wehub-resource-sync
155 lines
5.7 KiB
Python
155 lines
5.7 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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"""Tests for array operations."""
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from tensorflow.core.config import flags
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from tensorflow.python.eager import backprop
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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 array_ops
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from tensorflow.python.ops import gen_array_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.platform import test
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class ArrayOpTest(test.TestCase):
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def testGatherGradHasPartialStaticShape(self):
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# Create a tensor with an unknown dim 1.
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x = random_ops.random_normal([4, 10, 10])
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x = array_ops.gather(
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x, array_ops.reshape(array_ops.where_v2(x[0, :, 0] > 0.5), [-1]), axis=1
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)
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x.shape.assert_is_compatible_with([4, None, 10])
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with backprop.GradientTape() as tape:
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tape.watch(x)
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a = array_ops.gather(array_ops.gather(x, [0, 1]), [0, 1])
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grad_a = tape.gradient(a, x)
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with backprop.GradientTape() as tape:
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tape.watch(x)
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b = array_ops.gather(array_ops.gather(x, [2, 3], axis=2), [0, 1])
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grad_b = tape.gradient(b, x)
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# We make sure that the representation of the shapes are correct; the shape
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# equality check will always eval to false due to the shapes being partial.
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grad_a.shape.assert_is_compatible_with([None, None, 10])
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grad_b.shape.assert_is_compatible_with([4, None, 10])
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def testReshapeShapeInference(self):
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# Create a tensor with an unknown dim 1.
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x = random_ops.random_normal([4, 10, 10])
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x = array_ops.gather(
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x, array_ops.reshape(array_ops.where_v2(x[0, :, 0] > 0.5), [-1]), axis=1
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)
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x.shape.assert_is_compatible_with([4, None, 10])
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a = array_ops.reshape(x, array_ops.shape(x))
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a.shape.assert_is_compatible_with([4, None, 10])
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b = array_ops.reshape(x, math_ops.cast(array_ops.shape(x), dtypes.int64))
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b.shape.assert_is_compatible_with([4, None, 10])
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# We do not shape-infer across a tf.cast into anything that's not tf.int32
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# or tf.int64, since they might end up mangling the shape.
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c = array_ops.reshape(
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x,
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math_ops.cast(
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math_ops.cast(array_ops.shape(x), dtypes.float32), dtypes.int32
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),
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)
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c.shape.assert_is_compatible_with([None, None, None])
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def testEmptyMeshgrid(self):
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self.assertEqual(array_ops.meshgrid(), [])
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def testSlicedPartialShapeInference(self):
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@def_function.function(autograph=False)
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def g(x):
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return array_ops.zeros([array_ops.shape(x)[0]])
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conc = g.get_concrete_function(tensor_spec.TensorSpec([10, None]))
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self.assertAllEqual(conc.output_shapes.as_list(), [10])
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def testIdentityOnSlicedPartialShapeInference(self):
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@def_function.function(autograph=False)
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def g(x):
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return array_ops.zeros([array_ops.identity(array_ops.shape(x)[0])])
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conc = g.get_concrete_function(tensor_spec.TensorSpec([10, None]))
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self.assertAllEqual(conc.output_shapes.as_list(), [10])
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@test_util.run_in_graph_and_eager_modes
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def testParallelConcatFailsWithRankZeroShape(self):
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op = array_ops.ParallelConcat
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para = {"shape": 0, "values": [1]}
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def func():
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y = op(**para)
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return y
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with self.assertRaisesRegex(
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Exception, "(rank|dimension) of .* must be greater than .* 0"
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):
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func()
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@test_util.run_in_graph_and_eager_modes
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def testUpperBoundValuesWrongRank(self):
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# Used to cause a segfault, b/266336058
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arg0 = array_ops.zeros([2, 3], dtype=dtypes.float32)
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arg1 = array_ops.zeros([2, 1, 0], dtype=dtypes.float32)
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with self.assertRaisesRegex(
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Exception, "Shape must be rank 2 but is rank 3"
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):
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gen_array_ops.upper_bound(arg0, arg1)
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def testLowerBoundValuesWrongRank(self):
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# Used to cause a segfault, b/266336058
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arg0 = array_ops.zeros([2, 3], dtype=dtypes.float32)
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arg1 = array_ops.zeros([2, 1, 0], dtype=dtypes.float32)
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with self.assertRaisesRegex(
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Exception, "Shape must be rank 2 but is rank 3"
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):
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gen_array_ops.lower_bound(arg0, arg1)
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def testUpperBoundInputsWrongRank(self):
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# Used to cause a segfault, b/266336058
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arg0 = array_ops.zeros([2, 1, 0], dtype=dtypes.float32)
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arg1 = array_ops.zeros([2, 3], dtype=dtypes.float32)
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with self.assertRaisesRegex(
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Exception, "Shape must be rank 2 but is rank 3"
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):
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gen_array_ops.upper_bound(arg0, arg1)
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def testLowerBoundInputsWrongRank(self):
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# Used to cause a segfault, b/266336058
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arg0 = array_ops.zeros([2, 1, 0], dtype=dtypes.float32)
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arg1 = array_ops.zeros([2, 3], dtype=dtypes.float32)
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with self.assertRaisesRegex(
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Exception, "Shape must be rank 2 but is rank 3"
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):
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gen_array_ops.lower_bound(arg0, arg1)
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def testShapeDefaultIn32(self):
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# The tf_shape_default_int64 flag should NOT be set when this test runs
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self.assertFalse(flags.config().tf_shape_default_int64.value())
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s1 = array_ops.shape_v2(array_ops.zeros([1, 2]))
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self.assertEqual(s1.dtype, dtypes.int32)
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if __name__ == "__main__":
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test.main()
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