/external/tensorflow/tensorflow/contrib/slim/python/slim/data/ |
D | prefetch_queue_test.py | 54 batches = input_lib.batch( 57 batches = prefetch_queue.prefetch_queue(batches).dequeue() 63 results = sess.run(batches) 72 sess.run(batches) 91 batches = input_lib.batch( 94 batches = prefetch_queue.prefetch_queue(batches).dequeue() 101 results = sess.run(batches) 112 sess.run(batches) 131 batches = input_lib.batch( 134 batcher = prefetch_queue.prefetch_queue(batches) [all …]
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/external/tensorflow/tensorflow/python/kernel_tests/ |
D | record_input_test.py | 102 batches = 2 114 batches=batches) 121 for _ in range(int(files * records_per_file / batches)): 123 self.assertTrue(len(op_list) is batches) 159 batches = 2 171 batches=batches) 178 for _ in range(int(files * records_per_file / batches)): 180 self.assertTrue(len(op_list) is batches)
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/external/tensorflow/tensorflow/python/data/experimental/kernel_tests/ |
D | bucket_by_sequence_length_test.py | 147 batches = [] 150 batches.append(batch) 170 for batch in batches: 243 batches = [] 246 batches.append(batch) 252 for batch in batches: 294 batches = [] 297 batches.append(batch) 303 for batch in batches: 332 batches = [] [all …]
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/external/mesa3d/src/gallium/drivers/freedreno/ |
D | freedreno_batch_cache.c | 208 cache->batches[batch->idx] = NULL; in fd_bc_invalidate_batch() 265 for (unsigned i = 0; i < ARRAY_SIZE(cache->batches); i++) { in fd_bc_alloc_batch() 266 batch = cache->batches[i]; in fd_bc_alloc_batch() 280 for (unsigned i = 0; i < ARRAY_SIZE(cache->batches); i++) { in fd_bc_alloc_batch() 281 if ((cache->batches[i] == ctx->batch) || in fd_bc_alloc_batch() 282 !cache->batches[i]->needs_flush) in fd_bc_alloc_batch() 284 if (!flush_batch || (cache->batches[i]->seqno < flush_batch->seqno)) in fd_bc_alloc_batch() 285 fd_batch_reference_locked(&flush_batch, cache->batches[i]); in fd_bc_alloc_batch() 303 for (unsigned i = 0; i < ARRAY_SIZE(cache->batches); i++) { in fd_bc_alloc_batch() 304 struct fd_batch *other = cache->batches[i]; in fd_bc_alloc_batch() [all …]
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D | freedreno_batch_cache.h | 50 struct fd_batch *batches[32]; member 60 for (uint32_t _m = (mask); _m && ((batch) = (cache)->batches[u_bit_scan(&_m)]); _m &= (mask))
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/external/tensorflow/tensorflow/lite/kernels/internal/reference/ |
D | fully_connected.h | 44 const int batches = FlatSizeSkipDim(output_shape, output_dims_count - 1); in FullyConnected() local 48 for (int b = 0; b < batches; ++b) { in FullyConnected() 90 const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); in FullyConnected() local 94 for (int b = 0; b < batches; ++b) { in FullyConnected() 138 const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); in FullyConnected() local 142 for (int b = 0; b < batches; ++b) { in FullyConnected() 192 const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); in ShuffledFullyConnected() local 201 if (batches == 1) { in ShuffledFullyConnected() 205 } else if (batches == 4) { in ShuffledFullyConnected() 225 if (batches == 1) { in ShuffledFullyConnected() [all …]
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D | depthwiseconv_float.h | 44 const int batches = MatchingDim(input_shape, 0, output_shape, 0); in DepthwiseConv() local 56 for (int b = 0; b < batches; ++b) { in DepthwiseConv()
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/external/tensorflow/tensorflow/core/kernels/ |
