Home
last modified time | relevance | path

Searched refs:inputShape (Results 1 – 25 of 49) sorted by relevance

12

/aosp12/packages/modules/NeuralNetworks/common/operations/
H A DL2Normalization.cpp53 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in l2normFloat32Impl()
55 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in l2normFloat32Impl()
80 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in l2normQuant8Impl()
82 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in l2normQuant8Impl()
114 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in l2normQuant8SignedImpl()
141 int32_t ndim = getNumberOfDimensions(inputShape); in l2normFloat32()
142 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in l2normFloat32()
170 int32_t ndim = getNumberOfDimensions(inputShape); in l2normQuant8()
171 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in l2normQuant8()
186 int32_t ndim = getNumberOfDimensions(inputShape); in l2normQuant8Signed()
[all …]
H A DReshape.cpp34 bool copyData(const void* inputData, const Shape& inputShape, void* outputData, in copyData() argument
37 size_t count = nonExtensionOperandSizeOfData(inputShape.type, inputShape.dimensions); in copyData()
43 bool depthToSpaceGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize, in depthToSpaceGeneric() argument
50 template bool depthToSpaceGeneric<float>(const float* inputData, const Shape& inputShape,
64 bool spaceToDepthGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize, in spaceToDepthGeneric() argument
94 int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape)); in padGeneric()
112 tflite::RuntimeShape::ExtendedShape(4, convertShapeToTflshape(inputShape)); in padGeneric()
184 template bool padGeneric<float>(const float* inputData, const Shape& inputShape,
187 template bool padGeneric<_Float16>(const _Float16* inputData, const Shape& inputShape,
190 template bool padGeneric<uint8_t>(const uint8_t* inputData, const Shape& inputShape,
[all …]
H A DActivation.cpp56 int numElements = getNumberOfElements(inputShape); in reluFloat()
92 int numElements = getNumberOfElements(inputShape); in tanhFloat16()
102 int numElements = getNumberOfElements(inputShape); in tanhFloat32()
113 int numElements = getNumberOfElements(inputShape); in logisticFloat()
127 int numElements = getNumberOfElements(inputShape); in reluXQuant8()
167 int numElements = getNumberOfElements(inputShape); in tanhQuant8()
182 tflite::optimized_ops::Tanh(inputData, convertShapeToTflshape(inputShape), inputShape.offset, in tanhQuant8()
197 int numElements = getNumberOfElements(inputShape); in logisticQuant8()
213 inputData, convertShapeToTflshape(inputShape), inputShape.offset, input_range_radius, in logisticQuant8()
331 params.input_zero_point = inputShape.offset; in hardSwishQuant()
[all …]
H A DDequantize.cpp37 const int numElements = getNumberOfElements(inputShape); in compute()
38 const int32_t zeroPoint = inputShape.offset; in compute()
39 const float scale = inputShape.scale; in compute()
56 stride *= getSizeOfDimension(inputShape, i); in computePerChannel()
59 const int numElements = getNumberOfElements(inputShape); in computePerChannel()
60 const int32_t zeroPoint = inputShape.offset; in computePerChannel()
125 return compute(inputBuffer, inputShape, in execute()
133 return compute(inputBuffer, inputShape, in execute()
141 return compute(inputBuffer, inputShape, in execute()
149 return computePerChannel(inputBuffer, inputShape, in execute()
[all …]
H A DSplit.cpp29 bool splitGeneric(const Scalar* inputData, const Shape& inputShape, int32_t axis, in splitGeneric() argument
32 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in splitGeneric()
