wehub-resource-sync
76 lines
2.5 KiB
Python
76 lines
2.5 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, line-too-long, unused-variable, too-many-locals
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# ruff: noqa: E741
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"""LRN in python"""
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from itertools import product
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import numpy as np
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def lrn_python(a_np, size, axis, bias, alpha, beta):
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"""Local response normalization operator in NCHW layout.
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Parameters
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----------
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a_np : numpy.ndarray
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4-D with shape [batch, in_channel, in_height, in_width]
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size : int
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normalization window size
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axis : int
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input data layout channel axis
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bias : float
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offset to avoid dividing by 0. constant value
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alpha : float
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constant value
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beta : float
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exponent constant value
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Returns
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-------
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lrn_out : np.ndarray
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4-D with shape [batch, out_channel, out_height, out_width]
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"""
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radius = size // 2
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sqr_sum = np.zeros(shape=a_np.shape).astype(a_np.dtype)
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for i, j, k, l in product(*[range(_axis) for _axis in a_np.shape]):
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axis_size = a_np.shape[axis]
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if axis == 1:
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# NCHW layout
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sum_start = j - radius if j - radius >= 0 else 0
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sum_end = j + radius + 1 if j + radius + 1 < axis_size else axis_size
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sqr_sum[i, j, k, l] = sum(
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a_np[i, sum_start:sum_end, k, l] * a_np[i, sum_start:sum_end, k, l]
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)
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elif axis == 3:
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# NHWC layout
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sum_start = l - radius if l - radius >= 0 else 0
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sum_end = l + radius + 1 if l + radius + 1 < axis_size else axis_size
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sqr_sum[i, j, k, l] = sum(
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a_np[i, j, k, sum_start:sum_end] * a_np[i, j, k, sum_start:sum_end]
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)
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sqr_sum_up = np.power((bias + (alpha * sqr_sum / size)), beta)
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lrn_out = np.divide(a_np, sqr_sum_up)
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return lrn_out
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