wehub-resource-sync
1300 lines
44 KiB
Python
1300 lines
44 KiB
Python
# Copyright (c) 2022 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 typing
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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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from paddle.distribution import transform, variable
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np.random.seed(2022)
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paddle.seed(2022)
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@param.place(config.DEVICES)
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class TestTransform(unittest.TestCase):
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def setUp(self):
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self._t = transform.Transform()
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@param.param_func(
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[
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(
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paddle.distribution.Distribution(),
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paddle.distribution.TransformedDistribution,
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),
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(
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paddle.distribution.ExpTransform(),
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paddle.distribution.ChainTransform,
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),
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]
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)
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def test_call(self, input, expected_type):
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t = transform.Transform()
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self.assertIsInstance(t(input), expected_type)
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@param.param_func(
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[
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(transform.Type.BIJECTION, True),
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(transform.Type.INJECTION, True),
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(transform.Type.SURJECTION, False),
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(transform.Type.OTHER, False),
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]
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)
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def test_is_injective(self, type, expected):
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transform.Transform._type = type
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self.assertEqual(self._t._is_injective(), expected)
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def test_domain(self):
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self.assertTrue(isinstance(self._t._domain, variable.Real))
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def test_codomain(self):
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self.assertTrue(isinstance(self._t._codomain, variable.Real))
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@param.param_func(
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[(0, TypeError), (paddle.rand((2, 3)), NotImplementedError)]
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)
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def test_forward(self, input, expected):
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with self.assertRaises(expected):
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self._t.forward(input)
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@param.param_func(
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[(0, TypeError), (paddle.rand((2, 3)), NotImplementedError)]
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)
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def test_inverse(self, input, expected):
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with self.assertRaises(expected):
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self._t.inverse(input)
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@param.param_func(
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[(0, TypeError), (paddle.rand((2, 3)), NotImplementedError)]
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)
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def test_forward_log_det_jacobian(self, input, expected):
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with self.assertRaises(expected):
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self._t.forward_log_det_jacobian(input)
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@param.param_func(
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[(0, TypeError), (paddle.rand((2, 3)), NotImplementedError)]
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)
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def test_inverse_log_det_jacobian(self, input, expected):
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with self.assertRaises(expected):
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self._t.inverse_log_det_jacobian(input)
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@param.param_func([(0, TypeError)])
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def test_forward_shape(self, shape, expected):
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with self.assertRaises(expected):
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self._t.forward_shape(shape)
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@param.param_func([(0, TypeError)])
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def test_inverse_shape(self, shape, expected):
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with self.assertRaises(expected):
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self._t.inverse_shape(shape)
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@param.place(config.DEVICES)
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class TestAbsTransform(unittest.TestCase):
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def setUp(self):
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self._t = transform.AbsTransform()
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def test_is_injective(self):
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self.assertFalse(self._t._is_injective())
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def test_domain(self):
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self.assertTrue(isinstance(self._t._domain, variable.Real))
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self.assertEqual(self._t._domain.event_rank, 0)
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self.assertEqual(self._t._domain.is_discrete, False)
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def test_codomain(self):
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self.assertTrue(isinstance(self._t._codomain, variable.Positive))