35 outerSize *= inputShape.dimensions[i]; in splitGeneric()
38 int concatDimensions = getNumberOfDimensions(inputShape); in splitGeneric()
40 baseInnerSize *= inputShape.dimensions[i]; in splitGeneric()
55 bool splitFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis, in splitFloat16() argument
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
76 bool splitQuant8Signed(const int8_t* inputData, const Shape& inputShape, int32_t axis, in splitQuant8Signed() argument
[all …]
H A DSoftmax.cpp56 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in softmaxSlowFloat32()
57 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in softmaxSlowFloat32()
59 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in softmaxSlowFloat32()
87 int32_t ndim = getNumberOfDimensions(inputShape); in softmaxFloat32()
88 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in softmaxFloat32()
131 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in softmaxQuant8Impl()
132 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in softmaxQuant8Impl()
134 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in softmaxQuant8Impl()
205 int32_t ndim = getNumberOfDimensions(inputShape); in softmaxQuant8()
206 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in softmaxQuant8()
[all …]
H A DPooling.cpp81 Shape inputShape = context->getInputShape(kInputTensor); in initialize() local
82 int32_t input_height = getSizeOfDimension(inputShape, useNchw ? 2 : 1); in initialize()
83 int32_t input_width = getSizeOfDimension(inputShape, useNchw ? 3 : 2); in initialize()
154 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); in averagePoolNhwc()
158 averagePoolNhwc(inputDataFloat32.data(), inputShape, param, outputDataFloat32.data(), in averagePoolNhwc()
200 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); in l2PoolNhwc()
244 std::vector<float> inputData_float32(getNumberOfElements(inputShape)); in maxPoolNhwc()
248 maxPoolNhwc(inputData_float32.data(), inputShape, param, outputData_float32.data(), in maxPoolNhwc()
259 NN_RET_CHECK(input.initialize(inputData, inputShape)); in averagePool()
272 NN_RET_CHECK(input.initialize(inputData, inputShape)); in l2Pool()
[all …]
H A DSimpleMath.cpp33 bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis, in meanFloat16() argument
37 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); in meanFloat16()
41 meanGeneric<float, float>(inputDataFloat32.data(), inputShape, axis, axisShape, keepDims, in meanFloat16()
48 bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape, in meanGeneric() argument
52 int32_t* scratchBuffer = new int32_t[getNumberOfDimensions(inputShape)]; in meanGeneric()
66 inputData, reinterpret_cast<const int*>(inputShape.dimensions.data()), in meanGeneric()
67 getNumberOfDimensions(inputShape), outputData, 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,
H A DArgMinMax.cpp29 static void argMinMaxImpl(const In* inputData, const Shape& inputShape, int32_t axis, bool isArgMin, in argMinMaxImpl() argument
31 const int outerSize = getNumberOfElements(inputShape, 0, axis); in argMinMaxImpl()
32 const int axisSize = getSizeOfDimension(inputShape, axis); in argMinMaxImpl()
34 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in argMinMaxImpl()
51 bool argMinMaxGeneric(const uint8_t* inputData, const Shape& inputShape, int32 axis, bool isArgMin, in argMinMaxGeneric() argument
54 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in argMinMaxGeneric()
57 if (inputShape.type == operandType) { \ in argMinMaxGeneric()
59 argMinMaxImpl(reinterpret_cast<const dataType*>(inputData), inputShape, axis, isArgMin, \ in argMinMaxGeneric()
H A DLocalResponseNormalization.cpp51 inline bool localResponseNormFloat32Impl(const float* inputData, const Shape& inputShape, in localResponseNormFloat32Impl() argument
