327 lines
11 KiB
Python
327 lines
11 KiB
Python
# Copyright 2015 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 for tensorflow.ops.reverse_sequence_op."""
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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 itertools
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import sys
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import numpy as np
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from tensorflow.python.client import session
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.platform import benchmark
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from tensorflow.python.platform import test
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class WhereOpTest(test.TestCase):
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def _testWhere(self, x, truth, expected_err_re=None, fn=array_ops.where):
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with self.cached_session(use_gpu=True):
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ans = fn(x)
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self.assertTrue(ans.get_shape().is_compatible_with([None, x.ndim]))
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if expected_err_re is None:
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tf_ans = self.evaluate(ans)
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self.assertAllClose(tf_ans, truth, atol=1e-10)
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else:
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with self.assertRaisesOpError(expected_err_re):
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self.evaluate(ans)
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def _testWrongNumbers(self, fn=array_ops.where):
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with self.session(use_gpu=True):
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with self.assertRaises(ValueError):
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fn([False, True], [1, 2], None)
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with self.assertRaises(ValueError):
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fn([False, True], None, [1, 2])
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def _testBasicVec(self, fn=array_ops.where):
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x = np.asarray([True, False])
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truth = np.asarray([[0]], dtype=np.int64)
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self._testWhere(x, truth, None, fn)
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x = np.asarray([False, True, False])
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truth = np.asarray([[1]], dtype=np.int64)
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self._testWhere(x, truth, None, fn)
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x = np.asarray([False, False, True, False, True])
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truth = np.asarray([[2], [4]], dtype=np.int64)
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self._testWhere(x, truth, None, fn)
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def _testRandomVec(self, fn=array_ops.where):
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x = np.random.rand(1000000) > 0.5
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truth = np.vstack([np.where(x)[0].astype(np.int64)]).T
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self._testWhere(x, truth, None, fn)
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def _testBasicMat(self, fn=array_ops.where):
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x = np.asarray([[True, False], [True, False]])
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# Ensure RowMajor mode
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truth = np.asarray([[0, 0], [1, 0]], dtype=np.int64)
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self._testWhere(x, truth, None, fn)
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def _testBasic3Tensor(self, fn=array_ops.where):
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x = np.asarray([[[True, False], [True, False]],
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[[False, True], [False, True]],
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[[False, False], [False, True]]])
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# Ensure RowMajor mode
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truth = np.asarray(
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[[0, 0, 0], [0, 1, 0], [1, 0, 1], [1, 1, 1], [2, 1, 1]], dtype=np.int64)
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self._testWhere(x, truth, None, fn)
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def _testRandom(self, dtype, expected_err_re=None, fn=array_ops.where):
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shape = [127, 33, 53]
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x = np.random.randn(*shape) + 1j * np.random.randn(*shape)
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x = (np.random.randn(*shape) > 0).astype(dtype)
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truth = np.where(np.abs(x) > 0) # Tuples of indices by axis.
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truth = np.vstack(truth).T # Convert to [num_true, indices].
