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Searched refs:outputData (Results 1 – 25 of 51) sorted by relevance

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/packages/modules/NeuralNetworks/common/operations/
DReshape.cpp34 bool copyData(const void* inputData, const Shape& inputShape, void* outputData, in copyData() argument
38 memcpy(outputData, inputData, count); in copyData()
44 T* outputData, const Shape& outputShape) { in depthToSpaceGeneric() argument
47 outputData, convertShapeToDims(outputShape)); in depthToSpaceGeneric()
51 int32_t blockSize, float* outputData,
54 int32_t blockSize, _Float16* outputData,
57 int32_t blockSize, uint8_t* outputData,
60 int32_t blockSize, int8_t* outputData,
65 T* outputData, const Shape& outputShape) { in spaceToDepthGeneric() argument
68 outputData, convertShapeToDims(outputShape)); in spaceToDepthGeneric()
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DActivation.cpp53 bool reluFloat(const T* inputData, const Shape& inputShape, T* outputData, const Shape& outputShape, in reluFloat() argument
57 for (int i = 0; i < numElements; i++, inputData++, outputData++) { in reluFloat()
58 *outputData = static_cast<T>( in reluFloat()
63 template bool reluFloat<float>(const float* inputData, const Shape& inputShape, float* outputData,
66 _Float16* outputData, const Shape& outputShape, float reluMin,
70 bool relu1Float(const T* inputData, const Shape& inputShape, T* outputData, in relu1Float() argument
72 return reluFloat(inputData, inputShape, outputData, outputShape, -1.f, 1.f); in relu1Float()
74 template bool relu1Float<float>(const float* inputData, const Shape& inputShape, float* outputData,
77 _Float16* outputData, const Shape& outputShape);
80 bool relu6Float(const T* inputData, const Shape& inputShape, T* outputData, in relu6Float() argument
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DPooling.cpp142 float* outputData, const Shape& outputShape) { in averagePoolNhwc() argument
147 convertShapeToTflshape(outputShape), outputData); in averagePoolNhwc()
152 _Float16* outputData, const Shape& outputShape) { in averagePoolNhwc() argument
160 convertFloat32ToFloat16(outputDataFloat32, outputData); in averagePoolNhwc()
165 uint8_t* outputData, const Shape& outputShape) { in averagePoolNhwc() argument
170 convertShapeToTflshape(outputShape), outputData); in averagePoolNhwc()
175 int8_t* outputData, const Shape& outputShape) { in averagePoolNhwc() argument
183 outputData); in averagePoolNhwc()
188 float* outputData, const Shape& outputShape) { in l2PoolNhwc() argument
193 convertShapeToTflshape(outputShape), outputData); in l2PoolNhwc()
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DL2Normalization.cpp49 float* outputData, const Shape& outputShape) { in l2normFloat32Impl() argument
59 float* outputBeg = outputData + outer * axisSize * innerSize; in l2normFloat32Impl()
77 uint8_t* outputData, const Shape& outputShape) { in l2normQuant8Impl() argument
86 uint8_t* outputBeg = outputData + outer * axisSize * innerSize; in l2normQuant8Impl()
109 int8_t* outputData, const Shape& outputShape) { in l2normQuant8SignedImpl() argument
118 int8_t* outputBeg = outputData + outer * axisSize * innerSize; in l2normQuant8SignedImpl()
139 bool l2normFloat32(const float* inputData, const Shape& inputShape, int32_t axis, float* outputData, in l2normFloat32() argument
148 convertShapeToTflshape(outputShape), outputData); in l2normFloat32()
151 return l2normFloat32Impl(inputData, inputShape, axis, outputData, outputShape); in l2normFloat32()
156 _Float16* outputData, const Shape& outputShape) { in l2normFloat16() argument
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DSimpleMath.cpp34 const Shape& axisShape, bool keepDims, _Float16* outputData, in meanFloat16() argument
43 convertFloat32ToFloat16(outputDataFloat32, outputData); in meanFloat16()
49 bool keepDims, T* outputData, const Shape& outputShape) { in meanGeneric() argument
67 getNumberOfDimensions(inputShape), outputData, in meanGeneric()
79 float* outputData, const Shape& outputShape);
82 bool keepDims, uint8_t* outputData,
86 bool keepDims, int8_t* outputData,
DSoftmax.cpp54 int32_t axis, float* outputData, const Shape& outputShape) { in softmaxSlowFloat32() argument