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self.assertEqual(self._t._codomain.event_rank, 0)
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self.assertEqual(self._t._codomain.is_discrete, False)
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@param.param_func(
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[
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(np.array([-1.0, 1.0, 0.0]), np.array([1.0, 1.0, 0.0])),
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(
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np.array([[1.0, -1.0, -0.1], [-3.0, -0.1, 0]]),
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np.array([[1.0, 1.0, 0.1], [3.0, 0.1, 0]]),
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),
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]
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)
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def test_forward(self, input, expected):
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np.testing.assert_allclose(
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self._t.forward(paddle.to_tensor(input)).numpy(),
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expected,
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rtol=config.RTOL.get(str(input.dtype)),
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atol=config.ATOL.get(str(input.dtype)),
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)
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@param.param_func([(np.array(1.0), (-np.array(1.0), np.array(1.0)))])
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def test_inverse(self, input, expected):
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actual0, actual1 = self._t.inverse(paddle.to_tensor(input))
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expected0, expected1 = expected
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np.testing.assert_allclose(
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actual0.numpy(),
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expected0,
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rtol=config.RTOL.get(str(input.dtype)),
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atol=config.ATOL.get(str(input.dtype)),
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)
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np.testing.assert_allclose(
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actual1.numpy(),
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expected1,
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rtol=config.RTOL.get(str(input.dtype)),
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atol=config.ATOL.get(str(input.dtype)),
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)
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def test_forward_log_det_jacobian(self):
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with self.assertRaises(NotImplementedError):
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self._t.forward_log_det_jacobian(paddle.rand((10,)))
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@param.param_func(
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[
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(np.array(1.0), (np.array(0.0), np.array(0.0))),
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]
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)
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def test_inverse_log_det_jacobian(self, input, expected):
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actual0, actual1 = self._t.inverse_log_det_jacobian(
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paddle.to_tensor(input)
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)
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expected0, expected1 = expected
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np.testing.assert_allclose(
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actual0.numpy(),
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expected0,
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rtol=config.RTOL.get(str(input.dtype)),
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atol=config.ATOL.get(str(input.dtype)),
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)
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np.testing.assert_allclose(
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actual1.numpy(),
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expected1,
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rtol=config.RTOL.get(str(input.dtype)),
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atol=config.ATOL.get(str(input.dtype)),
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)
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@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
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def test_forward_shape(self, shape, expected_shape):
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self.assertEqual(self._t.forward_shape(shape), expected_shape)
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@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
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def test_inverse_shape(self, shape, expected_shape):
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self.assertEqual(self._t.forward_shape(shape), expected_shape)
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@param.param_func([(np.array(1.0), np.array(1.0))])
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def test_zerodim(self, input, expected):
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x = paddle.to_tensor(input).astype('float32')
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self.assertEqual(self._t.forward(x).shape, [])
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self.assertEqual(self._t.inverse(x)[0].shape, [])
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self.assertEqual(self._t.inverse(x)[1].shape, [])
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self.assertEqual(self._t.inverse_log_det_jacobian(x)[0].shape, [])
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self.assertEqual(self._t.inverse_log_det_jacobian(x)[1].shape, [])
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self.assertEqual(self._t.forward_shape(x.shape), [])
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self.assertEqual(self._t.inverse_shape(x.shape), [])
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@param.place(config.DEVICES)
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@param.param_cls(
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(param.TEST_CASE_NAME, 'loc', 'scale'),
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[
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('normal', np.random.rand(8, 10), np.random.rand(8, 10)),
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('broadcast', np.random.rand(2, 10), np.random.rand(10)),
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],
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)
