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/packages/modules/NeuralNetworks/common/cpu_operations/
DReshape.cpp40 bool copyData(const void* inputData, const Shape& inputShape, void* outputData, in copyData() argument
44 memcpy(outputData, inputData, count); in copyData()
49 bool depthToSpaceGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize, in depthToSpaceGeneric() argument
52 tflite::optimized_ops::DepthToSpace(inputData, convertShapeToDims(inputShape), blockSize, in depthToSpaceGeneric()
56 template bool depthToSpaceGeneric<float>(const float* inputData, const Shape& inputShape,
59 template bool depthToSpaceGeneric<_Float16>(const _Float16* inputData, const Shape& inputShape,
62 template bool depthToSpaceGeneric<uint8_t>(const uint8_t* inputData, const Shape& inputShape,
65 template bool depthToSpaceGeneric<int8_t>(const int8_t* inputData, const Shape& inputShape,
70 bool spaceToDepthGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize, in spaceToDepthGeneric() argument
73 tflite::optimized_ops::SpaceToDepth(inputData, convertShapeToDims(inputShape), blockSize, in spaceToDepthGeneric()
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DActivation.cpp54 bool reluFloat(const T* inputData, const Shape& inputShape, T* outputData, in reluFloat() argument
59 for (int i = 0; i < numElements; i++, inputData++, outputData++) { in reluFloat()
61 std::min(std::max(reluMin, static_cast<float>(*inputData)), reluMax)); in reluFloat()
65 template bool reluFloat<float>(const float* inputData, const Shape& inputShape, float* outputData,
67 template bool reluFloat<_Float16>(const _Float16* inputData, const Shape& inputShape,
72 bool relu1Float(const T* inputData, const Shape& inputShape, T* outputData, in relu1Float() argument
74 return reluFloat(inputData, inputShape, outputData, outputShape, -1.f, 1.f); in relu1Float()
76 template bool relu1Float<float>(const float* inputData, const Shape& inputShape, float* outputData,
78 template bool relu1Float<_Float16>(const _Float16* inputData, const Shape& inputShape,
82 bool relu6Float(const T* inputData, const Shape& inputShape, T* outputData, in relu6Float() argument
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DSplit.cpp31 bool splitGeneric(const Scalar* inputData, const Shape& inputShape, int32_t axis, in splitGeneric() argument
45 const Scalar* inputPtr = inputData; in splitGeneric()
57 bool splitFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis, in splitFloat16() argument
61 return splitGeneric<_Float16>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitFloat16()
64 bool splitFloat32(const float* inputData, const Shape& inputShape, int32_t axis, in splitFloat32() argument
68 return splitGeneric<float>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitFloat32()
71 bool splitQuant8(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in splitQuant8() argument
75 return splitGeneric<uint8_t>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitQuant8()
78 bool splitQuant8Signed(const int8_t* inputData, const Shape& inputShape, int32_t axis, in splitQuant8Signed() argument
82 return splitGeneric<int8_t>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitQuant8Signed()
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DPooling.cpp143 bool averagePoolNhwc(const float* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
148 tflite::optimized_ops::AveragePool(op_params, convertShapeToTflshape(inputShape), inputData, in averagePoolNhwc()
153 bool averagePoolNhwc(const _Float16* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
159 convertFloat16ToFloat32(inputData, &inputDataFloat32); in averagePoolNhwc()
166 bool averagePoolNhwc(const uint8_t* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
171 tflite::optimized_ops::AveragePool(op_params, convertShapeToTflshape(inputShape), inputData, in averagePoolNhwc()
176 bool averagePoolNhwc(const int8_t* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
184 inputData, convertShapeToTflshape(outputShape), in averagePoolNhwc()
189 bool l2PoolNhwc(const float* inputData, const Shape& inputShape, const PoolingParam& param, in l2PoolNhwc() argument
