WIP

test/test_transforms_v2.py

@@ -131,6 +131,10 @@ def _check_kernel_compiled_vs_eager(kernel, input, *args, rtol, atol, **kwargs):
     actual = kernel_compiled(input, *args, **kwargs)
     expected = kernel(input, *args, **kwargs)
 
+    print("input:", input.shape, input.dtype, input.stride())
+    print("actual:", actual.shape, actual.dtype, actual.stride())
+    print("args, kwargs:", args, kwargs)
+
     assert_close(
         actual,
         expected,
@@ -611,6 +615,8 @@ def test_kernel_image(self, size, interpolation, use_max_size, antialias, dtype,
         # Internally, it uses the float path. Thus, we need to test with an enormous tolerance here to account for that.
         atol = 30 if (interpolation is transforms.InterpolationMode.BICUBIC and dtype is torch.uint8) else 1
         check_cuda_vs_cpu_tolerances = dict(rtol=0, atol=atol / 255 if dtype.is_floating_point else atol)
+        # check_compiled_vs_eager_tolerances = dict(rtol=0, atol=atol / 255 if dtype.is_floating_point else atol)
+        check_compiled_vs_eager_tolerances = True
 
         check_kernel(
             F.resize_image,
@@ -621,9 +627,7 @@ def test_kernel_image(self, size, interpolation, use_max_size, antialias, dtype,
             antialias=antialias,
             check_cuda_vs_cpu=check_cuda_vs_cpu_tolerances,
             check_scripted_vs_eager=not isinstance(size, int),
-            check_compiled_vs_eager=dict(rtol=0, atol=1)
-            if (dtype is torch.uint8 and interpolation == transforms.InterpolationMode.BILINEAR)
-            else True,
+            check_compiled_vs_eager=check_compiled_vs_eager_tolerances,
         )
 
     @pytest.mark.parametrize("format", list(tv_tensors.BoundingBoxFormat))