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Searched refs:outputShape (Results 1 – 25 of 78) sorted by relevance

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/aosp12/packages/modules/NeuralNetworks/common/operations/
H A DReshape.cpp35 const Shape& outputShape) { in copyData() argument
44 T* outputData, const Shape& outputShape) { in depthToSpaceGeneric() argument
52 const Shape& outputShape);
65 T* outputData, const Shape& outputShape) { in spaceToDepthGeneric() argument
86 T* outputData, const Shape& outputShape) { in padGeneric() argument
186 const Shape& outputShape);
189 const Shape& outputShape);
192 const Shape& outputShape);
195 const Shape& outputShape);
225 const Shape& outputShape) { in spaceToBatchGeneric() argument
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H A DActivation.cpp71 const Shape& outputShape) { in relu1Float() argument
81 const Shape& outputShape) { in relu6Float() argument
90 const Shape& outputShape) { in tanhFloat16() argument
100 const Shape& outputShape) { in tanhFloat32() argument
142 const Shape& outputShape) { in reluQuant8() argument
148 const Shape& outputShape) { in relu1Quant8() argument
160 const Shape& outputShape) { in tanhQuant8() argument
162 if (outputShape.offset != 128 || outputShape.scale != 1.f / 128) { in tanhQuant8()
192 if (outputShape.offset != 0 || outputShape.scale != 1.f / 256) { in logisticQuant8()
257 if (outputShape.offset != 0 || outputShape.scale != 1.f / 128) { in tanhQuant8Signed()
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H A DPooling.cpp144 auto op_params = param.toTfliteParam(outputShape); in averagePoolNhwc()
159 outputShape); in averagePoolNhwc()
167 auto op_params = param.toTfliteParam(outputShape); in averagePoolNhwc()
177 auto op_params = param.toTfliteParam(outputShape); in averagePoolNhwc()
190 auto op_params = param.toTfliteParam(outputShape); in l2PoolNhwc()
212 auto op_params = param.toTfliteParam(outputShape); in maxPoolNhwc()
222 auto op_params = param.toTfliteParam(outputShape); in maxPoolNhwc()
232 auto op_params = param.toTfliteParam(outputShape); in maxPoolNhwc()
249 outputShape); in maxPoolNhwc()
269 const Shape& outputShape) { in l2Pool() argument
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H A DMaximumMinimum.cpp36 bool isMinimum, T* outputData, const Shape& outputShape) { in evalGeneric() argument
39 IndexedShapeWrapper outputShapeIndexed(outputShape); in evalGeneric()
41 std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0); in evalGeneric()
62 bool isMinimum, T* outputData, const Shape& outputShape) { in evalQuant8() argument
65 IndexedShapeWrapper outputShapeIndexed(outputShape); in evalQuant8()
67 std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0); in evalQuant8()
77 T aValue = requantize<T>(aData[aFlatIndex], aShape, outputShape); in evalQuant8()
78 T bValue = requantize<T>(bData[bFlatIndex], bShape, outputShape); in evalQuant8()
96 bool isMinimum, void* output, const Shape& outputShape) { in eval() argument
107 reinterpret_cast<float*>(output), outputShape); in eval()
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H A DSimpleMath.cpp35 const Shape& outputShape) { in meanFloat16() argument
40 std::vector<float> outputDataFloat32(getNumberOfElements(outputShape)); in meanFloat16()
42 outputDataFloat32.data(), outputShape); in meanFloat16()
49 bool keepDims, T* outputData, const Shape& outputShape) { in meanGeneric() argument
59 U* tempSumBuffer = new (std::nothrow) U[getNumberOfElements(outputShape)]; in meanGeneric()
68 reinterpret_cast<const int*>(outputShape.dimensions.data()), in meanGeneric()
69 getNumberOfDimensions(outputShape), axis, axisSize, keepDims, scratchBuffer, in meanGeneric()
79 float* outputData, const Shape& outputShape);
83 const Shape& outputShape);
87 const Shape& outputShape);
H A DFullyConnected.cpp59 float* outputData, const Shape& outputShape) { in fullyConnectedFloat32() argument
66 uint32_t batch_size = getSizeOfDimension(outputShape, 0); in fullyConnectedFloat32()
89 _Float16* outputData, const Shape& outputShape) { in fullyConnectedFloat16() argument
98 std::vector<float> outputDataFloat32(getNumberOfElements(outputShape)); in fullyConnectedFloat16()
101 outputDataFloat32.data(), outputShape); in fullyConnectedFloat16()
