| /frameworks/ml/nn/common/operations/ |
| D | QLSTM.cpp | 182 const uint32_t batchSize = getSizeOfDimension(inputShape, 0); in prepare() local
299 NN_RET_CHECK_EQ(getSizeOfDimension(outputStateShape, 0), batchSize); in prepare()
303 NN_RET_CHECK_EQ(getSizeOfDimension(cellStateShape, 0), batchSize); in prepare()
390 const uint32_t batchSize = inputShape.dimensions[0]; in execute() local
655 std::vector<int16_t> inputGateBuffer(batchSize * numUnits); in execute()
656 std::vector<int16_t> forgetGateBuffer(batchSize * numUnits); in execute()
657 std::vector<int16_t> cellGateBuffer(batchSize * numUnits); in execute()
658 std::vector<int16_t> outputGateBuffer(batchSize * numUnits); in execute()
659 std::vector<int8_t> buffer8(batchSize * numUnits); in execute()
674 inputToForgetEffectiveScaleB, batchSize, inputSize, in execute()
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| D | BidirectionalSequenceRNN.cpp | 220 const uint32_t batchSize = getSizeOfDimension(inputShape, 1); in executeTyped() local
252 tempHiddenState.resize(std::max(batchSize * fwNumUnits, batchSize * bwNumUnits)); in executeTyped()
259 const T* inputBatchPtr = input + i * batchSize * inputSize; in executeTyped()
262 auxInputBatchPtr = auxInput + i * batchSize * auxInputSize; in executeTyped()
265 T* fwOutputBatchPtr = fwOutput + i * batchSize * fwOutputBatchStride; in executeTyped()
278 const T* inputBatchPtr = bwInput + i * batchSize * inputSize; in executeTyped()
281 auxInputBatchPtr = auxInput + i * batchSize * auxInputSize; in executeTyped()
289 bwOutputBatchPtr = fwOutput + i * batchSize * bwOutputBatchStride; in executeTyped()
292 bwOutputBatchPtr = bwOutput + i * batchSize * bwOutputBatchStride; in executeTyped()
386 const uint32_t batchSize = in prepare() local
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| D | UnidirectionalSequenceRNN.cpp | 98 const uint32_t batchSize = getSizeOfDimension(inputShape, 1); in executeTyped() local
111 input += batchSize * inputSize; in executeTyped()
113 output += batchSize * numUnits; in executeTyped()
124 std::copy(hiddenState, hiddenState + batchSize * numUnits, stateOutput); in executeTyped()
162 const uint32_t batchSize = in prepare() local
179 NN_RET_CHECK_EQ(batchSize, getSizeOfDimension(hiddenState, 0)); in prepare()
184 output.dimensions[0] = timeMajor ? maxTime : batchSize; in prepare()
185 output.dimensions[1] = timeMajor ? batchSize : maxTime; in prepare()
192 outputStateShape.dimensions[0] = batchSize; in prepare()
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| D | LSTM.cpp | 434 const uint32_t batchSize = (inputRank == 3) ? getSizeOfDimension(input_shape, timeMajor ? 1 : 0) in LSTMEvalFloat32() local
441 batchInputShape.dimensions = {batchSize, inputSize}; in LSTMEvalFloat32()
442 const uint32_t batchInputSize = batchSize * inputSize; in LSTMEvalFloat32()
443 const uint32_t batchOutputSize = batchSize * outputSize; in LSTMEvalFloat32()
470 output_state_in_buffer, output_state_in_buffer + batchSize * outputSize); in LSTMEvalFloat32()
472 cell_state_in_buffer + batchSize * numCells); in LSTMEvalFloat32()
507 output_state_out_buffer + batchSize * outputSize); in LSTMEvalFloat32()
509 cell_state_out_buffer + batchSize * numCells); in LSTMEvalFloat32()
554 const uint32_t batchSize = (inputRank == 3) ? getSizeOfDimension(input_shape, timeMajor ? 1 : 0) in LSTMEvalFloat16() local
561 batchInputShape.dimensions = {batchSize, inputSize}; in LSTMEvalFloat16()
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| D | UnidirectionalSequenceLSTM.cpp | 202 const uint32_t batchSize = getSizeOfDimension(inputShape, isTimeMajor(context) ? 1 : 0); in prepare() local
319 NN_RET_CHECK_EQ(getSizeOfDimension(outputStateShape, 0), batchSize); in prepare()
323 NN_RET_CHECK_EQ(getSizeOfDimension(cellStateShape, 0), batchSize); in prepare()
383 outputStateOutTensor.dimensions[0] = batchSize; in prepare()
389 cellStateOutTensor.dimensions[0] = batchSize; in prepare()
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| D | ResizeImageOps.cpp | 68 const int batchSize = getSizeOfDimension(inputShape, 0); in resizeNearestNeighbor() local
81 for (int b = 0; b < batchSize; ++b) { in resizeNearestNeighbor()
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| D | GenerateProposals.cpp | 968 uint32_t batchSize = height * width * numAnchors; in generateProposalsNhwcFloat32Compute() local
969 uint32_t roiBufferSize = batchSize * kRoiDim; in generateProposalsNhwcFloat32Compute()
997 tempRoiShape.dimensions = {batchSize, kRoiDim}; in generateProposalsNhwcFloat32Compute()
999 tempBBoxDeltasShape.dimensions = {batchSize, kRoiDim}; in generateProposalsNhwcFloat32Compute()
1000 std::vector<int32_t> tempBatchSplitData(batchSize, 0); in generateProposalsNhwcFloat32Compute()
1001 Shape tempbatchSplitShape = {.dimensions = {batchSize}}; in generateProposalsNhwcFloat32Compute()
