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| # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
| #
| # Licensed under the Apache License, Version 2.0 (the "License");
| # you may not use this file except in compliance with the License.
| # You may obtain a copy of the License at
| #
| # http://www.apache.org/licenses/LICENSE-2.0
| #
| # Unless required by applicable law or agreed to in writing, software
| # distributed under the License is distributed on an "AS IS" BASIS,
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
| # See the License for the specific language governing permissions and
| # limitations under the License.
| # ==============================================================================
| """Test cases for ternary operators."""
|
| from __future__ import absolute_import
| from __future__ import division
| from __future__ import print_function
|
| import numpy as np
|
| from tensorflow.compiler.tests import xla_test
| from tensorflow.python.framework import dtypes
| from tensorflow.python.ops import array_ops
| from tensorflow.python.ops import gen_math_ops
| from tensorflow.python.ops import math_ops
| from tensorflow.python.platform import googletest
|
|
| class TernaryOpsTest(xla_test.XLATestCase):
|
| def _testTernary(self, op, a, b, c, expected):
| with self.cached_session() as session:
| with self.test_scope():
| pa = array_ops.placeholder(dtypes.as_dtype(a.dtype), a.shape, name="a")
| pb = array_ops.placeholder(dtypes.as_dtype(b.dtype), b.shape, name="b")
| pc = array_ops.placeholder(dtypes.as_dtype(c.dtype), c.shape, name="c")
| output = op(pa, pb, pc)
| result = session.run(output, {pa: a, pb: b, pc: c})
| self.assertAllClose(result, expected, rtol=1e-3)
|
| def testLinspace(self):
| self._testTernary(
| math_ops.linspace,
| np.float32(1),
| np.float32(2),
| np.int32(1),
| expected=np.array([1], dtype=np.float32))
| self._testTernary(
| math_ops.linspace,
| np.float32(1),
| np.float32(4),
| np.int32(3),
| expected=np.array([1, 2.5, 4], dtype=np.float32))
|
| def testRange(self):
| self._testTernary(
| math_ops.range,
| np.int32(1),
| np.int32(2),
| np.int32(1),
| expected=np.array([1], dtype=np.int32))
| self._testTernary(
| math_ops.range,
| np.int32(1),
| np.int32(7),
| np.int32(2),
| expected=np.array([1, 3, 5], dtype=np.int32))
|
| def testSelect(self):
| for dtype in self.numeric_types:
| self._testTernary(
| array_ops.where,
| np.array(0, dtype=np.bool),
| np.array(2, dtype=dtype),
| np.array(7, dtype=dtype),
| expected=np.array(7, dtype=dtype))
|
| self._testTernary(
| array_ops.where,
| np.array(1, dtype=np.bool),
| np.array([1, 2, 3, 4], dtype=dtype),
| np.array([5, 6, 7, 8], dtype=dtype),
| expected=np.array([1, 2, 3, 4], dtype=dtype))
|
| self._testTernary(
| array_ops.where,
| np.array(0, dtype=np.bool),
| np.array([[1, 2], [3, 4], [5, 6]], dtype=dtype),
| np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype),
| expected=np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype))
|
| self._testTernary(
| array_ops.where,
| np.array([0, 1, 1, 0], dtype=np.bool),
| np.array([1, 2, 3, 4], dtype=dtype),
| np.array([5, 6, 7, 8], dtype=dtype),
| expected=np.array([5, 2, 3, 8], dtype=dtype))
|
| self._testTernary(
| array_ops.where,
| np.array([0, 1, 0], dtype=np.bool),
| np.array([[1, 2], [3, 4], [5, 6]], dtype=dtype),
| np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype),
| expected=np.array([[7, 8], [3, 4], [11, 12]], dtype=dtype))
|
| def testSlice(self):
| for dtype in self.numeric_types:
| self._testTernary(
| array_ops.slice,
| np.array([[], [], []], dtype=dtype),
| np.array([1, 0], dtype=np.int32),
| np.array([2, 0], dtype=np.int32),
| expected=np.array([[], []], dtype=dtype))
|
| self._testTernary(
| array_ops.slice,
| np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype),
| np.array([0, 1], dtype=np.int32),
| np.array([2, 1], dtype=np.int32),
| expected=np.array([[2], [5]], dtype=dtype))
|
| def testClipByValue(self):
| for dtype in self.numeric_types - self.complex_types:
| test_cases = [
| (np.array([2, 4, 5], dtype=dtype), dtype(7)), #
| (dtype(1), np.array([2, 4, 5], dtype=dtype)), #
| (np.array([-2, 7, 7], dtype=dtype), np.array([-2, 9, 8], dtype=dtype))
| ]
| x = np.array([-2, 10, 6], dtype=dtype)
| for lower, upper in test_cases:
| self._testTernary(
| gen_math_ops._clip_by_value,
| x,
| lower,
| upper,
| expected=np.minimum(np.maximum(x, lower), upper))
|
|
| if __name__ == "__main__":
| googletest.main()
|
|
