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/packages/modules/NeuralNetworks/common/operations/
DReshape.cpp34 bool copyData(const void* inputData, const Shape& inputShape, void* outputData, in copyData() argument
38 memcpy(outputData, inputData, count); in copyData()
43 bool depthToSpaceGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize, in depthToSpaceGeneric() argument
46 tflite::optimized_ops::DepthToSpace(inputData, convertShapeToDims(inputShape), blockSize, in depthToSpaceGeneric()
50 template bool depthToSpaceGeneric<float>(const float* inputData, const Shape& inputShape,
53 template bool depthToSpaceGeneric<_Float16>(const _Float16* inputData, const Shape& inputShape,
56 template bool depthToSpaceGeneric<uint8_t>(const uint8_t* inputData, const Shape& inputShape,
59 template bool depthToSpaceGeneric<int8_t>(const int8_t* inputData, const Shape& inputShape,
64 bool spaceToDepthGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize, in spaceToDepthGeneric() argument
67 tflite::optimized_ops::SpaceToDepth(inputData, convertShapeToDims(inputShape), blockSize, in spaceToDepthGeneric()
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DActivation.cpp53 bool reluFloat(const T* inputData, const Shape& inputShape, T* outputData, const Shape& outputShape, in reluFloat() argument
57 for (int i = 0; i < numElements; i++, inputData++, outputData++) { in reluFloat()
59 std::min(std::max(reluMin, static_cast<float>(*inputData)), reluMax)); in reluFloat()
63 template bool reluFloat<float>(const float* inputData, const Shape& inputShape, float* outputData,
65 template bool reluFloat<_Float16>(const _Float16* inputData, const Shape& inputShape,
70 bool relu1Float(const T* inputData, const Shape& inputShape, T* outputData, in relu1Float() argument
72 return reluFloat(inputData, inputShape, outputData, outputShape, -1.f, 1.f); in relu1Float()
74 template bool relu1Float<float>(const float* inputData, const Shape& inputShape, float* outputData,
76 template bool relu1Float<_Float16>(const _Float16* inputData, const Shape& inputShape,
80 bool relu6Float(const T* inputData, const Shape& inputShape, T* outputData, in relu6Float() argument
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DSplit.cpp29 bool splitGeneric(const Scalar* inputData, const Shape& inputShape, int32_t axis, in splitGeneric() argument
43 const Scalar* inputPtr = inputData; in splitGeneric()
55 bool splitFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis, in splitFloat16() argument
59 return splitGeneric<_Float16>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitFloat16()
62 bool splitFloat32(const float* inputData, const Shape& inputShape, int32_t axis, in splitFloat32() argument
66 return splitGeneric<float>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitFloat32()
69 bool splitQuant8(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in splitQuant8() argument
73 return splitGeneric<uint8_t>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitQuant8()
76 bool splitQuant8Signed(const int8_t* inputData, const Shape& inputShape, int32_t axis, in splitQuant8Signed() argument
80 return splitGeneric<int8_t>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitQuant8Signed()
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DPooling.cpp141 bool averagePoolNhwc(const float* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
146 tflite::optimized_ops::AveragePool(op_params, convertShapeToTflshape(inputShape), inputData, in averagePoolNhwc()
151 bool averagePoolNhwc(const _Float16* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
157 convertFloat16ToFloat32(inputData, &inputDataFloat32); in averagePoolNhwc()
164 bool averagePoolNhwc(const uint8_t* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
