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/external/openthread/src/cli/
DREADME_DATASET.md5 Thread network configuration parameters are managed using Active and Pending Operational Dataset ob…
7 ### Active Operational Dataset
9 …ctive Operational Dataset includes parameters that are currently in use across an entire Thread ne…
21 ### Pending Operational Dataset
23 …Dataset is used to communicate changes to the Active Operational Dataset before they take effect. …
35 > dataset init new
37 > dataset
51 2. Commit new dataset to the Active Operational Dataset in non-volatile storage.
54 dataset commit active
73 …lly attaches to a Thread network, the device will retrieve the complete Active Operational Dataset.
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Dcli_dataset.cpp39 #include <openthread/dataset.h>
48 otOperationalDatasetTlvs Dataset::sDatasetTlvs;
50 const Dataset::ComponentMapper *Dataset::LookupMapper(const char *aName) const in LookupMapper()
56 &Dataset::OutputActiveTimestamp, in LookupMapper()
57 &Dataset::ParseActiveTimestamp, in LookupMapper()
62 &Dataset::OutputChannel, in LookupMapper()
63 &Dataset::ParseChannel, in LookupMapper()
68 &Dataset::OutputChannelMask, in LookupMapper()
69 &Dataset::ParseChannelMask, in LookupMapper()
74 &Dataset::OutputDelay, in LookupMapper()
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/external/tensorflow/tensorflow/python/data/experimental/kernel_tests/
Dauto_shard_dataset_test.py57 def getAllDatasetElements(self, dataset): argument
59 next_fn = self.getNext(dataset)
67 def assertDatasetProducesWithShuffle(self, dataset, expected, batch, argument
71 next_fn = self.getNext(dataset)
83 self.assertDatasetProduces(dataset, list(chunk(expected, batch)))
90 dataset = dataset_ops.Dataset.list_files(
92 dataset = dataset.flat_map(core_readers.TFRecordDataset)
93 dataset = dataset.batch(5)
94 dataset = distribute._AutoShardDataset(dataset, 5, 3)
101 self.assertDatasetProducesWithShuffle(dataset, expected, 5, 4, shuffle)
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Ddirected_interleave_dataset_test.py32 return combinations.combine(weights_type=["list", "tensor", "dataset"])
40 return dataset_ops.Dataset.from_tensors(weights_list).repeat()
48 selector_dataset = dataset_ops.Dataset.range(10).repeat(100)
50 dataset_ops.Dataset.from_tensors(i).repeat(100) for i in range(10)
52 dataset = dataset_ops._DirectedInterleaveDataset(selector_dataset,
54 next_element = self.getNext(dataset)
87 # Create a dataset that samples each integer in `[0, num_datasets)`
89 dataset = dataset_ops.Dataset.sample_from_datasets([
90 dataset_ops.Dataset.from_tensors(i).repeat() for i in range(classes)
92 dataset = dataset.take(num_samples)
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Drebatch_dataset_test.py150 def _flat_shapes(dataset): argument
153 for ts in nest.flatten(dataset_ops.get_legacy_output_shapes(dataset))
165 dataset = dataset_ops.Dataset.range(8).batch(4, drop_remainder=True)
167 dataset, batch_sizes=[2, 1, 1])
175 dataset = dataset_ops.Dataset.range(8).batch(4, drop_remainder=True)
177 dataset, batch_sizes=[2, 2], drop_remainder=drop_remainder)
184 dataset = dataset_ops.Dataset.range(8).batch(4, drop_remainder=False)
186 dataset, batch_sizes=[2, 2], drop_remainder=False)
193 dataset = dataset_ops.Dataset.range(8).batch(4, drop_remainder=False)
195 dataset, batch_sizes=[2, 2], drop_remainder=True)
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/external/tensorflow/tensorflow/python/data/experimental/kernel_tests/service/
Dauto_shard_test.py75 dataset = dataset_ops.Dataset.range(20)
76 dataset = self.make_distributed_dataset(
77 dataset, cluster=cluster, processing_mode=sharding_policy)
78 self.assertDatasetProduces(dataset, [1, 6, 11, 16])
83 dataset = dataset_ops.Dataset.range(20)
84 dataset = self.make_distributed_dataset(
85 dataset,
89 "Found an unshardable source dataset"):
90 self.getDatasetOutput(dataset)
98 dataset = dataset_ops.Dataset.range(20)
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/external/tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/
Dfilter_parallelization_test.py15 """Tests for `tf.data.Dataset.filter()`."""