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class TestAffineTransform(unittest.TestCase):
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def setUp(self):
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self._t = transform.AffineTransform(
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paddle.to_tensor(self.loc), paddle.to_tensor(self.scale)
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)
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@param.param_func(
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[
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(paddle.rand([1]), 0, TypeError),
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(0, paddle.rand([1]), TypeError),
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]
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)
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def test_init_exception(self, loc, scale, exc):
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with self.assertRaises(exc):
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paddle.distribution.AffineTransform(loc, scale)
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def test_scale(self):
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np.testing.assert_allclose(self._t.scale, self.scale)
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def test_loc(self):
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np.testing.assert_allclose(self._t.loc, self.loc)
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def test_is_injective(self):
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self.assertTrue(self._t._is_injective())
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def test_domain(self):
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self.assertTrue(isinstance(self._t._domain, variable.Real))
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self.assertEqual(self._t._domain.event_rank, 0)
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self.assertEqual(self._t._domain.is_discrete, False)
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def test_codomain(self):
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self.assertTrue(isinstance(self._t._codomain, variable.Real))
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self.assertEqual(self._t._codomain.event_rank, 0)
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self.assertEqual(self._t._codomain.is_discrete, False)
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def test_forward(self):
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x = np.random.random(self.loc.shape)
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np.testing.assert_allclose(
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self._t.forward(paddle.to_tensor(x)).numpy(),
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self._np_forward(x),
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rtol=config.RTOL.get(str(self._t.loc.numpy().dtype)),
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atol=config.ATOL.get(str(self._t.loc.numpy().dtype)),
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)
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def test_inverse(self):
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y = np.random.random(self.loc.shape)
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np.testing.assert_allclose(
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self._t.inverse(paddle.to_tensor(y)).numpy(),
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self._np_inverse(y),
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rtol=config.RTOL.get(str(self._t.loc.numpy().dtype)),
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atol=config.ATOL.get(str(self._t.loc.numpy().dtype)),
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)
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def _np_forward(self, x):
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return self.loc + self.scale * x
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def _np_inverse(self, y):
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return (y - self.loc) / self.scale
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def _np_forward_jacobian(self, x):
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return np.log(np.abs(self.scale))
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def _np_inverse_jacobian(self, y):
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return -self._np_forward_jacobian(self._np_inverse(y))
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def test_inverse_log_det_jacobian(self):
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y = np.random.random(self.scale.shape)
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np.testing.assert_allclose(
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self._t.inverse_log_det_jacobian(paddle.to_tensor(y)).numpy(),
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self._np_inverse_jacobian(y),
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rtol=config.RTOL.get(str(self._t.loc.numpy().dtype)),
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atol=config.ATOL.get(str(self._t.loc.numpy().dtype)),
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)
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def test_forward_log_det_jacobian(self):
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x = np.random.random(self.scale.shape)
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np.testing.assert_allclose(
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self._t.forward_log_det_jacobian(paddle.to_tensor(x)).numpy(),
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self._np_forward_jacobian(x),
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rtol=config.RTOL.get(str(self._t.loc.numpy().dtype)),
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atol=config.ATOL.get(str(self._t.loc.numpy().dtype)),
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)
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def test_forward_shape(self):
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shape = self.loc.shape
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self.assertEqual(
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tuple(self._t.forward_shape(shape)),
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np.broadcast(np.random.random(shape), self.loc, self.scale).shape,
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)
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def test_inverse_shape(self):
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shape = self.scale.shape
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self.assertEqual(
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tuple(self._t.forward_shape(shape)),
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np.broadcast(np.random.random(shape), self.loc, self.scale).shape,
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)
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@param.param_func([(np.array(1.0), np.array(1.0))])
