wehub-resource-sync
54 lines
1.9 KiB
Python
54 lines
1.9 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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"""Root mean square normalization in python"""
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import numpy as np
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def rms_norm_python(data, weight, axis, epsilon=1e-5):
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"""Root mean square normalization operator in Python.
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Parameters
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----------
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data : numpy.ndarray
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N-D with shape (d_0, d_1, ..., d_{N-1})
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weight: numpy.ndarray
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K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k
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bias: numpy.ndarray
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Optional, K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k
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axis : int or tuple of ints
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Axis over the normalization applied
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epsilon : float
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The epsilon value to avoid division by zero.
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Returns
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-------
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result : np.ndarray
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N-D with shape (d_0, d_1, ..., d_{N-1})
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"""
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dtype = data.dtype
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data = data.astype("float32")
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weight = weight.astype("float32")
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square_mean = np.mean(np.square(data), axis, keepdims=True)
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result = data * weight / np.sqrt(square_mean + epsilon)
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return result.astype(dtype)
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