100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
# Copyright 2017 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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"""Functional tests for ArgMin and ArgMax Ops."""
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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.compiler.tests import xla_test
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import test
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class ArgMinMaxTest(xla_test.XLATestCase):
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def _assertOpOutputMatchesExpected(self, op, axis, output_type, op_input,
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expected):
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"""Verifies that 'op' produces 'expected' when fed input 'op_input' .
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Args:
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op: argmin or argmax operator to test.
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axis: integer axis to reduce across.
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output_type: numpy datatype of the output to produce.
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op_input: numpy input array to use as input to 'op'.
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expected: numpy array representing the expected output of 'op'.
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"""
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with self.session() as session:
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with self.test_scope():
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pinp = array_ops.placeholder(
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dtypes.as_dtype(op_input.dtype), op_input.shape, name="a")
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output = op(pinp, axis=axis, output_type=output_type)
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result = session.run(output, {pinp: op_input})
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self.assertAllEqual(result, expected)
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def testArgMinMax(self):
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# Complex numbers do not support argmin/argmax.
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minmax_types = self.all_types & {np.int32, np.int64}
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for dtype in self.int_types | self.float_types:
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# output_type is a numpy data type that is used to specify the desired
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# output type of the op as well as to convert the Python number to the
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# array scalar of the type.
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for output_type in minmax_types:
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self._assertOpOutputMatchesExpected(
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math_ops.argmax,
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axis=0,
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output_type=output_type,
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op_input=np.array([1, 10, 27, 3, 3, 4], dtype=dtype),
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expected=output_type(2))
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self._assertOpOutputMatchesExpected(
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math_ops.argmax,
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axis=0,
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output_type=output_type,
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op_input=np.array([[4, 1, 7], [3, 2, 4]], dtype=dtype),
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expected=np.array([0, 1, 0], dtype=output_type))
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self._assertOpOutputMatchesExpected(
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math_ops.argmax,
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axis=1,
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output_type=output_type,
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op_input=np.array([[4, 1], [3, 2]], dtype=dtype),
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expected=np.array([0, 0], dtype=output_type))
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self._assertOpOutputMatchesExpected(
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math_ops.argmin,
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axis=0,
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output_type=output_type,
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op_input=np.array([3, 10, 27, 3, 2, 4], dtype=dtype),
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expected=output_type(4))
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self._assertOpOutputMatchesExpected(
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math_ops.argmin,
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axis=0,
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output_type=output_type,
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op_input=np.array([[4, 1, 7], [3, 2, 4]], dtype=dtype),
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expected=np.array([1, 0, 1], dtype=output_type))
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self._assertOpOutputMatchesExpected(
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math_ops.argmin,
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axis=1,
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output_type=output_type,
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op_input=np.array([[4, 1], [3, 2]], dtype=dtype),
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expected=np.array([1, 1], dtype=output_type))
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if __name__ == "__main__":
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test.main()
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