| /packages/modules/NeuralNetworks/runtime/test/fuzzing/operation_signatures/ |
| D | Convolutions.cpp | 36 bool useNchw = false; in conv2DExplicitConstructor() local
39 useNchw = op->inputs[10]->value<bool8>(); in conv2DExplicitConstructor()
45 int heightIndex = useNchw ? 2 : 1; in conv2DExplicitConstructor()
46 int widthIndex = useNchw ? 3 : 2; in conv2DExplicitConstructor()
47 int channelIndex = useNchw ? 1 : 3; in conv2DExplicitConstructor()
88 bool useNchw = false; in conv2DImplicitConstructor() local
91 useNchw = op->inputs[7]->value<bool8>(); in conv2DImplicitConstructor()
97 int heightIndex = useNchw ? 2 : 1; in conv2DImplicitConstructor()
98 int widthIndex = useNchw ? 3 : 2; in conv2DImplicitConstructor()
99 int channelIndex = useNchw ? 1 : 3; in conv2DImplicitConstructor()
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| D | BoundingBox.cpp | 44 bool useNchw; in roiConstructor() local
46 useNchw = op->inputs[9]->value<bool8>(); in roiConstructor()
48 useNchw = op->inputs[7]->value<bool8>(); in roiConstructor()
56 auto outDepth = op->inputs[0]->dimensions[useNchw ? 1 : 3]; in roiConstructor()
59 if (useNchw) { in roiConstructor()
89 bool useNchw; in roiFinalizer() local
91 useNchw = op->inputs[9]->value<bool8>(); in roiFinalizer()
93 useNchw = op->inputs[7]->value<bool8>(); in roiFinalizer()
97 uint32_t height = op->inputs[0]->dimensions[useNchw ? 2 : 1].getValue(); in roiFinalizer()
98 uint32_t width = op->inputs[0]->dimensions[useNchw ? 3 : 2].getValue(); in roiFinalizer()
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| D | Poolings.cpp | 36 bool useNchw = false; in poolingExplicitOpConstructor() local
37 if (op->inputs.size() > 10) useNchw = op->inputs[10]->value<bool8>(); in poolingExplicitOpConstructor()
38 int heightIndex = useNchw ? 2 : 1; in poolingExplicitOpConstructor()
39 int widthIndex = useNchw ? 3 : 2; in poolingExplicitOpConstructor()
40 int channelIndex = useNchw ? 1 : 3; in poolingExplicitOpConstructor()
75 bool useNchw = false; in poolingImplicitOpConstructor() local
76 if (op->inputs.size() > 7) useNchw = op->inputs[7]->value<bool8>(); in poolingImplicitOpConstructor()
77 int heightIndex = useNchw ? 2 : 1; in poolingImplicitOpConstructor()
78 int widthIndex = useNchw ? 3 : 2; in poolingImplicitOpConstructor()
79 int channelIndex = useNchw ? 1 : 3; in poolingImplicitOpConstructor()
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| D | Reshape.cpp | 29 bool useNchw = false; in spaceToDepthConstructor() local
30 if (op->inputs.size() > 2) useNchw = op->inputs[2]->value<bool8>(); in spaceToDepthConstructor()
31 int heightIndex = useNchw ? 2 : 1; in spaceToDepthConstructor()
32 int widthIndex = useNchw ? 3 : 2; in spaceToDepthConstructor()
33 int depthIndex = useNchw ? 1 : 3; in spaceToDepthConstructor()
42 if (useNchw) { in spaceToDepthConstructor()
84 bool useNchw = false; in depthToSpaceConstructor() local
85 if (op->inputs.size() > 2) useNchw = op->inputs[2]->value<bool8>(); in depthToSpaceConstructor()
86 int heightIndex = useNchw ? 2 : 1; in depthToSpaceConstructor()
87 int widthIndex = useNchw ? 3 : 2; in depthToSpaceConstructor()
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| D | Resize.cpp | 27 bool useNchw = op->inputs.size() > 3 ? static_cast<bool>(op->inputs[3]->value<bool8>()) : false; in resizeOpConstructor() local
28 int heightIndex = useNchw ? 2 : 1; in resizeOpConstructor()
29 int widthIndex = useNchw ? 3 : 2; in resizeOpConstructor()
54 if (useNchw) { in resizeOpConstructor()
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| /packages/modules/NeuralNetworks/common/cpu_operations/ |