194 tflite::optimized_ops::L2Pool(op_params, convertShapeToTflshape(inputShape), inputData, in l2PoolNhwc()
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DL2Normalization.cpp46 inline bool l2normFloat32Impl(const float* inputData, const Shape& inputShape, int32_t axis, in l2normFloat32Impl() argument
55 const float* inputBeg = inputData + outer * axisSize * innerSize; in l2normFloat32Impl()
74 inline bool l2normQuant8Impl(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8Impl() argument
82 const uint8_t* inputBeg = inputData + outer * axisSize * innerSize; in l2normQuant8Impl()
106 inline bool l2normQuant8SignedImpl(const int8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8SignedImpl() argument
114 const int8_t* inputBeg = inputData + outer * axisSize * innerSize; in l2normQuant8SignedImpl()
137 bool l2normFloat32(const float* inputData, const Shape& inputShape, int32_t axis, float* outputData, in l2normFloat32() argument
145 tflite::optimized_ops::L2Normalization(param, convertShapeToTflshape(inputShape), inputData, in l2normFloat32()
149 return l2normFloat32Impl(inputData, inputShape, axis, outputData, outputShape); in l2normFloat32()
153 bool l2normFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis, in l2normFloat16() argument
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DSimpleMath.cpp39 bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis, in meanFloat16() argument
44 convertFloat16ToFloat32(inputData, &inputDataFloat32); in meanFloat16()
54 bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape, in meanGeneric() argument
72 inputData, reinterpret_cast<const int*>(inputShape.dimensions.data()), in meanGeneric()
83 template bool meanGeneric<float, float>(float* inputData, const Shape& inputShape,
86 template bool meanGeneric<uint8_t, int32_t>(uint8_t* inputData, const Shape& inputShape,
90 template bool meanGeneric<int8_t, int32_t>(int8_t* inputData, const Shape& inputShape,
DLocalResponseNormalization.cpp45 inline bool localResponseNormFloat32Impl(const float* inputData, const Shape& inputShape, in localResponseNormFloat32Impl() argument
55 const float* inputBase = inputData + outer * axisSize * innerSize; in localResponseNormFloat32Impl()
76 bool localResponseNorm(const T* inputData, const Shape& inputShape, int32_t radius, T bias, T alpha,
80 bool localResponseNorm<float>(const float* inputData, const Shape& inputShape, int32_t radius, in localResponseNorm() argument
92 param, convertShapeToTflshape(inputShape), inputData, in localResponseNorm()
96 return localResponseNormFloat32Impl(inputData, inputShape, radius, bias, alpha, beta, axis, in localResponseNorm()
102 bool localResponseNorm<_Float16>(const _Float16* inputData, const Shape& inputShape, int32_t radius, in localResponseNorm() argument
107 convertFloat16ToFloat32(inputData, &inputDataFloat32); in localResponseNorm()
DSoftmax.cpp50 inline bool softmaxSlowFloat32(const float* inputData, const Shape& inputShape, const float beta, in softmaxSlowFloat32() argument
58 const float* inputBeg = inputData + outer * axisSize * innerSize; in softmaxSlowFloat32()
82 bool softmaxFloat32(const float* inputData, const Shape& inputShape, const float beta, int32_t axis, in softmaxFloat32() argument
90 tflite::optimized_ops::Softmax(param, convertShapeToTflshape(inputShape), inputData, in softmaxFloat32()
94 return softmaxSlowFloat32(inputData, inputShape, beta, axis, outputData, outputShape); in softmaxFloat32()
98 bool softmaxFloat16(const _Float16* inputData, const Shape& inputShape, const float beta, in softmaxFloat16() argument
102 convertFloat16ToFloat32(inputData, &inputData_float32); in softmaxFloat16()
113 bool softmaxQuant8Impl(const T* inputData, const Shape& inputShape, const float /*beta*/, in softmaxQuant8Impl() argument
133 const T* inputBeg = inputData + outer * axisSize * innerSize; in softmaxQuant8Impl()
200 bool softmaxQuant8(const T* inputData, const Shape& inputShape, const float beta, int32_t axis, in softmaxQuant8() argument
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DCast.cpp44 bool copyToTensor(const FromT* inputData, int numElements, uint8_t* outputData, in copyToTensor() argument
