wehub-resource-sync
80 lines
2.5 KiB
Python
80 lines
2.5 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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@param.place(config.DEVICES)
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@param.param_cls(
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(param.TEST_CASE_NAME, 'base', 'transforms'),
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[
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(
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'base_normal',
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paddle.distribution.Normal(0.0, 1.0),
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[paddle.distribution.ExpTransform()],
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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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self._t = paddle.distribution.TransformedDistribution(
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self.base, self.transforms
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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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value = paddle.to_tensor([0.5])
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np.testing.assert_allclose(
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self.simple_log_prob(value, self.base, self.transforms),
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self._t.log_prob(value),
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rtol=config.RTOL.get(str(value.numpy().dtype)),
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atol=config.ATOL.get(str(value.numpy().dtype)),
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)
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def simple_log_prob(self, value, base, transforms):
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log_prob = 0.0
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y = value
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for t in reversed(transforms):
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x = t.inverse(y)
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log_prob = log_prob - t.forward_log_det_jacobian(x)
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y = x
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log_prob += base.log_prob(y)
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return log_prob
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# TODO(cxxly): Add Kolmogorov-Smirnov test for sample result.
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def test_sample(self):
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shape = [5, 10, 8]
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expected_shape = (5, 10, 8)
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data = self._t.sample(shape)
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self.assertEqual(tuple(data.shape), expected_shape)
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self.assertEqual(data.dtype, self.base.loc.dtype)
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def test_rsample(self):
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shape = [5, 10, 8]
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expected_shape = (5, 10, 8)
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data = self._t.rsample(shape)
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self.assertEqual(tuple(data.shape), expected_shape)
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self.assertEqual(data.dtype, self.base.loc.dtype)
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if __name__ == '__main__':
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unittest.main()
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