56 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in localResponseNormFloat32Impl()
57 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in localResponseNormFloat32Impl()
59 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in localResponseNormFloat32Impl()
86 bool localResponseNorm<float>(const float* inputData, const Shape& inputShape, int32_t radius,
89 int32_t ndim = getNumberOfDimensions(inputShape);
90 NN_CHECK(handleNegativeAxis(inputShape, &axis));
91 radius = std::min(radius, static_cast<int32_t>(inputShape.dimensions[axis]));
98 param, convertShapeToTflshape(inputShape), inputData,
112 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape));
[all …]
H A DUnidirectionalSequenceRNN.cpp50 const uint32_t firstDimSize = getSizeOfDimension(inputShape, 0); in transposeFirstTwoDims()
52 const uint32_t inputSize = getSizeOfDimension(inputShape, 2); in transposeFirstTwoDims()
67 Shape inputShape = context->getInputShape(kInputTensor); in executeTyped() local
87 inputTransposed.resize(getNumberOfElements(inputShape)); in executeTyped()
92 std::swap(inputShape.dimensions[0], inputShape.dimensions[1]); in executeTyped()
96 const uint32_t maxTime = getSizeOfDimension(inputShape, 0); in executeTyped()
97 const uint32_t batchSize = getSizeOfDimension(inputShape, 1); in executeTyped()
98 const uint32_t inputSize = getSizeOfDimension(inputShape, 2); in executeTyped()
102 Shape fixedTimeInputShape = inputShape; in executeTyped()
104 fixedTimeInputShape.dimensions[0] = inputShape.dimensions[1]; in executeTyped()
[all …]
H A DTopK_V2.cpp41 bool evalGeneric(const T* inputData, const Shape& inputShape, const int32_t k, T* valuesData, in evalGeneric() argument
43 const int rowSize = inputShape.dimensions.back(); in evalGeneric()
44 const int totalSize = getNumberOfElements(inputShape); in evalGeneric()
96 const Shape inputShape = context->getInputShape(kInputTensor); in prepare() local
99 NN_RET_CHECK_LE(k, inputShape.dimensions.back()); in prepare()
103 Shape outputValuesShape = inputShape; in prepare()
107 outputIndicesShape.dimensions = inputShape.dimensions; in prepare()
114 const Shape inputShape = context->getInputShape(kInputTensor); in execute() local
115 switch (inputShape.type) { in execute()
132 LOG(ERROR) << "Unsupported data type: " << inputShape.type; in execute()
H A DConv2D.cpp233 int32_t inputOffset = -inputShape.offset; in convNhwc()
287 inputShape.offset += 128; in convNhwc()
341 NN_RET_CHECK(input.initialize(inputData, inputShape)); in conv()
362 uint32_t numBatches = getSizeOfDimension(inputShape, 0); in convQuant8PerChannelNhwc()
364 uint32_t inputWidth = getSizeOfDimension(inputShape, 2); in convQuant8PerChannelNhwc()
365 uint32_t inputDepth = getSizeOfDimension(inputShape, 3); in convQuant8PerChannelNhwc()
373 int32_t inputOffset = -inputShape.offset; in convQuant8PerChannelNhwc()
455 uint32_t numBatches = getSizeOfDimension(inputShape, 0); in convQuant8PerChannelNhwc()
466 int32_t inputOffset = -inputShape.offset; in convQuant8PerChannelNhwc()
488 convParams.input_offset = -inputShape.offset; in convQuant8PerChannelNhwc()
[all …]
H A DGroupedConv2D.cpp34 uint32_t numBatches = getSizeOfDimension(inputShape, 0); \
35 uint32_t inputHeight = getSizeOfDimension(inputShape, 1); \
36 uint32_t inputWidth = getSizeOfDimension(inputShape, 2); \
37 uint32_t inputDepth = getSizeOfDimension(inputShape, 3); \
113 int32_t inputOffset = -inputShape.offset; in groupedConvQuant8()
200 bool groupedConvQuant8PerChannel(const T* inputData, const Shape& inputShape, in groupedConvQuant8PerChannel() argument
210 int32_t inputOffset = -inputShape.offset; in groupedConvQuant8PerChannel()
