| /frameworks/ml/nn/runtime/test/specs/ |
| D | svdf.mod.py | 17 batches = 2 variable
24 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
28 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
31 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
126 input_sequence_size = int(len(test_inputs) / input_size / batches)
131 batch_start = i * input_size * batches
132 batch_end = batch_start + input_size * batches
134 input0[state_in] = [0 for _ in range(batches * (memory_size - 1) * units)]
135 output0 = {state_out:[0 for x in range(batches * (memory_size - 1) * units)],
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| D | rnn.mod.py | 17 batches = 2 variable
23 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
27 hidden_state_in = Input("hidden_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
31 hidden_state_out = IgnoredOutput("hidden_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units…
32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
185 input_sequence_size = int(len(test_inputs) / input_size / batches)
194 input0[hidden_state_in] = [0 for x in range(batches * units)]
196 hidden_state_out: [0 for x in range(batches * units)],
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| D | rnn_state.mod.py | 17 batches = 2 variable
23 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
27 hidden_state_in = Input("hidden_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
31 hidden_state_out = IgnoredOutput("hidden_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units…
32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
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| D | svdf_state.mod.py | 17 batches = 2 variable
24 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
28 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
31 state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
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| /frameworks/ml/nn/common/operations/internal/reference/ |
| D | reference_ops.h | 168 const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); in Conv() local
180 for (int batch = 0; batch < batches; ++batch) { in Conv()
241 const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); in Conv() local
251 for (int batch = 0; batch < batches; ++batch) { in Conv()
386 const int batches = ArraySize(output_dims, 1) * ArraySize(output_dims, 2) * in FullyConnected() local
392 for (int b = 0; b < batches; ++b) { in FullyConnected()
433 const int batches = ArraySize(output_dims, 1) * ArraySize(output_dims, 2) * in FullyConnected() local
439 for (int b = 0; b < batches; ++b) { in FullyConnected()
467 const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); in NonGlobalBatchNormalization() local
478 for (int b = 0; b < batches; ++b) { in NonGlobalBatchNormalization()
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| D | depthwiseconv_float.h | 34 const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); in DepthwiseConv() local
45 for (int b = 0; b < batches; ++b) { in DepthwiseConv()
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| D | depthwiseconv_uint8.h | 50 const int batches = MatchingArraySize(input_dims, 3, output_dims, 3); in DepthwiseConv() local
61 for (int b = 0; b < batches; ++b) { in DepthwiseConv()
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| /frameworks/ml/nn/common/ |
| D | OperationsUtils.cpp | 240 uint32_t batches = getSizeOfDimension(input, 0); in convPrepare() local
248 output->dimensions = {batches, outHeight, outWidth, channels_out}; in convPrepare()
276 uint32_t batches = getSizeOfDimension(input, 0); in depthwiseConvPrepare() local
284 output->dimensions = {batches, outHeight, outWidth, channels_out}; in depthwiseConvPrepare()
297 uint32_t batches = getSizeOfDimension(input, 0); in genericPoolingPrepare() local
308 output->dimensions = {batches, outHeight, outWidth, channels_out}; in genericPoolingPrepare()
437 uint32_t batches = getSizeOfDimension(input, 0); in resizeBilinearPrepare() local
441 output->dimensions = {batches, (uint32_t)height, (uint32_t)width, channels}; in resizeBilinearPrepare()
452 uint32_t batches = getSizeOfDimension(input, 0); in depthToSpacePrepare() local
459 output->dimensions = {batches, in depthToSpacePrepare()
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| /frameworks/ml/nn/common/operations/internal/optimized/ |
| D | optimized_ops.h | 609 const int batches = ArraySize(output_dims, 1) * ArraySize(output_dims, 2) *
613 if (batches == 1 && !(output_size % 4)) {
635 input_data, filter_cols, batches, filter_cols);
637 output_data, output_rows, batches, output_rows);
741 const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
751 for (int b = 0; b < batches; ++b) {
1032 const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
1043 for (int b = 0; b < batches; ++b) {
1068 const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
1075 for (int b = 0; b < batches; ++b) {
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| D | depthwiseconv_float.h | 626 const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
692 for (int b = 0; b < batches; ++b) {
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| D | depthwiseconv_uint8.h | 1366 const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
1441 for (int b = 0; b < batches; ++b) {
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| /frameworks/ml/nn/common/operations/ |
| D | RNNTest.cpp | 152 BasicRNNOpModel(uint32_t batches, uint32_t units, uint32_t size) in BasicRNNOpModel() argument
153 : batches_(batches), in BasicRNNOpModel()
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| D | SVDFTest.cpp | 122 SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, in SVDFOpModel() argument
124 : batches_(batches), in SVDFOpModel()
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| /frameworks/base/services/core/java/com/android/server/ |
| D | AlarmManagerService.java | 2442 void recordWakeupAlarms(ArrayList<Batch> batches, long nowELAPSED, long nowRTC) { in recordWakeupAlarms() argument
2443 final int numBatches = batches.size(); in recordWakeupAlarms()
2445 Batch b = batches.get(nextBatch); in recordWakeupAlarms()
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