49 copyCast(inputData, reinterpret_cast<dataType*>(outputData), numElements); \ in copyToTensor()
72 bool eval(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, in eval() argument
80 copyToTensor(reinterpret_cast<const dataType*>(inputData), numElements, outputData, \ in eval()
92 return copyData(inputData, inputShape, outputData, outputShape); in eval()
DArgMinMax.cpp31 static void argMinMaxImpl(const In* inputData, const Shape& inputShape, int32_t axis, bool isArgMin, in argMinMaxImpl() argument
39 auto minMaxValue = inputData[outer * axisSize * innerSize + inner]; in argMinMaxImpl()
42 const auto& value = inputData[(outer * axisSize + i) * innerSize + inner]; in argMinMaxImpl()
53 bool argMinMaxGeneric(const uint8_t* inputData, const Shape& inputShape, int32 axis, bool isArgMin, in argMinMaxGeneric() argument
61 argMinMaxImpl(reinterpret_cast<const dataType*>(inputData), inputShape, axis, isArgMin, \ in argMinMaxGeneric()
DFullyConnected.cpp52 bool fullyConnectedFloat32(const float* inputData, const Shape& inputShape, in fullyConnectedFloat32() argument
66 tflite::reference_ops::FullyConnected(inputData, convertShapeToDims(inputShape), in fullyConnectedFloat32()
73 tflite::optimized_ops::FullyConnected(inputData, convertShapeToDims(inputShape), in fullyConnectedFloat32()
82 bool fullyConnectedFloat16(const _Float16* inputData, const Shape& inputShape, in fullyConnectedFloat16() argument
88 convertFloat16ToFloat32(inputData, &inputDataFloat32); in fullyConnectedFloat16()
103 bool fullyConnectedQuant8(const uint8_t* inputData, const Shape& inputShape, in fullyConnectedQuant8() argument
134 tflite::optimized_ops::FullyConnected(inputData, convertShapeToDims(inputShape), inputOffset, in fullyConnectedQuant8()
144 bool fullyConnectedQuant8(const int8_t* inputData, const Shape& inputShape, in fullyConnectedQuant8() argument
173 params, convertShapeToTflshape(inputShape), inputData, in fullyConnectedQuant8()
DInstanceNormalization.cpp39 inline bool instanceNormNhwc(const T* inputData, const Shape& inputShape, T gamma, T beta, in instanceNormNhwc() argument
54 T val = inputData[indexBase + (h * width + w) * depth]; in instanceNormNhwc()
63 T val = inputData[indexBase + (h * width + w) * depth] - mean; in instanceNormNhwc()
73 outputData[ind] = (inputData[ind] - mean) * gamma / sigma + beta; in instanceNormNhwc()
82 inline bool instanceNorm(const T* inputData, const Shape& inputShape, T gamma, T beta, T epsilon, in instanceNorm() argument
86 NN_RET_CHECK(input.initialize(inputData, inputShape)); in instanceNorm()
DResizeImageOps.cpp61 bool resizeNearestNeighbor(const T* inputData, const Shape& inputShape, bool alignCorners, in resizeNearestNeighbor() argument
91 std::copy_n(inputData + b * inHeight * inWidth * channels + in resizeNearestNeighbor()
104 bool resizeImageOpNhwc(OperationType opType, const T* inputData, const Shape& inputShape, in resizeImageOpNhwc() argument
119 convertShapeToTflshape(inputShape), inputData, convertShapeToTflshape(outDimShape), in resizeImageOpNhwc()
124 resizeNearestNeighbor(inputData, inputShape, alignCorners, halfPixelCenters, outputData, in resizeImageOpNhwc()
131 bool resizeImageOpNhwc<_Float16>(OperationType opType, const _Float16* inputData, in resizeImageOpNhwc() argument
136 convertFloat16ToFloat32(inputData, &inputData_float32); in resizeImageOpNhwc()
145 bool resizeImageOp(OperationType opType, const T* inputData, const Shape& inputShape, bool useNchw, in resizeImageOp() argument
150 NN_RET_CHECK(input.initialize(inputData, inputShape)); in resizeImageOp()
DGroupedConv2D.cpp50 bool groupedConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData, in groupedConvFloat32() argument
62 const float* inputBase = inputData; in groupedConvFloat32()
108 bool groupedConvQuant8(const T* inputData, const Shape& inputShape, const T* filterData, in groupedConvQuant8() argument
134 const T* inputBase = inputData; in groupedConvQuant8()
185 template bool groupedConvQuant8<int8_t>(const int8_t* inputData, const Shape& inputShape,
194 template bool groupedConvQuant8<uint8_t>(const uint8_t* inputData, const Shape& inputShape,
204 bool groupedConvQuant8PerChannel(const T* inputData, const Shape& inputShape, in groupedConvQuant8PerChannel() argument