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def test_zerodim(self, input, expected):
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affine = transform.AffineTransform(paddle.zeros([]), paddle.ones([]))
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x = paddle.to_tensor(input).astype('float32')
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self.assertEqual(affine.forward(x).shape, [])
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self.assertEqual(affine.inverse(x).shape, [])
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self.assertEqual(affine.forward_log_det_jacobian(x).shape, [])
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self.assertEqual(affine.inverse_log_det_jacobian(x).shape, [])
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self.assertEqual(affine.forward_shape(x.shape), ())
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self.assertEqual(affine.inverse_shape(x.shape), ())
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@param.place(config.DEVICES)
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class TestExpTransform(unittest.TestCase):
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def setUp(self):
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self._t = transform.ExpTransform()
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def test_is_injective(self):
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self.assertTrue(self._t._is_injective())
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def test_domain(self):
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self.assertTrue(isinstance(self._t._domain, variable.Real))
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self.assertEqual(self._t._domain.event_rank, 0)
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self.assertEqual(self._t._domain.is_discrete, False)
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def test_codomain(self):
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self.assertTrue(isinstance(self._t._codomain, variable.Positive))
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self.assertEqual(self._t._codomain.event_rank, 0)
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self.assertEqual(self._t._codomain.is_discrete, False)
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@param.param_func(
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[
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(
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np.array([0.0, 1.0, 2.0, 3.0]),
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np.exp(np.array([0.0, 1.0, 2.0, 3.0])),
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),
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(
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np.array([[0.0, 1.0, 2.0, 3.0], [-5.0, 6.0, 7.0, 8.0]]),
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np.exp(np.array([[0.0, 1.0, 2.0, 3.0], [-5.0, 6.0, 7.0, 8.0]])),
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),
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]
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)
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def test_forward(self, input, expected):
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np.testing.assert_allclose(
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self._t.forward(paddle.to_tensor(input)).numpy(),
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expected,
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rtol=config.RTOL.get(str(input.dtype)),
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atol=config.ATOL.get(str(input.dtype)),
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)
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@param.param_func(
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[
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(np.array([1.0, 2.0, 3.0]), np.log(np.array([1.0, 2.0, 3.0]))),
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(
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np.array([[1.0, 2.0, 3.0], [6.0, 7.0, 8.0]]),
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np.log(np.array([[1.0, 2.0, 3.0], [6.0, 7.0, 8.0]])),
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),
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]
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)
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def test_inverse(self, input, expected):
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np.testing.assert_allclose(
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self._t.inverse(paddle.to_tensor(input)).numpy(),
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expected,
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rtol=config.RTOL.get(str(input.dtype)),
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atol=config.ATOL.get(str(input.dtype)),
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)
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@param.param_func(
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[
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(np.array([1.0, 2.0, 3.0]),),
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(np.array([[1.0, 2.0, 3.0], [6.0, 7.0, 8.0]]),),
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]
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)
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def test_forward_log_det_jacobian(self, input):
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np.testing.assert_allclose(
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self._t.forward_log_det_jacobian(paddle.to_tensor(input)).numpy(),
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self._np_forward_jacobian(input),
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rtol=config.RTOL.get(str(input.dtype)),
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atol=config.ATOL.get(str(input.dtype)),
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)
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def _np_forward_jacobian(self, x):
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return x
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|
|
@param.param_func(
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[
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(np.array([1.0, 2.0, 3.0]),),
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(np.array([[1.0, 2.0, 3.0], [6.0, 7.0, 8.0]]),),
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]
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)
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def test_inverse_log_det_jacobian(self, input):
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np.testing.assert_allclose(
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self._t.inverse_log_det_jacobian(paddle.to_tensor(input)).numpy(),
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self._np_inverse_jacobian(input),
