| /external/tensorflow/tensorflow/python/distribute/v1/ |
| D | all_reduce.py | 254 def build_ring_all_reduce(input_tensors, num_workers, num_subchunks, argument
277 if len(input_tensors) < 2:
279 input_tensors, shape = _flatten_tensors(input_tensors)
280 devices = [t.device for t in input_tensors]
284 input_tensors, devices,
297 def _build_ring_gather(input_tensors, devices, num_subchunks, argument
317 num_devices = len(input_tensors)
321 return input_tensors
322 shape = input_tensors[0].shape
332 splits, split_pad_len = _padded_split(input_tensors[d], num_chunks)
[all …]
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| D | all_reduce_test.py | 93 input_tensors = []
100 input_tensors.append(array_ops.identity(t8))
101 return input_tensors, device_names
106 input_tensors, device_names = self._buildInput(1, 1)
108 output_tensors = ar._build_ring_gather(input_tensors, device_names, 1,
111 self.assertEqual(output_tensors, input_tensors)
113 input_tensors, device_names = self._buildInput(1, 4)
116 input_tensors, device_names, 2, pred_by_c_d, rank_by_c_d, math_ops.add)
119 self.assertEqual(len(output_tensors), len(input_tensors))
120 num_chunks = 2 * len(input_tensors)
[all …]
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| /external/tensorflow/tensorflow/core/ops/ |
| D | array_ops_test.cc | 310 op.input_tensors.resize(2); in TEST()
316 op.input_tensors[0] = &in_t; in TEST()
342 op.input_tensors.resize(3); in TEST()
343 op.input_tensors[2] = &axis_dim_t; in TEST()
450 op.input_tensors.resize(2); in TEST()
471 op.input_tensors[1] = &paddings_t; in TEST()
481 op.input_tensors.resize(3); in TEST()
502 op.input_tensors[1] = &paddings_t; in TEST()
511 op.input_tensors.resize(2); in TEST()
535 op.input_tensors[1] = &paddings_t; in TEST()
[all …]
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| D | image_ops_test.cc | 35 op.input_tensors.resize(2); in TEST()
49 op.input_tensors[1] = &size_tensor; in TEST()
212 op.input_tensors.resize(2); in TEST()
234 op.input_tensors[1] = &size_tensor; in TEST()
246 op.input_tensors.resize(4); in TEST()
262 op.input_tensors[3] = &size_tensor; in TEST()
277 op.input_tensors.resize(2); in TEST()
288 op.input_tensors[1] = &size_tensor; in TEST()
294 op.input_tensors.resize(4); in TEST()
304 op.input_tensors[3] = &image_size; in TEST()
[all …]
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| D | random_ops_test.cc | 26 op.input_tensors.resize(2); in TEST()
34 op.input_tensors[1] = &num_samples; in TEST()
42 op.input_tensors.resize(2); in TEST()
49 op.input_tensors[0] = &shape; in TEST()
57 op.input_tensors.resize(2); in TEST()
64 op.input_tensors[0] = &shape; in TEST()
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| D | math_ops_test.cc | 294 op.input_tensors.resize(3); in TEST()
306 op.input_tensors[0] = &start_t; in TEST()
309 op.input_tensors[1] = &limit_t; in TEST()
313 op.input_tensors[2] = &delta_t; in TEST()
337 op.input_tensors.resize(3); in TEST()
347 op.input_tensors[2] = &num_t; in TEST()
357 op.input_tensors.resize(3); in TEST()
368 op.input_tensors[2] = &num_segments_t; in TEST()
379 op.input_tensors.resize(3); in TEST()
392 op.input_tensors.resize(4); in TEST()
[all …]
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| D | data_flow_ops_test.cc | 148 op.input_tensors.push_back(nullptr); in TEST()
149 op.input_tensors.push_back(&tensor_5); in TEST()
152 op.input_tensors[0] = &tensor_2; in TEST()
153 op.input_tensors[1] = nullptr; in TEST()
157 op.input_tensors[1] = &tensor_5; in TEST()
242 op.input_tensors.push_back(nullptr); in TEST()
243 op.input_tensors.push_back(&n_tensor); in TEST()
274 op.input_tensors.push_back(nullptr); in TEST()
275 op.input_tensors.push_back(&n_tensor); in TEST()
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| D | spectral_ops_test.cc | 74 op.input_tensors.resize(2); in TEST()
76 op.input_tensors[1] = &fft_length; in TEST()
134 op.input_tensors.resize(2); in TEST()
136 op.input_tensors[1] = &fft_length; in TEST()
194 op.input_tensors.resize(2); in TEST()
196 op.input_tensors[1] = &fft_length; in TEST()
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| /external/tensorflow/tensorflow/python/keras/ |
| D | models.py | 110 tensor in tensor_map for tensor in nest.flatten(node.input_tensors)):
