Add python API for MVN-6 (#3816)

* Add python api for MVN-6

* Fix code style

* Fix codestyle

* Apply feedback

* Fix test

ngraph/python/src/ngraph/opset6/__init__.py

@@ -98,7 +98,7 @@ from ngraph.opset1.ops import minimum
 from ngraph.opset4.ops import mish
 from ngraph.opset1.ops import mod
 from ngraph.opset1.ops import multiply
-from ngraph.opset2.ops import mvn
+from ngraph.opset6.ops import mvn
 from ngraph.opset1.ops import negative
 from ngraph.opset5.ops import non_max_suppression
 from ngraph.opset3.ops import non_zero

ngraph/python/src/ngraph/opset6/ops.py

@@ -111,3 +111,34 @@ def gather_elements(
     }
 
     return _get_node_factory_opset6().create("GatherElements", inputs, attributes)
+
+
+@nameable_op
+def mvn(
+    data: Node,
+    axes: Node,
+    normalize_variance: bool,
+    eps: float,
+    eps_mode: str,
+    name: Optional[str] = None,
+) -> Node:
+    """Return a node which performs MeanVarianceNormalization (MVN).
+
+    @param data: The node with data tensor.
+    @param axes: The node with axes to reduce on.
+    @param normalize_variance: Denotes whether to perform variance normalization.
+    @param eps: The number added to the variance to avoid division by zero
+               when normalizing the value. Scalar value.
+    @param eps_mode: how eps is applied (`inside_sqrt` or `outside_sqrt`)
+    @param name: Optional output node name.
+    @return The new node performing a MVN operation on input tensor.
+    """
+    inputs = as_nodes(data, axes)
+
+    attributes = {
+        "normalize_variance": normalize_variance,
+        "eps": eps,
+        "eps_mode": eps_mode
+    }
+
+    return _get_node_factory_opset6().create("MVN", inputs, attributes)

ngraph/python/tests/test_ngraph/test_normalization.py

@@ -115,3 +115,43 @@ def test_batch_norm_inference():
     result = run_op_node([data, gamma, beta, mean, variance], ng.batch_norm_inference, epsilon)
 
     assert np.allclose(result, excepted)
+
+
+def test_mvn_no_variance():
+    data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9,
+                     1, 2, 3, 4, 5, 6, 7, 8, 9,
+                     1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.float32).reshape([1, 3, 3, 3])
+    axes = np.array([2, 3], dtype=np.int64)
+    epsilon = 1e-9
+    normalize_variance = False
+    eps_mode = "outside_sqrt"
+    excepted = np.array([-4, -3, -2, -1, 0, 1, 2, 3, 4,
+                         -4, -3, -2, -1, 0, 1, 2, 3, 4,
+                         -4, -3, -2, -1, 0, 1, 2, 3, 4], dtype=np.float32).reshape([1, 3, 3, 3])
+
+    result = run_op_node([data], ng.mvn, axes, normalize_variance, epsilon, eps_mode)
+
+    assert np.allclose(result, excepted)
+
+
+def test_mvn():
+    data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9,
+                     1, 2, 3, 4, 5, 6, 7, 8, 9,
+                     1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.float32).reshape([1, 3, 3, 3])
+    axes = np.array([2, 3], dtype=np.int64)
+    epsilon = 1e-9
+    normalize_variance = True
+    eps_mode = "outside_sqrt"
+    excepted = np.array([-1.5491934, -1.161895, -0.7745967,
+                         -0.38729835, 0., 0.38729835,
+                         0.7745967, 1.161895, 1.5491934,
+                         -1.5491934, -1.161895, -0.7745967,
+                         -0.38729835, 0., 0.38729835,
+                         0.7745967, 1.161895, 1.5491934,
+                         -1.5491934, -1.161895, -0.7745967,
+                         -0.38729835, 0., 0.38729835,
+                         0.7745967, 1.161895, 1.5491934], dtype=np.float32).reshape([1, 3, 3, 3])
+
+    result = run_op_node([data], ng.mvn, axes, normalize_variance, epsilon, eps_mode)
+
+    assert np.allclose(result, excepted)