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80 lines
2.5 KiB
80 lines
2.5 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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def huber_loss_forward(val, delta):
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abs_val = abs(val)
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if abs_val <= delta:
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return 0.5 * val * val
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else:
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return delta * (abs_val - 0.5 * delta)
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class TestHuberLossOp(OpTest):
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def setUp(self):
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self.op_type = 'huber_loss'
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self.samples_num = 64
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self.delta = 1.0
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self.init_input()
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residual = self.inputs['Y'].reshape(
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self.samples_num, 1) - self.inputs['X'].reshape(self.samples_num, 1)
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loss = np.vectorize(huber_loss_forward)(residual,
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self.delta).astype('float32')
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self.attrs = {'delta': self.delta}
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self.outputs = {
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'Residual': residual,
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'Out': loss.reshape((self.samples_num, 1))
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}
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def init_input(self):
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self.inputs = {
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'X': np.random.uniform(0, 1.,
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(self.samples_num, 1)).astype('float32'),
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'Y': np.random.uniform(0, 1.,
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(self.samples_num, 1)).astype('float32'),
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}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.008)
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def test_check_grad_ingore_x(self):
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self.check_grad(
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['Y'], 'Out', max_relative_error=0.008, no_grad_set=set("residual"))
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def test_check_grad_ingore_y(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=0.008, no_grad_set=set('residual'))
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def TestHuberLossOp1(TestHuberLossOp):
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def init_input(self):
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self.inputs = {
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'X': np.random.uniform(0, 1.,
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(self.samples_num, 1)).astype('float32'),
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'Y': np.random.uniform(0, 1., (self.samples_num)).astype('float32'),
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}
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if __name__ == '__main__':
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unittest.main()
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