169 tflite::optimized_ops::AveragePool(op_params, convertShapeToTflshape(inputShape), inputData, in averagePoolNhwc()
174 bool averagePoolNhwc(const int8_t* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
182 inputData, convertShapeToTflshape(outputShape), in averagePoolNhwc()
187 bool l2PoolNhwc(const float* inputData, const Shape& inputShape, const PoolingParam& param, in l2PoolNhwc() argument
192 tflite::optimized_ops::L2Pool(op_params, convertShapeToTflshape(inputShape), inputData, in l2PoolNhwc()
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DL2Normalization.cpp48 inline bool l2normFloat32Impl(const float* inputData, const Shape& inputShape, int32_t axis, in l2normFloat32Impl() argument
57 const float* inputBeg = inputData + outer * axisSize * innerSize; in l2normFloat32Impl()
76 inline bool l2normQuant8Impl(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8Impl() argument
84 const uint8_t* inputBeg = inputData + outer * axisSize * innerSize; in l2normQuant8Impl()
108 inline bool l2normQuant8SignedImpl(const int8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8SignedImpl() argument
116 const int8_t* inputBeg = inputData + outer * axisSize * innerSize; in l2normQuant8SignedImpl()
139 bool l2normFloat32(const float* inputData, const Shape& inputShape, int32_t axis, float* outputData, in l2normFloat32() argument
147 tflite::optimized_ops::L2Normalization(param, convertShapeToTflshape(inputShape), inputData, in l2normFloat32()
151 return l2normFloat32Impl(inputData, inputShape, axis, outputData, outputShape); in l2normFloat32()
155 bool l2normFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis, in l2normFloat16() argument
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DSimpleMath.cpp33 bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis, in meanFloat16() argument
38 convertFloat16ToFloat32(inputData, &inputDataFloat32); in meanFloat16()
48 bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape, in meanGeneric() argument
66 inputData, reinterpret_cast<const int*>(inputShape.dimensions.data()), in meanGeneric()
77 template bool meanGeneric<float, float>(float* inputData, const Shape& inputShape,
80 template bool meanGeneric<uint8_t, int32_t>(uint8_t* inputData, const Shape& inputShape,
84 template bool meanGeneric<int8_t, int32_t>(int8_t* inputData, const Shape& inputShape,
DSoftmax.cpp53 inline bool softmaxSlowFloat32(const float* inputData, const Shape& inputShape, const float beta, in softmaxSlowFloat32() argument
61 const float* inputBeg = inputData + outer * axisSize * innerSize; in softmaxSlowFloat32()
85 bool softmaxFloat32(const float* inputData, const Shape& inputShape, const float beta, int32_t axis, in softmaxFloat32() argument
93 tflite::optimized_ops::Softmax(param, convertShapeToTflshape(inputShape), inputData, in softmaxFloat32()
97 return softmaxSlowFloat32(inputData, inputShape, beta, axis, outputData, outputShape); in softmaxFloat32()
101 bool softmaxFloat16(const _Float16* inputData, const Shape& inputShape, const float beta, in softmaxFloat16() argument
105 convertFloat16ToFloat32(inputData, &inputData_float32); in softmaxFloat16()
116 bool softmaxQuant8Impl(const T* inputData, const Shape& inputShape, const float beta, int32_t axis, in softmaxQuant8Impl() argument
136 const T* inputBeg = inputData + outer * axisSize * innerSize; in softmaxQuant8Impl()
203 bool softmaxQuant8(const T* inputData, const Shape& inputShape, const float beta, int32_t axis, in softmaxQuant8() argument
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DGroupedConv2D.cpp46 bool groupedConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData, in groupedConvFloat32() argument
58 const float* inputBase = inputData; in groupedConvFloat32()
104 bool groupedConvQuant8(const T* inputData, const Shape& inputShape, const T* filterData, in groupedConvQuant8() argument