110 uint8_t* outputData, const Shape& outputShape) { in fullyConnectedQuant8() argument
114 int32_t outputOffset = outputShape.offset; in fullyConnectedQuant8()
151 int8_t* outputData, const Shape& outputShape) { in fullyConnectedQuant8() argument
163 CalculateActivationRangeInt8(activation, outputShape, &outputActivationMin, in fullyConnectedQuant8()
169 params.output_offset = outputShape.offset; in fullyConnectedQuant8()
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H A DConv2D.cpp199 float* outputData, const Shape& outputShape) { in convNhwc() argument
235 int32_t outputOffset = outputShape.offset; in convNhwc()
282 int8_t* outputData, Shape outputShape) { in convNhwc() argument
294 outputShape.offset += 128; in convNhwc()
338 const Shape& outputShape) { in conv() argument
342 NN_RET_CHECK(output.initialize(outputData, outputShape)); in conv()
359 const Shape& outputShape) { in convQuant8PerChannelNhwc() argument
374 int32_t outputOffset = outputShape.offset; in convQuant8PerChannelNhwc()
452 const Shape& outputShape) { in convQuant8PerChannelNhwc() argument
467 int32_t outputOffset = outputShape.offset; in convQuant8PerChannelNhwc()
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H A DQuantize.cpp40 bool quantizeToQuant8(const T* inputData, uint8_t* outputData, const Shape& outputShape) { in quantizeToQuant8() argument
42 uint32_t size = getNumberOfElements(outputShape); in quantizeToQuant8()
45 0.0f, std::min<float>(255.0f, outputShape.offset + std::round(inputData[i] / in quantizeToQuant8()
46 outputShape.scale)))); in quantizeToQuant8()
52 bool quantizeToQuant8Signed(const T* inputData, int8_t* outputData, const Shape& outputShape) { in quantizeToQuant8Signed() argument
54 uint32_t size = getNumberOfElements(outputShape); in quantizeToQuant8Signed()
58 std::min<float>(127.0f, outputShape.offset + in quantizeToQuant8Signed()
59 std::round(inputData[i] / outputShape.scale)))); in quantizeToQuant8Signed()
H A DGroupedConv2D.cpp41 uint32_t outputHeight = getSizeOfDimension(outputShape, 1); \
42 uint32_t outputWidth = getSizeOfDimension(outputShape, 2); \
43 uint32_t outputDepth = getSizeOfDimension(outputShape, 3); \
51 const Shape& outputShape) { in groupedConvFloat32() argument
109 const Shape& outputShape) { in groupedConvQuant8() argument
115 int32_t outputOffset = outputShape.offset; in groupedConvQuant8()
188 const Shape& outputShape);
197 const Shape& outputShape);
211 int32_t outputOffset = outputShape.offset; in groupedConvQuant8PerChannel()
305 outputData_float32.data(), outputShape); in groupedConvFloat16()
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H A DCast.cpp45 const Shape& outputShape) { in copyToTensor() argument
53 switch (outputShape.type) { in copyToTensor()
73 const Shape& outputShape) { in eval() argument
81 outputShape); \ in eval()
91 if (inputShape.type == outputShape.type) { in eval()
92 return copyData(inputData, inputShape, outputData, outputShape); in eval()
H A DPow.cpp35 const Shape& exponentShape, T* outputData, const Shape& outputShape) { in evalGeneric() argument
38 IndexedShapeWrapper outputShapeIndexed(outputShape); in evalGeneric()
40 std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0); in evalGeneric()
70 const Shape& exponentShape, void* outputData, const Shape& outputShape) { in eval() argument
75 reinterpret_cast<_Float16*>(outputData), outputShape); in eval()
80 reinterpret_cast<float*>(outputData), outputShape); in eval()
H A DL2Normalization.cpp49 float* outputData, const Shape& outputShape) { in l2normFloat32Impl() argument
77 uint8_t* outputData, const Shape& outputShape) { in l2normQuant8Impl() argument
109 int8_t* outputData, const Shape& outputShape) { in l2normQuant8SignedImpl() argument
140 const Shape& outputShape) { in l2normFloat32() argument
151 return l2normFloat32Impl(inputData, inputShape, axis, outputData, outputShape); in l2normFloat32()
156 _Float16* outputData, const Shape& outputShape) { in l2normFloat16() argument
160 std::vector<float> outputDataFloat32(getNumberOfElements(outputShape)); in l2normFloat16()
169 uint8_t* outputData, const Shape& outputShape) { in l2normQuant8() argument
180 return l2normQuant8Impl(inputData, inputShape, axis, outputData, outputShape); in l2normQuant8()
