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Searched refs:inputShape (Results 1 – 25 of 47) sorted by relevance

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/frameworks/ml/nn/common/operations/
DDequantize.cpp37 bool compute(const InputType* inputData, const Shape& inputShape, OutputType* outputData) { in compute() argument
38 const int numElements = getNumberOfElements(inputShape); in compute()
39 const int32_t zeroPoint = inputShape.offset; in compute()
40 const float scale = inputShape.scale; in compute()
49 bool computePerChannel(const int8_t* inputData, const Shape& inputShape, OutputType* outputData) { in computePerChannel() argument
53 const int channelDim = inputShape.extraParams.channelQuant().channelDim; in computePerChannel()
55 for (int i = getNumberOfDimensions(inputShape) - 1; i > channelDim; --i) { in computePerChannel()
56 stride *= getSizeOfDimension(inputShape, i); in computePerChannel()
59 const int numElements = getNumberOfElements(inputShape); in computePerChannel()
60 const int32_t zeroPoint = inputShape.offset; in computePerChannel()
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DReshape.cpp32 bool copyData(const void* inputData, const Shape& inputShape, void* outputData, in copyData() argument
35 size_t count = nonExtensionOperandSizeOfData(inputShape.type, inputShape.dimensions); in copyData()
41 bool depthToSpaceGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize, in depthToSpaceGeneric() argument
44 tflite::optimized_ops::DepthToSpace(inputData, convertShapeToDims(inputShape), blockSize, in depthToSpaceGeneric()
48 template bool depthToSpaceGeneric<float>(const float* inputData, const Shape& inputShape,
51 template bool depthToSpaceGeneric<_Float16>(const _Float16* inputData, const Shape& inputShape,
54 template bool depthToSpaceGeneric<uint8_t>(const uint8_t* inputData, const Shape& inputShape,
59 bool spaceToDepthGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize, in spaceToDepthGeneric() argument
62 tflite::optimized_ops::SpaceToDepth(inputData, convertShapeToDims(inputShape), blockSize, in spaceToDepthGeneric()
66 template bool spaceToDepthGeneric<float>(const float* inputData, const Shape& inputShape,
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DSplit.cpp28 bool splitGeneric(const Scalar* inputData, const Shape& inputShape, int32_t axis, in splitGeneric() argument
31 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in splitGeneric()
34 outerSize *= inputShape.dimensions[i]; in splitGeneric()
37 int concatDimensions = getNumberOfDimensions(inputShape); in splitGeneric()
39 baseInnerSize *= inputShape.dimensions[i]; in splitGeneric()
54 bool splitFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis, in splitFloat16() argument
58 return splitGeneric<_Float16>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitFloat16()
61 bool splitFloat32(const float* inputData, const Shape& inputShape, int32_t axis, in splitFloat32() argument
65 return splitGeneric<float>(inputData, inputShape, axis, outputDataPtrs, outputShapes); in splitFloat32()
68 bool splitQuant8(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in splitQuant8() argument
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DL2Normalization.cpp39 inline bool l2normFloat32Impl(const float* inputData, const Shape& inputShape, int32_t axis, in l2normFloat32Impl() argument
42 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in l2normFloat32Impl()
43 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in l2normFloat32Impl()
45 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in l2normFloat32Impl()
66 inline bool l2normQuant8Impl(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8Impl() argument
69 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in l2normQuant8Impl()
70 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in l2normQuant8Impl()
72 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in l2normQuant8Impl()
80 int32_t val = static_cast<int32_t>(*p) - inputShape.offset; in l2normQuant8Impl()
87 int32_t val = static_cast<int32_t>(*p) - inputShape.offset; in l2normQuant8Impl()
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DActivation.cpp40 bool reluFloat(const T* inputData, const Shape& inputShape, T* outputData, const Shape& outputShape, in reluFloat() argument
