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- # copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
- #
- # Licensed 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.
- import numpy as np
- from ...utils.benchmark import benchmark
- @benchmark.timeit
- class NormalizeFeatures:
- """Normalize Features Transform"""
- def _normalize(self, preds):
- """normalize"""
- feas_norm = np.sqrt(np.sum(np.square(preds[0]), axis=0, keepdims=True))
- features = np.divide(preds[0], feas_norm)
- return features
- def __call__(self, preds):
- normalized_features = [self._normalize(feature) for feature in preds]
- return normalized_features
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