add reference test for normalize_image_tensor

test/prototype_transforms_kernel_infos.py

@@ -2232,6 +2232,22 @@ def sample_inputs_normalize_image_tensor():
         yield ArgsKwargs(image_loader, mean=mean, std=std)
 
 
+def reference_normalize_image_tensor(image, mean, std, inplace=False):
+    mean = torch.tensor(mean).view(-1, 1, 1)
+    std = torch.tensor(std).view(-1, 1, 1)
+
+    sub = torch.Tensor.sub_ if inplace else torch.Tensor.sub
+    return sub(image, mean).div_(std)
+
+
+def reference_inputs_normalize_image_tensor():
+    yield ArgsKwargs(
+        make_image_loader(size=(32, 32), color_space=datapoints.ColorSpace.RGB, extra_dims=[1]),
+        mean=[0.5, 0.5, 0.5],
+        std=[1.0, 1.0, 1.0],
+    )
+
+
 def sample_inputs_normalize_video():
     mean, std = _NORMALIZE_MEANS_STDS[0]
     for video_loader in make_video_loaders(
@@ -2246,6 +2262,8 @@ def sample_inputs_normalize_video():
             F.normalize_image_tensor,
             kernel_name="normalize_image_tensor",
             sample_inputs_fn=sample_inputs_normalize_image_tensor,
+            reference_fn=reference_normalize_image_tensor,
+            reference_inputs_fn=reference_inputs_normalize_image_tensor,
             test_marks=[
                 xfail_jit_python_scalar_arg("mean"),
                 xfail_jit_python_scalar_arg("std"),

test/test_prototype_transforms_functional.py

@@ -13,7 +13,12 @@
 
 import torchvision.prototype.transforms.utils
 from common_utils import cache, cpu_and_gpu, needs_cuda, set_rng_seed
-from prototype_common_utils import assert_close, make_bounding_boxes, parametrized_error_message
+from prototype_common_utils import (
+    assert_close,
+    DEFAULT_SQUARE_SPATIAL_SIZE,
+    make_bounding_boxes,
+    parametrized_error_message,
+)
 from prototype_transforms_dispatcher_infos import DISPATCHER_INFOS
 from prototype_transforms_kernel_infos import KERNEL_INFOS
 from torch.utils._pytree import tree_map
@@ -538,6 +543,22 @@ def test_convert_dtype_image_tensor_dtype_and_device(info, args_kwargs, device):
     assert output.device == input.device
 
 
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize("num_channels", [1, 3])
+def test_normalize_image_tensor_stats(device, num_channels):
+    stats = pytest.importorskip("scipy.stats", reason="SciPy is not available")
+
+    def assert_samples_from_standard_normal(t):
+        p_value = stats.kstest(t.flatten(), cdf="norm", args=(0, 1)).pvalue
+        return p_value > 1e-4
+
+    image = torch.rand(num_channels, DEFAULT_SQUARE_SPATIAL_SIZE, DEFAULT_SQUARE_SPATIAL_SIZE)
+    mean = image.mean(dim=(1, 2)).tolist()
+    std = image.std(dim=(1, 2)).tolist()
+
+    assert_samples_from_standard_normal(F.normalize_image_tensor(image, mean, std))
+
+
 # TODO: All correctness checks below this line should be ported to be references on a `KernelInfo` in
 #  `prototype_transforms_kernel_infos.py`