449 lines
15 KiB
Python
449 lines
15 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 the datasets shape inference."""
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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.data.ops import dataset_ops
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from tensorflow.python.data.ops import iterator_ops
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import meta_graph
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.grappler import item
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from tensorflow.python.ops import array_ops
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from tensorflow.python.platform import test
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class GrapplerTest(test.TestCase):
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def testFromTensors(self):
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test_cases = [{
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'tensor': 0,
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'shape': tensor_shape.TensorShape([])
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}, {
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'tensor': np.array([1, 2, 3]),
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'shape': tensor_shape.TensorShape([3])
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}, {
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'tensor': np.array([[1, 2, 3]]),
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'shape': tensor_shape.TensorShape([1, 3])
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}]
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for test_case in test_cases:
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with ops.Graph().as_default() as g:
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dataset = dataset_ops.Dataset.from_tensors(test_case['tensor'])
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iterator = dataset_ops.make_one_shot_iterator(dataset)
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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self.assertEqual(test_case['shape'],
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op_properties['IteratorGetNext'][0].shape)
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def testFromTensorSlices(self):
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test_cases = [{
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'tensor': np.array([1, 2, 3]),
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'shape': tensor_shape.TensorShape([])
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}, {
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'tensor': np.array([[1, 2, 3]]),
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'shape': tensor_shape.TensorShape([3])
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}, {
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'tensor': np.array([[[1, 2, 3]]]),
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'shape': tensor_shape.TensorShape([1, 3])
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}]
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for test_case in test_cases:
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with ops.Graph().as_default() as g:
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dataset = dataset_ops.Dataset.from_tensor_slices(test_case['tensor'])
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iterator = dataset_ops.make_one_shot_iterator(dataset)
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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self.assertEqual(test_case['shape'],
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op_properties['IteratorGetNext'][0].shape)
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def testFromGenerator(self):
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test_cases = [{
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'tensor': 0,
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'shape': tensor_shape.TensorShape([])
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}, {
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'tensor': np.array([1, 2, 3]),
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'shape': tensor_shape.TensorShape([3])
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}, {
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'tensor': np.array([[1, 2, 3]]),
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'shape': tensor_shape.TensorShape([1, 3])
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}]
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for test_case in test_cases:
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def make_generator(tensor):
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def generator():
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yield tensor
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return generator
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with ops.Graph().as_default() as g:
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dataset = dataset_ops.Dataset.from_generator(
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make_generator(test_case['tensor']),
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dtypes.int64,
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output_shapes=test_case['shape'])
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iterator = dataset_ops.make_one_shot_iterator(dataset)
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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self.assertEqual(test_case['shape'],
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op_properties['IteratorGetNext'][0].shape)
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def testRange(self):
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with ops.Graph().as_default() as g:
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dataset = dataset_ops.Dataset.range(42)
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iterator = dataset_ops.make_one_shot_iterator(dataset)
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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self.assertEqual(
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tensor_shape.TensorShape([]),
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op_properties['IteratorGetNext'][0].shape)
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def _testTransformation(self, fn):
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test_cases = [{
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'tensor': 0,
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'shape': tensor_shape.TensorShape({})
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}, {
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'tensor': np.array([1, 2, 3]),
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'shape': tensor_shape.TensorShape([3])
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}, {
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'tensor': np.array([[1, 2, 3]]),
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'shape': tensor_shape.TensorShape([1, 3])
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}]
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for test_case in test_cases:
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with ops.Graph().as_default() as g:
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dataset = dataset_ops.Dataset.from_tensors(test_case['tensor'])
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dataset = fn(dataset, test_case['tensor'], test_case['shape'])
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iterator = dataset_ops.make_one_shot_iterator(dataset)
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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self.assertEqual(test_case['shape'],
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op_properties['IteratorGetNext'][0].shape)
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def testConcatenate(self):
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def fn(dataset, tensor, shape):
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del shape