D | crop_and_resize_op_benchmark_test.cc | 24 static Graph* BM_CropAndResize(int batches, int width, int height, int depth, in BM_CropAndResize() argument 27 Tensor in(DT_FLOAT, TensorShape({batches, height, width, depth})); in BM_CropAndResize() 29 Tensor boxes(DT_FLOAT, TensorShape({batches, 4})); in BM_CropAndResize() 31 Tensor box_ind(DT_INT32, TensorShape({batches})); in BM_CropAndResize() 33 for (int i = 0; i < batches; ++i) { in BM_CropAndResize()
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D | adjust_contrast_op_benchmark_test.cc | 24 static Graph* BM_AdjustContrast(int batches, int width, int height) { in BM_AdjustContrast() argument 26 Tensor in(DT_FLOAT, TensorShape({batches, width, height, 3})); in BM_AdjustContrast()
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D | resize_op_benchmark_test.cc | 24 static Graph* BM_Resize(const char* algorithm, int batches, int width, in BM_Resize() argument 27 Tensor in(DT_FLOAT, TensorShape({batches, width, height, 3})); in BM_Resize()
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/external/mesa3d/src/mesa/main/ |
D | glthread.c | 90 glthread->batches[i].ctx = ctx; in _mesa_glthread_init() 91 util_queue_fence_init(&glthread->batches[i].fence); in _mesa_glthread_init() 119 util_queue_fence_destroy(&glthread->batches[i].fence); in _mesa_glthread_destroy() 149 struct glthread_batch *next = &glthread->batches[glthread->next]; in _mesa_glthread_flush_batch() 193 struct glthread_batch *last = &glthread->batches[glthread->last]; in _mesa_glthread_finish() 194 struct glthread_batch *next = &glthread->batches[glthread->next]; in _mesa_glthread_finish()
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/external/tensorflow/tensorflow/lite/kernels/ |
D | svdf_test.cc | 131 BaseSVDFOpModel(int batches, int units, int input_size, int memory_size, in BaseSVDFOpModel() argument 135 : batches_(batches), in BaseSVDFOpModel() 146 TensorData{TensorType_FLOAT32, {batches, memory_size * num_filters}}, in BaseSVDFOpModel() 157 {batches, memory_size * num_filters} // activation_state tensor in BaseSVDFOpModel() 205 HybridSVDFOpModel(int batches, int units, int input_size, int memory_size, in HybridSVDFOpModel() argument 207 : BaseSVDFOpModel(batches, units, input_size, memory_size, rank, in HybridSVDFOpModel()
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D | fully_connected_test.cc | 137 TfLiteRegistration* registration, int units, int batches, in BaseFullyConnectedOpModel() argument 143 : batches_(batches), units_(units) { 276 HybridFullyConnectedOpModel(int units, int batches, const TensorData& input, in HybridFullyConnectedOpModel() argument 279 : batches_(batches), units_(units) { 550 int batches, FullyConnectedOptionsWeightsFormat weights_format) { in SimpleTestQuantizedInt16OutputCase() argument 563 registration, output_depth, batches, in SimpleTestQuantizedInt16OutputCase() 565 {TensorType_UINT8, {batches, input_depth}, kInputMin, kInputMax}, in SimpleTestQuantizedInt16OutputCase() 590 CHECK(batches == 1 || batches == 4); in SimpleTestQuantizedInt16OutputCase() 598 std::vector<float> input_data(input_depth * batches); in SimpleTestQuantizedInt16OutputCase() 617 std::vector<float> expected_output_data(output_depth * batches); in SimpleTestQuantizedInt16OutputCase() [all …]
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D | basic_rnn_test.cc | 176 RNNOpModel(int batches, int units, int size, in RNNOpModel() argument 179 : batches_(batches), units_(units), input_size_(size) { in RNNOpModel() 236 HybridRNNOpModel(int batches, int units, int size, TensorType tensor_type) in HybridRNNOpModel() argument 237 : RNNOpModel(batches, units, size, tensor_type, tensor_type) { in HybridRNNOpModel()
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D | log_softmax_test.cc | 34 LogSoftmaxOpModel(int batches, int size) in LogSoftmaxOpModel() argument 35 : batches_(batches), input_size_(size) { in LogSoftmaxOpModel()