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self._testWhere(x, truth, expected_err_re, fn)
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def _testThreeArgument(self, fn=array_ops.where):
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x = np.array([[-2, 3, -1], [1, -3, -3]])
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np_val = np.where(x > 0, x * x, -x)
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with self.test_session(use_gpu=True):
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tf_val = self.evaluate(fn(constant_op.constant(x) > 0, x * x, -x))
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self.assertAllEqual(tf_val, np_val)
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def testWrongNumbers(self):
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self._testWrongNumbers()
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@test_util.run_deprecated_v1
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def testBasicVec(self):
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self._testBasicVec()
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@test_util.run_deprecated_v1
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def testRandomVec(self):
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self._testRandomVec()
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@test_util.run_deprecated_v1
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def testBasicMat(self):
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self._testBasicMat()
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@test_util.run_deprecated_v1
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def testBasic3Tensor(self):
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self._testBasic3Tensor()
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@test_util.run_deprecated_v1
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def testRandomBool(self):
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self._testRandom(np.bool)
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@test_util.run_deprecated_v1
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def testRandomInt32(self):
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self._testRandom(np.int32)
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@test_util.run_deprecated_v1
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def testRandomInt64(self):
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self._testRandom(np.int64)
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@test_util.run_deprecated_v1
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def testRandomFloat(self):
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self._testRandom(np.float32)
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@test_util.run_deprecated_v1
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def testRandomDouble(self):
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self._testRandom(np.float64)
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@test_util.run_deprecated_v1
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def testRandomComplex64(self):
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self._testRandom(np.complex64)
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@test_util.run_deprecated_v1
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def testRandomComplex128(self):
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self._testRandom(np.complex128)
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@test_util.run_deprecated_v1
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def testRandomUint8(self):
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self._testRandom(np.uint8)
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@test_util.run_deprecated_v1
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def testRandomInt8(self):
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self._testRandom(np.int8)
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@test_util.run_deprecated_v1
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def testRandomInt16(self):
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self._testRandom(np.int16)
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@test_util.run_deprecated_v1
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def testThreeArgument(self):
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self._testThreeArgument()
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def testV2WrongNumbers(self):
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self._testWrongNumbers(array_ops.where_v2)
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def testV2BasicVec(self):
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self._testBasicVec(array_ops.where_v2)
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def testV2RandomVec(self):
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self._testRandomVec(array_ops.where_v2)
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def testV2BasicMat(self):
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self._testBasicMat(array_ops.where_v2)
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def testV2Basic3Tensor(self):
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self._testBasic3Tensor(array_ops.where_v2)
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def testV2RandomBool(self):
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self._testRandom(np.bool, None, array_ops.where_v2)
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def testV2RandomInt32(self):
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self._testRandom(np.int32, None, array_ops.where_v2)
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def testV2RandomInt64(self):
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self._testRandom(np.int64, None, array_ops.where_v2)
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def testV2RandomFloat(self):
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self._testRandom(np.float32, None, array_ops.where_v2)
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def testV2RandomDouble(self):
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self._testRandom(np.float64, None, array_ops.where_v2)
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def testV2RandomComplex64(self):
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self._testRandom(np.complex64, None, array_ops.where_v2)
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def testV2RandomComplex128(self):
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self._testRandom(np.complex128, None, array_ops.where_v2)
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def testV2RandomUint8(self):
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self._testRandom(np.uint8, None, array_ops.where_v2)
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def testV2RandomInt8(self):
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self._testRandom(np.int8, None, array_ops.where_v2)
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def testV2RandomInt16(self):
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self._testRandom(np.int16, None, array_ops.where_v2)
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def testV2ThreeArgument(self):
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self._testThreeArgument(array_ops.where_v2)
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def testV2Broadcasting(self):
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f = np.random.normal(0, 1, (3, 5, 1, 1))
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x = np.zeros((7, 11))
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y = np.ones((7, 11))
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np_val = np.where(f < 0, x, y)
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with self.test_session(use_gpu=True):
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tf_val = self.evaluate(
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array_ops.where_v2(constant_op.constant(f) < 0, x, y))