185 int8_t* outputData, const Shape& outputShape) { in l2normQuant8Signed() argument
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H A DConcatenation.cpp52 const Shape& outputShape) { in concatenation() argument
63 getNumberOfDimensions(outputShape) - axis - 1, inputDataPtrs.data(), in concatenation()
64 inputDimsPtr.data(), num_inputs, outputData, convertShapeToDims(outputShape)); in concatenation()
72 uint8_t* outputData, const Shape& outputShape) {
88 getNumberOfDimensions(outputShape) - axis - 1, inputDataPtrs.data(),
90 convertShapeToDims(outputShape), outputShape.offset, outputShape.scale);
131 Shape outputShape(context->getOutputShape(kOutputTensor)); variable
132 outputShape.offset += 128;
134 output_uint8.data(), outputShape));
H A DSoftmax.cpp54 int32_t axis, float* outputData, const Shape& outputShape) { in softmaxSlowFloat32() argument
86 float* outputData, const Shape& outputShape) { in softmaxFloat32() argument
94 convertShapeToTflshape(outputShape), outputData); in softmaxFloat32()
102 int32_t axis, _Float16* outputData, const Shape& outputShape) { in softmaxFloat16() argument
106 std::vector<float> outputData_float32(getNumberOfElements(outputShape)); in softmaxFloat16()
109 outputShape); in softmaxFloat16()
118 T* outputData, const Shape& outputShape) { in softmaxQuant8Impl() argument
204 T* outputData, const Shape& outputShape) { in softmaxQuant8() argument
210 outputShape.offset != -128) || in softmaxQuant8()
211 outputShape.scale != 1.f / 256) { in softmaxQuant8()
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H A DDepthwiseConv2D.cpp139 const Shape& outputShape) { in depthwiseConvNhwc() argument
187 outputShape); in depthwiseConvNhwc()
199 const Shape& outputShape) { in depthwiseConvNhwc() argument
228 .output_offset = outputShape.offset, in depthwiseConvNhwc()
250 Shape outputShape) { in depthwiseConvNhwc() argument
262 outputShape.offset += 128; in depthwiseConvNhwc()
297 uint32_t outputWidth = getSizeOfDimension(outputShape, 2); in depthwiseConvQuant8PerChannelNhwc()
301 int32_t outputOffset = outputShape.offset; in depthwiseConvQuant8PerChannelNhwc()
379 const Shape& outputShape) { in depthwiseConv() argument
383 NN_RET_CHECK(output.initialize(outputData, outputShape)); in depthwiseConv()
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H A DPRelu.cpp48 const Shape& outputShape) { in eval() argument
51 IndexedShapeWrapper outputShapeIndexed(outputShape); in eval()
52 std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0); in eval()
71 T* outputData, const Shape& outputShape) { in evalQuant8() argument
74 const int32_t output_offset = outputShape.offset; in evalQuant8()
76 const double real_multiplier_pos = aShape.scale / outputShape.scale; in evalQuant8()
77 const double real_multiplier_neg = input_product_scale / outputShape.scale; in evalQuant8()
98 aData, aShape, bData, bShape, outputData, outputShape); in evalQuant8()
H A DResizeImageOps.cpp74 const int outHeight = getSizeOfDimension(outputShape, 1); in resizeNearestNeighbor()
75 const int outWidth = getSizeOfDimension(outputShape, 2); in resizeNearestNeighbor()
113 const Shape& outputShape) { in resizeImageOpNhwc() argument
115 int32_t height = static_cast<int32_t>(getSizeOfDimension(outputShape, 1)); in resizeImageOpNhwc()
116 int32_t width = static_cast<int32_t>(getSizeOfDimension(outputShape, 2)); in resizeImageOpNhwc()
127 outDimData, convertShapeToTflshape(outputShape), outputData); in resizeImageOpNhwc()
132 outputShape); in resizeImageOpNhwc()
140 _Float16* outputData, const Shape& outputShape) {
144 std::vector<float> outputData_float32(getNumberOfElements(outputShape));
154 const Shape& outputShape) { in resizeImageOp() argument
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H A DTransposeConv2D.cpp129 const Shape& outputShape) { in transposeConvNhwc() argument
136 memset(outputData, 0, getNumberOfElements(outputShape) * sizeof(float)); in transposeConvNhwc()
205 int32_t outputOffset = outputShape.offset; in transposeConvNhwc()
217 CalculateActivationRange<T>(activation, outputShape, &outputActivationMin, in transposeConvNhwc()
281 const Shape& outputShape) { in transposeConvNhwc() argument