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rtol=config.RTOL.get(str(input.dtype)),
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atol=config.ATOL.get(str(input.dtype)),
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)
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def _np_inverse_jacobian(self, y):
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return -self._np_forward_jacobian(np.log(y))
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|
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@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
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def test_forward_shape(self, shape, expected_shape):
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self.assertEqual(self._t.forward_shape(shape), expected_shape)
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|
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@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
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def test_inverse_shape(self, shape, expected_shape):
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self.assertEqual(self._t.forward_shape(shape), expected_shape)
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|
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@param.param_func([(np.array(1.0), np.array(1.0))])
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def test_zerodim(self, input, expected):
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x = paddle.to_tensor(input).astype('float32')
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self.assertEqual(self._t.forward(x).shape, [])
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self.assertEqual(self._t.inverse(x).shape, [])
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self.assertEqual(self._t.forward_log_det_jacobian(x).shape, [])
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self.assertEqual(self._t.inverse_log_det_jacobian(x).shape, [])
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self.assertEqual(self._t.forward_shape(x.shape), [])
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self.assertEqual(self._t.inverse_shape(x.shape), [])
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|
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@param.place(config.DEVICES)
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class TestChainTransform(unittest.TestCase):
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@param.param_func(
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[(paddle.distribution.Transform, TypeError), ([0], TypeError)]
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)
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def test_init_exception(self, transforms, exception):
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with self.assertRaises(exception):
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paddle.distribution.ChainTransform(transforms)
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|
|
@param.param_func(
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|
(
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(
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transform.ChainTransform(
|
|
(
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transform.AbsTransform(),
|
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transform.AffineTransform(
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paddle.rand([1]), paddle.rand([1])
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),
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)
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),
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False,
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),
|
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(
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transform.ChainTransform(
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(
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transform.AffineTransform(
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paddle.rand([1]), paddle.rand([1])
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),
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transform.ExpTransform(),
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)
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),
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True,
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),
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)
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|
)
|
|
def test_is_injective(self, chain, expected):
|
|
self.assertEqual(chain._is_injective(), expected)
|
|
|
|
@param.param_func(
|
|
(
|
|
(
|
|
transform.ChainTransform(
|
|
(
|
|
transform.IndependentTransform(
|
|
transform.ExpTransform(), 1
|
|
),
|
|
transform.IndependentTransform(
|
|
transform.ExpTransform(), 10
|
|
),
|
|
transform.IndependentTransform(
|
|
transform.ExpTransform(), 8
|
|
),
|
|
)
|
|
),
|
|
variable.Independent(variable.real, 10),
|
|
),
|
|
)
|
|
)
|
|
def test_domain(self, input, expected):
|
|
self.assertIsInstance(input._domain, type(expected))
|
|
self.assertEqual(input._domain.event_rank, expected.event_rank)
|
|
self.assertEqual(input._domain.is_discrete, expected.is_discrete)
|
|
|
|
@param.param_func(
|
|
(
|
|
(
|
|
transform.ChainTransform(
|
|
(
|
|
transform.IndependentTransform(
|
|
transform.ExpTransform(), 9
|
|
),
|
|
transform.IndependentTransform(
|
|
transform.ExpTransform(), 4
|
|
),
|
|
transform.IndependentTransform(
|
|
transform.ExpTransform(), 5
|
|
),
|
|
)
|
|
),
|
|
variable.Independent(variable.real, 9),
|
|
),
|
|
)
|
|
)
|
|
def test_codomain(self, input, expected):
|
|
self.assertIsInstance(input._codomain, variable.Independent)
|
|
self.assertEqual(input._codomain.event_rank, expected.event_rank)
|
|
self.assertEqual(input._codomain.is_discrete, expected.is_discrete)
|
|
|
|
@param.param_func(
|
|
[
|
|
(
|
|
transform.ChainTransform(
|
|
(
|
|
transform.AffineTransform(
|
|
paddle.to_tensor([0.0]), paddle.to_tensor([1.0])
|
|
),
|
|
transform.ExpTransform(),
|
|
)
|
|
),
|
|
np.array([0.0, 1.0, 2.0, 3.0]),
|
|
np.exp(np.array([0.0, 1.0, 2.0, 3.0]) * 1.0),
|
|
),
|
|
(
|
|
transform.ChainTransform(
|
|
(transform.ExpTransform(), transform.TanhTransform())
|
|
),
|
|
np.array([[0.0, -1.0, 2.0, -3.0], [-5.0, 6.0, 7.0, -8.0]]),
|
|
np.tanh(
|
|
np.exp(
|
|
np.array(
|
|
[[0.0, -1.0, 2.0, -3.0], [-5.0, 6.0, 7.0, -8.0]]
|
|
)
|
|
)
|
|
),
|
|
),
|
|
]
|
|
)
|
|
def test_forward(self, chain, input, expected):
|
|
np.testing.assert_allclose(
|
|
chain.forward(paddle.to_tensor(input)).numpy(),
|
|
expected,
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func(
|
|
[
|
|
(
|
|
transform.ChainTransform(
|
|
(
|
|
transform.AffineTransform(
|
|
paddle.to_tensor([0.0]), paddle.to_tensor([-1.0])
|
|
),
|
|
transform.ExpTransform(),
|
|
)
|
|
),
|
|
np.array([0.0, 1.0, 2.0, 3.0]),
|
|
np.log(np.array([0.0, 1.0, 2.0, 3.0])) / (-1.0),
|
|
),
|
|
(
|
|
transform.ChainTransform(
|
|
(transform.ExpTransform(), transform.TanhTransform())