239 const T* inputBase = inputData; in groupedConvQuant8PerChannel()
290 bool groupedConvFloat16(const _Float16* inputData, const Shape& inputShape, in groupedConvFloat16() argument
303 convertFloat16ToFloat32(inputData, &inputData_float32); in groupedConvFloat16()
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DRNN.cpp116 bool RNN::RNNStep(const T* inputData, const Shape& inputShape, const T* hiddenStateInputData, in RNNStep() argument
124 return RNNStep<T>(inputData, inputShape, /*auxInputData=*/nullptr, /*auxInputShape=*/dummyShape, in RNNStep()
136 bool RNN::RNNStep(const T* inputData, const Shape& inputShape, const T* auxInputData, in RNNStep() argument
162 const T* input_ptr_batch = inputData + b * input_size; in RNNStep()
223 template bool RNN::RNNStep<_Float16>(const _Float16* inputData, const Shape& inputShape,
229 template bool RNN::RNNStep<_Float16>(const _Float16* inputData, const Shape& inputShape,
239 template bool RNN::RNNStep<float>(const float* inputData, const Shape& inputShape,
245 template bool RNN::RNNStep<float>(const float* inputData, const Shape& inputShape,
DQuantize.cpp36 bool quantizeToQuant8(const T* inputData, uint8_t* outputData, const Shape& outputShape) { in quantizeToQuant8() argument
41 0.0f, std::min<float>(255.0f, outputShape.offset + std::round(inputData[i] / in quantizeToQuant8()
49 bool quantizeToQuant8Signed(const T* inputData, int8_t* outputData, const Shape& outputShape) { in quantizeToQuant8Signed() argument
56 std::round(inputData[i] / outputShape.scale)))); in quantizeToQuant8Signed()
DRoiPooling.cpp42 inline bool roiPoolingNhwc(const T_Input* inputData, const Shape& inputShape, const T_Roi* roiData, in roiPoolingNhwc() argument
94 const T_Input* batchBase = inputData + batchId * inHeight * inWidth * inDepth; in roiPoolingNhwc()
131 inline bool roiPooling(const T_Input* inputData, const Shape& inputShape, const T_Roi* roiData, in roiPooling() argument
137 NN_RET_CHECK(input.initialize(inputData, inputShape)); in roiPooling()
147 inline bool roiPooling<uint8_t, uint16_t>(const uint8_t* inputData, const Shape& inputShape, in roiPooling() argument
155 NN_RET_CHECK(roiPooling(inputData, inputShape, roi_float32.data(), roiShape, batchSplitData, in roiPooling()
162 inline bool roiPooling<int8_t, uint16_t>(const int8_t* inputData, const Shape& inputShape, in roiPooling() argument
170 NN_RET_CHECK(roiPooling(inputData, inputShape, roi_float32.data(), roiShape, batchSplitData, in roiPooling()
DTile.cpp69 void tileImpl(const T* inputData, const Shape& inputShape, const int32_t* multiples, T* outputData, in tileImpl() argument
71 TileOneDimension(inputShape, inputData, multiples, outputData, 0); in tileImpl()
90 bool eval(const uint8_t* inputData, const Shape& inputShape, const int32_t* multiples, in eval() argument
96 tileImpl(reinterpret_cast<const dataType*>(inputData), inputShape, multiples, \ in eval()
DDepthwiseConv2D.cpp129 bool depthwiseConvNhwc(const float* inputData, const Shape& inputShape, const float* filterData, in depthwiseConvNhwc() argument
155 tflite::reference_ops::DepthwiseConv(params, convertShapeToTflshape(inputShape), inputData, in depthwiseConvNhwc()
163 bool depthwiseConvNhwc(const _Float16* inputData, const Shape& inputShape, in depthwiseConvNhwc() argument
172 convertFloat16ToFloat32(inputData, &inputDataFloat32); in depthwiseConvNhwc()
189 bool depthwiseConvNhwc(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, in depthwiseConvNhwc() argument
231 tflite::reference_ops::DepthwiseConv(params, convertShapeToTflshape(inputShape), inputData, in depthwiseConvNhwc()
240 bool depthwiseConvNhwc(const int8_t* inputData, Shape inputShape, const int8_t* filterData, in depthwiseConvNhwc() argument
250 convertInt8ToUInt8(inputData, &unsignedInput); in depthwiseConvNhwc()
273 const T* inputData, const Shape& inputShape, const int8_t* filterData, in depthwiseConvQuant8PerChannelNhwc() argument
319 const T* inputBase = inputData; in depthwiseConvQuant8PerChannelNhwc()
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DConv2D.cpp191 bool convNhwc(const float* inputData, const Shape& inputShape, const float* filterData, in convNhwc() argument