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return dataset.concatenate(dataset_ops.Dataset.from_tensors(tensor))
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self._testTransformation(fn)
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def testPrefetch(self):
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def fn(dataset, tensor, shape):
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del tensor, shape
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return dataset.prefetch(42)
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self._testTransformation(fn)
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def testRepeat(self):
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def fn(dataset, tensor, shape):
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del tensor, shape
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return dataset.repeat(42)
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self._testTransformation(fn)
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def testShuffle(self):
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def fn(dataset, tensor, shape):
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del tensor, shape
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return dataset.shuffle(42)
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self._testTransformation(fn)
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def testCache(self):
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def fn(dataset, tensor, shape):
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del tensor, shape
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return dataset.cache()
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self._testTransformation(fn)
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def testTake(self):
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def fn(dataset, tensor, shape):
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del tensor, shape
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return dataset.take(42)
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self._testTransformation(fn)
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def testSkip(self):
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def fn(dataset, tensor, shape):
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del tensor, shape
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return dataset.skip(42)
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self._testTransformation(fn)
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def testShard(self):
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def fn(dataset, tensor, shape):
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del tensor, shape
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return dataset.shard(42, 0)
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self._testTransformation(fn)
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def testFilter(self):
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def fn(dataset, tensor, shape):
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del tensor, shape
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return dataset.filter(lambda x: True)
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self._testTransformation(fn)
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def as_tensor_shape(self, proto_with_symbolic_values):
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for i in range(len(proto_with_symbolic_values.dim)):
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if proto_with_symbolic_values.dim[i].size < -1:
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proto_with_symbolic_values.dim[i].size = -1
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return tensor_shape.TensorShape(proto_with_symbolic_values)
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def testBatch(self):
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test_cases = [{
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'tensor': 0,
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'shape': tensor_shape.TensorShape([None])
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}, {
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'tensor': np.array([1, 2, 3]),
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'shape': tensor_shape.TensorShape([None, 3])
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}, {
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'tensor': np.array([[1, 2, 3]]),
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'shape': tensor_shape.TensorShape([None, 1, 3])
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}]
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for test_case in test_cases:
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with ops.Graph().as_default() as g:
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dataset = dataset_ops.Dataset.from_tensors(test_case['tensor'])
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dataset = dataset.batch(42)
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iterator = dataset_ops.make_one_shot_iterator(dataset)
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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inferred_shape = self.as_tensor_shape(
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op_properties['IteratorGetNext'][0].shape)
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self.assertTrue(test_case['shape'].dims[0].is_compatible_with(
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inferred_shape[0]))
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self.assertEqual(test_case['shape'][1:], inferred_shape[1:])
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def testPaddedBatch(self):
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test_cases = [{
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'tensor': 0,
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'shape': tensor_shape.TensorShape([None])
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}, {
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'tensor': np.array([1, 2, 3]),
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'shape': tensor_shape.TensorShape([None, 4])
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}, {
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'tensor': np.array([[1, 2, 3]]),
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'shape': tensor_shape.TensorShape([None, 2, 4])
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}]
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for test_case in test_cases:
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with ops.Graph().as_default() as g:
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dataset = dataset_ops.Dataset.from_tensors(test_case['tensor'])
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dataset = dataset.padded_batch(42, padded_shapes=test_case['shape'][1:])
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iterator = dataset_ops.make_one_shot_iterator(dataset)
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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inferred_shape = self.as_tensor_shape(
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op_properties['IteratorGetNext'][0].shape)
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self.assertTrue(test_case['shape'].dims[0].is_compatible_with(
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inferred_shape[0]))
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self.assertEqual(test_case['shape'][1:], inferred_shape[1:])
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def testFlatMap(self):
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test_cases = [{
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'tensor': 0,
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'shape': tensor_shape.TensorShape([])
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}, {
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'tensor': np.array([1, 2, 3]),
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'shape': tensor_shape.TensorShape([3])
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}, {
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'tensor': np.array([[1, 2, 3]]),