36 def filter_fn(dataset, predicate): argument
37 return dataset.filter(predicate)
39 def legacy_filter_fn(dataset, predicate): argument
40 return dataset.filter_with_legacy_function(predicate)
59 def enableFilterParallelization(self, dataset): argument
62 return dataset.with_options(options)
75 dataset = dataset_ops.Dataset.from_tensor_slices(components).map(
77 dataset = self.enableFilterParallelization(dataset)
78 dataset = dataset.apply(testing.assert_next(["ParallelFilter"]))
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Doptimization_test.py41 return dataset_ops.Dataset.from_tensors(0).map(lambda x: x + var)
44 return dataset_ops.Dataset.from_tensors(
45 0).flat_map(lambda _: dataset_ops.Dataset.from_tensors(var))
48 return dataset_ops.Dataset.from_tensors(0).filter(lambda x: x < var)
55 return dataset_ops.Dataset.from_tensors(0).apply(
63 return dataset_ops.Dataset.range(5).apply(
70 return dataset_ops.Dataset.from_tensors(var)
72 return dataset_ops.Dataset.from_tensors(0).repeat(10).apply(
76 return dataset_ops.Dataset.from_tensors(0).apply(
104 dataset = dataset_ops.Dataset.range(
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Dmake_deterministic_test.py48 # Set the seed, since in graph mode some non-random dataset ops call
51 # TODO(reedwm): Ensure such dataset ops do not raise an error when no seed
71 return dataset_ops.Dataset.range(2).map(map_fn)
77 dataset = dataset_ops.Dataset.range(5)
79 dataset = dataset.apply(
82 dataset = dataset.interleave(
87 dataset,
108 dataset = dataset_ops.Dataset.range(5)
109 dataset = dataset.map(map_fn, num_parallel_calls=5)
113 dataset,
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/external/tensorflow/tensorflow/python/data/experimental/benchmarks/
Dautotune_benchmark.py28 def _run_benchmark(self, dataset, autotune, benchmark_iters, benchmark_label, argument
33 dataset = dataset.with_options(options)
37 dataset=dataset,
58 dataset = dataset_ops.Dataset.from_tensors(
60 dataset = dataset.map(math_ops.matmul)
61 dataset = dataset.batch(
64 dataset=dataset,
77 dataset = dataset_ops.Dataset.from_tensors(
79 dataset = dataset.map(
82 dataset=dataset,
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/external/openthread/src/core/meshcop/
Ddataset_manager.hpp45 #include "meshcop/dataset.hpp"
66 * Restores the Operational Dataset from non-volatile memory.
68 * @retval kErrorNone Successfully restore the dataset.
69 * @retval kErrorNotFound There is no corresponding dataset stored in non-volatile memory.
75 * Retrieves the dataset from non-volatile memory.
77 * @param[out] aDataset Where to place the dataset.
79 * @retval kErrorNone Successfully retrieved the dataset.
80 * @retval kErrorNotFound There is no corresponding dataset stored in non-volatile memory.
83 Error Read(Dataset &aDataset) const { return mLocal.Read(aDataset); } in Read()
86 * Retrieves the dataset from non-volatile memory.
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Ddataset.hpp40 #include <openthread/dataset.h>
56 * Represents MeshCop Dataset.
59 class Dataset class
64 …uint8_t kMaxSize = OT_OPERATIONAL_DATASET_MAX_LENGTH; ///< Max size of MeshCoP Dataset (bytes)
69 * Represents the Dataset type (active or pending).
74 kActive, ///< Active Dataset
75 kPending, ///< Pending Dataset
79 * Represents a Dataset as a sequence of TLVs.
85 * Represents presence of different components in Active or Pending Operational Dataset.
92 * Indicates whether or not the Active Timestamp is present in the Dataset.
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Ddataset_local.hpp41 #include "meshcop/dataset.hpp"
54 * @param[in] aType The type of the dataset, active or pending.
57 DatasetLocal(Instance &aInstance, Dataset::Type aType);
60 * Indicates whether this is an Active or Pending Dataset.
62 * @returns The Dataset type.
65 Dataset::Type GetType(void) const { return mType; } in GetType()
68 * Clears the Dataset.