43 int numElements = getNumberOfElements(inputShape); in reluFloat()
50 template bool reluFloat<float>(const float* inputData, const Shape& inputShape, float* outputData,
52 template bool reluFloat<_Float16>(const _Float16* inputData, const Shape& inputShape,
57 bool relu1Float(const T* inputData, const Shape& inputShape, T* outputData, in relu1Float() argument
59 return reluFloat(inputData, inputShape, outputData, outputShape, -1.f, 1.f); in relu1Float()
61 template bool relu1Float<float>(const float* inputData, const Shape& inputShape, float* outputData,
63 template bool relu1Float<_Float16>(const _Float16* inputData, const Shape& inputShape,
67 bool relu6Float(const T* inputData, const Shape& inputShape, T* outputData, in relu6Float() argument
69 return reluFloat(inputData, inputShape, outputData, outputShape, 0.f, 6.f); in relu6Float()
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DNormalization.cpp29 inline bool localResponseNormFloat32Impl(const float* inputData, const Shape& inputShape, in localResponseNormFloat32Impl() argument
34 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in localResponseNormFloat32Impl()
35 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in localResponseNormFloat32Impl()
37 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in localResponseNormFloat32Impl()
59 bool localResponseNormFloat16(const _Float16* inputData, const Shape& inputShape, int32_t radius, in localResponseNormFloat16() argument
63 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); in localResponseNormFloat16()
67 localResponseNormFloat32(inputDataFloat32.data(), inputShape, radius, bias, alpha, beta, axis, in localResponseNormFloat16()
74 bool localResponseNormFloat32(const float* inputData, const Shape& inputShape, int32_t radius, in localResponseNormFloat32() argument
77 int32_t ndim = getNumberOfDimensions(inputShape); in localResponseNormFloat32()
78 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in localResponseNormFloat32()
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DArgMinMax.cpp30 static void argMinMaxImpl(const In* inputData, const Shape& inputShape, in argMinMaxImpl() argument
33 const int outerSize = getNumberOfElements(inputShape, 0, axis); in argMinMaxImpl()
34 const int axisSize = getSizeOfDimension(inputShape, axis); in argMinMaxImpl()
36 inputShape, axis + 1, getNumberOfDimensions(inputShape)); in argMinMaxImpl()
55 bool argMinMaxGeneric(const uint8_t* inputData, const Shape& inputShape, in argMinMaxGeneric() argument
59 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in argMinMaxGeneric()
62 if (inputShape.type == operandType) { \ in argMinMaxGeneric()
66 inputShape, \ in argMinMaxGeneric()
DSoftmax.cpp42 inline bool softmaxSlowFloat32(const float* inputData, const Shape& inputShape, const float beta, in softmaxSlowFloat32() argument
45 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in softmaxSlowFloat32()
46 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in softmaxSlowFloat32()
48 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in softmaxSlowFloat32()
74 bool softmaxFloat32(const float* inputData, const Shape& inputShape, const float beta, int32_t axis, in softmaxFloat32() argument
76 int32_t ndim = getNumberOfDimensions(inputShape); in softmaxFloat32()
77 NN_CHECK(handleNegativeAxis(inputShape, &axis)); in softmaxFloat32()
82 tflite::optimized_ops::Softmax(param, convertShapeToTflshape(inputShape), inputData, in softmaxFloat32()
86 return softmaxSlowFloat32(inputData, inputShape, beta, axis, outputData, outputShape); in softmaxFloat32()
90 bool softmaxFloat16(const _Float16* inputData, const Shape& inputShape, const float beta, in softmaxFloat16() argument
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DTopK_V2.cpp29 bool evalGeneric(const T* inputData, const Shape& inputShape, const int32_t k, T* valuesData, in evalGeneric() argument
32 const int rowSize = inputShape.dimensions.back(); in evalGeneric()
33 const int totalSize = getNumberOfElements(inputShape); in evalGeneric()
67 bool eval(const void* inputData, const Shape& inputShape, const int32_t k, void* valuesData, in eval() argument
69 switch (inputShape.type) { in eval()
71 return evalGeneric(reinterpret_cast<const _Float16*>(inputData), inputShape, k, in eval()
