/frameworks/ml/nn/common/operations/ |
H A D | Normalization.cpp | 25 bool l2normFloat32(const float* inputData, const Shape& inputShape, argument 28 inputData, convertShapeToDims(inputShape), 34 bool l2normQuant8(const uint8_t* inputData, const Shape& inputShape, argument 37 inputData, convertShapeToDims(inputShape), 38 inputShape.offset, 44 bool localResponseNormFloat32(const float* inputData, const Shape& inputShape, argument 48 inputData, convertShapeToDims(inputShape),
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H A D | Reshape.cpp | 30 bool reshapeGeneric(const void* inputData, const Shape& inputShape, argument 32 size_t count = sizeOfData(inputShape.type, inputShape.dimensions); 37 bool resizeBilinearFloat32(const float* inputData, const Shape& inputShape, argument 48 inputData, convertShapeToDims(inputShape), 54 bool depthToSpaceGeneric(const uint8_t* inputData, const Shape& inputShape, argument 57 if (inputShape.type == OperandType::TENSOR_FLOAT32) { 60 convertShapeToDims(inputShape), 64 } else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM) { 67 convertShapeToDims(inputShape), 78 spaceToDepthGeneric(const uint8_t* inputData, const Shape& inputShape, int32_t blockSize, uint8_t* outputData, const Shape& outputShape) argument 102 padGeneric(const uint8_t* inputData, const Shape& inputShape, const int32_t* paddings, uint8_t* outputData, const Shape& outputShape) argument 136 batchToSpaceGeneric(const uint8_t* inputData, const Shape& inputShape, const int32_t* blockSize, uint8_t* outputData, const Shape& outputShape) argument 162 spaceToBatchGeneric(const uint8_t* inputData, const Shape& inputShape, const int32_t* blockSize, const int32_t* padding, const Shape& paddingShape, uint8_t* outputData, const Shape& outputShape) argument 191 squeezeGeneric(const void* inputData, const Shape& inputShape, void* outputData, const Shape& outputShape) argument 198 transposeGeneric(const uint8_t* inputData, const Shape& inputShape, const int32_t* perm, const Shape& permShape, uint8_t* outputData, const Shape& outputShape) argument [all...] |
H A D | Activation.cpp | 25 bool reluFloat32(const float* inputData, const Shape& inputShape, argument 27 int numElements = getNumberOfElements(inputShape); 34 bool relu1Float32(const float* inputData, const Shape& inputShape, argument 36 int numElements = getNumberOfElements(inputShape); 43 bool relu6Float32(const float* inputData, const Shape& inputShape, argument 45 int numElements = getNumberOfElements(inputShape); 52 bool tanhFloat32(const float* inputData, const Shape& inputShape, argument 54 int numElements = getNumberOfElements(inputShape); 61 bool logisticFloat32(const float* inputData, const Shape& inputShape, argument 63 int numElements = getNumberOfElements(inputShape); 70 softmaxFloat32(const float* inputData, const Shape& inputShape, const float beta, float* outputData, const Shape& outputShape) argument 108 reluQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, const Shape& outputShape) argument 114 relu1Quant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, const Shape& outputShape) argument 120 relu6Quant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, const Shape& outputShape) argument 128 logisticQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, const Shape& outputShape) argument 161 softmaxQuant8(const uint8_t* inputData, const Shape& inputShape, const float beta, uint8_t* outputData, const Shape& outputShape) argument [all...] |
H A D | FullyConnected.cpp | 31 bool fullyConnectedFloat32(const float* inputData, const Shape& inputShape, argument 43 uint32_t input_n_elements = getNumberOfElements(inputShape); 46 inputData, convertShapeToDims(inputShape), 53 inputData, convertShapeToDims(inputShape), 62 bool fullyConnectedQuant8(const uint8_t* inputData, const Shape& inputShape, argument 67 int32_t inputOffset = -inputShape.offset; 77 if (!GetQuantizedConvolutionMultipler(inputShape, weightsShape, biasShape, 95 inputData, convertShapeToDims(inputShape), inputOffset,
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H A D | StridedSlice.cpp | 29 bool stridedSliceGeneric(const uint8_t* inputData, const Shape& inputShape, argument 42 int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape)); 44 int32_t dim = static_cast<int32_t>(getSizeOfDimension(inputShape, idx)); 72 if (inputShape.type == OperandType::TENSOR_FLOAT32) { 75 convertShapeToDims(inputShape), 80 } else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM) { 83 convertShapeToDims(inputShape),
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H A D | Pooling.cpp | 26 uint32_t height = getSizeOfDimension(inputShape, 1); \ 27 uint32_t width = getSizeOfDimension(inputShape, 2); \ 34 bool averagePoolFloat32(const float* inputData, const Shape& inputShape, argument 48 inputData, convertShapeToDims(inputShape), 57 bool averagePoolQuant8(const uint8_t* inputData, const Shape& inputShape, argument 74 inputData, convertShapeToDims(inputShape), 83 bool l2PoolFloat32(const float* inputData, const Shape& inputShape, argument 97 inputData, convertShapeToDims(inputShape), 106 bool maxPoolFloat32(const float* inputData, const Shape& inputShape, argument 120 inputData, convertShapeToDims(inputShape), 129 maxPoolQuant8(const uint8_t* inputData, const Shape& inputShape, int32_t padding_left, int32_t padding_right, int32_t padding_top, int32_t padding_bottom, int32_t stride_width, int32_t stride_height, int32_t filter_width, int32_t filter_height, int32_t activation, uint8_t* outputData, const Shape& outputShape) argument [all...] |
