chore: import upstream snapshot with attribution

python/tvm/topi/testing/l2_normalize_python.py

@@ -0,0 +1,45 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
+"""L2 normalize in python"""
+
+import numpy as np
+
+
+def l2_normalize_python(a_np, eps, axis=None):
+    """L2 normalize operator in NCHW layout.
+
+    Parameters
+    ----------
+    a_np : numpy.ndarray
+        4-D with shape [batch, in_channel, in_height, in_width]
+
+    eps : float
+        epsilon constant value
+    axis : list of int
+        axis over the normalization applied
+
+    Returns
+    -------
+    l2_normalize_out : np.ndarray
+        4-D with shape [batch, out_channel, out_height, out_width]
+    """
+    dot_value = np.power(a_np, 2.0)
+    sqr_sum = np.sum(dot_value, axis, keepdims=True)
+    sqrt_sum = np.sqrt(np.maximum(np.broadcast_to(sqr_sum, a_np.shape), eps))
+    l2_normalize_out = np.divide(a_np, sqrt_sum)
+    return l2_normalize_out