1017 std::vector<uint32_t> select(batchSize); in generateProposalsNhwcFloat32Compute()
1047 scoresBase += batchSize; in generateProposalsNhwcFloat32Compute()
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| /frameworks/native/services/inputflinger/reader/ |
| D | InputReader.cpp | 146 size_t batchSize = 1; in processEventsLocked() local
149 while (batchSize < count) { in processEventsLocked()
150 if (rawEvent[batchSize].type >= EventHubInterface::FIRST_SYNTHETIC_EVENT || in processEventsLocked()
151 rawEvent[batchSize].deviceId != deviceId) { in processEventsLocked()
154 batchSize += 1; in processEventsLocked()
157 ALOGD("BatchSize: %zu Count: %zu", batchSize, count); in processEventsLocked()
159 processEventsForDeviceLocked(deviceId, rawEvent, batchSize); in processEventsLocked()
176 count -= batchSize; in processEventsLocked()
177 rawEvent += batchSize; in processEventsLocked()
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| /frameworks/av/media/libstagefright/ |
| D | CameraSource.cpp | 985 uint32_t batchSize = 0; in releaseRecordingFrame() local
989 batchSize = mInflightBatchSizes[0]; in releaseRecordingFrame()
992 if (batchSize == 0) { // return buffers one by one in releaseRecordingFrame()
1001 if (mInflightReturnedHandles.size() == batchSize) { in releaseRecordingFrame()
1272 int batchSize = 0; in recordingFrameHandleCallbackTimestampBatch() local
1293 ++batchSize; in recordingFrameHandleCallbackTimestampBatch()
1309 if (batchSize > 0) { in recordingFrameHandleCallbackTimestampBatch()
1311 mInflightBatchSizes.push_back(batchSize); in recordingFrameHandleCallbackTimestampBatch()
1313 for (int i = 0; i < batchSize; i++) { in recordingFrameHandleCallbackTimestampBatch()
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| /frameworks/ml/nn/runtime/test/ |
| D | TestValidateOperations.cpp | 3926 const uint32_t batchSize = 2; in bidirectionlSequenceRNNTest() local
3931 uint32_t inputDims[3] = {maxTime, batchSize, inputSize}; in bidirectionlSequenceRNNTest()
3935 uint32_t hiddenStateDims[2] = {batchSize, numUnits}; in bidirectionlSequenceRNNTest()
3936 uint32_t outputDims[2] = {batchSize, numUnits}; in bidirectionlSequenceRNNTest()
4002 const uint32_t batchSize = 2; in unidirectionlSequenceRNNTest() local
4007 uint32_t inputDims[3] = {maxTime, batchSize, inputSize}; in unidirectionlSequenceRNNTest()
4011 uint32_t hiddenStateDims[2] = {batchSize, numUnits}; in unidirectionlSequenceRNNTest()
4012 uint32_t outputDims[2] = {batchSize, numUnits}; in unidirectionlSequenceRNNTest()
4068 const uint32_t batchSize = 3; in unidirectionalSequenceLSTMTest() local
4073 uint32_t inputDims[3] = {maxTime, batchSize, inputSize}; in unidirectionalSequenceLSTMTest()
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| /frameworks/av/services/camera/libcameraservice/device3/ |
| D | Camera3Device.cpp | 4540 size_t batchSize = requests.size(); in processBatchCaptureRequests() local
4542 captureRequests_3_4.resize(batchSize); in processBatchCaptureRequests()
4544 captureRequests.resize(batchSize); in processBatchCaptureRequests()
4550 for (size_t i = 0; i < batchSize; i++) { in processBatchCaptureRequests()
4581 for (size_t i = 0; i < batchSize; i++) { in processBatchCaptureRequests()
4660 if (status == common::V1_0::Status::OK && *numRequestProcessed != batchSize) { in processBatchCaptureRequests()
4662 __FUNCTION__, *numRequestProcessed, batchSize); in processBatchCaptureRequests()
5189 size_t batchSize = mNextRequests.size(); in sendRequestsBatch() local
5190 std::vector<camera3_capture_request_t*> requests(batchSize); in sendRequestsBatch()
5192 for (size_t i = 0; i < batchSize; i++) { in sendRequestsBatch()
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| /frameworks/ml/nn/tools/api/ |
| D | types.spec | 3493 * it is set to true, then the input has a shape [maxTime, batchSize,
3494 * inputSize], otherwise the input has a shape [batchSize, maxTime,
3503 * A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden
3512 * A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden
3517 * it is set to true, then the input has a shape [maxTime, batchSize,
3518 * auxInputSize], otherwise the input has a shape [batchSize, maxTime,
3550 * two dimensions are [maxTime, batchSize], otherwise they are set to
3551 * [batchSize, maxTime]. If mergeOutputs is set to true, then the third
3558 * [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
3559 * [batchSize, maxTime, bwNumUnits].
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| /frameworks/base/services/core/java/com/android/server/ |
| D | AlarmManagerService.java | 1018 final int batchSize = alarms.size(); in haveAlarmsTimeTickAlarm() local
1019 for (int j = 0; j < batchSize; j++) { in haveAlarmsTimeTickAlarm()
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