1956f5ad87
* Change op runtime error string to match with graph construction. * Remove session scopes * Fix invalid argument tests to be eager friendly by constructing and evaluating in the same line. PiperOrigin-RevId: 320693166 Change-Id: I42ee0c4a4e141074863a366aef55f9960f0ea806
147 lines
4.9 KiB
Python
147 lines
4.9 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 scalar strictness and scalar leniency."""
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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.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 gen_io_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import sparse_ops
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import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
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from tensorflow.python.platform import test
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# TODO(rmlarsen) : Remove this test completely after we stop supporting GraphDef
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# version 5 and remove support of legacy scalars from Concat, Fill, Range,
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# and Reshape.
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class ScalarTest(test.TestCase):
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def check(self, op, args, error, correct=None, lenient=None, strict=[5, 6]):
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if lenient is None:
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lenient = []
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# Use placeholders to bypass shape inference, since only the C++
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# G raphDef level is ever scalar lenient.
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def placeholders(args, feed):
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if isinstance(args, tuple):
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return [placeholders(x, feed) for x in args]
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else:
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x = ops.convert_to_tensor(args).eval()
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fake = array_ops.placeholder(np.asarray(x).dtype)
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feed[fake] = x
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return fake
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# Test various GraphDef versions
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for version in strict + lenient:
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with ops.Graph().as_default() as g:
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test_util.set_producer_version(g, version)
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with self.session(graph=g) as sess:
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feed = {}
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xs = placeholders(args, feed)
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x = op(*xs)
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if version in strict:
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with self.assertRaisesOpError(error):
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sess.run(x, feed_dict=feed)
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else:
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r = sess.run(x, feed_dict=feed)
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if correct is not None:
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self.assertAllEqual(r, correct)
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def testConcat(self):
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for data in (2, [3], 7), ([2], 3, 7), ([2], [3], 7):
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self.check(array_ops.concat, (data, 0),
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r'Ranks of all input tensors should match', [2, 3, 7])
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def testFill(self):
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self.check(
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array_ops.fill, (2, 3),
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'dims must be a vector', [3, 3],
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lenient=[5, 6],
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strict=[])
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self.check(
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array_ops.fill, ([2], [3]),
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'value must be a scalar', [3, 3],
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lenient=[5, 6],
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strict=[])
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def testPad(self):
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self.check(array_ops.pad, (7, [[1, 2]]),
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'The first dimension of paddings must be the rank of inputs',
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[0, 7, 0, 0])
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def testRandom(self):
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self.check(random_ops.random_uniform, (3,), 'shape must be a vector')
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def testReshape(self):
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self.check(
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array_ops.reshape, (7, 1),
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'sizes input must be 1-D', [7],
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lenient=[5, 6],
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strict=[])
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def testShardedFilename(self):
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self.check(gen_io_ops.sharded_filename, ('foo', 4, [100]),
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'must be a scalar', b'foo-00004-of-00100')
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def testShardedFilespec(self):
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self.check(gen_io_ops.sharded_filespec, ('foo', [100]), 'must be a scalar',
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b'foo-?????-of-00100')
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def testUnsortedSegmentSum(self):
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self.check(math_ops.unsorted_segment_sum, (7, 1, [4]),
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'num_segments should be a scalar', [0, 7, 0, 0])
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def testRange(self):
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self.check(
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math_ops.range, ([0], 3, 2),
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'start must be a scalar', [0, 2],
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lenient=[5, 6],
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strict=[])
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self.check(
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math_ops.range, (0, [3], 2),
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'limit must be a scalar', [0, 2],
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lenient=[5, 6],
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strict=[])
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self.check(
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math_ops.range, (0, 3, [2]),
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'delta must be a scalar', [0, 2],
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lenient=[5, 6],
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strict=[])
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def testSlice(self):
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data = np.arange(10)
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error = 'Expected begin and size arguments to be 1-D tensors'
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self.check(array_ops.slice, (data, 2, 3), error, [2, 3, 4])
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self.check(array_ops.slice, (data, [2], 3), error, [2, 3, 4])
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self.check(array_ops.slice, (data, 2, [3]), error, [2, 3, 4])
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def testSparseToDense(self):
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self.check(sparse_ops.sparse_to_dense, (1, 4, 7),
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'output_shape must be rank 1', [0, 7, 0, 0])
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def testTile(self):
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self.check(array_ops.tile, ([7], 2), 'Expected multiples to be 1-D', [7, 7])
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if __name__ == '__main__':
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test.main()
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