|
|
),
|
|
np.array([[0.0, 1.0, 2.0, 3.0], [5.0, 6.0, 7.0, 8.0]]),
|
|
np.log(
|
|
np.arctanh(
|
|
np.array([[0.0, 1.0, 2.0, 3.0], [5.0, 6.0, 7.0, 8.0]])
|
|
)
|
|
),
|
|
),
|
|
]
|
|
)
|
|
def test_inverse(self, chain, input, expected):
|
|
np.testing.assert_allclose(
|
|
chain.inverse(paddle.to_tensor(input)).numpy(),
|
|
expected,
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func(
|
|
[
|
|
(
|
|
transform.ChainTransform(
|
|
(
|
|
transform.AffineTransform(
|
|
paddle.to_tensor([0.0]), paddle.to_tensor([-1.0])
|
|
),
|
|
transform.PowerTransform(
|
|
paddle.to_tensor([2.0], dtype='float64')
|
|
),
|
|
)
|
|
),
|
|
np.array([1.0, 2.0, 3.0]),
|
|
np.log(2.0 * np.array([1.0, 2.0, 3.0])),
|
|
),
|
|
]
|
|
)
|
|
def test_forward_log_det_jacobian(self, chain, input, expected):
|
|
np.testing.assert_allclose(
|
|
chain.forward_log_det_jacobian(paddle.to_tensor(input)).numpy(),
|
|
expected,
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func(
|
|
[
|
|
(
|
|
transform.ChainTransform(
|
|
(
|
|
transform.AffineTransform(
|
|
paddle.to_tensor([0.0]), paddle.to_tensor([-1.0])
|
|
),
|
|
transform.ExpTransform(),
|
|
)
|
|
),
|
|
(2, 3, 5),
|
|
(2, 3, 5),
|
|
),
|
|
]
|
|
)
|
|
def test_forward_shape(self, chain, shape, expected_shape):
|
|
self.assertEqual(chain.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func(
|
|
[
|
|
(
|
|
transform.ChainTransform(
|
|
(
|
|
transform.AffineTransform(
|
|
paddle.to_tensor([0.0]), paddle.to_tensor([-1.0])
|
|
),
|
|
transform.ExpTransform(),
|
|
)
|
|
),
|
|
(2, 3, 5),
|
|
(2, 3, 5),
|
|
),
|
|
]
|
|
)
|
|
def test_inverse_shape(self, chain, shape, expected_shape):
|
|
self.assertEqual(chain.inverse_shape(shape), expected_shape)
|
|
|
|
|
|
@param.place(config.DEVICES)
|
|
@param.param_cls(
|
|
(param.TEST_CASE_NAME, 'base', 'reinterpreted_batch_rank', 'x'),
|
|
[
|
|
(
|
|
'rank-over-zero',
|
|
transform.ExpTransform(),
|
|
2,
|
|
np.random.rand(2, 3, 3),
|
|
),
|
|
],
|
|
)
|
|
class TestIndependentTransform(unittest.TestCase):
|
|
def setUp(self):
|
|
self._t = transform.IndependentTransform(
|
|
self.base, self.reinterpreted_batch_rank
|
|
)
|
|
|
|
@param.param_func(
|
|
[(0, 0, TypeError), (paddle.distribution.Transform(), -1, ValueError)]
|
|
)
|
|
def test_init_exception(self, base, rank, exc):
|
|
with self.assertRaises(exc):
|
|
paddle.distribution.IndependentTransform(base, rank)
|
|
|
|
def test_is_injective(self):
|
|
self.assertEqual(self._t._is_injective(), self.base._is_injective())
|
|
|
|
def test_domain(self):
|
|
self.assertTrue(isinstance(self._t._domain, variable.Independent))
|
|
self.assertEqual(
|
|
self._t._domain.event_rank,
|
|
self.base._domain.event_rank + self.reinterpreted_batch_rank,
|
|
)
|
|
self.assertEqual(
|
|
self._t._domain.is_discrete, self.base._domain.is_discrete
|
|
)
|
|
|
|
def test_codomain(self):
|
|
self.assertTrue(isinstance(self._t._codomain, variable.Independent))
|
|
self.assertEqual(
|
|
self._t._codomain.event_rank,
|
|
self.base._codomain.event_rank + self.reinterpreted_batch_rank,
|
|
)
|
|
self.assertEqual(
|
|
self._t._codomain.is_discrete, self.base._codomain.is_discrete
|
|
)
|
|
|
|
def test_forward(self):
|
|
np.testing.assert_allclose(
|
|
self._t.forward(paddle.to_tensor(self.x)).numpy(),
|
|
self.base.forward(paddle.to_tensor(self.x)).numpy(),
|
|
rtol=config.RTOL.get(str(self.x.dtype)),
|
|
atol=config.ATOL.get(str(self.x.dtype)),
|
|
)
|
|
|
|
def test_inverse(self):
|
|
np.testing.assert_allclose(
|
|
self._t.inverse(paddle.to_tensor(self.x)).numpy(),
|
|
self.base.inverse(paddle.to_tensor(self.x)).numpy(),
|
|
rtol=config.RTOL.get(str(self.x.dtype)),
|
|
atol=config.ATOL.get(str(self.x.dtype)),
|
|
)
|
|
|
|
def test_forward_log_det_jacobian(self):
|
|
actual = self._t.forward_log_det_jacobian(paddle.to_tensor(self.x))
|
|
self.assertEqual(
|
|
tuple(actual.shape), self.x.shape[: -self.reinterpreted_batch_rank]
|
|
)
|
|
expected = self.base.forward_log_det_jacobian(
|
|
paddle.to_tensor(self.x)
|
|
).sum(list(range(-self.reinterpreted_batch_rank, 0)))
|
|
np.testing.assert_allclose(
|
|
actual.numpy(),
|
|
expected.numpy(),
|
|
rtol=config.RTOL.get(str(self.x.dtype)),
|
|
atol=config.ATOL.get(str(self.x.dtype)),
|
|
)
|
|
|
|
@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
|
|
def test_forward_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
|
|
def test_inverse_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
|
|
@param.place(config.DEVICES)
|
|
class TestPowerTransform(unittest.TestCase):
|
|
def setUp(self):
|
|
self._t = transform.PowerTransform(paddle.to_tensor([2.0]))
|
|
|
|
def test_init(self):
|
|
with self.assertRaises(TypeError):
|
|
transform.PowerTransform(1.0)
|
|
|
|
def test_is_injective(self):
|
|
self.assertTrue(self._t._is_injective())
|
|
|
|
def test_domain(self):
|
|
self.assertTrue(isinstance(self._t._domain, variable.Real))
|
|
self.assertEqual(self._t._domain.event_rank, 0)
|
|
self.assertEqual(self._t._domain.is_discrete, False)
|
|
|
|
def test_codomain(self):
|
|
self.assertTrue(isinstance(self._t._codomain, variable.Positive))
|
|
self.assertEqual(self._t._codomain.event_rank, 0)
|
|
self.assertEqual(self._t._codomain.is_discrete, False)
|
|
|
|
@param.param_func(
|
|
[
|
|
(
|
|
np.array([2.0]),
|
|
np.array([0.0, -1.0, 2.0]),
|
|
np.power(np.array([0.0, -1.0, 2.0]), 2.0),
|
|
),
|
|
(
|
|
np.array([[0.0], [3.0]]),
|
|
np.array([[1.0, 0.0], [5.0, 6.0]]),
|
|
np.power(
|
|
np.array([[1.0, 0.0], [5.0, 6.0]]), np.array([[0.0], [3.0]])
|
|
),
|
|
),
|
|
]
|
|
)
|
|
def test_forward(self, power, x, y):
|
|
t = transform.PowerTransform(paddle.to_tensor(power))
|
|
np.testing.assert_allclose(
|
|
t.forward(paddle.to_tensor(x)).numpy(),
|
|
y,
|
|
rtol=config.RTOL.get(str(x.dtype)),
|
|
atol=config.ATOL.get(str(x.dtype)),
|
|
)
|
|
|
|
@param.param_func([(np.array([2.0]), np.array([4.0]), np.array([2.0]))])
|
|
def test_inverse(self, power, y, x):
|
|
t = transform.PowerTransform(paddle.to_tensor(power))
|
|
np.testing.assert_allclose(
|
|
t.inverse(paddle.to_tensor(y)).numpy(),
|
|
x,
|
|
rtol=config.RTOL.get(str(x.dtype)),
|
|
atol=config.ATOL.get(str(x.dtype)),
|
|
)
|
|
|
|
@param.param_func(((np.array([2.0]), np.array([3.0, 1.4, 0.8])),))
|
|
def test_forward_log_det_jacobian(self, power, x):
|
|
t = transform.PowerTransform(paddle.to_tensor(power))
|
|
np.testing.assert_allclose(
|
|
t.forward_log_det_jacobian(paddle.to_tensor(x)).numpy(),
|
|
self._np_forward_jacobian(power, x),
|
|
rtol=config.RTOL.get(str(x.dtype)),
|
|
atol=config.ATOL.get(str(x.dtype)),
|
|
)
|
|
|
|
def _np_forward_jacobian(self, alpha, x):
|
|
return np.abs(np.log(alpha * np.power(x, alpha - 1)))
|
|
|
|
@param.param_func([((2, 3, 5), (2, 3, 5))])
|
|
def test_forward_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func([((2, 3, 5), (2, 3, 5))])
|
|
def test_inverse_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func([(np.array(2.0), np.array(1.0))])
|
|
def test_zerodim(self, input, expected):
|
|
power = transform.PowerTransform(paddle.full([], 2.0))
|
|
|
|
