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/frameworks/ml/nn/runtime/test/specs/V1_0/
Dsvdf.mod.py17 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
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Dsvdf_bias_present.mod.py17 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 …]
Dsvdf2.mod.py17 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 …]
Drnn.mod.py17 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)],
Drnn_state.mod.py17 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))
Dsvdf_state.mod.py17 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))
/frameworks/ml/nn/runtime/test/specs/V1_2/
Dsvdf_bias_present_float16.mod.py17 batches = 2 variable
26 input = Input("input", "TENSOR_FLOAT16", "{%d, %d}" % (batches, input_size))
30 state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*feature…
34 output = Output("output", "TENSOR_FLOAT16", "{%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
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Dsvdf_float16.mod.py17 batches = 2 variable
26 input = Input("input", "TENSOR_FLOAT16", "{%d, %d}" % (batches, input_size))
30 state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*features))
33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*feature…
34 output = Output("output", "TENSOR_FLOAT16", "{%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 …]
Drnn_float16.mod.py17 batches = 2 variable
23 input = Input("input", "TENSOR_FLOAT16", "{%d, %d}" % (batches, input_size))
27 hidden_state_in = Input("hidden_state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, units))
31 hidden_state_out = IgnoredOutput("hidden_state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, units…
32 output = Output("output", "TENSOR_FLOAT16", "{%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)],
Dsvdf_state_float16.mod.py17 batches = 2 variable
24 input = Input("input", "TENSOR_FLOAT16", "{%d, %d}" % (batches, input_size))
28 state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*units))
31 state_out = Output("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*units))
32 output = Output("output", "TENSOR_FLOAT16", "{%d, %d}" % (batches, units))
/frameworks/ml/nn/runtime/test/specs/V1_1/
Dsvdf2_relaxed.mod.py17 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 …]
Dsvdf_relaxed.mod.py17 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 …]
Dsvdf_bias_present_relaxed.mod.py17 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 …]
Drnn_relaxed.mod.py17 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)],
Drnn_state_relaxed.mod.py17 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))
Dsvdf_state_relaxed.mod.py17 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))
/frameworks/ml/nn/common/
DOperationsUtils.cpp426 uint32_t batches = getSizeOfDimension(input, 0); in depthToSpacePrepare() local
433 output->dimensions = {batches, height * blockSize, width * blockSize, in depthToSpacePrepare()
445 uint32_t batches = getSizeOfDimension(input, 0); in spaceToDepthPrepare() local
454 output->dimensions = {batches, height / blockSize, width / blockSize, in spaceToDepthPrepare()
544 uint32_t batches = getSizeOfDimension(input, 0); in batchToSpacePrepare() local
549 NN_OPS_CHECK(batches % (blockSizeData[0] * blockSizeData[1]) == 0); in batchToSpacePrepare()
551 output->dimensions = {batches / (blockSizeData[0] * blockSizeData[1]), in batchToSpacePrepare()
577 uint32_t batches = getSizeOfDimension(input, 0); in spaceToBatchPrepare() local
589 output->dimensions = {batches * (blockSizeData[0] * blockSizeData[1]), in spaceToBatchPrepare()
743 uint32_t batches = getSizeOfDimension(input, 0); in groupedConvPrepare() local
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/frameworks/ml/nn/common/operations/
DRNNTest.cpp133 BasicRNNOpModel(uint32_t batches, uint32_t units, uint32_t size) in BasicRNNOpModel() argument
134 : batches_(batches), units_(units), input_size_(size), activation_(kActivationRelu) { in BasicRNNOpModel()
DResizeImageOps.cpp235 uint32_t batches = getSizeOfDimension(input, 0); in prepare() local
268 output.dimensions = {batches, channels, (uint32_t)height, (uint32_t)width}; in prepare()
270 output.dimensions = {batches, (uint32_t)height, (uint32_t)width, channels}; in prepare()
DSVDFTest.cpp166 SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, uint32_t memory_size, in SVDFOpModel() argument
168 : batches_(batches), in SVDFOpModel()
DPooling.cpp364 uint32_t batches = getSizeOfDimension(input, 0); in prepare() local
379 output.dimensions = {batches, channels, outHeight, outWidth}; in prepare()
381 output.dimensions = {batches, outHeight, outWidth, channels}; in prepare()
DTransposeConv2D.cpp507 uint32_t batches = getSizeOfDimension(input, 0); in prepare() local
534 output.dimensions = {batches, channels_out, outHeight, outWidth}; in prepare()
536 output.dimensions = {batches, outHeight, outWidth, channels_out}; in prepare()
DConv2D.cpp628 uint32_t batches = getSizeOfDimension(input, 0); in prepare() local
660 output.dimensions = {batches, channels_out, outHeight, outWidth}; in prepare()
662 output.dimensions = {batches, outHeight, outWidth, channels_out}; in prepare()
/frameworks/ml/nn/tools/api/
Dtypes.spec134 * Since %{APILevel29}, zero batches is supported for this tensor.
330 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
359 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
384 * [batches, out_height, out_width, depth].
1192 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
1221 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
1246 * [batches, out_height, out_width, depth].
1284 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
1686 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
1715 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
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/frameworks/base/services/core/java/com/android/server/
DAlarmManagerService.java990 static int getAlarmCount(ArrayList<Batch> batches) { in getAlarmCount() argument
993 final int size = batches.size(); in getAlarmCount()
995 ret += batches.get(i).size(); in getAlarmCount()
1013 boolean haveBatchesTimeTickAlarm(ArrayList<Batch> batches) { in haveBatchesTimeTickAlarm() argument
1014 final int numBatches = batches.size(); in haveBatchesTimeTickAlarm()
1016 if (haveAlarmsTimeTickAlarm(batches.get(i).alarms)) { in haveBatchesTimeTickAlarm()
3780 void recordWakeupAlarms(ArrayList<Batch> batches, long nowELAPSED, long nowRTC) { in recordWakeupAlarms() argument
3781 final int numBatches = batches.size(); in recordWakeupAlarms()
3783 Batch b = batches.get(nextBatch); in recordWakeupAlarms()