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D | unidirectional_sequence_rnn_test.cc | 175 int batches, int sequence_len, int units, int size, bool time_major, in UnidirectionalRNNOpModel() argument 178 : batches_(batches), in UnidirectionalRNNOpModel() 250 HybridUnidirectionalRNNOpModel(int batches, int sequence_len, int units, in HybridUnidirectionalRNNOpModel() argument 253 : UnidirectionalRNNOpModel(batches, sequence_len, units, size, time_major, in HybridUnidirectionalRNNOpModel()
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D | softmax_test.cc | 34 SoftmaxOpModel(int batches, int size, float beta) in SoftmaxOpModel() argument 35 : batches_(batches), input_size_(size), beta_(beta) { in SoftmaxOpModel()
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/external/tensorflow/tensorflow/lite/kernels/internal/reference/integer_ops/ |
D | pooling.h | 31 const int batches = MatchingDim(input_shape, 0, output_shape, 0); in AveragePool() local 39 for (int batch = 0; batch < batches; ++batch) { in AveragePool() 92 const int batches = MatchingDim(input_shape, 0, output_shape, 0); in MaxPool() local 100 for (int batch = 0; batch < batches; ++batch) { in MaxPool()
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D | fully_connected.h | 42 const int batches = output_shape.Dims(0); in FullyConnected() local 46 for (int b = 0; b < batches; ++b) { in FullyConnected()
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D | conv.h | 50 const int batches = MatchingDim(input_shape, 0, output_shape, 0); in ConvPerChannel() local 64 for (int batch = 0; batch < batches; ++batch) { in ConvPerChannel()
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/external/tensorflow/tensorflow/contrib/hvx/hexagon_controller/src_impl/ |
D | graph_functions_wrapper.c | 213 const uint32_t nn_id, const uint32_t batches, const uint32_t height, in hexagon_controller_ExecuteGraph() argument 220 TFMLOGD("Input: %d, %d, %d, %d, %d, %d", batches, height, width, depth, in hexagon_controller_ExecuteGraph() 229 input.batches = batches; in hexagon_controller_ExecuteGraph() 243 *out_batches = output.batches; in hexagon_controller_ExecuteGraph()
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/external/tensorflow/tensorflow/python/keras/engine/ |
D | training_generator_test.py | 339 def _make_dataset(self, inputs, batches): argument 340 return dataset_ops.DatasetV2.from_tensors(inputs).repeat(batches) 342 def _make_iterator(self, inputs, batches): argument 344 self._make_dataset(inputs, batches)) 346 def _make_generator(self, inputs, batches): argument 349 for _ in range(batches):
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/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_BatchFunction.pbtxt | 25 Number of scheduling threads for processing batches of work. 26 Determines the number of batches processed in parallel. 45 Maximum number of batches enqueued. Default: 10. 53 batches up to one of those sizes. The entries must increase monotonically, and
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D | api_def_Batch.pbtxt | 23 num_batch_threads: Number of scheduling threads for processing batches of work. 24 Determines the number of batches processed in parallel. 30 batches up to one of those sizes. The entries must increase monotonically, and
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/external/tensorflow/tensorflow/lite/kernels/internal/ |
D | tensor_utils_test.cc | 414 const int b_rows = 29, b_cols = 1, batches = 2; in TEST() local 427 int8_t b_int8_data[b_rows * b_cols * batches]; in TEST() 429 float scaling_factor_b[batches]; in TEST() 446 for (int i = 0; i < b_rows * b_cols * batches; ++i) { in TEST() 453 float c_float_data[a_rows * b_cols * batches]; in TEST() 454 for (int i = 0; i < a_rows * b_cols * batches; ++i) { in TEST() 464 scaling_factor_c, batches, c_float_data, in TEST() 471 for (int i = 0; i < a_rows * b_cols * batches; ++i) { in TEST()
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