| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| D | svdf2.mod.py | 17 batches = 2 variable
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature…
34 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
75 state_in: [0 for _ in range(batches * memory_size * features)],
142 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
147 batch_start = i * input_size * batches
148 batch_end = batch_start + input_size * batches
150 golden_start = i * units * batches
[all …]
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| D | svdf.mod.py | 17 batches = 2 variable
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature…
34 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
60 state_in: [0 for _ in range(batches * memory_size * features)],
127 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
132 batch_start = i * input_size * batches
133 batch_end = batch_start + input_size * batches
135 golden_start = i * units * batches
[all …]
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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))
184 input_sequence_size = int(len(test_inputs) / input_size / batches)
193 input0[hidden_state_in] = [0 for x in range(batches * units)]
195 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/runtime/test/specs/V1_1/ |
| D | svdf_relaxed.mod.py | 17 batches = 2 variable
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature…
34 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
61 state_in: [0 for _ in range(batches * memory_size * features)],
128 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
133 batch_start = i * input_size * batches
134 batch_end = batch_start + input_size * batches
136 golden_start = i * units * batches
[all …]
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| D | svdf2_relaxed.mod.py | 17 batches = 2 variable
26 input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
30 state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*feature…
34 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
76 state_in: [0 for _ in range(batches * memory_size * features)],
143 output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
148 batch_start = i * input_size * batches
149 batch_end = batch_start + input_size * batches
151 golden_start = i * units * batches
[all …]
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| D | rnn_relaxed.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_relaxed.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_relaxed.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/ |
| D | OperationsUtils.cpp | 262 uint32_t batches = getSizeOfDimension(input, 0); in convPrepare() local
270 output->dimensions = {batches, outHeight, outWidth, channels_out}; in convPrepare()
298 uint32_t batches = getSizeOfDimension(input, 0); in depthwiseConvPrepare() local
306 output->dimensions = {batches, outHeight, outWidth, channels_out}; in depthwiseConvPrepare()
319 uint32_t batches = getSizeOfDimension(input, 0); in genericPoolingPrepare() local
330 output->dimensions = {batches, outHeight, outWidth, channels_out}; in genericPoolingPrepare()
462 uint32_t batches = getSizeOfDimension(input, 0); in resizeBilinearPrepare() local
466 output->dimensions = {batches, (uint32_t)height, (uint32_t)width, channels}; in resizeBilinearPrepare()
477 uint32_t batches = getSizeOfDimension(input, 0); in depthToSpacePrepare() local
484 output->dimensions = {batches, in depthToSpacePrepare()
[all …]
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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 | 186 SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, in SVDFOpModel() argument
188 : batches_(batches), in SVDFOpModel()
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| /frameworks/base/services/core/java/com/android/server/ |
| D | AlarmManagerService.java | 806 static int getAlarmCount(ArrayList<Batch> batches) { in getAlarmCount() argument
809 final int size = batches.size(); in getAlarmCount()
811 ret += batches.get(i).size(); in getAlarmCount()
829 boolean haveBatchesTimeTickAlarm(ArrayList<Batch> batches) { in haveBatchesTimeTickAlarm() argument
830 final int numBatches = batches.size(); in haveBatchesTimeTickAlarm()
832 if (haveAlarmsTimeTickAlarm(batches.get(i).alarms)) { in haveBatchesTimeTickAlarm()
3365 void recordWakeupAlarms(ArrayList<Batch> batches, long nowELAPSED, long nowRTC) { in recordWakeupAlarms() argument
3366 final int numBatches = batches.size(); in recordWakeupAlarms()
3368 Batch b = batches.get(nextBatch); in recordWakeupAlarms()
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