63 float* outputBeg = outputData + outer * axisSize * innerSize; in softmaxSlowFloat32()
86 float* outputData, const Shape& outputShape) { in softmaxFloat32() argument
94 convertShapeToTflshape(outputShape), outputData); in softmaxFloat32()
97 return softmaxSlowFloat32(inputData, inputShape, beta, axis, outputData, outputShape); in softmaxFloat32()
102 int32_t axis, _Float16* outputData, const Shape& outputShape) { in softmaxFloat16() argument
110 convertFloat32ToFloat16(outputData_float32, outputData); in softmaxFloat16()
118 T* outputData, const Shape& outputShape) { in softmaxQuant8Impl() argument
138 T* outputBeg = outputData + outer * axisSize * innerSize; in softmaxQuant8Impl()
204 T* outputData, const Shape& outputShape) { in softmaxQuant8() argument
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DGroupedConv2D.cpp50 int32_t numGroups, int32_t activation, float* outputData, in groupedConvFloat32() argument
59 float* outPtr = outputData; in groupedConvFloat32()
108 int32_t numGroups, int32_t activation, T* outputData, in groupedConvQuant8() argument
131 T* outPtr = outputData; in groupedConvQuant8()
187 int32_t numGroups, int32_t activation, int8_t* outputData,
196 int32_t numGroups, int32_t activation, uint8_t* outputData,
206 int32_t activation, T* outputData, const Shape& outputShape) { in groupedConvQuant8PerChannel() argument
235 T* outPtr = outputData; in groupedConvQuant8PerChannel()
290 int32_t activation, _Float16* outputData, const Shape& outputShape) { in groupedConvFloat16() argument
306 convertFloat32ToFloat16(outputData_float32, outputData); in groupedConvFloat16()
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DLocalResponseNormalization.cpp53 int32_t axis, float* outputData, in localResponseNormFloat32Impl() argument
62 float* outputBase = outputData + outer * axisSize * innerSize; in localResponseNormFloat32Impl()
83 T beta, int32_t axis, T* outputData, const Shape& outputShape);
87 float bias, float alpha, float beta, int32_t axis, float* outputData, in localResponseNorm() argument
99 convertShapeToTflshape(outputShape), outputData); in localResponseNorm()
103 outputData, outputShape); in localResponseNorm()
110 _Float16* outputData, const Shape& outputShape) { in localResponseNorm() argument
118 convertFloat32ToFloat16(outputDataFloat32, outputData); in localResponseNorm()
DCast.cpp44 bool copyToTensor(const FromT* inputData, int numElements, uint8_t* outputData, in copyToTensor() argument
49 copyCast(inputData, reinterpret_cast<dataType*>(outputData), numElements); \ in copyToTensor()
72 bool eval(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, in eval() argument
80 copyToTensor(reinterpret_cast<const dataType*>(inputData), numElements, outputData, \ in eval()
92 return copyData(inputData, inputShape, outputData, outputShape); in eval()
DPow.cpp35 const Shape& exponentShape, T* outputData, const Shape& outputShape) { in evalGeneric() argument
50 outputData[outputFlatIndex] = std::pow(static_cast<float>(baseData[baseFlatIndex]), 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()
DRNN.cpp119 const int32_t activation, T* outputData) { in RNNStep() argument
128 /*outputBatchStride=*/numUnits, /*outputBatchOffset=*/0, outputData); in RNNStep()
141 const uint32_t outputBatchStride, const uint32_t outputBatchOffset, T* outputData, in RNNStep() argument
168 T* output_ptr_batch = outputData + b * outputBatchStride + outputBatchOffset; in RNNStep()
228 _Float16* outputData);
237 const uint32_t outputBatchOffset, _Float16* outputData,
244 float* outputData);
253 float* outputData, float* hiddenStateOutput);
DFullyConnected.cpp59 float* outputData, const Shape& outputShape) { in fullyConnectedFloat32() argument
74 outputData, convertShapeToDims(outputShape)); in fullyConnectedFloat32()
81 outputData, convertShapeToDims(outputShape)); in fullyConnectedFloat32()
89 _Float16* outputData, const Shape& outputShape) { in fullyConnectedFloat16() argument
102 convertFloat32ToFloat16(outputDataFloat32, outputData); in fullyConnectedFloat16()
110 uint8_t* outputData, const Shape& outputShape) { in fullyConnectedQuant8() argument
142 outputActivationMin, outputActivationMax, outputData, in fullyConnectedQuant8()
