replace torch.testing.assert_allclose with torch.testing.assert_close

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/409

`assert_close` is preferred over `assert_allclose`: https://github.com/pytorch/pytorch/issues/61844

The `assert_allclose` was removed yesterday in https://github.com/pytorch/pytorch/pull/87974, causing test to fail, eg. https://github.com/facebookresearch/d2go/actions/runs/3389194553/jobs/5632021291

Reviewed By: sstsai-adl

Differential Revision: D41000306

fbshipit-source-id: 7bd1cb9d5edf0a4609a909e2283df411bcabdf13

tests/export/test_api.py

@@ -174,7 +174,7 @@ class MultiTensorInSingleTensorOut(nn.Module):
 
     @staticmethod
     def check_outputs(new_output, original_output):
-        torch.testing.assert_allclose(new_output, torch.tensor([5]))
+        torch.testing.assert_close(new_output, torch.tensor([5]))
 
 
 # NOTE: caffe2 wrapper assumes tensors are fp32
@@ -191,7 +191,7 @@ class SingleListInSingleListOut(nn.Module):
     @staticmethod
     def check_outputs(new_output, original_output):
         assert len(new_output) == 1
-        torch.testing.assert_allclose(new_output[0], torch.tensor([5.0]))
+        torch.testing.assert_close(new_output[0], torch.tensor([5.0]))
 
 
 class MultiDictInMultiDictOut(nn.Module):
@@ -210,10 +210,10 @@ class MultiDictInMultiDictOut(nn.Module):
     @staticmethod
     def check_outputs(new_output, original_output):
         first, second = original_output
-        torch.testing.assert_allclose(first["add"], torch.tensor([4]))
-        torch.testing.assert_allclose(first["sub"], torch.tensor([-2]))
-        torch.testing.assert_allclose(second["add"], torch.tensor([6]))
-        torch.testing.assert_allclose(second["sub"], torch.tensor([-2]))
+        torch.testing.assert_close(first["add"], torch.tensor([4]))
+        torch.testing.assert_close(first["sub"], torch.tensor([-2]))
+        torch.testing.assert_close(second["add"], torch.tensor([6]))
+        torch.testing.assert_close(second["sub"], torch.tensor([-2]))
 
 
 MODEL_EXPORT_METHOD_TEST_CASES = [

tests/modeling/test_box_with_nms_limit.py

@@ -47,7 +47,7 @@ class TestBoxWithNMSLimit(unittest.TestCase):
             topk_per_image=detections_per_im,
         )
         for result, kept_index, score in zip(ref_results, ref_kept_indices, scores):
-            torch.testing.assert_allclose(
+            torch.testing.assert_close(
                 score[kept_index, result.pred_classes],
                 result.scores,
             )
@@ -99,7 +99,7 @@ class TestBoxWithNMSLimit(unittest.TestCase):
         for _score_nms, _class_nms, _keeps_nms, _score in zip(
             roi_score_nms, roi_class_nms, roi_keeps_nms, scores
         ):
-            torch.testing.assert_allclose(
+            torch.testing.assert_close(
                 _score[_keeps_nms.to(torch.int64), _class_nms.to(torch.int64)],
                 _score_nms,
             )
@@ -109,9 +109,9 @@ class TestBoxWithNMSLimit(unittest.TestCase):
         ):
             s1, i1 = s.sort()
             s2, i2 = ref.scores.sort()
-            torch.testing.assert_allclose(s1, s2)
-            torch.testing.assert_allclose(b[i1], ref.pred_boxes.tensor[i2])
-            torch.testing.assert_allclose(c.to(torch.int64)[i1], ref.pred_classes[i2])
+            torch.testing.assert_close(s1, s2)
+            torch.testing.assert_close(b[i1], ref.pred_boxes.tensor[i2])
+            torch.testing.assert_close(c.to(torch.int64)[i1], ref.pred_classes[i2])
 
         for ref, k in zip(ref_kept_indices, roi_keeps_nms):
             # NOTE: order might be different due to implementation

tests/modeling/test_meta_arch_semantic_seg.py

@@ -97,7 +97,7 @@ class BaseSemanticSegTestCase:
                 for predictor_output, pytorch_output in zip(
                     predicotr_outputs, pytorch_outputs
                 ):
-                    torch.testing.assert_allclose(
+                    torch.testing.assert_close(
                         predictor_output["sem_seg"], pytorch_output["sem_seg"]
                     )