| D | ResizeImageOps.cpp | 145 bool resizeImageOp(OperationType opType, const T* inputData, const Shape& inputShape, bool useNchw, in resizeImageOp() argument
148 InputWithLayout<T> input(useNchw); in resizeImageOp()
149 OutputWithLayout<T> output(useNchw); in resizeImageOp()
173 const bool useNchw = getOptionalScalar(context, kLayoutScalar); in prepare() local
181 uint32_t inHeight = getSizeOfDimension(input, useNchw ? 2 : 1); in prepare()
182 uint32_t inWidth = getSizeOfDimension(input, useNchw ? 3 : 2); in prepare()
183 uint32_t channels = getSizeOfDimension(input, useNchw ? 1 : 3); in prepare()
212 if (useNchw) { in prepare()
224 const bool useNchw = getOptionalScalar(context, kLayoutScalar); in execute() local
231 context->getInputShape(kInputTensor), useNchw, alignCorners, in execute()
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| D | Pooling.cpp | 53 bool useNchw = false; member
69 useNchw = context->getInputValue<bool>(10); in initialize()
79 useNchw = context->getInputValue<bool>(7); in initialize()
84 int32_t input_height = getSizeOfDimension(inputShape, useNchw ? 2 : 1); in initialize()
85 int32_t input_width = getSizeOfDimension(inputShape, useNchw ? 3 : 2); in initialize()
259 InputWithLayout<T> input(param.useNchw); in averagePool()
260 OutputWithLayout<T> output(param.useNchw); in averagePool()
272 InputWithLayout<T> input(param.useNchw); in l2Pool()
273 OutputWithLayout<T> output(param.useNchw); in l2Pool()
285 InputWithLayout<T> input(param.useNchw); in maxPool()
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| D | RoiPooling.cpp | 134 bool useNchw, T_Input* outputData, const Shape& outputShape) { in roiPooling() argument
135 InputWithLayout<T_Input> input(useNchw); in roiPooling()
136 OutputWithLayout<T_Input> output(useNchw); in roiPooling()
151 float widthStride, bool useNchw, uint8_t* outputData, in roiPooling() argument
156 batchSplitShape, heightStride, widthStride, useNchw, outputData, in roiPooling()
166 float widthStride, bool useNchw, int8_t* outputData, in roiPooling() argument
171 batchSplitShape, heightStride, widthStride, useNchw, outputData, in roiPooling()
179 bool useNchw = context->getInputValue<bool>(kLayoutScalar); in prepare() local
187 [[maybe_unused]] uint32_t inHeight = getSizeOfDimension(input, useNchw ? 2 : 1); in prepare()
188 [[maybe_unused]] uint32_t inWidth = getSizeOfDimension(input, useNchw ? 3 : 2); in prepare()
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| D | DepthwiseConv2D.cpp | 52 bool useNchw = false; member
65 useNchw = context->getInputValue<bool>(8); in initialize()
82 useNchw = context->getInputValue<bool>(11); in initialize()
94 int32_t input_width = getSizeOfDimension(inputShape, useNchw ? 3 : 2); in initialize()
95 int32_t input_height = getSizeOfDimension(inputShape, useNchw ? 2 : 1); in initialize()
374 int32_t depthMultiplier, int32_t activation, bool useNchw, T_Input* outputData, in depthwiseConv() argument
376 InputWithLayout<T_Input> input(useNchw); in depthwiseConv()
377 OutputWithLayout<T_Input> output(useNchw); in depthwiseConv()
397 int32_t depthMultiplier, int32_t activation, bool useNchw, in depthwiseConvQuant8PerChannel() argument
399 InputWithLayout<T> input(useNchw); in depthwiseConvQuant8PerChannel()
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| D | Conv2D.cpp | 67 bool useNchw = false; member
79 useNchw = context->getInputValue<bool>(7); in initialize()
95 useNchw = context->getInputValue<bool>(10); in initialize()
107 int32_t input_width = getSizeOfDimension(inputShape, useNchw ? 3 : 2); in initialize()
108 int32_t input_height = getSizeOfDimension(inputShape, useNchw ? 2 : 1); in initialize()
