135 lines
5.5 KiB
Python
135 lines
5.5 KiB
Python
# Copyright 2019 The TensorFlow 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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# ==============================================================================
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"""Tests Sobol sequence generator."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import googletest
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class SobolSampleOpTest(test_util.TensorFlowTestCase):
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def test_basic(self):
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for dtype in [np.float64, np.float32]:
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expected = np.array([[.5, .5], [.75, .25], [.25, .75], [.375, .375]])
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sample = self.evaluate(math_ops.sobol_sample(2, 4, dtype=dtype))
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self.assertAllClose(expected, sample, 0.001)
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def test_more_known_values(self):
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for dtype in [np.float64, np.float32]:
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sample = math_ops.sobol_sample(5, 31, dtype=dtype)
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expected = [[0.50, 0.50, 0.50, 0.50, 0.50],
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[0.75, 0.25, 0.25, 0.25, 0.75],
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[0.25, 0.75, 0.75, 0.75, 0.25],
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[0.375, 0.375, 0.625, 0.875, 0.375],
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[0.875, 0.875, 0.125, 0.375, 0.875],
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[0.625, 0.125, 0.875, 0.625, 0.625],
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[0.125, 0.625, 0.375, 0.125, 0.125],
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[0.1875, 0.3125, 0.9375, 0.4375, 0.5625],
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[0.6875, 0.8125, 0.4375, 0.9375, 0.0625],
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[0.9375, 0.0625, 0.6875, 0.1875, 0.3125],
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[0.4375, 0.5625, 0.1875, 0.6875, 0.8125],
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[0.3125, 0.1875, 0.3125, 0.5625, 0.9375],
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[0.8125, 0.6875, 0.8125, 0.0625, 0.4375],
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[0.5625, 0.4375, 0.0625, 0.8125, 0.1875],
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[0.0625, 0.9375, 0.5625, 0.3125, 0.6875],
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[0.09375, 0.46875, 0.46875, 0.65625, 0.28125],
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[0.59375, 0.96875, 0.96875, 0.15625, 0.78125],
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[0.84375, 0.21875, 0.21875, 0.90625, 0.53125],
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[0.34375, 0.71875, 0.71875, 0.40625, 0.03125],
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[0.46875, 0.09375, 0.84375, 0.28125, 0.15625],
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[0.96875, 0.59375, 0.34375, 0.78125, 0.65625],
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[0.71875, 0.34375, 0.59375, 0.03125, 0.90625],
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[0.21875, 0.84375, 0.09375, 0.53125, 0.40625],
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[0.15625, 0.15625, 0.53125, 0.84375, 0.84375],
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[0.65625, 0.65625, 0.03125, 0.34375, 0.34375],
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[0.90625, 0.40625, 0.78125, 0.59375, 0.09375],
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[0.40625, 0.90625, 0.28125, 0.09375, 0.59375],
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[0.28125, 0.28125, 0.15625, 0.21875, 0.71875],
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[0.78125, 0.78125, 0.65625, 0.71875, 0.21875],
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[0.53125, 0.03125, 0.40625, 0.46875, 0.46875],
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[0.03125, 0.53125, 0.90625, 0.96875, 0.96875]]
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self.assertAllClose(expected, self.evaluate(sample), .001)
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def test_skip(self):
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dim = 10
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n = 50
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skip = 17
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for dtype in [np.float64, np.float32]:
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sample_noskip = math_ops.sobol_sample(dim, n + skip, dtype=dtype)
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sample_skip = math_ops.sobol_sample(dim, n, skip, dtype=dtype)
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self.assertAllClose(
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self.evaluate(sample_noskip)[skip:, :], self.evaluate(sample_skip))
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def test_static_shape(self):
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s = math_ops.sobol_sample(10, 100, dtype=np.float32)
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self.assertAllEqual([100, 10], s.shape.as_list())
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def test_static_shape_using_placeholder_for_dim(self):
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@def_function.function(
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input_signature=[tensor_spec.TensorSpec(shape=[], dtype=dtypes.int32)])
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def f(dim):
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s = math_ops.sobol_sample(dim, 100, dtype=dtypes.float32)
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assert s.shape.as_list() == [100, None]
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return s
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self.assertAllEqual([100, 10], self.evaluate(f(10)).shape)
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def test_static_shape_using_placeholder_for_num_results(self):
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@def_function.function(
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input_signature=[tensor_spec.TensorSpec(shape=[], dtype=dtypes.int32)])
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def f(num_results):
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s = math_ops.sobol_sample(10, num_results, dtype=dtypes.float32)
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assert s.shape.as_list() == [None, 10]
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return s
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self.assertAllEqual([100, 10], self.evaluate(f(100)).shape)
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def test_static_shape_using_only_placeholders(self):
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@def_function.function(
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input_signature=[tensor_spec.TensorSpec(shape=[], dtype=dtypes.int32)] *
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2)
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def f(dim, num_results):
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s = math_ops.sobol_sample(dim, num_results, dtype=dtypes.float32)
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assert s.shape.as_list() == [None, None]
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return s
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self.assertAllEqual([100, 10], self.evaluate(f(10, 100)).shape)
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def test_dynamic_shape(self):
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s = math_ops.sobol_sample(10, 100, dtype=dtypes.float32)
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self.assertAllEqual([100, 10], self.evaluate(s).shape)
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def test_default_dtype(self):
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# Create an op without specifying the dtype. Dtype should be float32 in
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# this case.
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s = math_ops.sobol_sample(10, 100)
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self.assertEqual(dtypes.float32, s.dtype)
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if __name__ == '__main__':
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googletest.main()
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