76 return evalGeneric(reinterpret_cast<const float*>(inputData), inputShape, k, in eval()
81 return evalGeneric(reinterpret_cast<const int32_t*>(inputData), inputShape, k, in eval()
86 return evalGeneric(reinterpret_cast<const uint8_t*>(inputData), inputShape, k, in eval()
91 LOG(ERROR) << "Unsupported data type: " << toString(inputShape.type); in eval()
DPooling.cpp71 Shape inputShape = context->getInputShape(kInputTensor); in initialize() local
72 int32_t input_height = getSizeOfDimension(inputShape, useNchw ? 2 : 1); in initialize()
73 int32_t input_width = getSizeOfDimension(inputShape, useNchw ? 3 : 2); in initialize()
121 bool averagePoolNhwc(const float* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
126 tflite::optimized_ops::AveragePool(op_params, convertShapeToTflshape(inputShape), inputData, in averagePoolNhwc()
131 bool averagePoolNhwc(const _Float16* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
134 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); in averagePoolNhwc()
138 averagePoolNhwc(inputDataFloat32.data(), inputShape, param, outputDataFloat32.data(), in averagePoolNhwc()
144 bool averagePoolNhwc(const uint8_t* inputData, const Shape& inputShape, const PoolingParam& param, in averagePoolNhwc() argument
149 tflite::optimized_ops::AveragePool(op_params, convertShapeToTflshape(inputShape), inputData, in averagePoolNhwc()
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DDepthwiseConv2D.cpp28 bool depthwiseConvFloat16(const _Float16* inputData, const Shape& inputShape, in depthwiseConvFloat16() argument
36 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); in depthwiseConvFloat16()
44 depthwiseConvFloat32(inputDataFloat32.data(), inputShape, filterDataFloat32.data(), filterShape, in depthwiseConvFloat16()
55 uint32_t height = getSizeOfDimension(inputShape, 1); \
56 uint32_t width = getSizeOfDimension(inputShape, 2); \
65 bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData, in depthwiseConvFloat32() argument
90 tflite::optimized_ops::DepthwiseConv(params, convertShapeToTflshape(inputShape), inputData, in depthwiseConvFloat32()
98 bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape, in depthwiseConvQuant8() argument
115 NN_RET_CHECK(GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape, outputShape, in depthwiseConvQuant8()
132 .input_offset = -inputShape.offset, in depthwiseConvQuant8()
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DSimpleMath.cpp51 bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis, in meanFloat16() argument
55 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); in meanFloat16()
59 meanGeneric<float, float>(inputDataFloat32.data(), inputShape, axis, axisShape, keepDims, in meanFloat16()
66 bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape, in meanGeneric() argument
70 int32_t* scratchBuffer = new int32_t[getNumberOfDimensions(inputShape)]; in meanGeneric()
84 inputData, reinterpret_cast<const int*>(inputShape.dimensions.data()), in meanGeneric()
85 getNumberOfDimensions(inputShape), outputData, in meanGeneric()
95 template bool meanGeneric<float, float>(float* inputData, const Shape& inputShape,
98 template bool meanGeneric<uint8_t, int32_t>(uint8_t* inputData, const Shape& inputShape,
DStridedSlice.cpp31 bool stridedSliceGeneric(const uint8_t* inputData, const Shape& inputShape, in stridedSliceGeneric() argument
44 int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape)); in stridedSliceGeneric()
61 if (inputShape.type == OperandType::TENSOR_FLOAT32) { in stridedSliceGeneric()
64 reinterpret_cast<const float*>(inputData), convertShapeToDims(inputShape), in stridedSliceGeneric()
67 } else if (inputShape.type == OperandType::TENSOR_FLOAT16) { in stridedSliceGeneric()
70 reinterpret_cast<const _Float16*>(inputData), convertShapeToDims(inputShape), in stridedSliceGeneric()
73 } else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM) { in stridedSliceGeneric()
76 reinterpret_cast<const uint8_t*>(inputData), convertShapeToDims(inputShape), in stridedSliceGeneric()
DUnidirectionalSequenceRNN.cpp41 void transposeFirstTwoDims(const T* input, const Shape& inputShape, T* output) { in transposeFirstTwoDims() argument
42 const uint32_t firstDimSize = getSizeOfDimension(inputShape, 0); in transposeFirstTwoDims()
43 const uint32_t secondDimSize = getSizeOfDimension(inputShape, 1); in transposeFirstTwoDims()
