Revert the expected value and use original eps=0 value for flaky tests.

Author: datumbox

test/expect/ModelTester.test_keypointrcnn_resnet50_fpn_expect.pkl

@@ -148,7 +148,19 @@ def _test_detection_model(self, name, dev):
         kwargs = {}
         if "retinanet" in name:
             kwargs["score_thresh"] = 0.013
-        model = models.detection.__dict__[name](num_classes=50, pretrained_backbone=False, **kwargs)
+
+        # Workaround for flaky tests
+        from torchvision.ops.misc import FrozenBatchNorm2d
+        class OverwriteEPS:
+            def __enter__(self):
+                self.default = FrozenBatchNorm2d._DEFAULT_EPS
+                FrozenBatchNorm2d._DEFAULT_EPS = 0.0
+
+            def __exit__(self, type, value, traceback):
+                FrozenBatchNorm2d._DEFAULT_EPS = self.default
+
+        with OverwriteEPS():
+            model = models.detection.__dict__[name](num_classes=50, pretrained_backbone=False, **kwargs)
         model.eval().to(device=dev)
         input_shape = (3, 300, 300)
         # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests

test/expect/ModelTester.test_retinanet_resnet50_fpn_expect.pkl

@@ -47,13 +47,18 @@ class FrozenBatchNorm2d(torch.nn.Module):
     BatchNorm2d where the batch statistics and the affine parameters
     are fixed
     """
+    _DEFAULT_EPS = 1e-5
 
     def __init__(
         self,
         num_features: int,
-        eps: float = 1e-5,
+        eps: float = None,
         n: Optional[int] = None,
     ):
+        # eps=None for unit-test stability
+        if eps is None:
+            eps = FrozenBatchNorm2d._DEFAULT_EPS
+
         # n=None for backward-compatibility
         if n is not None:
             warnings.warn("`n` argument is deprecated and has been renamed `num_features`",