x = paddle.to_tensor(input).astype('float32')
|
|
self.assertEqual(power.forward(x).shape, [])
|
|
self.assertEqual(power.inverse(x).shape, [])
|
|
self.assertEqual(power.forward_log_det_jacobian(x).shape, [])
|
|
self.assertEqual(power.inverse_log_det_jacobian(x).shape, [])
|
|
self.assertEqual(power.forward_shape(x.shape), ())
|
|
self.assertEqual(power.inverse_shape(x.shape), ())
|
|
|
|
|
|
@param.place(config.DEVICES)
|
|
class TestTanhTransform(unittest.TestCase):
|
|
def setUp(self):
|
|
self._t = transform.TanhTransform()
|
|
|
|
def test_is_injective(self):
|
|
self.assertTrue(self._t._is_injective())
|
|
|
|
def test_domain(self):
|
|
self.assertTrue(isinstance(self._t._domain, variable.Real))
|
|
self.assertEqual(self._t._domain.event_rank, 0)
|
|
self.assertEqual(self._t._domain.is_discrete, False)
|
|
|
|
def test_codomain(self):
|
|
self.assertTrue(isinstance(self._t._codomain, variable.Variable))
|
|
self.assertEqual(self._t._codomain.event_rank, 0)
|
|
self.assertEqual(self._t._codomain.is_discrete, False)
|
|
self.assertEqual(self._t._codomain._constraint._lower, -1)
|
|
self.assertEqual(self._t._codomain._constraint._upper, 1)
|
|
|
|
@param.param_func(
|
|
[
|
|
(
|
|
np.array([0.0, 1.0, 2.0, 3.0]),
|
|
np.tanh(np.array([0.0, 1.0, 2.0, 3.0])),
|
|
),
|
|
(
|
|
np.array([[0.0, 1.0, 2.0, 3.0], [-5.0, 6.0, 7.0, 8.0]]),
|
|
np.tanh(
|
|
np.array([[0.0, 1.0, 2.0, 3.0], [-5.0, 6.0, 7.0, 8.0]])
|
|
),
|
|
),
|
|
]
|
|
)
|
|
def test_forward(self, input, expected):
|
|
np.testing.assert_allclose(
|
|
self._t.forward(paddle.to_tensor(input)).numpy(),
|
|
expected,
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func(
|
|
[
|
|
(np.array([1.0, 2.0, 3.0]), np.arctanh(np.array([1.0, 2.0, 3.0]))),
|
|
(
|
|
np.array([[1.0, 2.0, 3.0], [6.0, 7.0, 8.0]]),
|
|
np.arctanh(np.array([[1.0, 2.0, 3.0], [6.0, 7.0, 8.0]])),
|
|
),
|
|
]
|
|
)
|
|
def test_inverse(self, input, expected):
|
|
np.testing.assert_allclose(
|
|
self._t.inverse(paddle.to_tensor(input)).numpy(),
|
|
expected,
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func(
|
|
[
|
|
(np.array([1.0, 2.0, 3.0]),),
|
|
(np.array([[1.0, 2.0, 3.0], [6.0, 7.0, 8.0]]),),
|
|
]
|
|
)
|
|
def test_forward_log_det_jacobian(self, input):
|
|
np.testing.assert_allclose(
|
|
self._t.forward_log_det_jacobian(paddle.to_tensor(input)).numpy(),
|
|
self._np_forward_jacobian(input),
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
def _np_forward_jacobian(self, x):
|
|
return 2.0 * (np.log(2.0) - x - self._np_softplus(-2.0 * x))
|
|
|
|
def _np_softplus(self, x, beta=1.0, threshold=20.0):
|
|
if np.any(beta * x > threshold):
|
|
return x
|
|
return 1.0 / beta * np.log1p(np.exp(beta * x))
|
|
|
|
def _np_inverse_jacobian(self, y):
|
|
return -self._np_forward_jacobian(np.arctanh(y))
|
|
|
|
@param.param_func(
|
|
[
|
|
(np.array([1.0, 2.0, 3.0]),),
|
|
(np.array([[1.0, 2.0, 3.0], [6.0, 7.0, 8.0]]),),
|
|
]
|
|
)
|
|
def test_inverse_log_det_jacobian(self, input):
|
|
np.testing.assert_allclose(
|
|
self._t.inverse_log_det_jacobian(paddle.to_tensor(input)).numpy(),
|
|
self._np_inverse_jacobian(input),
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
|
|
def test_forward_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
|
|
def test_inverse_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func([(np.array(1.0), np.array(1.0))])
|
|
def test_zerodim(self, input, expected):
|
|
x = paddle.to_tensor(input).astype('float32')
|
|
self.assertEqual(self._t.forward(x).shape, [])
|
|
self.assertEqual(self._t.inverse(x).shape, [])
|
|
self.assertEqual(self._t.forward_log_det_jacobian(x).shape, [])
|
|
self.assertEqual(self._t.inverse_log_det_jacobian(x).shape, [])
|
|
self.assertEqual(self._t.forward_shape(x.shape), [])
|
|
self.assertEqual(self._t.inverse_shape(x.shape), [])
|
|
|
|
|
|
@param.place(config.DEVICES)
|
|
@param.param_cls(
|
|
(param.TEST_CASE_NAME, 'in_event_shape', 'out_event_shape'),
|
|
[
|
|
('regular_shape', (2, 3), (3, 2)),
|
|
],
|
|
)
|
|
class TestReshapeTransform(unittest.TestCase):
|
|
def setUp(self):
|
|
self._t = transform.ReshapeTransform(
|
|
self.in_event_shape, self.out_event_shape
|
|
)
|
|
|
|
@param.param_func([(0, 0, TypeError), ((1, 2), (1, 3), ValueError)])
|
|
def test_init_exception(self, in_event_shape, out_event_shape, exc):
|
|
with self.assertRaises(exc):
|
|
paddle.distribution.ReshapeTransform(
|
|
in_event_shape, out_event_shape
|
|
)
|
|
|
|
def test_is_injective(self):
|
|
self.assertTrue(self._t._is_injective())
|
|
|
|
def test_domain(self):
|
|
self.assertTrue(isinstance(self._t._domain, variable.Independent))
|
|
|
|
def test_codomain(self):
|
|
self.assertTrue(isinstance(self._t._codomain, variable.Independent))
|
|
|
|
def test_forward(self):
|
|
x = paddle.ones(self.in_event_shape)
|
|
np.testing.assert_allclose(
|
|
self._t.forward(x),
|
|
paddle.ones(self.out_event_shape),
|
|
rtol=config.RTOL.get(str(x.numpy().dtype)),
|
|
atol=config.ATOL.get(str(x.numpy().dtype)),
|
|
)
|
|
|
|
def test_inverse(self):
|
|
x = paddle.ones(self.out_event_shape)
|
|
np.testing.assert_allclose(
|
|
self._t.inverse(x).numpy(),
|
|
paddle.ones(self.in_event_shape).numpy(),
|
|
rtol=config.RTOL.get(str(x.numpy().dtype)),
|
|
atol=config.ATOL.get(str(x.numpy().dtype)),
|
|
)
|
|
|
|
def test_forward_log_det_jacobian(self):
|
|
x = paddle.ones(self.in_event_shape)
|
|
np.testing.assert_allclose(
|
|
self._t.forward_log_det_jacobian(x).numpy(),
|
|
paddle.zeros([1]).numpy(),
|
|
rtol=config.RTOL.get(str(x.numpy().dtype)),
|
|
atol=config.ATOL.get(str(x.numpy().dtype)),
|
|
)
|
|
|
|
def test_in_event_shape(self):
|
|
self.assertEqual(self._t.in_event_shape, self.in_event_shape)
|
|
|
|
def test_out_event_shape(self):
|
|
self.assertEqual(self._t.out_event_shape, self.out_event_shape)
|
|
|
|
@param.param_func([((), ValueError), ((1, 2), ValueError)])
|
|
def test_forward_shape_exception(self, shape, exc):
|
|
with self.assertRaises(exc):
|
|
self._t.forward_shape(shape)
|
|
|
|
@param.param_func([((), ValueError), ((1, 2), ValueError)])
|
|
def test_inverse_shape_exception(self, shape, exc):
|
|
with self.assertRaises(exc):
|
|
self._t.inverse_shape(shape)
|
|
|
|
@param.param_func([(np.array(2.0), np.array(1.0))])
|
|
def test_zerodim(self, input, expected):
|
|
reshape = transform.ReshapeTransform((), (1, 1))
|
|
|
|
x = paddle.to_tensor(input).astype('float32')
|
|
out = reshape.forward(x)
|
|
|
|
self.assertEqual(out.shape, [1, 1])
|
|
self.assertEqual(reshape.inverse(out).shape, [])
|
|
self.assertEqual(reshape.forward_log_det_jacobian(x).shape, [])
|
|
self.assertEqual(reshape.inverse_log_det_jacobian(out).shape, [])
|
|
self.assertEqual(reshape.forward_shape(x.shape), (1, 1))
|
|
self.assertEqual(reshape.inverse_shape(out.shape), ())
|
|
|
|
|
|
def _np_softplus(x, beta=1.0, threshold=20.0):
|
|
if np.any(beta * x > threshold):
|
|
return x
|
|
return 1.0 / beta * np.log1p(np.exp(beta * x))
|
|
|
|
|
|
class TestSigmoidTransform(unittest.TestCase):
|
|
def setUp(self):
|
|
self._t = transform.SigmoidTransform()
|
|
|
|
def test_is_injective(self):
|
|
