chore: import upstream snapshot with attribution

python/tvm/topi/nn/local_response_norm.py

@@ -0,0 +1,59 @@
+# 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
+"""TVM operator for local response norm compute."""
+
+from .. import cpp
+
+
+def lrn(data, size, axis=1, alpha=0.0001, beta=0.75, bias=2):
+    """Perform the across channels local response normalisation
+    on the input data.
+
+    sum_sqr_up^i{x, y} = (bias+((alpha/size)* \
+                                {sum_{j=max(0, i-size/2)}^{min(N-1,i+size/2)} \
+                                     (data^j{x,y})^2}))^beta
+    output^i{x, y} = data^i{x, y}/sum_sqr_up^i{x, y}
+    N is the number for input channels
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        4-D with shape [batch, channel, height, width]
+
+    size : int
+        normalisation window size
+
+    axis : int
+        input data layout channel axis
+        default value is 1 for NCHW format
+
+    bias : float
+        offset to avoid dividing by 0
+
+    alpha : float
+        to be divided
+
+    beta : float
+        exponent
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        4-D output with same shape
+    """
+    return cpp.nn.lrn(data, size, axis, alpha, beta, bias)