forked from huawei/mindspore2022
50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
# Copyright 2019 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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This module py_transforms is implemented basing on python. It provides common
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operations including OneHotOp.
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"""
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from .validators import check_one_hot_op
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from .vision import py_transforms_util as util
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class OneHotOp:
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"""
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Apply one hot encoding transformation to the input label, make label be more smoothing and continuous.
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Args:
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num_classes (int): Num class of object in dataset, type is int and value over 0.
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smoothing_rate (float): The adjustable Hyper parameter decides the label smoothing level , 0.0 means not do it.
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"""
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@check_one_hot_op
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def __init__(self, num_classes, smoothing_rate=0.0):
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self.num_classes = num_classes
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self.smoothing_rate = smoothing_rate
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def __call__(self, label):
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"""
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Call method.
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Args:
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label (numpy.ndarray): label to be applied label smoothing.
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Returns:
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label (numpy.ndarray), label after being Smoothed.
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"""
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return util.one_hot_encoding(label, self.num_classes, self.smoothing_rate)
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