wehub-resource-sync
60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# pylint: disable=invalid-name
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"""TVM operator for local response norm compute."""
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from .. import cpp
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def lrn(data, size, axis=1, alpha=0.0001, beta=0.75, bias=2):
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"""Perform the across channels local response normalisation
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on the input data.
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sum_sqr_up^i{x, y} = (bias+((alpha/size)* \
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{sum_{j=max(0, i-size/2)}^{min(N-1,i+size/2)} \
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(data^j{x,y})^2}))^beta
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output^i{x, y} = data^i{x, y}/sum_sqr_up^i{x, y}
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N is the number for input channels
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Parameters
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----------
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data : tvm.te.Tensor
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4-D with shape [batch, channel, height, width]
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size : int
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normalisation window size
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axis : int
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input data layout channel axis
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default value is 1 for NCHW format
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bias : float
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offset to avoid dividing by 0
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alpha : float
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to be divided
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beta : float
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exponent
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Returns
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-------
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output : tvm.te.Tensor
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4-D output with same shape
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"""
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return cpp.nn.lrn(data, size, axis, alpha, beta, bias)
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