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Searched refs:batches (Results 1 – 25 of 30) sorted by relevance

/packages/modules/NeuralNetworks/runtime/test/specs/V1_2/
D	svdf_float16.mod.py	17 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_sizefeatures)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_sizefeature… 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 …]
D	svdf_bias_present_float16.mod.py	17 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_sizefeatures)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_sizefeature… 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 …]
D	rnn_float16.mod.py	17 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)],
D	svdf_state_float16.mod.py	`17 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_sizeunits)) 31 state_out = Output("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_sizeunits)) 32 output = Output("output", "TENSOR_FLOAT16", "{%d, %d}" % (batches, units))`
/packages/modules/NeuralNetworks/runtime/test/specs/V1_0/
D	svdf_bias_present.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_sizefeatures)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_sizefeature… 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 …]
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_sizefeatures)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_sizefeature… 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 …]
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_sizefeatures)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_sizefeature… 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 …]
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)],
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))`
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_sizeunits)) 31 state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_sizeunits)) 32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))`
/packages/modules/NeuralNetworks/runtime/test/specs/V1_1/
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_sizefeatures)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_sizefeature… 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 …]
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_sizefeatures)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_sizefeature… 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 …]
D	svdf_bias_present_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_sizefeatures)) 33 state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_sizefeature… 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 …]
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)],
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))`
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_sizeunits)) 31 state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_sizeunits)) 32 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))`
/packages/modules/NeuralNetworks/common/
D	OperationsUtils.cpp	424 uint32_t batches = getSizeOfDimension(input, 0); in depthToSpacePrepare() local 431 output->dimensions = {batches, height * blockSize, width * blockSize, in depthToSpacePrepare() 443 uint32_t batches = getSizeOfDimension(input, 0); in spaceToDepthPrepare() local 452 output->dimensions = {batches, height / blockSize, width / blockSize, in spaceToDepthPrepare() 538 uint32_t batches = getSizeOfDimension(input, 0); in batchToSpacePrepare() local 543 NN_OPS_CHECK(batches % (blockSizeData[0] * blockSizeData[1]) == 0); in batchToSpacePrepare() 545 output->dimensions = {batches / (blockSizeData[0] * blockSizeData[1]), in batchToSpacePrepare() 571 uint32_t batches = getSizeOfDimension(input, 0); in spaceToBatchPrepare() local 583 output->dimensions = {batches * (blockSizeData[0] * blockSizeData[1]), in spaceToBatchPrepare() 737 uint32_t batches = getSizeOfDimension(input, 0); in groupedConvPrepare() local [all …]
/packages/apps/Dialer/java/com/android/dialer/calllog/database/
D	MutationApplier.java	`104 Iterable<List<Long>> batches = Iterables.partition(mutations.getDeletes(), 999); in applyToDatabaseInternal() local 105 for (List<Long> idsInBatch : batches) { in applyToDatabaseInternal()`
/packages/modules/NeuralNetworks/common/operations/
D	ResizeImageOps.cpp	`241 uint32_t batches = getSizeOfDimension(input, 0); in prepare() local 274 output.dimensions = {batches, channels, (uint32_t)height, (uint32_t)width}; in prepare() 276 output.dimensions = {batches, (uint32_t)height, (uint32_t)width, channels}; in prepare()`
D	RNNTest.cpp	`134 BasicRNNOpModel(uint32_t batches, uint32_t units, uint32_t size) in BasicRNNOpModel() argument 135 : batches_(batches), units_(units), input_size_(size), activation_(kActivationRelu) { in BasicRNNOpModel()`
D	SVDFTest.cpp	`168 SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, uint32_t memory_size, in SVDFOpModel() argument 170 : batches_(batches), in SVDFOpModel()`
D	Pooling.cpp	`369 uint32_t batches = getSizeOfDimension(input, 0); in prepare() local 384 output.dimensions = {batches, channels, outHeight, outWidth}; in prepare() 386 output.dimensions = {batches, outHeight, outWidth, channels}; in prepare()`
D	TransposeConv2D.cpp	`512 uint32_t batches = getSizeOfDimension(input, 0); in prepare() local 539 output.dimensions = {batches, channels_out, outHeight, outWidth}; in prepare() 541 output.dimensions = {batches, outHeight, outWidth, channels_out}; in prepare()`
/packages/modules/NeuralNetworks/tools/api/
D	types.spec	181 * Since %{NNAPILevel3}, zero batches is supported for this tensor. 425 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying 454 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying 479 * [batches, out_height, out_width, depth]. 1291 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying 1320 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying 1345 * [batches, out_height, out_width, depth]. 1383 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying 1785 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying 1814 * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying [all …]
/packages/apps/Dialer/java/com/android/dialer/calllog/datasources/systemcalllog/
D	SystemCallLogDataSource.java	`486 Iterable<List<Long>> batches = Iterables.partition(matchingIds, 999); in getIdsFromSystemCallLogThatMatch() local 487 for (List<Long> idsInBatch : batches) { in getIdsFromSystemCallLogThatMatch()`

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