286 std::vector<float> outputData_float32(getNumberOfElements(outputShape)); in transposeConvNhwc()
294 outputShape); in transposeConvNhwc()
304 const Shape& outputShape) { in transposeConv() argument
308 NN_RET_CHECK(output.initialize(outputData, outputShape)); in transposeConv()
340 int32_t outputOffset = outputShape.offset; in transposeConvQuant8PerChannelNhwc()
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H A DReduce.cpp55 const Shape outputShape = context->getOutputShape(kOutputTensor); in compute() local
64 reinterpret_cast<const int32_t*>(outputShape.dimensions.data()), in compute()
65 outputShape.dimensions.size(), context->getInputBuffer<int32_t>(kInputAxes), numAxes, in compute()
147 Shape outputShape = inputShape; in prepare() local
148 outputShape.dimensions.clear(); in prepare()
153 outputShape.dimensions.push_back(1); in prepare()
156 outputShape.dimensions.push_back(getSizeOfDimension(inputShape, axis)); in prepare()
161 if (outputShape.dimensions.empty()) { in prepare()
162 outputShape.dimensions.push_back(1); in prepare()
165 return context->setOutputShape(kOutputTensor, outputShape); in prepare()
H A DLocalResponseNormalization.cpp54 const Shape& outputShape) { in localResponseNormFloat32Impl() argument
83 T beta, int32_t axis, T* outputData, const Shape& outputShape);
88 const Shape& outputShape) {
99 convertShapeToTflshape(outputShape), outputData);
103 outputData, outputShape);
110 _Float16* outputData, const Shape& outputShape) {
114 std::vector<float> outputDataFloat32(getNumberOfElements(outputShape));
117 outputDataFloat32.data(), outputShape);
H A DSlice.cpp55 T* outputData, const Shape& outputShape) { in evalGeneric() argument
56 const int outputSize = getNumberOfElements(outputShape); in evalGeneric()
57 const IndexedShapeWrapper indexedOutput = IndexedShapeWrapper(outputShape); in evalGeneric()
59 std::vector<uint32_t> outputIndex(getNumberOfDimensions(outputShape), 0); in evalGeneric()
126 Shape outputShape = context->getOutputShape(kOutputTensor); in prepare() local
127 outputShape.dimensions.resize(n_dims); in prepare()
137 outputShape.dimensions[i] = sliceSize; in prepare()
139 return context->setOutputShape(kOutputTensor, outputShape); in prepare()
H A DMultinomial.cpp66 Shape* outputShape) { in Prepare() argument
78 outputShape->type = OperandType::TENSOR_INT32; in Prepare()
79 outputShape->dimensions = {batch_size, sample_count}; in Prepare()
80 outputShape->offset = inputShape.offset; in Prepare()
81 outputShape->scale = inputShape.scale; in Prepare()
H A DLSHProjection.cpp45 Shape* outputShape) { in Prepare() argument
69 outputShape->dimensions = {SizeOfDimension(hash, 0)}; in Prepare()
76 outputShape->dimensions = {SizeOfDimension(hash, 0) * SizeOfDimension(hash, 1)}; in Prepare()
83 outputShape->type = OperandType::TENSOR_INT32; in Prepare()
84 outputShape->offset = 0; in Prepare()
85 outputShape->scale = 0.f; in Prepare()
/aosp12/packages/modules/NeuralNetworks/common/include/
H A DOperations.h65 const Shape& outputShape);
81 const Shape& outputShape);
88 const Shape& outputShape);
91 const Shape& outputShape);
95 T* outputData, const Shape& outputShape);
98 T* outputData, const Shape& outputShape);
102 T* outputData, const Shape& outputShape);
111 const Shape& outputShape);
115 const Shape& outputShape);
160 const Shape& outputShape);
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/aosp12/packages/modules/NeuralNetworks/common/
H A DOperationsUtils.cpp50 const auto scale = outputShape.scale; in CalculateActivationRangeImpl()
51 const auto zero_point = outputShape.offset; in CalculateActivationRangeImpl()
267 *multiplier = input_product_scale / outputShape.scale; in GetQuantizedConvolutionMultipler()
467 outputShape->type = valueShape.type; in embeddingLookupPrepare()
468 outputShape->dimensions = {lookups, columns}; in embeddingLookupPrepare()
472 outputShape->offset = valueShape.offset; in embeddingLookupPrepare()
473 outputShape->scale = valueShape.scale; in embeddingLookupPrepare()
485 outputShape->type = valueShape.type; in hashtableLookupPrepare()
486 outputShape->dimensions = {lookups}; in hashtableLookupPrepare()
490 outputShape->offset = valueShape.offset; in hashtableLookupPrepare()
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