151 int8_t* outputData, const Shape& outputShape) { in fullyConnectedQuant8() argument
179 biasData, convertShapeToTflshape(outputShape), outputData); in fullyConnectedQuant8()
DResizeImageOps.cpp69 bool halfPixelCenters, T* outputData, const Shape& outputShape) { in resizeNearestNeighbor() argument
101 outputData + b * outHeight * outWidth * channels + in resizeNearestNeighbor()
112 bool alignCorners, bool halfPixelCenters, T* outputData, in resizeImageOpNhwc() argument
127 outDimData, convertShapeToTflshape(outputShape), outputData); in resizeImageOpNhwc()
131 resizeNearestNeighbor(inputData, inputShape, alignCorners, halfPixelCenters, outputData, in resizeImageOpNhwc()
140 _Float16* outputData, const Shape& outputShape) { in resizeImageOpNhwc() argument
147 convertFloat32ToFloat16(outputData_float32, outputData); in resizeImageOpNhwc()
153 bool alignCorners, bool halfPixelCenters, T* outputData, in resizeImageOp() argument
158 NN_RET_CHECK(output.initialize(outputData, outputShape)); in resizeImageOp()
DTransposeConv2D.cpp128 const TransposeConv2dParam& param, float* outputData, in transposeConvNhwc() argument
136 memset(outputData, 0, getNumberOfElements(outputShape) * sizeof(float)); in transposeConvNhwc()
139 float* outputBase = outputData; in transposeConvNhwc()
171 float* outPtr = outputData; in transposeConvNhwc()
185 const TransposeConv2dParam& param, T* outputData, const Shape& outputShape) { in transposeConvNhwc() argument
263 T* outPtr = outputData; in transposeConvNhwc()
280 const TransposeConv2dParam& param, _Float16* outputData, in transposeConvNhwc() argument
295 convertFloat32ToFloat16(outputData_float32, outputData); in transposeConvNhwc()
303 const TransposeConv2dParam& param, T_Input* outputData, in transposeConv() argument
308 NN_RET_CHECK(output.initialize(outputData, outputShape)); in transposeConv()
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DConv2D.cpp199 float* outputData, const Shape& outputShape) { in convNhwc() argument
218 output_activation_min, output_activation_max, outputData, in convNhwc()
228 uint8_t* outputData, const Shape& outputShape) { in convNhwc() argument
269 output_activation_min, output_activation_max, outputData, in convNhwc()
282 int8_t* outputData, Shape outputShape) { in convNhwc() argument
301 convertUInt8ToInt8(unsignedOutput, outputData); in convNhwc()
311 _Float16* outputData, const Shape& outputShape) { in convNhwc() argument
327 convertFloat32ToFloat16(outputData_float32, outputData); in convNhwc()
337 int32_t dilation_height_factor, int32_t activation, bool useNchw, T_Input* outputData, in conv() argument
342 NN_RET_CHECK(output.initialize(outputData, outputShape)); in conv()
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DDepthwiseConv2D.cpp138 int32_t depthMultiplier, int32_t activation, float* outputData, in depthwiseConvNhwc() argument
162 convertShapeToTflshape(outputShape), outputData); in depthwiseConvNhwc()
173 _Float16* outputData, const Shape& outputShape) { in depthwiseConvNhwc() argument
189 convertFloat32ToFloat16(outputDataFloat32, outputData); in depthwiseConvNhwc()
198 int32_t depthMultiplier, int32_t activation, uint8_t* outputData, in depthwiseConvNhwc() argument
238 convertShapeToTflshape(outputShape), outputData); in depthwiseConvNhwc()
249 int32_t depthMultiplier, int32_t activation, int8_t* outputData, in depthwiseConvNhwc() argument
270 convertUInt8ToInt8(unsignedOutput, outputData); in depthwiseConvNhwc()
283 int32_t depthMultiplier, int32_t activation, T* outputData, const Shape& outputShape) { in depthwiseConvQuant8PerChannelNhwc() argument
324 T* outPtr = outputData; in depthwiseConvQuant8PerChannelNhwc()
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DArgMinMax.cpp30 Out* outputData, const Shape& outputShape) { in argMinMaxImpl() argument
46 outputData[outer * innerSize + inner] = minMaxIndex; in argMinMaxImpl()
52 uint8_t* outputData, const Shape& outputShape) { in argMinMaxGeneric() argument
60 reinterpret_cast<int32_t*>(outputData), outputShape); \ in argMinMaxGeneric()
DRoiPooling.cpp58 T_Input* outputData, const Shape& outputShape) { in roiPoolingNhwc() argument
74 T_Input* outPtr = outputData; in roiPoolingNhwc()
