Cleanup, fix call, add TestRoiAlign, add json file

pytest.ini

@@ -11,5 +11,3 @@ addopts =
 testpaths =
     test
 xfail_strict = True
-markers =
-    opcheck_only_one: only opcheck one parametrization

test/conftest.py

@@ -19,6 +19,7 @@ def pytest_configure(config):
     config.addinivalue_line("markers", "needs_cuda: mark for tests that rely on a CUDA device")
     config.addinivalue_line("markers", "needs_mps: mark for tests that rely on a MPS device")
     config.addinivalue_line("markers", "dont_collect: mark for tests that should not be collected")
+    config.addinivalue_line("markers", "opcheck_only_one: only opcheck one parametrization")
 
 
 def pytest_collection_modifyitems(items):

test/test_ops.py

@@ -4,7 +4,6 @@
 from functools import lru_cache
 from itertools import product
 from typing import Callable, List, Tuple
-import unittest
 
 import numpy as np
 import pytest
@@ -21,6 +20,14 @@
 from torchvision.models.feature_extraction import get_graph_node_names
 
 
+OPTESTS = [
+    "test_schema",
+    "test_autograd_registration",
+    "test_faketensor",
+    "test_aot_dispatch_dynamic",
+]
+
+
 # Context manager for setting deterministic flag and automatically
 # resetting it to its original value
 class DeterministicGuard:
@@ -464,9 +471,10 @@ def test_boxes_shape(self):
 
     @pytest.mark.parametrize("aligned", (True, False))
     @pytest.mark.parametrize("device", cpu_and_cuda_and_mps())
-    @pytest.mark.parametrize("x_dtype", (torch.float16, torch.float32, torch.float64), ids=str)
+    @pytest.mark.parametrize("x_dtype", (torch.float16, torch.float32, torch.float64))  # , ids=str)
     @pytest.mark.parametrize("contiguous", (True, False))
     @pytest.mark.parametrize("deterministic", (True, False))
+    @pytest.mark.opcheck_only_one()
     def test_forward(self, device, contiguous, deterministic, aligned, x_dtype, rois_dtype=None):
         if deterministic and device == "cpu":
             pytest.skip("cpu is always deterministic, don't retest")
@@ -484,6 +492,7 @@ def test_forward(self, device, contiguous, deterministic, aligned, x_dtype, rois
     @pytest.mark.parametrize("deterministic", (True, False))
     @pytest.mark.parametrize("x_dtype", (torch.float, torch.half))
     @pytest.mark.parametrize("rois_dtype", (torch.float, torch.half))
+    @pytest.mark.opcheck_only_one()
     def test_autocast(self, aligned, deterministic, x_dtype, rois_dtype):
         with torch.cuda.amp.autocast():
             self.test_forward(
@@ -499,6 +508,7 @@ def test_autocast(self, aligned, deterministic, x_dtype, rois_dtype):
     @pytest.mark.parametrize("device", cpu_and_cuda_and_mps())
     @pytest.mark.parametrize("contiguous", (True, False))
     @pytest.mark.parametrize("deterministic", (True, False))
+    @pytest.mark.opcheck_only_one()
     def test_backward(self, seed, device, contiguous, deterministic):
         if deterministic and device == "cpu":
             pytest.skip("cpu is always deterministic, don't retest")
@@ -513,6 +523,7 @@ def _make_rois(self, img_size, num_imgs, dtype, num_rois=1000):
     @pytest.mark.parametrize("aligned", (True, False))
     @pytest.mark.parametrize("scale, zero_point", ((1, 0), (2, 10), (0.1, 50)))
     @pytest.mark.parametrize("qdtype", (torch.qint8, torch.quint8, torch.qint32))
+    @pytest.mark.opcheck_only_one()
     def test_qroialign(self, aligned, scale, zero_point, qdtype):
         """Make sure quantized version of RoIAlign is close to float version"""
         pool_size = 5
@@ -582,6 +593,15 @@ def test_jit_boxes_list(self):
         self._helper_jit_boxes_list(model)
 
 
+optests.generate_opcheck_tests(
+    testcase=TestRoIAlign,
+    namespaces=["torchvision"],
+    failures_dict_path="test/optests_failures_dict.json",
+    additional_decorators=[],
+    test_utils=OPTESTS,
+)
+
+
 class TestPSRoIAlign(RoIOpTester):
     mps_backward_atol = 5e-2
 
@@ -837,20 +857,13 @@ def test_batched_nms_implementations(self, seed):
         empty = torch.empty((0,), dtype=torch.int64)
         torch.testing.assert_close(empty, ops.batched_nms(empty, None, None, None))
 
-data_dependent_torchvision_test_checks = [
-    "test_schema",
-    "test_autograd_registration",
-    "test_faketensor",
-    "test_aot_dispatch_dynamic",
-]
 
 optests.generate_opcheck_tests(
-    TestNMS,
-    ["torchvision"],
-    {},
-    "test/test_ops.py",
-    [],
-    data_dependent_torchvision_test_checks,
+    testcase=TestNMS,
+    namespaces=["torchvision"],
+    failures_dict_path="test/optests_failures_dict.json",
+    additional_decorators=[],
+    test_utils=OPTESTS,
 )