129 def _clone_functional_model(model, input_tensors=None, layer_fn=_clone_layer): argument
172 if input_tensors is not None:
174 input_tensors = nest.flatten(input_tensors)
175 for i, input_tensor in enumerate(input_tensors):
195 input_tensors, output_tensors, created_layers = (
199 model = Model(input_tensors, output_tensors, name=model.name)
279 def _clone_sequential_model(model, input_tensors=None, layer_fn=_clone_layer): argument
321 if isinstance(layer, InputLayer) and input_tensors is not None:
332 if input_tensors is None:
[all …]
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| /external/tensorflow/tensorflow/lite/ |
| D | graph_info_test.cc | 117 EXPECT_EQ(generated_subgraphs[subgraph_index].input_tensors, in CheckPartitionSubgraphs()
118 expected_subgraphs[subgraph_index].input_tensors); in CheckPartitionSubgraphs()
162 expected_subgraph.input_tensors = {0}; in TEST()
180 expected_subgraph.input_tensors = {0}; in TEST()
200 expected_subgraph.input_tensors = {}; in TEST()
220 expected_subgraph.input_tensors = {0}; in TEST()
243 expected_subgraph0.input_tensors = {0}; in TEST()
248 expected_subgraph1.input_tensors = {1}; in TEST()
270 expected_subgraph0.input_tensors = {0}; in TEST()
275 expected_subgraph1.input_tensors = {1}; in TEST()
[all …]
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| /external/armnn/python/pyarmnn/test/ |
| D | test_runtime.py | 36 input_tensors = [const_tensor_pair]
49 yield preferred_backends, network, runtime, input_tensors, output_tensors
75 input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data])
85 yield runtime, net_id, input_tensors, output_tensors
178 input_tensors = random_runtime[3]
185 runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
192 input_tensors = []
200 runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
214 input_tensors = mock_model_runtime[2]
220 runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
[all …]
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| D | test_tensor_conversion.py | 49 input_tensors = ann.make_input_tensors(input_tensor_info, input_data)
50 assert len(input_tensors) == 1
52 for tensor, tensor_info in zip(input_tensors, input_tensor_info):
90 input_tensors = ann.make_input_tensors(input_tensor_info, input_data)
91 assert len(input_tensors) == 1
93 for tensor, tensor_info in zip(input_tensors, input_tensor_info):
|
| /external/tensorflow/tensorflow/lite/experimental/mlir/testing/ |
| D | mlir_convert.py | 30 def mlir_convert(options, graph_def, input_tensors, output_tensors, **kwargs): argument
49 input_arrays = [x[0] for x in input_tensors]
50 input_shapes = zip_test_utils.get_input_shapes_map(input_tensors)
76 def representative_dataset(input_tensors): argument
78 for _, shape, _ in input_tensors:
88 yield representative_dataset(input_tensors)
126 input_tensors, argument
156 for input_tensor in input_tensors:
161 input_types = ",".join([x[2] for x in input_tensors])
176 ",".join([x[0] for x in input_tensors]),
|
| /external/tensorflow/tensorflow/lite/python/ |
| D | lite.py | 808 input_tensors = [
816 return graph_def, input_tensors, output_tensors
819 def _validate_inputs(self, graph_def, input_tensors): argument
839 for tensor in input_tensors:
863 def _optimize_tf_model(self, graph_def, input_tensors, output_tensors, argument
883 input_tensors,
889 def convert(self, graph_def, input_tensors, output_tensors): argument
907 self._validate_inputs(graph_def, input_tensors)
923 input_tensors=input_tensors,
979 graph_def, input_tensors, output_tensors = self._load_saved_model(
[all …]
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| /external/tensorflow/tensorflow/compiler/mlir/tfrt/benchmarks/ |
| D | benchmark_mlir_function.cc | 37 llvm::SmallVector<Tensor> input_tensors; in GetInputTensors() local
42 input_tensors.emplace_back(spec.dtype, shape); in GetInputTensors()
47 input_tensors.back().flat<float>().setRandom(); in GetInputTensors()
54 return input_tensors; in GetInputTensors()
77 llvm::SmallVector<Tensor> input_tensors = GetInputTensors(input_specs); in RunMlirBenchmark() local
81 for (const Tensor& tensor : input_tensors) in RunMlirBenchmark()
133 llvm::SmallVector<Tensor> input_tensors = GetInputTensors(input_specs); in RunEigenBenchmark() local
137 compute(input_tensors, device); in RunEigenBenchmark()
|
| /external/tensorflow/tensorflow/lite/testing/ |
