Searched refs:inputShape (Results 1 - 17 of 17) sorted by relevance

/frameworks/ml/nn/common/operations/
H A DNormalization.cpp25 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),
H A DReshape.cpp30 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
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H A DActivation.cpp25 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 DFullyConnected.cpp31 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,
H A DStridedSlice.cpp29 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),
H A DPooling.cpp26 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 DDepthwiseConv2D.cpp27 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,
H A DConv2D.cpp35 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,
H A DSVDF.cpp90 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;
H A DSimpleMath.cpp279 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),
H A DLSTM.cpp268 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
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H A DRNN.cpp67 const Shape &inputShape = input->shape(); local
70 hiddenStateShape->type = inputShape.type;
74 outputShape->type = inputShape.type;
/frameworks/ml/nn/common/include/
H A DOperations.h63 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 DOperationsUtils.h86 bool GetQuantizedConvolutionMultipler(const Shape& inputShape,
/frameworks/ml/nn/runtime/test/benchmark/src/com/example/android/nn/benchmark/
H A DNNTestBase.java39 private synchronized native boolean resizeInputTensors(long modelHandle, int[] inputShape); argument
48 public NNTestBase(String modelName, int[] inputShape) { argument
50 mInputShape = inputShape;
/frameworks/ml/nn/common/
H A DCpuExecutor.cpp490 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 DOperationsUtils.cpp110 bool GetQuantizedConvolutionMultipler(const Shape& inputShape, argument
115 const float input_product_scale = inputShape.scale * filterShape.scale;

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