147 bool useNchw, T_Input* outputData, const Shape& outputShape) { in roiPooling() argument
151 NN_RET_CHECK(output.initialize(outputData, outputShape)); in roiPooling()
164 float widthStride, bool useNchw, uint8_t* outputData, in roiPooling() argument
169 batchSplitShape, heightStride, widthStride, useNchw, outputData, in roiPooling()
179 float widthStride, bool useNchw, int8_t* outputData, in roiPooling() argument
184 batchSplitShape, heightStride, widthStride, useNchw, outputData, in roiPooling()
DTile.cpp69 void tileImpl(const T* inputData, const Shape& inputShape, const int32_t* multiples, T* outputData, in tileImpl() argument
71 TileOneDimension(inputShape, inputData, multiples, outputData, 0); in tileImpl()
91 uint8_t* outputData, const Shape& outputShape) { in eval() argument
97 reinterpret_cast<dataType*>(outputData), outputShape); \ in eval()
DQuantize.cpp40 bool quantizeToQuant8(const T* inputData, uint8_t* outputData, const Shape& outputShape) { in quantizeToQuant8() argument
44 outputData[i] = static_cast<uint8_t>(std::max<float>( in quantizeToQuant8()
52 bool quantizeToQuant8Signed(const T* inputData, int8_t* outputData, const Shape& outputShape) { in quantizeToQuant8Signed() argument
56 outputData[i] = static_cast<int8_t>(std::max<float>( in quantizeToQuant8Signed()
DMaximumMinimum.cpp36 bool isMinimum, T* outputData, const Shape& outputShape) { in evalGeneric() argument
51 outputData[outputFlatIndex] = isMinimum ? std::min(aData[aFlatIndex], bData[bFlatIndex]) in evalGeneric()
62 bool isMinimum, T* outputData, const Shape& outputShape) { in evalQuant8() argument
79 outputData[outputFlatIndex] = in evalQuant8()
DSVDF.cpp164 float* outputData, float* outputStateData) { in EvalFloat32() argument
206 tflite::tensor_utils::ReductionSumVector(scratch, outputData, batch_size * num_units, rank); in EvalFloat32()
210 tflite::tensor_utils::VectorBatchVectorAdd(biasData, num_units, batch_size, outputData); in EvalFloat32()
214 tflite::tensor_utils::ApplyActivationToVector(outputData, batch_size * num_units, in EvalFloat32()
215 params_.activation_, outputData); in EvalFloat32()
/packages/modules/NeuralNetworks/common/include/
DOperations.h49 bool floorFloat16(const _Float16* inputData, _Float16* outputData, const Shape& shape);
50 bool floorFloat32(const float* inputData, float* outputData, const Shape& shape);
58 _Float16* outputData, const Shape& outputShape);
64 int32_t depthMultiplier, int32_t activation, float* outputData,
72 uint8_t* outputData, const Shape& outputShape);
80 int32_t depthMultiplier, int32_t activation, uint8_t* outputData,
85 _Float16* outputData, const Shape& outputShape);
87 float bias, float alpha, float beta, int32_t axis, float* outputData,
90 bool copyData(const void* inputData, const Shape& inputShape, void* outputData,
95 T* outputData, const Shape& outputShape);
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/packages/apps/Camera2/src/com/android/camera/app/
DMemoryQuery.java104 HashMap outputData = new HashMap(); in queryMemory() local
105 outputData.put(KEY_TIMESTAMP, new Long(timestamp)); in queryMemory()
106 outputData.put(KEY_MEMORY_AVAILABLE, new Long(availMem)); in queryMemory()
107 outputData.put(KEY_TOTAL_MEMORY, new Long(totalMem)); in queryMemory()
108 outputData.put(KEY_TOTAL_PSS, new Long(totalPSS)); in queryMemory()
109 outputData.put(KEY_LAST_TRIM_LEVEL, new Integer(info.lastTrimLevel)); in queryMemory()
110 outputData.put(KEY_TOTAL_PRIVATE_DIRTY, new Long(totalPrivateDirty)); in queryMemory()
111 outputData.put(KEY_TOTAL_SHARED_DIRTY, new Long(totalSharedDirty)); in queryMemory()
112 outputData.put(KEY_MEMORY_CLASS, new Long(memoryClass)); in queryMemory()
113 outputData.put(KEY_LARGE_MEMORY_CLASS, new Long(largeMemoryClass)); in queryMemory()
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/packages/modules/NeuralNetworks/runtime/test/
DTestMemoryInternal.cpp152 uint8_t* outputData = in TEST_F() local
154 ASSERT_NE(outputData, nullptr); in TEST_F()
155 memset(outputData, 0, outputSize); in TEST_F()
169 CompareMatrices(expected3, *reinterpret_cast<Matrix3x4*>(outputData + offsetForActual)), in TEST_F()
174 munmap(outputData, outputSize); in TEST_F()

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