wehub-resource-sync
98 lines
3.2 KiB
Python
98 lines
3.2 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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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', 'transforms'),
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[
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(
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'base_normal',
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paddle.distribution.Normal,
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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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value = np.array([0.5])
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loc = np.array([0.0])
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scale = np.array([1.0])
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shape = [5, 10, 8]
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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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static_value = paddle.static.data('value', value.shape, value.dtype)
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static_loc = paddle.static.data('loc', loc.shape, loc.dtype)
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static_scale = paddle.static.data('scale', scale.shape, scale.dtype)
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self.base = self.base(static_loc, static_scale)
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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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actual_log_prob = self._t.log_prob(static_value)
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expected_log_prob = self.transformed_log_prob(
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static_value, self.base, self.transforms
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)
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sample_data = self._t.sample(shape)
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exe.run(sp)
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[
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self.actual_log_prob,
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self.expected_log_prob,
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self.sample_data,
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] = exe.run(
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mp,
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feed={'value': value, 'loc': loc, 'scale': scale},
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fetch_list=[actual_log_prob, expected_log_prob, sample_data],
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)
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def test_log_prob(self):
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np.testing.assert_allclose(
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self.actual_log_prob,
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self.expected_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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def transformed_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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expected_shape = (5, 10, 8, 1)
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self.assertEqual(tuple(self.sample_data.shape), expected_shape)
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self.assertEqual(self.sample_data.dtype, self.dtype)
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if __name__ == '__main__':
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unittest.main()
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