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108 lines
4.2 KiB
108 lines
4.2 KiB
# Copyright (c) 2020 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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import unittest
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import collections
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import numpy as np
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from op_test import OpTest
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class TestSampleLogitsOp(OpTest):
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def setUp(self):
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self.op_type = "sample_logits"
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self.dtype = np.float64
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self.use_mkldnn = False
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bs = 2
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K = 20
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NT = 10
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S = 5
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Samples = np.random.random([bs, NT + S]).astype('int64')
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Probabilities = np.random.random([bs, NT + S]).astype('float64')
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LogitsDim = np.array([bs, K], dtype=np.int64)
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LabelsDim = np.array([bs, NT], dtype=np.int64)
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SampledLogits = np.random.random([bs, NT + S]).astype('float64')
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SampledLabels = np.random.random([bs, NT]).astype('int64')
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self.bs = bs
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self.K = K
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self.NT = NT
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self.S = S
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Labels = np.array(list(range(self.NT)) * self.bs).astype('int64')
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self.Labels = Labels.reshape(self.bs, -1)
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self.Logits = np.random.random([self.bs, self.K]).astype('float64')
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self.inputs = {"Logits": self.Logits, "Labels": self.Labels}
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self.fetch_list = [
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'Samples', 'Probabilities', 'SampledLogits', 'SampledLabels'
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]
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self.outputs = collections.OrderedDict(
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(('Samples', Samples), ('Probabilities', Probabilities),
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('LogitsDim', LogitsDim), ('LabelsDim', LabelsDim),
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('SampledLogits', SampledLogits), ('SampledLabels',
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SampledLabels)))
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self.attrs = {'num_samples': self.S}
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def test_check_output(self):
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places = self._get_places()
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for p in places:
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(Samples, Probabilities, SampledLogits,
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SampledLabels) = [np.array(o) for o in self.calc_output(p)]
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assert Samples.dtype == np.int64, \
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"Samples dtype is {}, not int64".format(Samples.dtype)
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assert Probabilities.dtype == np.float64, \
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"Probabilities dtype is {}, not float64".format(
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Probabilities.dtype)
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assert SampledLogits.dtype == np.float64, \
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"SampledLogits dtype is {}, not float64".format(
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SampledLogits.dtype)
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assert SampledLabels.dtype == np.int64, \
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"SampledLabels dtype is {}, not int64".format(
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SampledLabels.dtype)
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assert Samples.shape == (self.bs, self.NT + self.S)
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assert Probabilities.shape == (self.bs, self.NT + self.S)
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assert SampledLogits.shape == (self.bs, self.NT + self.S)
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assert SampledLabels.shape == (self.bs, self.NT)
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assert (SampledLabels == self.Labels).all()
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sampled_logits = self.Logits[:, Samples[0][:self.NT]]
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sampled_logits -= np.log(Probabilities[:, :self.NT])
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np.testing.assert_almost_equal(sampled_logits,
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SampledLogits[:, :self.NT])
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def test_check_grad(self):
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self._check_grad_helper()
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for p in self._get_places():
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grads = self._get_gradient(['Logits'], p, ['SampledLogits'], [])
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np.testing.assert_almost_equal(grads[0].sum(), np.array([1.]))
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class TestSampleLogitsOpNoUniq(TestSampleLogitsOp):
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def setUp(self):
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super(TestSampleLogitsOpNoUniq, self).setUp()
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self.attrs = {'num_samples': self.S, 'uniq': False}
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class TestSampleLogitsOpWithAccidentalHits(TestSampleLogitsOp):
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def setUp(self):
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super(TestSampleLogitsOpWithAccidentalHits, self).setUp()
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self.attrs = {'num_samples': self.S, 'remove_accidental_hits': False}
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if __name__ == "__main__":
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unittest.main()
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