212 inputData, convertShapeToDims(inputShape), filterData, convertShapeToDims(filterShape), in convNhwc()
220 bool convNhwc(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, in convNhwc() argument
261 tflite::optimized_ops::Conv(inputData, convertShapeToDims(inputShape), inputOffset, filterData, in convNhwc()
274 bool convNhwc(const int8_t* inputData, Shape inputShape, const int8_t* filterData, in convNhwc() argument
283 convertInt8ToUInt8(inputData, &unsignedInput); in convNhwc()
303 bool convNhwc(const _Float16* inputData, const Shape& inputShape, const _Float16* filterData, in convNhwc() argument
316 convertFloat16ToFloat32(inputData, &inputData_float32); in convNhwc()
330 bool conv(const T_Input* inputData, const Shape& inputShape, const T_Filter* filterData, in conv() argument
338 NN_RET_CHECK(input.initialize(inputData, inputShape)); in conv()
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DMirrorPad.cpp116 inputData(theInputData), in EvalData()
126 const T* inputData = nullptr; member
178 const auto* inputData = evalData.inputData; in run() local
181 outputData[i] = inputData[getFlatIndex(i, evalData)]; in run()
/packages/modules/NeuralNetworks/common/include/
DOperations.h32 bool floorFloat16(const _Float16* inputData, _Float16* outputData, const Shape& shape);
33 bool floorFloat32(const float* inputData, float* outputData, const Shape& shape);
35 bool depthwiseConvFloat16(const _Float16* inputData, const Shape& inputShape,
42 bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData,
49 bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape,
56 bool depthwiseConvQuant8PerChannel(const uint8_t* inputData, const Shape& inputShape,
66 bool localResponseNormFloat16(const _Float16* inputData, const Shape& inputShape, int32_t radius,
69 bool localResponseNormFloat32(const float* inputData, const Shape& inputShape, int32_t radius,
73 bool copyData(const void* inputData, const Shape& inputShape, void* outputData,
77 bool depthToSpaceGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize,
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/packages/providers/MediaProvider/src/com/android/providers/media/photopicker/sync/
DPickerSyncManager.java183 final Data inputData = in schedulePeriodicSyncs() local
185 final PeriodicWorkRequest periodicSyncRequest = getPeriodicProactiveSyncRequest(inputData); in schedulePeriodicSyncs()
207 final Data inputData = in schedulePeriodicAlbumReset() local
215 getPeriodicAlbumResetRequest(inputData); in schedulePeriodicAlbumReset()
245 final Data inputData = new Data(Map.of(SYNC_WORKER_INPUT_SYNC_SOURCE, syncSource)); in syncMediaProactively() local
246 final OneTimeWorkRequest syncRequest = getOneTimeProactiveSyncRequest(inputData); in syncMediaProactively()
273 final Data inputData = new Data(Map.of(SYNC_WORKER_INPUT_SYNC_SOURCE, syncSource)); in syncMediaImmediately() local
275 buildOneTimeWorkerRequest(ImmediateSyncWorker.class, inputData); in syncMediaImmediately()
314 final Data inputData = in syncAlbumMediaForProviderImmediately() local
322 buildOneTimeWorkerRequest(MediaResetWorker.class, inputData); in syncAlbumMediaForProviderImmediately()
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/packages/modules/DeviceLock/DeviceLockController/src/com/android/devicelockcontroller/provision/worker/
DReportDeviceProvisionStateWorker.java84 Data inputData = new Data.Builder() in reportSetupFailed() local
88 enqueueReportWork(inputData, workManager); in reportSetupFailed()
95 Data inputData = new Data.Builder() in reportSetupCompleted() local
98 enqueueReportWork(inputData, workManager); in reportSetupCompleted()
105 Data inputData = new Data.Builder() in reportCurrentFailedStep() local
109 enqueueReportWork(inputData, workManager); in reportCurrentFailedStep()
112 private static void enqueueReportWork(Data inputData, WorkManager workManager) { in enqueueReportWork() argument
125 .setInputData(inputData) in enqueueReportWork()
/packages/modules/NeuralNetworks/runtime/test/specs/experimental/
Ddensify_7.mod.py22 inputData = [0.0] * 210 variable
23 inputData[0:22:7] = [11.0, 13.0, 17.0, 19.0]
26 inputData}

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