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self.assertAllEqual(tf_val, np_val)
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def testV2ScalarBroadcasting(self):
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x = np.zeros((7, 11))
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y = np.ones((7, 11))
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np_val = np.where(True, x, y)
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with self.test_session(use_gpu=True):
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tf_val = self.evaluate(
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array_ops.where_v2(
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constant_op.constant(True, dtype=dtypes.bool), x, y))
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self.assertAllEqual(tf_val, np_val)
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def testV2VectorBroadcasting(self):
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x = np.zeros(7)
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y = np.ones(7)
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np_val = np.where([True], x, y)
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with self.test_session(use_gpu=True):
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tf_val = self.evaluate(
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array_ops.where_v2(
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constant_op.constant([True], dtype=dtypes.bool), x, y))
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self.assertAllEqual(tf_val, np_val)
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def testV2PredBroadcasting(self):
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pred = np.array([1, 0, 0]).reshape((3, 1))
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x = np.random.randn(3, 4)
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y = np.random.randn(3, 4)
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np_val = np.where(pred, x, y)
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with self.test_session(use_gpu=True):
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tf_val = self.evaluate(array_ops.where_v2(pred, x, y))
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self.assertAllClose(tf_val, np_val)
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@test_util.run_deprecated_v1
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def testBatchSelect(self):
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x = np.array([[-2, 3, -1] * 64, [1, -3, -3] * 64] * 8192) # [16384, 192]
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c_mat = np.array([[False] * 192, [True] * 192] * 8192) # [16384, 192]
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c_vec = np.array([False, True] * 8192) # [16384]
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np_val = np.where(c_mat, x * x, -x)
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with self.session(use_gpu=True):
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tf_val = array_ops.where(c_vec, x * x, -x).eval()
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self.assertAllEqual(tf_val, np_val)
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class WhereBenchmark(test.Benchmark):
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def benchmarkWhere(self):
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for (m, n, p, use_gpu) in itertools.product(
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[10],
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[10, 100, 1000, 10000, 100000, 1000000],
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[0.01, 0.5, 0.99],
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[False, True]):
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name = "m_%d_n_%d_p_%g_use_gpu_%s" % (m, n, p, use_gpu)
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device = "/%s:0" % ("gpu" if use_gpu else "cpu")
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with ops.Graph().as_default():
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with ops.device(device):
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x = random_ops.random_uniform((m, n), dtype=dtypes.float32) <= p
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v = resource_variable_ops.ResourceVariable(x)
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op = array_ops.where(v)
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with session.Session(config=benchmark.benchmark_config()) as sess:
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v.initializer.run()
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r = self.run_op_benchmark(sess, op, min_iters=100, name=name)
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gb_processed_input = m * n / 1.0e9
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# approximate size of output: m*n*p int64s for each axis.
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gb_processed_output = 2 * 8 * m * n * p / 1.0e9
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gb_processed = gb_processed_input + gb_processed_output
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throughput = gb_processed / r["wall_time"]
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print("Benchmark: %s \t wall_time: %0.03g s \t "
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"Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput))
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sys.stdout.flush()
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def benchmarkBatchSelect(self):
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for (m, n, use_gpu) in itertools.product([1000, 10000, 100000],
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[10, 100, 1000], [False, True]):
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name = "m_%d_n_%d_use_gpu_%s" % (m, n, use_gpu)
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device = "/%s:0" % ("gpu" if use_gpu else "cpu")
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with ops.Graph().as_default():
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with ops.device(device):
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x_gen = random_ops.random_uniform([m, n], dtype=dtypes.float32)
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y_gen = random_ops.random_uniform([m, n], dtype=dtypes.float32)
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c_gen = random_ops.random_uniform([m], dtype=dtypes.float32) <= 0.5
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x = resource_variable_ops.ResourceVariable(x_gen)
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y = resource_variable_ops.ResourceVariable(y_gen)
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c = resource_variable_ops.ResourceVariable(c_gen)
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op = array_ops.where(c, x, y)
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with session.Session(config=benchmark.benchmark_config()) as sess:
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x.initializer.run()
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y.initializer.run()
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c.initializer.run()
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r = self.run_op_benchmark(sess, op, min_iters=100, name=name)
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# approximate size of output: m*n*2 floats for each axis.
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gb_processed = m * n * 8 / 1.0e9
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throughput = gb_processed / r["wall_time"]
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print("Benchmark: %s \t wall_time: %0.03g s \t "
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"Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput))
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sys.stdout.flush()
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if __name__ == "__main__":
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test.main()
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