【易用性提升No.21】新增paddle.nn.functional.group_norm

python/paddle/nn/functional/__init__.py

@@ -127,6 +127,7 @@
     layer_norm,
     local_response_norm,
     normalize,
+    group_norm,
 )
 from .pooling import (
     adaptive_avg_pool1d,
@@ -262,6 +263,7 @@
     'batch_norm',
     'layer_norm',
     'instance_norm',
+    'group_norm',
     'class_center_sample',
     'sparse_attention',
     'fold',

python/paddle/nn/functional/norm.py

@@ -633,3 +633,58 @@ def local_response_norm(
     div = paddle.pow(div, beta)
     res = paddle.divide(x, div, name=name)
     return res
+
+
+def group_norm(
+    x,
+    num_groups,
+    weight=None,
+    bias=None,
+    epsilon=1e-05,
+    data_format="NCHW",
+    name=None,
+):
+    if data_format not in ['NCHW', 'NHWC']:
+        raise ValueError("unsupported data layout:" + data_format)
+    if in_dynamic_or_pir_mode():
+        return _C_ops.group_norm(
+            x,
+            weight,
+            bias,
+            epsilon,
+            num_groups,
+            data_format,
+        )
+    helper = LayerHelper('group_norm', **locals())
+    mean_out = helper.create_variable_for_type_inference(
+        dtype=x.dtype, stop_gradient=True
+    )
+    variance_out = helper.create_variable_for_type_inference(
+        dtype=x.dtype, stop_gradient=True
+    )
+
+    inputs = {'X': x}
+    if bias is not None:
+        inputs['Bias'] = bias
+    if weight is not None:
+        inputs['Scale'] = weight
+
+    # create output
+    group_norm_out = helper.create_variable_for_type_inference(dtype=x.dtype)
+
+    helper.append_op(
+        type="group_norm",
+        inputs=inputs,
+        outputs={
+            "Y": group_norm_out,
+            "Mean": mean_out,
+            "Variance": variance_out,
+        },
+        attrs={
+            "epsilon": epsilon,
+            "groups": num_groups,
+            "data_layout": data_format,
+        },
+    )
+
+    return helper.append_activation(group_norm_out)

test/legacy_test/test_group_norm_op.py

@@ -75,8 +75,8 @@ def group_norm_wrapper(
 ):
     if data_format == "AnyLayout":
         data_format = "NCDHW"
-    return paddle._C_ops.group_norm(
-        input, weight, bias, epsilon, num_groups, data_format
+    return paddle.nn.functional.group_norm(
+        input, num_groups, weight, bias, epsilon, data_format
     )
 
 
@@ -525,6 +525,18 @@ def attr_data_format():
 
             self.assertRaises(ValueError, attr_data_format)
 
+    def test_func_dataformat_exception(self):
+        paddle.disable_static()
+        data = paddle.rand([1, 3, 3, 4], dtype="float64")
+
+        def func_data_format():
+            out = paddle.nn.functional.group_norm(
+                data, 2, None, None, 1e-5, data_format="NDHW"
+            )
+
+        self.assertRaises(ValueError, func_data_format)
+        paddle.enable_static()
+
 
 class TestGroupNormEager(unittest.TestCase):
     def test_dygraph_api(self):