wehub-resource-sync
134 lines
4.0 KiB
Python
134 lines
4.0 KiB
Python
# Copyright (c) 2021 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 numpy as np
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import parameterize as param
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from distribution import config
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import paddle
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np.random.seed(2022)
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paddle.enable_static()
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@param.place(config.DEVICES)
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@param.param_cls(
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(param.TEST_CASE_NAME, 'base', 'reinterpreted_batch_rank', 'alpha', 'beta'),
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[
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(
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'base_beta',
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paddle.distribution.Beta,
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1,
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np.random.rand(1, 2),
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np.random.rand(1, 2),
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)
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],
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)
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class TestIndependent(unittest.TestCase):
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def setUp(self):
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value = np.random.rand(1)
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self.dtype = value.dtype
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exe = paddle.static.Executor()
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sp = paddle.static.Program()
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mp = paddle.static.Program()
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with paddle.static.program_guard(mp, sp):
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alpha = paddle.static.data(
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'alpha', self.alpha.shape, self.alpha.dtype
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)
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beta = paddle.static.data('beta', self.beta.shape, self.beta.dtype)
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self.base = self.base(alpha, beta)
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t = paddle.distribution.Independent(
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self.base, self.reinterpreted_batch_rank
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)
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mean = t.mean
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variance = t.variance
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entropy = t.entropy()
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static_value = paddle.static.data('value', value.shape, value.dtype)
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log_prob = t.log_prob(static_value)
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base_mean = self.base.mean
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base_variance = self.base.variance
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base_entropy = self.base.entropy()
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base_log_prob = self.base.log_prob(static_value)
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fetch_list = [
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mean,
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variance,
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entropy,
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log_prob,
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base_mean,
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base_variance,
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base_entropy,
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base_log_prob,
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]
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exe.run(sp)
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[
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self.mean,
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self.variance,
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self.entropy,
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self.log_prob,
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self.base_mean,
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self.base_variance,
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self.base_entropy,
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self.base_log_prob,
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] = exe.run(
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mp,
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feed={'value': value, 'alpha': self.alpha, 'beta': self.beta},
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fetch_list=fetch_list,
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)
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def test_mean(self):
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np.testing.assert_allclose(
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self.mean,
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self.base_mean,
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rtol=config.RTOL.get(str(self.dtype)),
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atol=config.ATOL.get(str(self.dtype)),
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)
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def test_variance(self):
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np.testing.assert_allclose(
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self.variance,
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self.base_variance,
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rtol=config.RTOL.get(str(self.dtype)),
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atol=config.ATOL.get(str(self.dtype)),
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)
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def test_entropy(self):
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np.testing.assert_allclose(
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self._np_sum_rightmost(
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self.base_entropy, self.reinterpreted_batch_rank
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),
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self.entropy,
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rtol=config.RTOL.get(str(self.dtype)),
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atol=config.ATOL.get(str(self.dtype)),
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)
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def _np_sum_rightmost(self, value, n):
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return np.sum(value, tuple(range(-n, 0))) if n > 0 else value
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def test_log_prob(self):
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np.testing.assert_allclose(
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self._np_sum_rightmost(
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self.base_log_prob, self.reinterpreted_batch_rank
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),
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self.log_prob,
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rtol=config.RTOL.get(str(self.dtype)),
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atol=config.ATOL.get(str(self.dtype)),
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)
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if __name__ == '__main__':
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unittest.main()
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