334 int32_t dilation_height_factor, int32_t activation, bool useNchw, T_Input* outputData, in conv() argument
336 InputWithLayout<T_Input> input(useNchw); in conv()
337 OutputWithLayout<T_Input> output(useNchw); in conv()
511 int32_t dilationHeightFactor, int32_t activation, bool useNchw, in convQuant8PerChannel() argument
513 InputWithLayout<T> input(useNchw); in convQuant8PerChannel()
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| D | TransposeConv2D.cpp | 59 bool useNchw = false; member
69 useNchw = context->getInputValue<bool>(8); in initialize()
76 int32_t outputWidth = useNchw ? outputShapeData[3] : outputShapeData[2]; in initialize()
77 int32_t outputHeight = useNchw ? outputShapeData[2] : outputShapeData[1]; in initialize()
90 useNchw = context->getInputValue<bool>(10); in initialize()
301 InputWithLayout<T_Input> input(param.useNchw); in transposeConv()
302 OutputWithLayout<T_Input> output(param.useNchw); in transposeConv()
421 InputWithLayout<T> input(param.useNchw); in transposeConvQuant8PerChannel()
422 OutputWithLayout<T> output(param.useNchw); in transposeConvQuant8PerChannel()
461 uint32_t height = getSizeOfDimension(input, param.useNchw ? 2 : 1); in prepare()
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| D | RoiAlign.cpp | 308 int32_t heightSamplingRatio, int32_t widthSamplingRatio, bool useNchw, in roiAlign() argument
310 InputWithLayout<T_Input> input(useNchw); in roiAlign()
311 OutputWithLayout<T_Input> output(useNchw); in roiAlign()
333 bool useNchw = context->getInputValue<bool>(kLayoutScalar); in prepare() local
341 uint32_t inHeight = getSizeOfDimension(input, useNchw ? 2 : 1); in prepare()
342 uint32_t inWidth = getSizeOfDimension(input, useNchw ? 3 : 2); in prepare()
343 uint32_t inDepth = getSizeOfDimension(input, useNchw ? 1 : 3); in prepare()
380 if (useNchw) { in prepare()
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| D | InstanceNormalization.cpp | 83 bool useNchw, T* outputData, const Shape& outputShape) { in instanceNorm() argument
84 InputWithLayout<T> input(useNchw); in instanceNorm()
85 OutputWithLayout<T> output(useNchw); in instanceNorm()
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| D | GenerateProposals.cpp | 954 bool useNchw, std::vector<float>* scoresOutData, in generateProposalsFloat32Compute() argument
957 InputWithLayout<float> score_nhwc(useNchw), delta_nhwc(useNchw); in generateProposalsFloat32Compute()
972 int32_t postNmsTopN, float iouThreshold, float minSize, bool useNchw, in generateProposalsFloat32() argument
979 iouThreshold, minSize, useNchw, &scoresOut_float32, &roiOut_float32, &batchesOut)); in generateProposalsFloat32()
1015 int32_t postNmsTopN, float iouThreshold, float minSize, bool useNchw, in generateProposalsFloat16() argument
1030 heightStride, widthStride, preNmsTopN, postNmsTopN, iouThreshold, minSize, useNchw, in generateProposalsFloat16()
1064 int32_t postNmsTopN, float iouThreshold, float minSize, bool useNchw, in generateProposalsQuant() argument
1082 heightStride, widthStride, preNmsTopN, postNmsTopN, iouThreshold, minSize, useNchw, in generateProposalsQuant()
1114 bool useNchw = context->getInputValue<bool>(kLayoutScalar); in prepare() local
1130 uint32_t height = getSizeOfDimension(scoreShape, useNchw ? 2 : 1); in prepare()
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| /packages/modules/NeuralNetworks/common/include/ |
| D | CpuOperationUtils.h | 160 InputWithLayout(bool useNchw) : mDataOriginal(nullptr), mUseNchw(useNchw) {} in InputWithLayout() argument
184 OutputWithLayout(bool useNchw) : mDataOriginal(nullptr), mUseNchw(useNchw) {} in OutputWithLayout() argument
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