44 const uint32_t inputSize = getSizeOfDimension(inputShape, 2); in transposeFirstTwoDims()
59 Shape inputShape = context->getInputShape(kInputTensor); in executeTyped() local
79 inputTransposed.resize(getNumberOfElements(inputShape)); in executeTyped()
81 transposeFirstTwoDims(input, inputShape, inputTransposed.data()); in executeTyped()
84 std::swap(inputShape.dimensions[0], inputShape.dimensions[1]); in executeTyped()
88 const uint32_t maxTime = getSizeOfDimension(inputShape, 0); in executeTyped()
89 const uint32_t batchSize = getSizeOfDimension(inputShape, 1); in executeTyped()
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DGroupedConv2D.cpp30 uint32_t numBatches = getSizeOfDimension(inputShape, 0); \
31 uint32_t inputHeight = getSizeOfDimension(inputShape, 1); \
32 uint32_t inputWidth = getSizeOfDimension(inputShape, 2); \
33 uint32_t inputDepth = getSizeOfDimension(inputShape, 3); \
42 bool groupedConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData, in groupedConvFloat32() argument
99 bool groupedConvQuant8(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, in groupedConvQuant8() argument
108 int32_t inputOffset = -inputShape.offset; in groupedConvQuant8()
115 NN_RET_CHECK(GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape, outputShape, in groupedConvQuant8()
176 bool groupedConvQuant8PerChannel(const uint8_t* inputData, const Shape& inputShape, in groupedConvQuant8PerChannel() argument
187 int32_t inputOffset = -inputShape.offset; in groupedConvQuant8PerChannel()
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DConv2D.cpp94 Shape inputShape = context->getInputShape(kInputTensor); in initialize() local
96 int32_t input_width = getSizeOfDimension(inputShape, useNchw ? 3 : 2); in initialize()
97 int32_t input_height = getSizeOfDimension(inputShape, useNchw ? 2 : 1); in initialize()
120 uint32_t height = getSizeOfDimension(inputShape, 1); \
121 uint32_t width = getSizeOfDimension(inputShape, 2); \
126 uint32_t inDepth = getSizeOfDimension(inputShape, 3); \
164 bool convNhwc(const float* inputData, const Shape& inputShape, const float* filterData, in convNhwc() argument
180 tflite::optimized_ops::Conv(inputData, convertShapeToDims(inputShape), filterData, in convNhwc()
189 bool convNhwc(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, in convNhwc() argument
199 int32_t inputOffset = -inputShape.offset; in convNhwc()
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DFullyConnected.cpp47 bool fullyConnectedFloat32(const float* inputData, const Shape& inputShape, in fullyConnectedFloat32() argument
58 uint32_t input_n_elements = getNumberOfElements(inputShape); in fullyConnectedFloat32()
61 tflite::reference_ops::FullyConnected(inputData, convertShapeToDims(inputShape), in fullyConnectedFloat32()
68 tflite::optimized_ops::FullyConnected(inputData, convertShapeToDims(inputShape), in fullyConnectedFloat32()
77 bool fullyConnectedFloat16(const _Float16* inputData, const Shape& inputShape, in fullyConnectedFloat16() argument
82 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); in fullyConnectedFloat16()
90 fullyConnectedFloat32(inputDataFloat32.data(), inputShape, weightsDataFloat32.data(), in fullyConnectedFloat16()
98 bool fullyConnectedQuant8(const uint8_t* inputData, const Shape& inputShape, in fullyConnectedQuant8() argument
103 int32_t inputOffset = -inputShape.offset; in fullyConnectedQuant8()
113 NN_RET_CHECK(GetQuantizedConvolutionMultipler(inputShape, weightsShape, biasShape, outputShape, in fullyConnectedQuant8()
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DSlice.cpp46 bool evalGeneric(const T* inputData, const Shape& inputShape, const int32_t* beginData, in evalGeneric() argument
51 const IndexedShapeWrapper indexedInput = IndexedShapeWrapper(inputShape); in evalGeneric()
54 std::vector<uint32_t> inputIndex(getNumberOfDimensions(inputShape)); in evalGeneric()
94 const Shape& inputShape = context->getInputShape(kInputTensor); in prepare() local
95 const int32_t n_dims = getNumberOfDimensions(inputShape); in prepare()
115 sliceSize = getSizeOfDimension(inputShape, i) - sliceBegin; in prepare()
117 NN_RET_CHECK_LE(beginData[i], getSizeOfDimension(inputShape, i)); in prepare()
119 NN_RET_CHECK_LE(sliceBegin + sliceSize, getSizeOfDimension(inputShape, i)); in prepare()