74 * Indicates whether an Active or Pending Dataset is saved in non-volatile memory.
76 * @retval TRUE if an Active or Pending Dataset is saved in non-volatile memory.
77 * @retval FALSE if an Active or Pending Dataset is not saved in non-volatile memory.
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/external/openthread/tests/scripts/expect/
Dcli-dataset.exp36 send "dataset active\n"
48 send "dataset pending\n"
50 send "dataset init active\n"
52 send "dataset activetimestamp 100\n"
54 send "dataset activetimestamp\n"
58 send "dataset channel 18\n"
60 send "dataset channel\n"
64 send "dataset channel 11\n"
66 send "dataset channel\n"
70 send "dataset channelmask 0x03fff800\n"
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/external/tensorflow/tensorflow/python/data/kernel_tests/
Dcardinality_test.py15 """Tests for `tf.data.Dataset.cardinality()`."""
29 ("Map1", lambda: dataset_ops.Dataset.range(5).map(lambda x: x),
31 ("Map2", lambda: dataset_ops.Dataset.range(5).map(
35 ("Map1", lambda: dataset_ops.Dataset.range(5).map(lambda x: x), 5),
36 ("Map2", lambda: dataset_ops.Dataset.range(5).map(
41 lambda: dataset_ops.Dataset.range(5).batch(2, drop_remainder=True), 2),
43 lambda: dataset_ops.Dataset.range(5).batch(2, drop_remainder=False), 3),
45 lambda: dataset_ops.Dataset.range(5).filter(lambda _: True).batch(2),
47 ("Batch4", lambda: dataset_ops.Dataset.range(5).repeat().batch(2),
49 ("Cache1", lambda: dataset_ops.Dataset.range(5).cache(), 5),
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Dsnapshot_test.py76 def assertDatasetProducesSet(self, dataset, expected): argument
78 next_fn = self.getNext(dataset)
116 dataset = dataset_ops.Dataset.from_tensors([1, 2, 3])
117 dataset.snapshot(path=self._snapshot_dir)
129 dataset = core_readers._TFRecordDataset(filenames)
130 dataset = dataset.snapshot(path=self._snapshot_dir)
131 self.assertDatasetProduces(dataset, expected)
153 dataset = core_readers._TFRecordDataset(filenames)
154 dataset = dataset.snapshot(path=self._snapshot_dir, compression="AUTO")
155 self.assertDatasetProduces(dataset, expected)
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Dmap_test.py15 """Tests for `tf.data.Dataset.map()`."""
71 def new_map_fn(dataset, *args, **kwargs): argument
72 return dataset.map(*args, **kwargs)
74 def legacy_map_fn(dataset, *args, **kwargs): argument
75 return dataset.map_with_legacy_function(*args, **kwargs)
92 def new_map_fn(dataset, *args, **kwargs): argument
93 return dataset.map(*args, **kwargs)
141 dataset = dataset_ops.Dataset.from_tensor_slices(components)
142 dataset = apply_map(dataset, _map_fn).repeat(count)
145 [shape for shape in dataset_ops.get_legacy_output_shapes(dataset)])
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Dfrom_tensors_test.py15 """Tests for `tf.data.Dataset.from_tensors()."""
52 """Test a dataset that represents a single tuple of tensors."""
55 dataset = dataset_ops.Dataset.from_tensors(components)
59 nest.flatten(dataset_ops.get_legacy_output_shapes(dataset)))
61 self.assertDatasetProduces(dataset, expected_output=[components])
65 """Test a dataset that represents a dataset."""
66 dataset = dataset_ops.Dataset.from_tensors(dataset_ops.Dataset.range(10))
67 dataset = dataset.flat_map(lambda x: x)
68 self.assertDatasetProduces(dataset, expected_output=range(10))
72 """Test a dataset that represents a TensorArray."""
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Ddataset_test.py15 """Tests for `tf.data.Dataset`."""