130 const T* inputBase = inputData; in groupedConvQuant8()
181 template bool groupedConvQuant8<int8_t>(const int8_t* inputData, const Shape& inputShape,
190 template bool groupedConvQuant8<uint8_t>(const uint8_t* inputData, const Shape& inputShape,
200 bool groupedConvQuant8PerChannel(const T* inputData, const Shape& inputShape, in groupedConvQuant8PerChannel() argument
234 const T* inputBase = inputData; in groupedConvQuant8PerChannel()
285 bool groupedConvFloat16(const _Float16* inputData, const Shape& inputShape, in groupedConvFloat16() argument
298 convertFloat16ToFloat32(inputData, &inputData_float32); in groupedConvFloat16()
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DLocalResponseNormalization.cpp51 inline bool localResponseNormFloat32Impl(const float* inputData, const Shape& inputShape, in localResponseNormFloat32Impl() argument
61 const float* inputBase = inputData + outer * axisSize * innerSize; in localResponseNormFloat32Impl()
82 bool localResponseNorm(const T* inputData, const Shape& inputShape, int32_t radius, T bias, T alpha,
86 bool localResponseNorm<float>(const float* inputData, const Shape& inputShape, int32_t radius, in localResponseNorm() argument
98 param, convertShapeToTflshape(inputShape), inputData, in localResponseNorm()
102 return localResponseNormFloat32Impl(inputData, inputShape, radius, bias, alpha, beta, axis, in localResponseNorm()
108 bool localResponseNorm<_Float16>(const _Float16* inputData, const Shape& inputShape, int32_t radius, in localResponseNorm() argument
113 convertFloat16ToFloat32(inputData, &inputDataFloat32); in localResponseNorm()
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.cpp29 static void argMinMaxImpl(const In* inputData, const Shape& inputShape, int32_t axis, bool isArgMin, in argMinMaxImpl() argument
37 auto minMaxValue = inputData[outer * axisSize * innerSize + inner]; in argMinMaxImpl()
40 const auto& value = inputData[(outer * axisSize + i) * innerSize + inner]; in argMinMaxImpl()
51 bool argMinMaxGeneric(const uint8_t* inputData, const Shape& inputShape, int32 axis, bool isArgMin, in argMinMaxGeneric() argument
59 argMinMaxImpl(reinterpret_cast<const dataType*>(inputData), inputShape, axis, isArgMin, \ in argMinMaxGeneric()
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,
DFullyConnected.cpp56 bool fullyConnectedFloat32(const float* inputData, const Shape& inputShape, in fullyConnectedFloat32() argument
70 tflite::reference_ops::FullyConnected(inputData, convertShapeToDims(inputShape), in fullyConnectedFloat32()
77 tflite::optimized_ops::FullyConnected(inputData, convertShapeToDims(inputShape), in fullyConnectedFloat32()
86 bool fullyConnectedFloat16(const _Float16* inputData, const Shape& inputShape, in fullyConnectedFloat16() argument
92 convertFloat16ToFloat32(inputData, &inputDataFloat32); in fullyConnectedFloat16()
107 bool fullyConnectedQuant8(const uint8_t* inputData, const Shape& inputShape, in fullyConnectedQuant8() argument
138 tflite::optimized_ops::FullyConnected(inputData, convertShapeToDims(inputShape), inputOffset, in fullyConnectedQuant8()
148 bool fullyConnectedQuant8(const int8_t* inputData, const Shape& inputShape, in fullyConnectedQuant8() argument
177 params, convertShapeToTflshape(inputShape), inputData, in fullyConnectedQuant8()
DInstanceNormalization.cpp49 inline bool instanceNormNhwc(const T* inputData, const Shape& inputShape, T gamma, T beta, in instanceNormNhwc() argument
64 T val = inputData[indexBase + (h * width + w) * depth]; in instanceNormNhwc()
73 T val = inputData[indexBase + (h * width + w) * depth] - mean; in instanceNormNhwc()
83 outputData[ind] = (inputData[ind] - mean) * gamma / sigma + beta; in instanceNormNhwc()
92 inline bool instanceNorm(const T* inputData, const Shape& inputShape, T gamma, T beta, T epsilon, in instanceNorm() argument