self.assertTrue(self._t._is_injective())
|
|
|
|
def test_domain(self):
|
|
self.assertTrue(isinstance(self._t._domain, variable.Real))
|
|
|
|
def test_codomain(self):
|
|
self.assertTrue(isinstance(self._t._codomain, variable.Variable))
|
|
|
|
@param.param_func(
|
|
((np.ones((5, 10)), 1 / (1 + np.exp(-np.ones((5, 10))))),)
|
|
)
|
|
def test_forward(self, input, expected):
|
|
np.testing.assert_allclose(
|
|
self._t.forward(paddle.to_tensor(input)),
|
|
expected,
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func(
|
|
((np.ones(10), np.log(np.ones(10)) - np.log1p(-np.ones(10))),)
|
|
)
|
|
def test_inverse(self, input, expected):
|
|
np.testing.assert_allclose(
|
|
self._t.inverse(paddle.to_tensor(input)).numpy(),
|
|
expected,
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func(
|
|
(
|
|
(
|
|
np.ones(10),
|
|
-_np_softplus(-np.ones(10)) - _np_softplus(np.ones(10)),
|
|
),
|
|
)
|
|
)
|
|
def test_forward_log_det_jacobian(self, input, expected):
|
|
np.testing.assert_allclose(
|
|
self._t.forward_log_det_jacobian(paddle.to_tensor(input)).numpy(),
|
|
expected,
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
|
|
def test_forward_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
|
|
def test_inverse_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func([(np.array(1.0), np.array(1.0))])
|
|
def test_zerodim(self, input, expected):
|
|
x = paddle.to_tensor(input).astype('float32')
|
|
self.assertEqual(self._t.forward(x).shape, [])
|
|
self.assertEqual(self._t.inverse(x).shape, [])
|
|
self.assertEqual(self._t.forward_log_det_jacobian(x).shape, [])
|
|
self.assertEqual(self._t.inverse_log_det_jacobian(x).shape, [])
|
|
self.assertEqual(self._t.forward_shape(x.shape), [])
|
|
self.assertEqual(self._t.inverse_shape(x.shape), [])
|
|
|
|
|
|
class TestSoftmaxTransform(unittest.TestCase):
|
|
def setUp(self):
|
|
self._t = transform.SoftmaxTransform()
|
|
|
|
def test_is_injective(self):
|
|
self.assertFalse(self._t._is_injective())
|
|
|
|
def test_domain(self):
|
|
self.assertTrue(isinstance(self._t._domain, variable.Independent))
|
|
|
|
def test_codomain(self):
|
|
self.assertTrue(isinstance(self._t._codomain, variable.Variable))
|
|
|
|
@param.param_func(((np.random.random((5, 10)),),))
|
|
def test_forward(self, input):
|
|
np.testing.assert_allclose(
|
|
self._t.forward(paddle.to_tensor(input)),
|
|
self._np_forward(input),
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func(((np.random.random(10),),))
|
|
def test_inverse(self, input):
|
|
np.testing.assert_allclose(
|
|
self._t.inverse(paddle.to_tensor(input)),
|
|
self._np_inverse(input),
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
def _np_forward(self, x):
|
|
x = np.exp(x - np.max(x, -1, keepdims=True)[0])
|
|
return x / np.sum(x, -1, keepdims=True)
|
|
|
|
def _np_inverse(self, y):
|
|
return np.log(y)
|
|
|
|
def test_forward_log_det_jacobian(self):
|
|
with self.assertRaises(NotImplementedError):
|
|
self._t.forward_log_det_jacobian(paddle.rand((2, 3)))
|
|
|
|
@param.param_func([((2, 3, 5), (2, 3, 5))])
|
|
def test_forward_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func([((), ValueError)])
|
|
def test_forward_shape_exception(self, shape, exc):
|
|
with self.assertRaises(exc):
|
|
self._t.forward_shape(shape)
|
|
|
|
@param.param_func([((), ValueError)])
|
|
def test_inverse_shape_exception(self, shape, exc):
|
|
with self.assertRaises(exc):
|
|
self._t.inverse_shape(shape)
|
|
|
|
@param.param_func([((2, 3, 5), (2, 3, 5))])
|
|
def test_inverse_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.inverse_shape(shape), expected_shape)
|
|
|
|
|
|
class TestStickBreakingTransform(unittest.TestCase):
|
|
def setUp(self):
|
|
self._t = transform.StickBreakingTransform()
|
|
|
|
def test_is_injective(self):
|
|
self.assertTrue(self._t._is_injective())
|
|
|
|
def test_domain(self):
|
|
self.assertTrue(isinstance(self._t._domain, variable.Independent))
|
|
|
|
def test_codomain(self):
|
|
self.assertTrue(isinstance(self._t._codomain, variable.Variable))
|
|
|
|
@param.param_func(((np.random.random(10),),))
|
|
def test_forward(self, input):
|
|
np.testing.assert_allclose(
|
|
self._t.inverse(self._t.forward(paddle.to_tensor(input))),
|
|
input,
|
|
rtol=config.RTOL.get(str(input.dtype)),
|
|
atol=config.ATOL.get(str(input.dtype)),
|
|
)
|
|
|
|
@param.param_func([((2, 3, 5), (2, 3, 6))])
|
|
def test_forward_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func([((2, 3, 5), (2, 3, 4))])
|
|
def test_inverse_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.inverse_shape(shape), expected_shape)
|
|
|
|
@param.param_func(((np.random.random(10),),))
|
|
def test_forward_log_det_jacobian(self, x):
|
|
self.assertEqual(
|
|
self._t.forward_log_det_jacobian(paddle.to_tensor(x)).shape, []
|
|
)
|
|
|
|
|
|
# Todo
|
|
@param.place(config.DEVICES)
|
|
@param.param_cls(
|
|
(param.TEST_CASE_NAME, 'transforms', 'axis'),
|
|
[
|
|
('simple_one_transform', [transform.ExpTransform()], 0),
|
|
],
|
|
)
|
|
class TestStackTransform(unittest.TestCase):
|
|
def setUp(self):
|
|
self._t = transform.StackTransform(self.transforms, self.axis)
|
|
|
|
def test_is_injective(self):
|
|
self.assertTrue(self._t._is_injective())
|
|
|
|
def test_domain(self):
|
|
self.assertTrue(isinstance(self._t._domain, variable.Stack))
|
|
|
|
def test_codomain(self):
|
|
self.assertTrue(isinstance(self._t._codomain, variable.Stack))
|
|
|
|
@param.param_func(
|
|
[
|
|
(np.array([[0.0, 1.0, 2.0, 3.0]]),),
|
|
(np.array([[-5.0, 6.0, 7.0, 8.0]]),),
|
|
]
|
|
)
|
|
def test_forward(self, input):
|
|
self.assertEqual(
|
|
tuple(self._t.forward(paddle.to_tensor(input)).shape), input.shape
|
|
)
|
|
|
|
@param.param_func(
|
|
[
|
|
(np.array([[1.0, 2.0, 3.0]]),),
|
|
(
|
|
np.array(
|
|
[[6.0, 7.0, 8.0]],
|
|
),
|
|
),
|
|
]
|
|
)
|
|
def test_inverse(self, input):
|
|
self.assertEqual(
|
|
tuple(self._t.inverse(paddle.to_tensor(input)).shape), input.shape
|
|
)
|
|
|
|
@param.param_func(
|
|
[(np.array([[1.0, 2.0, 3.0]]),), (np.array([[6.0, 7.0, 8.0]]),)]
|
|
)
|
|
def test_forward_log_det_jacobian(self, input):
|
|
self.assertEqual(
|
|
tuple(
|
|
self._t.forward_log_det_jacobian(paddle.to_tensor(input)).shape
|
|
),
|
|
input.shape,
|
|
)
|
|
|
|
@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
|
|
def test_forward_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
@param.param_func([((), ()), ((2, 3, 5), (2, 3, 5))])
|
|
def test_inverse_shape(self, shape, expected_shape):
|
|
self.assertEqual(self._t.forward_shape(shape), expected_shape)
|
|
|
|
def test_axis(self):
|
|
self.assertEqual(self._t.axis, self.axis)
|
|
|
|
@param.param_func(
|
|
[
|
|
(0, 0, TypeError),
|
|
([0], 0, TypeError),
|
|
([paddle.distribution.ExpTransform()], 'axis', TypeError),
|
|
]
|
|
)
|
|
def test_init_exception(self, transforms, axis, exc):
|
|
with self.assertRaises(exc):
|
|
paddle.distribution.StackTransform(transforms, axis)
|
|
|
|
def test_transforms(self):
|
|
self.assertIsInstance((self._t.transforms), typing.Sequence)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|