54 dataset = dataset_ops.Dataset.range(10)
56 self.evaluate(dataset._as_serialized_graph()))
60 dataset = dataset_ops.Dataset.range(10).map(
64 dataset._as_serialized_graph(external_state_policy=options_lib
71 init_source=["textfile", "keyvaluetensor", "dataset"])))
76 dataset = dataset_ops.Dataset.range(3)
77 dataset = dataset.map(table.lookup)
79 round_tripped = self.graphRoundTrip(dataset)
81 del dataset
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Dio_test.py52 dataset = dataset_ops.Dataset.range(42)
53 dataset.save(self._test_dir, compression=compression)
54 dataset2 = dataset_ops.Dataset.load(
55 self._test_dir, dataset.element_spec, compression=compression)
60 dataset = dataset_ops.Dataset.range(42)
61 dataset.save(self._test_dir)
62 dataset2 = dataset_ops.Dataset.load(self._test_dir, dataset.element_spec)
67 dataset = dataset_ops.Dataset.range(42)
68 dataset.save(self._test_dir, shard_func=lambda x: x // 21)
69 dataset2 = dataset_ops.Dataset.load(self._test_dir, dataset.element_spec)
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Dshard_test.py15 """Tests for `tf.data.Dataset.shard()`."""
31 dataset = dataset_ops.Dataset.range(10).shard(5, 2)
32 self.assertDatasetProduces(dataset, expected_output=[2, 7])
36 dataset_a = dataset_ops.Dataset.range(10)
37 dataset_b = dataset_ops.Dataset.range(10, 0, -1)
38 dataset = dataset_ops.Dataset.zip((dataset_a, dataset_b)).shard(5, 2)
39 self.assertDatasetProduces(dataset, expected_output=[(2, 8), (7, 3)])
43 dataset = dataset_ops.Dataset.range(10).shard(5, 0)
44 self.assertDatasetProduces(dataset, expected_output=[0, 5])
49 dataset = dataset_ops.Dataset.range(10).shard(5, 7)
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/external/tensorflow/tensorflow/python/distribute/
Dinput_ops_test.py43 def _getNext(self, dataset): argument
45 iterator = iter(dataset)
48 iterator = dataset_ops.make_one_shot_iterator(dataset)
99 def _verifySimpleShardingOutput(self, dataset, record_fn): argument
100 next_element_fn = self._getNext(dataset)
110 dataset = readers.TFRecordDataset(self._createTFRecordFiles())
111 dataset = input_ops.auto_shard_dataset(
112 dataset, self._num_shards, self._shard_index)
114 self._verifySimpleShardingOutput(dataset, self._record)
118 dataset = dataset_ops.Dataset.from_tensor_slices(
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/external/tensorflow/tensorflow/python/data/ops/
Ddataset_ops.py79 # tf.function->wrap_function->dataset->autograph->tf.function).
135 @tf_export("data.Dataset", v1=[])
143 The `tf.data.Dataset` API supports writing descriptive and efficient input
144 pipelines. `Dataset` usage follows a common pattern:
146 1. Create a source dataset from your input data.
147 2. Apply dataset transformations to preprocess the data.
148 3. Iterate over the dataset and process the elements.
150 Iteration happens in a streaming fashion, so the full dataset does not need to
155 The simplest way to create a dataset is to create it from a python `list`:
157 >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
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/external/ComputeLibrary/tests/validation/CL/
DCast.cpp52 …astQASYMM8toF32Dataset = combine(framework::dataset::make("DataType", DataType::QASYMM8), framewor…
55 …t auto CastU8toS8Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
56 …t auto CastU8toU16Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
57 …t auto CastU8toS16Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
58 …t auto CastU8toU32Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
59 …t auto CastU8toS32Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
60 …t auto CastU8toF16Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
61 …t auto CastU8toF32Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
64 …t auto CastS8toU8Dataset = combine(framework::dataset::make("DataType", DataType::S8), framework:…
65 …t auto CastS8toU16Dataset = combine(framework::dataset::make("DataType", DataType::S8), framework:…
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/external/ComputeLibrary/tests/validation/dynamic_fusion/gpu/cl/
DCast.cpp50 …astQASYMM8toF32Dataset = combine(framework::dataset::make("DataType", DataType::QASYMM8), framewor…
53 …t auto CastU8toS8Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
54 …t auto CastU8toU16Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
55 …t auto CastU8toS16Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
56 …t auto CastU8toU32Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
57 …t auto CastU8toS32Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
58 …t auto CastU8toF16Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
59 …t auto CastU8toF32Dataset = combine(framework::dataset::make("DataType", DataType::U8), framework:…
62 …t auto CastS8toU8Dataset = combine(framework::dataset::make("DataType", DataType::S8), framework:…
63 …t auto CastS8toU16Dataset = combine(framework::dataset::make("DataType", DataType::S8), framework:…
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