96 NN_RET_CHECK(input.initialize(inputData, inputShape)); in instanceNorm()
DResizeImageOps.cpp68 bool resizeNearestNeighbor(const T* inputData, const Shape& inputShape, bool alignCorners, in resizeNearestNeighbor() argument
98 std::copy_n(inputData + b * inHeight * inWidth * channels + in resizeNearestNeighbor()
111 bool resizeImageOpNhwc(OperationType opType, const T* inputData, const Shape& inputShape, in resizeImageOpNhwc() argument
126 convertShapeToTflshape(inputShape), inputData, convertShapeToTflshape(outDimShape), in resizeImageOpNhwc()
131 resizeNearestNeighbor(inputData, inputShape, alignCorners, halfPixelCenters, outputData, in resizeImageOpNhwc()
138 bool resizeImageOpNhwc<_Float16>(OperationType opType, const _Float16* inputData, in resizeImageOpNhwc() argument
143 convertFloat16ToFloat32(inputData, &inputData_float32); in resizeImageOpNhwc()
152 bool resizeImageOp(OperationType opType, const T* inputData, const Shape& inputShape, bool useNchw, in resizeImageOp() argument
157 NN_RET_CHECK(input.initialize(inputData, inputShape)); in resizeImageOp()
DConv2D.cpp194 bool convNhwc(const float* inputData, const Shape& inputShape, const float* filterData, in convNhwc() argument
215 inputData, convertShapeToDims(inputShape), filterData, convertShapeToDims(filterShape), in convNhwc()
223 bool convNhwc(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, in convNhwc() argument
264 tflite::optimized_ops::Conv(inputData, convertShapeToDims(inputShape), inputOffset, filterData, in convNhwc()
277 bool convNhwc(const int8_t* inputData, Shape inputShape, const int8_t* filterData, in convNhwc() argument
286 convertInt8ToUInt8(inputData, &unsignedInput); in convNhwc()
306 bool convNhwc(const _Float16* inputData, const Shape& inputShape, const _Float16* filterData, in convNhwc() argument
319 convertFloat16ToFloat32(inputData, &inputData_float32); in convNhwc()
333 bool conv(const T_Input* inputData, const Shape& inputShape, const T_Filter* filterData, in conv() argument
341 NN_RET_CHECK(input.initialize(inputData, inputShape)); in conv()
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DDepthwiseConv2D.cpp133 bool depthwiseConvNhwc(const float* inputData, const Shape& inputShape, const float* filterData, in depthwiseConvNhwc() argument
159 tflite::reference_ops::DepthwiseConv(params, convertShapeToTflshape(inputShape), inputData, in depthwiseConvNhwc()
167 bool depthwiseConvNhwc(const _Float16* inputData, const Shape& inputShape, in depthwiseConvNhwc() argument
176 convertFloat16ToFloat32(inputData, &inputDataFloat32); in depthwiseConvNhwc()
193 bool depthwiseConvNhwc(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, in depthwiseConvNhwc() argument
235 tflite::reference_ops::DepthwiseConv(params, convertShapeToTflshape(inputShape), inputData, in depthwiseConvNhwc()
244 bool depthwiseConvNhwc(const int8_t* inputData, Shape inputShape, const int8_t* filterData, in depthwiseConvNhwc() argument
254 convertInt8ToUInt8(inputData, &unsignedInput); in depthwiseConvNhwc()
277 const T* inputData, const Shape& inputShape, const int8_t* filterData, in depthwiseConvQuant8PerChannelNhwc() argument
323 const T* inputBase = inputData; in depthwiseConvQuant8PerChannelNhwc()
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DRoiPooling.cpp55 inline bool roiPoolingNhwc(const T_Input* inputData, const Shape& inputShape, const T_Roi* roiData, in roiPoolingNhwc() argument
107 const T_Input* batchBase = inputData + batchId * inHeight * inWidth * inDepth; in roiPoolingNhwc()
144 inline bool roiPooling(const T_Input* inputData, const Shape& inputShape, const T_Roi* roiData, in roiPooling() argument
150 NN_RET_CHECK(input.initialize(inputData, inputShape)); in roiPooling()
160 inline bool roiPooling<uint8_t, uint16_t>(const uint8_t* inputData, const Shape& inputShape, in roiPooling() argument