| D | toco_convert.py | 76 def toco_convert(options, graph_def, input_tensors, output_tensors, **kwargs): argument
101 input_arrays = [x[0] for x in input_tensors]
102 data_types = [zip_test_utils.TF_TYPE_INFO[x[2]][1] for x in input_tensors]
111 input_shapes = zip_test_utils.get_input_shapes_map(input_tensors)
122 def representative_dataset(input_tensors): argument
124 for _, shape, _ in input_tensors:
134 yield representative_dataset(input_tensors)
170 shapes=[x[1] for x in input_tensors],
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| /external/tensorflow/tensorflow/python/eager/ |
| D | tape.py | 181 def record_operation(op_type, output_tensors, input_tensors, backward_function, argument
185 input_tensors, backward_function,
189 def record_operation_backprop_only(op_type, output_tensors, input_tensors, argument
193 input_tensors,
197 def record_operation_forwardprop_only(op_type, output_tensors, input_tensors, argument
215 op_type, output_tensors, input_tensors, backward_function,
|
| /external/tensorflow/tensorflow/lite/testing/op_tests/ |
| D | add_n.py | 59 input_tensors = []
61 input_tensors.append(
66 out = tf.add_n(input_tensors)
67 return input_tensors, [out]
|
| D | identity.py | 43 input_tensors = []
46 input_tensors = [
59 inputs_doubled = [input_tensor * 2.0 for input_tensor in input_tensors]
66 return input_tensors, identity_outputs
|
| D | batch_to_space_nd.py | 81 input_tensors = [input_tensor]
90 input_tensors.append(block_shape)
99 input_tensors.append(crops)
102 return input_tensors, [out]
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | node_test.py | 46 self.assertListEqual(node.input_tensors, [a])
75 self.assertIs(dense._inbound_nodes[0].input_tensors, a)
76 self.assertIs(dense._inbound_nodes[1].input_tensors, b)
102 self.assertLen(merge_layer._inbound_nodes[0].input_tensors, 2)
103 self.assertEqual(merge_layer._inbound_nodes[0].input_tensors, [a_2, b_2])
150 self.assertLen(merge_layer._inbound_nodes[0].input_tensors, 2)
151 self.assertEqual(merge_layer._inbound_nodes[0].input_tensors, [a, b])
|
| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_feed.py | 377 def set_configuration_from_input_tensors(self, input_tensors): argument
391 if len(input_tensors) != self.number_of_tuple_elements:
393 % (str(input_tensors), self.number_of_tuple_elements))
394 self.set_tuple_shapes([t.shape for t in input_tensors])
395 self.set_tuple_types([t.dtype for t in input_tensors])
397 def set_configuration_from_sharded_input_tensors(self, input_tensors): argument
421 number_of_shards = len(input_tensors)
423 for t in input_tensors:
428 str(input_tensors), self.number_of_tuple_elements))
431 sharded_shapes = [[t[i].shape for t in input_tensors]
[all …]
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| /external/tensorflow/tensorflow/core/kernels/data/ |
| D | map_defun_op_test.cc | 44 std::vector<Tensor> input_tensors = arguments_; in GetInputTensors() local
45 input_tensors.insert(input_tensors.end(), captured_inputs_.begin(), in GetInputTensors()
47 return input_tensors; in GetInputTensors()
244 auto input_tensors = test_case.map_defun_op_params.GetInputTensors(); in TEST_P() local
246 for (auto& input : input_tensors) { in TEST_P()
273 auto input_tensors = test_case.map_defun_op_params.GetInputTensors(); in TEST_F() local
275 for (auto& input : input_tensors) { in TEST_F()
|
| /external/tflite-support/tensorflow_lite_support/cc/task/vision/core/ |
| D | base_vision_task_api.h | 114 absl::Status Preprocess(const std::vector<TfLiteTensor*>& input_tensors, in Preprocess() argument
131 if (input_tensors.size() != 1) { in Preprocess()
179 if (input_tensors[0]->bytes != input_data_byte_size) { in Preprocess()
187 input_data, input_data_byte_size / sizeof(uint8), input_tensors[0]); in Preprocess()
190 if (input_tensors[0]->bytes / sizeof(float) != in Preprocess()
200 input_tensors[0]); in Preprocess()
|
| /external/armnn/python/pyarmnn/examples/common/ |
| D | network_executor.py | 66 def execute_network(input_tensors: list, output_tensors: list, runtime, net_id: int) -> List[np.nda…
79 runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
98 def run(self, input_tensors: list) -> List[np.ndarray]:
108 return execute_network(input_tensors, self.output_tensors, self.runtime, self.network_id)
|