221 biasChannelShape.scale = filterScales[i] * inputShape.scale; in groupedConvQuant8PerChannel()
285 bool groupedConvFloat16(const _Float16* inputData, const Shape& inputShape, in groupedConvFloat16() argument
293 std::vector<float> inputData_float32(getNumberOfElements(inputShape)); in groupedConvFloat16()
[all …]
H A DFullyConnected.cpp56 bool fullyConnectedFloat32(const float* inputData, const Shape& inputShape, in fullyConnectedFloat32() argument
67 uint32_t input_n_elements = getNumberOfElements(inputShape); in fullyConnectedFloat32()
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
91 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); in fullyConnectedFloat16()
107 bool fullyConnectedQuant8(const uint8_t* inputData, const Shape& inputShape, in fullyConnectedQuant8() argument
112 int32_t inputOffset = -inputShape.offset; in fullyConnectedQuant8()
148 bool fullyConnectedQuant8(const int8_t* inputData, const Shape& inputShape, in fullyConnectedQuant8() argument
167 params.input_offset = -inputShape.offset; in fullyConnectedQuant8()
[all …]
H A DRNN.cpp68 const Shape& inputShape = input->shape(); in Prepare() local
71 hiddenStateShape->type = inputShape.type; in Prepare()
75 outputShape->type = inputShape.type; in Prepare()
116 bool RNN::RNNStep(const T* inputData, const Shape& inputShape, const T* hiddenStateInputData, in RNNStep() argument
136 bool RNN::RNNStep(const T* inputData, const Shape& inputShape, const T* auxInputData, in RNNStep() argument
145 const uint32_t batch_size = inputShape.dimensions[0]; in RNNStep()
147 const uint32_t input_size = inputShape.dimensions[1]; 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,
[all …]
H A DChannelShuffle.cpp38 inline bool eval(const T* inputData, const Shape& inputShape, int32_t numGroups, int32_t axis, in eval() argument
40 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in eval()
41 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in eval()
43 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in eval()
69 const Shape& inputShape = context->getInputShape(kInputTensor); in validate() local
70 if (hasKnownRank(inputShape)) { in validate()
71 NN_RET_CHECK_LE(getNumberOfDimensions(inputShape), 4); in validate()
H A DDepthwiseConv2D.cpp123 uint32_t height = getSizeOfDimension(inputShape, 1); \
124 uint32_t width = getSizeOfDimension(inputShape, 2); \
226 .input_offset = -inputShape.offset, in depthwiseConvNhwc()
255 inputShape.offset += 128; in depthwiseConvNhwc()
289 uint32_t numBatches = getSizeOfDimension(inputShape, 0); in depthwiseConvQuant8PerChannelNhwc()
290 uint32_t inputHeight = getSizeOfDimension(inputShape, 1); in depthwiseConvQuant8PerChannelNhwc()
291 uint32_t inputWidth = getSizeOfDimension(inputShape, 2); in depthwiseConvQuant8PerChannelNhwc()
292 uint32_t inputDepth = getSizeOfDimension(inputShape, 3); in depthwiseConvQuant8PerChannelNhwc()
300 int32_t inputOffset = -inputShape.offset; in depthwiseConvQuant8PerChannelNhwc()
382 NN_RET_CHECK(input.initialize(inputData, inputShape)); in depthwiseConv()
[all …]
H A DSqueeze.cpp76 const Shape inputShape = context->getInputShape(kInputTensor); in prepare() local
78 int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape)); in prepare()
80 NN_RET_CHECK_LE(getNumberOfDimensions(inputShape), 4); in prepare()
92 if (getSizeOfDimension(inputShape, idx) == 1) { in prepare()
103 getSizeOfDimension(inputShape, current) == 1); in prepare()
117 outDims[outIdx++] = getSizeOfDimension(inputShape, inIdx); in prepare()
121 Shape outputShape(inputShape); in prepare()
H A DCast.cpp72 bool eval(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, in eval() argument