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'shape': tensor_shape.TensorShape([1, 3])
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}]
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for test_case in test_cases:
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with ops.Graph().as_default() as g:
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dataset = dataset_ops.Dataset.range(42)
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def make_dataset(tensor):
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def dataset_fn(n):
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return dataset_ops.Dataset.from_tensors(tensor).repeat(n)
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return dataset_fn
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dataset = dataset.flat_map(make_dataset(test_case['tensor']))
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iterator = dataset_ops.make_one_shot_iterator(dataset)
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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self.assertEqual(test_case['shape'],
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op_properties['IteratorGetNext'][0].shape)
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def testInterleave(self):
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test_cases = [{
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'tensor': 0,
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'shape': tensor_shape.TensorShape([])
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}, {
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'tensor': np.array([1, 2, 3]),
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'shape': tensor_shape.TensorShape([3])
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}, {
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'tensor': np.array([[1, 2, 3]]),
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'shape': tensor_shape.TensorShape([1, 3])
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}]
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for test_case in test_cases:
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with ops.Graph().as_default() as g:
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dataset = dataset_ops.Dataset.range(42)
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def make_dataset(tensor):
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def dataset_fn(n):
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return dataset_ops.Dataset.from_tensors(tensor).repeat(n)
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return dataset_fn
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dataset = dataset.interleave(
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make_dataset(test_case['tensor']), cycle_length=42)
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iterator = dataset_ops.make_one_shot_iterator(dataset)
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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self.assertEqual(test_case['shape'],
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op_properties['IteratorGetNext'][0].shape)
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def testMap(self):
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test_cases = [{
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'tensor': 0,
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'shape': tensor_shape.TensorShape([])
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}, {
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'tensor': np.array([1, 2, 3]),
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'shape': tensor_shape.TensorShape([3])
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}, {
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'tensor': np.array([[1, 2, 3]]),
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'shape': tensor_shape.TensorShape([3, 1])
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}, {
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'tensor': np.array([[[1, 2, 3], [4, 5, 6]]]),
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'shape': tensor_shape.TensorShape([3, 2, 1])
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}]
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for test_case in test_cases:
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with ops.Graph().as_default() as g:
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dataset = dataset_ops.Dataset.from_tensors(test_case['tensor'])
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dataset = dataset.map(array_ops.transpose)
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iterator = dataset_ops.make_one_shot_iterator(dataset)
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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self.assertEqual(test_case['shape'],
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op_properties['IteratorGetNext'][0].shape)
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def testFromStructure(self):
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test_cases = [{
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'shape': tensor_shape.TensorShape([])
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}, {
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'shape': tensor_shape.TensorShape([3])
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}, {
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'shape': tensor_shape.TensorShape([1, 2])
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}, {
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'shape': tensor_shape.TensorShape([1, 2, 3])
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}]
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for test_case in test_cases:
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with ops.Graph().as_default() as g:
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iterator = iterator_ops.Iterator.from_structure(
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dtypes.int64, output_shapes=test_case['shape'])
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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self.assertEqual(test_case['shape'],
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op_properties['IteratorGetNext'][0].shape)
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def testFromStringHandle(self):
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test_cases = [{
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'shape': tensor_shape.TensorShape([])
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}, {
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'shape': tensor_shape.TensorShape([3])
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}, {
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'shape': tensor_shape.TensorShape([1, 2])
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}, {
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'shape': tensor_shape.TensorShape([1, 2, 3])
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}]
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for test_case in test_cases:
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with ops.Graph().as_default() as g:
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iterator = iterator_ops.Iterator.from_structure(dtypes.int64)
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handle = iterator.string_handle()
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iterator = iterator_ops.Iterator.from_string_handle(
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handle, dtypes.int64, output_shapes=test_case['shape'])
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get_next = iterator.get_next()
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(get_next)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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self.assertEqual(test_case['shape'],
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op_properties['IteratorGetNext'][0].shape)
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if __name__ == '__main__':
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test.main()
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