H A D | DepthwiseConv2D.cpp | 27 uint32_t height = getSizeOfDimension(inputShape, 1); \ 28 uint32_t width = getSizeOfDimension(inputShape, 2); \ 37 bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape, argument 53 inputData, convertShapeToDims(inputShape), 65 bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape, argument 83 if (!GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape, 93 uint32_t inputOffset = -inputShape.offset; 98 inputData, convertShapeToDims(inputShape), inputOffset,
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H A D | Conv2D.cpp | 35 uint32_t height = getSizeOfDimension(inputShape, 1); \ 36 uint32_t width = getSizeOfDimension(inputShape, 2); \ 41 uint32_t inDepth = getSizeOfDimension(inputShape, 3); \ 79 bool convFloat32(const float* inputData, const Shape& inputShape, argument 97 inputData, convertShapeToDims(inputShape), 107 bool convQuant8(const uint8_t* inputData, const Shape& inputShape, argument 118 int32_t inputOffset = -inputShape.offset; 128 if (!GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape, 146 inputData, convertShapeToDims(inputShape), inputOffset,
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H A D | SVDF.cpp | 90 const Shape &inputShape = input->shape(); local 91 stateShape->type = inputShape.type; 93 stateShape->offset = inputShape.offset; 94 stateShape->scale = inputShape.scale; 97 outputShape->type = inputShape.type; 99 outputShape->offset = inputShape.offset; 100 outputShape->scale = inputShape.scale;
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H A D | SimpleMath.cpp | 279 bool meanGeneric(const uint8_t* inputData, const Shape& inputShape, argument 283 int32_t* scratchBuffer = new int32_t[getNumberOfDimensions(inputShape)]; 290 if (inputShape.type == OperandType::TENSOR_FLOAT32) { 293 reinterpret_cast<const int*>(inputShape.dimensions.data()), 294 getNumberOfDimensions(inputShape), 299 } else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM) { 302 reinterpret_cast<const int*>(inputShape.dimensions.data()), 303 getNumberOfDimensions(inputShape),
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H A D | LSTM.cpp | 268 const Shape &inputShape = input->shape(); local 270 outputShape->type = inputShape.type; 272 outputShape->offset = inputShape.offset; 273 outputShape->scale = inputShape.scale; 275 outputStateShape->type = inputShape.type; 277 outputStateShape->offset = inputShape.offset; 278 outputStateShape->scale = inputShape.scale; 280 cellStateShape->type = inputShape.type; 282 cellStateShape->offset = inputShape.offset; 283 cellStateShape->scale = inputShape [all...] |
H A D | RNN.cpp | 67 const Shape &inputShape = input->shape(); local 70 hiddenStateShape->type = inputShape.type; 74 outputShape->type = inputShape.type;
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/frameworks/ml/nn/common/include/ |
H A D | Operations.h | 63 bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape, 71 bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape, 80 bool convFloat32(const float* inputData, const Shape& inputShape, 88 bool convQuant8(const uint8_t* inputData, const Shape& inputShape, 97 bool averagePoolFloat32(const float* inputData, const Shape& inputShape, 103 bool averagePoolQuant8(const uint8_t* inputData, const Shape& inputShape, 109 bool l2PoolFloat32(const float* inputData, const Shape& inputShape, 115 bool maxPoolFloat32(const float* inputData, const Shape& inputShape, 121 bool maxPoolQuant8(const uint8_t* inputData, const Shape& inputShape, 128 bool reluFloat32(const float* inputData, const Shape& inputShape, [all...] |
H A D | OperationsUtils.h | 86 bool GetQuantizedConvolutionMultipler(const Shape& inputShape,
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/frameworks/ml/nn/runtime/test/benchmark/src/com/example/android/nn/benchmark/ |
H A D | NNTestBase.java | 39 private synchronized native boolean resizeInputTensors(long modelHandle, int[] inputShape); argument 48 public NNTestBase(String modelName, int[] inputShape) { argument 50 mInputShape = inputShape;
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/frameworks/ml/nn/common/ |
H A D | CpuExecutor.cpp | 490 Shape inputShape = input.shape(); local 492 int32_t input_width = getSizeOfDimension(inputShape, 2); 493 int32_t input_height = getSizeOfDimension(inputShape, 1); 577 Shape inputShape = input.shape(); local 579 int32_t input_width = getSizeOfDimension(inputShape, 2); 580 int32_t input_height = getSizeOfDimension(inputShape, 1); 660 Shape inputShape = input.shape(); local 661 int32_t input_width = getSizeOfDimension(inputShape, 2); 662 int32_t input_height = getSizeOfDimension(inputShape, 1); 740 Shape inputShape local 804 Shape inputShape = input.shape(); local [all...] |
H A D | OperationsUtils.cpp | 110 bool GetQuantizedConvolutionMultipler(const Shape& inputShape, argument 115 const float input_product_scale = inputShape.scale * filterShape.scale;
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