info=PILKernelInfo(F.resize_image_pil),
-        skips=[
-            skip_integer_size_jit(),
+        test_marks=[
+            xfail_integer_size_jit(),
         ],
     ),
     DispatcherInfo(
@@ -135,9 +134,9 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
             features.Mask: F.affine_mask,
         },
         pil_kernel_info=PILKernelInfo(F.affine_image_pil),
-        skips=[
-            skip_dispatch_pil_if_fill_sequence_needs_broadcast,
-            skip_python_scalar_arg_jit("shear", reason="Scalar shear is not supported by JIT"),
+        test_marks=[
+            xfail_dispatch_pil_if_fill_sequence_needs_broadcast,
+            xfail_python_scalar_arg_jit("shear"),
         ],
     ),
     DispatcherInfo(
@@ -166,16 +165,6 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
             features.Mask: F.crop_mask,
         },
         pil_kernel_info=PILKernelInfo(F.crop_image_pil, kernel_name="crop_image_pil"),
-        skips=[
-            Skip(
-                "test_dispatch_feature",
-                condition=lambda args_kwargs, device: isinstance(args_kwargs.args[0], BoundingBoxLoader),
-                reason=(
-                    "F.crop expects 4 coordinates as input, but bounding box sample inputs only generate two "
-                    "since that is sufficient for the kernel."
-                ),
-            )
-        ],
     ),
     DispatcherInfo(
         F.resized_crop,
@@ -193,10 +182,20 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
             features.BoundingBox: F.pad_bounding_box,
             features.Mask: F.pad_mask,
         },
-        skips=[
-            skip_dispatch_pil_if_fill_sequence_needs_broadcast,
-        ],
         pil_kernel_info=PILKernelInfo(F.pad_image_pil, kernel_name="pad_image_pil"),
+        test_marks=[
+            TestMark(
+                ("TestDispatchers", "test_dispatch_pil"),
+                pytest.mark.xfail(
+                    reason=(
+                        "PIL kernel doesn't support sequences of length 1 for argument `fill` and "
+                        "`padding_mode='constant'`, if the number of color channels is larger."
+                    )
+                ),
+                condition=lambda args_kwargs: fill_sequence_needs_broadcast(args_kwargs)
+                and args_kwargs.kwargs.get("padding_mode", "constant") == "constant",
+            )
+        ],
     ),
     DispatcherInfo(
         F.perspective,
@@ -205,10 +204,10 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
             features.BoundingBox: F.perspective_bounding_box,
             features.Mask: F.perspective_mask,
         },
-        skips=[
-            skip_dispatch_pil_if_fill_sequence_needs_broadcast,
-        ],
         pil_kernel_info=PILKernelInfo(F.perspective_image_pil),
+        test_marks=[
+            xfail_dispatch_pil_if_fill_sequence_needs_broadcast,
+        ],
     ),
     DispatcherInfo(
         F.elastic,
@@ -227,8 +226,8 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
             features.Mask: F.center_crop_mask,
         },
         pil_kernel_info=PILKernelInfo(F.center_crop_image_pil),
-        skips=[
-            skip_integer_size_jit("output_size"),
+        test_marks=[
+            xfail_integer_size_jit("output_size"),
         ],
     ),
     DispatcherInfo(
@@ -237,9 +236,9 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
             features.Image: F.gaussian_blur_image_tensor,
         },
         pil_kernel_info=PILKernelInfo(F.gaussian_blur_image_pil),
-        skips=[
-            skip_python_scalar_arg_jit("kernel_size"),
-            skip_python_scalar_arg_jit("sigma"),
+        test_marks=[
+            xfail_python_scalar_arg_jit("kernel_size"),
+            xfail_python_scalar_arg_jit("sigma"),
         ],
     ),
     DispatcherInfo(
@@ -290,7 +289,7 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
             features.Image: F.erase_image_tensor,
         },
         pil_kernel_info=PILKernelInfo(F.erase_image_pil),
-        skips=[
+        test_marks=[
             skip_dispatch_feature,
         ],
     ),
@@ -335,8 +334,8 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
             features.Image: F.five_crop_image_tensor,
         },
         pil_kernel_info=PILKernelInfo(F.five_crop_image_pil),
-        skips=[
-            skip_integer_size_jit(),
+        test_marks=[
+            xfail_integer_size_jit(),
             skip_dispatch_feature,
         ],
     ),
@@ -345,18 +344,18 @@ def fill_sequence_needs_broadcast(args_kwargs, device):
         kernels={
             features.Image: F.ten_crop_image_tensor,
         },
-        pil_kernel_info=PILKernelInfo(F.ten_crop_image_pil),
-        skips=[
-            skip_integer_size_jit(),
+        test_marks=[
+            xfail_integer_size_jit(),
             skip_dispatch_feature,
         ],
+        pil_kernel_info=PILKernelInfo(F.ten_crop_image_pil),
     ),
     DispatcherInfo(
         F.normalize,
         kernels={
             features.Image: F.normalize_image_tensor,
         },
-        skips=[
+        test_marks=[
             skip_dispatch_feature,
         ],
     ),