75 int numElements = getNumberOfElements(inputShape); in eval()
85 switch (inputShape.type) { in eval()
91 if (inputShape.type == outputShape.type) { in eval()
92 return copyData(inputData, inputShape, outputData, outputShape); in eval()
H A DResizeImageOps.cpp68 bool resizeNearestNeighbor(const T* inputData, const Shape& inputShape, bool alignCorners, in resizeNearestNeighbor() argument
70 const int batchSize = getSizeOfDimension(inputShape, 0); in resizeNearestNeighbor()
71 const int inHeight = getSizeOfDimension(inputShape, 1); in resizeNearestNeighbor()
72 const int inWidth = getSizeOfDimension(inputShape, 2); in resizeNearestNeighbor()
73 const int channels = getSizeOfDimension(inputShape, 3); 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()
142 std::vector<float> inputData_float32(getNumberOfElements(inputShape));
145 NN_RET_CHECK(resizeImageOpNhwc(opType, inputData_float32.data(), inputShape, alignCorners,
[all …]
H A DInstanceNormalization.cpp49 inline bool instanceNormNhwc(const T* inputData, const Shape& inputShape, T gamma, T beta, in instanceNormNhwc() argument
52 uint32_t numBatches = getSizeOfDimension(inputShape, 0); in instanceNormNhwc()
53 uint32_t height = getSizeOfDimension(inputShape, 1); in instanceNormNhwc()
54 uint32_t width = getSizeOfDimension(inputShape, 2); in instanceNormNhwc()
55 uint32_t depth = getSizeOfDimension(inputShape, 3); 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()
H A DSlice.cpp53 bool evalGeneric(const T* inputData, const Shape& inputShape, const int32_t* beginData, in evalGeneric() argument
58 const IndexedShapeWrapper indexedInput = IndexedShapeWrapper(inputShape); in evalGeneric()
61 std::vector<uint32_t> inputIndex(getNumberOfDimensions(inputShape)); in evalGeneric()
111 const Shape& inputShape = context->getInputShape(kInputTensor); in prepare() local
112 const int32_t n_dims = getNumberOfDimensions(inputShape); in prepare()
132 sliceSize = getSizeOfDimension(inputShape, i) - sliceBegin; in prepare()
134 NN_RET_CHECK_LE(beginData[i], getSizeOfDimension(inputShape, i)); in prepare()
136 NN_RET_CHECK_LE(sliceBegin + sliceSize, getSizeOfDimension(inputShape, i)); in prepare()
H A DRoiPooling.cpp55 inline bool roiPoolingNhwc(const T_Input* inputData, const Shape& inputShape, const T_Roi* roiData, in roiPoolingNhwc() argument
65 uint32_t numBatches = getSizeOfDimension(inputShape, 0); in roiPoolingNhwc()
66 uint32_t inHeight = getSizeOfDimension(inputShape, 1); in roiPoolingNhwc()
67 uint32_t inWidth = getSizeOfDimension(inputShape, 2); in roiPoolingNhwc()
68 uint32_t inDepth = getSizeOfDimension(inputShape, 3); in roiPoolingNhwc()
123 T_Input maxValue = static_cast<T_Input>(inputShape.offset); 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,
168 NN_RET_CHECK(roiPooling(inputData, inputShape, roi_float32.data(), roiShape, batchSplitData,
[all …]
/aosp12/packages/modules/NeuralNetworks/common/include/
H A DOperations.h52 bool depthwiseConvFloat16(const _Float16* inputData, const Shape& inputShape,
66 bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape,
73 bool depthwiseConvQuant8PerChannel(const uint8_t* inputData, const Shape& inputShape,
90 bool copyData(const void* inputData, const Shape& inputShape, void* outputData,
113 bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis,
120 bool stridedSliceGeneric(const uint8_t* inputData, const Shape& inputShape,
125 bool argMinMaxGeneric(const uint8_t* inputData, const Shape& inputShape, int32_t axis,
128 bool splitFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis,
132 bool splitFloat32(const float* inputData, const Shape& inputShape, const int32_t axis,
148 bool groupedConvFloat16(const _Float16* inputData, const Shape& inputShape,
[all …]

12