168 NN_RET_CHECK(roiPooling(inputData, inputShape, roi_float32.data(), roiShape, batchSplitData, in roiPooling()
175 inline bool roiPooling<int8_t, uint16_t>(const int8_t* inputData, const Shape& inputShape, in roiPooling() argument
183 NN_RET_CHECK(roiPooling(inputData, inputShape, roi_float32.data(), roiShape, batchSplitData, in roiPooling()
DTransposeConv2D.cpp126 bool transposeConvNhwc(const float* inputData, const Shape& inputShape, const float* filterData, in transposeConvNhwc() argument
138 const float* inputBase = inputData; in transposeConvNhwc()
183 bool transposeConvNhwc(const T* inputData, const Shape& inputShape, const T* filterData, in transposeConvNhwc() argument
224 const T* inputPtr = inputData; in transposeConvNhwc()
277 bool transposeConvNhwc(const _Float16* inputData, const Shape& inputShape, in transposeConvNhwc() argument
288 convertFloat16ToFloat32(inputData, &inputData_float32); in transposeConvNhwc()
301 bool transposeConv(const T_Input* inputData, const Shape& inputShape, const T_Filter* filterData, in transposeConv() argument
307 NN_RET_CHECK(input.initialize(inputData, inputShape)); in transposeConv()
317 bool transposeConvQuant8PerChannelNhwc(const T* inputData, const Shape& inputShape, in transposeConvQuant8PerChannelNhwc() argument
366 const T* inputPtr = inputData; in transposeConvQuant8PerChannelNhwc()
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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()
DQuantize.cpp40 bool quantizeToQuant8(const T* inputData, uint8_t* outputData, const Shape& outputShape) { in quantizeToQuant8() argument
45 0.0f, std::min<float>(255.0f, outputShape.offset + std::round(inputData[i] / in quantizeToQuant8()
52 bool quantizeToQuant8Signed(const T* inputData, int8_t* outputData, const Shape& outputShape) { in quantizeToQuant8Signed() argument
59 std::round(inputData[i] / outputShape.scale)))); in quantizeToQuant8Signed()
DDequantize.cpp36 bool compute(const InputType* inputData, const Shape& inputShape, OutputType* outputData) { in compute() argument
41 const int32_t value = inputData[i]; in compute()
48 bool computePerChannel(const int8_t* inputData, const Shape& inputShape, OutputType* outputData) { in computePerChannel() argument
70 const int32_t value = inputData[i]; in computePerChannel()
DExpandDims.cpp40 bool eval(const uint8_t* inputData, const Shape& inputShape, int32_t axis, uint8_t* outputData, in eval() argument
42 memcpy(outputData, inputData, in eval()
/packages/modules/NeuralNetworks/common/include/
DOperations.h49 bool floorFloat16(const _Float16* inputData, _Float16* outputData, const Shape& shape);
50 bool floorFloat32(const float* inputData, float* outputData, const Shape& shape);
52 bool depthwiseConvFloat16(const _Float16* inputData, const Shape& inputShape,
59 bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData,
66 bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape,
73 bool depthwiseConvQuant8PerChannel(const uint8_t* inputData, const Shape& inputShape,
83 bool localResponseNormFloat16(const _Float16* inputData, const Shape& inputShape, int32_t radius,
86 bool localResponseNormFloat32(const float* inputData, const Shape& inputShape, int32_t radius,
90 bool copyData(const void* inputData, const Shape& inputShape, void* outputData,
94 bool depthToSpaceGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize,
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/packages/modules/NeuralNetworks/runtime/test/
DTestMemoryInternal.cpp141 uint8_t* inputData = in TEST_F() local
143 ASSERT_NE(inputData, nullptr); in TEST_F()
144 memcpy(inputData + offsetForMatrix1, matrix1, sizeof(Matrix3x4)); in TEST_F()
173 munmap(inputData, inputSize); in TEST_F()

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