DInstanceNormalization.cpp46 inline bool instanceNormNhwc(const T* inputData, const Shape& inputShape, T gamma, T beta, in instanceNormNhwc() argument
49 uint32_t numBatches = getSizeOfDimension(inputShape, 0); in instanceNormNhwc()
50 uint32_t height = getSizeOfDimension(inputShape, 1); in instanceNormNhwc()
51 uint32_t width = getSizeOfDimension(inputShape, 2); in instanceNormNhwc()
52 uint32_t depth = getSizeOfDimension(inputShape, 3); in instanceNormNhwc()
78 inline bool instanceNorm(const T* inputData, const Shape& inputShape, T gamma, T beta, T epsilon, in instanceNorm() argument
82 NN_RET_CHECK(input.initialize(inputData, inputShape)); in instanceNorm()
DRNN.cpp72 const Shape &inputShape = input->shape(); in Prepare() local
75 hiddenStateShape->type = inputShape.type; in Prepare()
79 outputShape->type = inputShape.type; in Prepare()
120 bool RNN::RNNStep(const T* inputData, const Shape& inputShape, const T* hiddenStateInputData, in RNNStep() argument
128 return RNNStep<T>(inputData, inputShape, /*auxInputData=*/nullptr, /*auxInputShape=*/dummyShape, in RNNStep()
140 bool RNN::RNNStep(const T* inputData, const Shape& inputShape, const T* auxInputData, in RNNStep() argument
149 const uint32_t batch_size = inputShape.dimensions[0]; in RNNStep()
151 const uint32_t input_size = inputShape.dimensions[1]; in RNNStep()
DTile.cpp65 void tileImpl(const T* inputData, const Shape& inputShape, const int32_t* multiples, T* outputData, in tileImpl() argument
67 TileOneDimension(inputShape, inputData, multiples, outputData, 0); in tileImpl()
86 bool eval(const uint8_t* inputData, const Shape& inputShape, const int32_t* multiples, in eval() argument
92 tileImpl(reinterpret_cast<const dataType*>(inputData), inputShape, multiples, \ in eval()
97 switch (inputShape.type) { in eval()
DExpandDims.cpp40 bool eval(const uint8_t* inputData, const Shape& inputShape, int32_t axis, uint8_t* outputData, in eval() argument
43 nonExtensionOperandSizeOfData(inputShape.type, inputShape.dimensions)); in eval()
DTransposeConv2D.cpp101 uint32_t numBatches = getSizeOfDimension(inputShape, 0); \
102 uint32_t inputHeight = getSizeOfDimension(inputShape, 1); \
103 uint32_t inputWidth = getSizeOfDimension(inputShape, 2); \
104 uint32_t inputDepth = getSizeOfDimension(inputShape, 3); \
115 bool transposeConvNhwc(const float* inputData, const Shape& inputShape, const float* filterData, in transposeConvNhwc() argument
171 bool transposeConvNhwc(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, in transposeConvNhwc() argument
192 int32_t inputOffset = -inputShape.offset; in transposeConvNhwc()
199 NN_RET_CHECK(GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape, outputShape, in transposeConvNhwc()
266 bool transposeConvNhwc(const _Float16* inputData, const Shape& inputShape, in transposeConvNhwc() argument
272 std::vector<float> inputData_float32(getNumberOfElements(inputShape)); in transposeConvNhwc()
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DChannelShuffle.cpp39 inline bool eval(const T* inputData, const Shape& inputShape, int32_t numGroups, int32_t axis, in eval() argument
41 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); in eval()
42 const uint32_t axisSize = getSizeOfDimension(inputShape, axis); in eval()
44 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); in eval()
/frameworks/ml/nn/common/include/
DOperations.h49 bool depthwiseConvFloat16(const _Float16* inputData, const Shape& inputShape,
56 bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape, const float* filterData,
63 bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape,
70 bool depthwiseConvQuant8PerChannel(const uint8_t* inputData, const Shape& inputShape,
80 bool localResponseNormFloat16(const _Float16* inputData, const Shape& inputShape, int32_t radius,
83 bool localResponseNormFloat32(const float* inputData, const Shape& inputShape, int32_t radius,
87 bool copyData(const void* inputData, const Shape& inputShape, void* outputData,
91 bool depthToSpaceGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize,
94 bool spaceToDepthGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize,
98 bool padGeneric(const T* inputData, const Shape& inputShape, const int32_t* paddings, T pad_value,
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