| /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 | 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 | 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 | 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/operations/ |
| D | ResizeImageOps.cpp | 152 bool resizeImageOp(OperationType opType, const T* inputData, const Shape& inputShape, bool useNchw, in resizeImageOp() argument
155 InputWithLayout<T> input(useNchw); in resizeImageOp()
156 OutputWithLayout<T> output(useNchw); in resizeImageOp()
234 const bool useNchw = getOptionalScalar(context, kLayoutScalar); in prepare() local
242 uint32_t inHeight = getSizeOfDimension(input, useNchw ? 2 : 1); in prepare()
243 uint32_t inWidth = getSizeOfDimension(input, useNchw ? 3 : 2); in prepare()
244 uint32_t channels = getSizeOfDimension(input, useNchw ? 1 : 3); in prepare()
273 if (useNchw) { in prepare()
285 const bool useNchw = getOptionalScalar(context, kLayoutScalar); in execute() local
292 context->getInputShape(kInputTensor), useNchw, alignCorners, in execute()
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| D | Pooling.cpp | 51 bool useNchw = false; member
67 useNchw = context->getInputValue<bool>(10); in initialize()
77 useNchw = context->getInputValue<bool>(7); in initialize()
82 int32_t input_height = getSizeOfDimension(inputShape, useNchw ? 2 : 1); in initialize()
83 int32_t input_width = getSizeOfDimension(inputShape, useNchw ? 3 : 2); in initialize()
257 InputWithLayout<T> input(param.useNchw); in averagePool()
258 OutputWithLayout<T> output(param.useNchw); in averagePool()
270 InputWithLayout<T> input(param.useNchw); in l2Pool()
271 OutputWithLayout<T> output(param.useNchw); in l2Pool()
283 InputWithLayout<T> input(param.useNchw); in maxPool()
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| D | DepthwiseConv2D.cpp | 56 bool useNchw = false; member
69 useNchw = context->getInputValue<bool>(8); in initialize()
86 useNchw = context->getInputValue<bool>(11); in initialize()
98 int32_t input_width = getSizeOfDimension(inputShape, useNchw ? 3 : 2); in initialize()
99 int32_t input_height = getSizeOfDimension(inputShape, useNchw ? 2 : 1); in initialize()
378 int32_t depthMultiplier, int32_t activation, bool useNchw, T_Input* outputData, in depthwiseConv() argument
380 InputWithLayout<T_Input> input(useNchw); in depthwiseConv()
381 OutputWithLayout<T_Input> output(useNchw); in depthwiseConv()
401 int32_t depthMultiplier, int32_t activation, bool useNchw, in depthwiseConvQuant8PerChannel() argument
403 InputWithLayout<T> input(useNchw); in depthwiseConvQuant8PerChannel()
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| D | RoiPooling.cpp | 147 bool useNchw, T_Input* outputData, const Shape& outputShape) { in roiPooling() argument
148 InputWithLayout<T_Input> input(useNchw); in roiPooling()
149 OutputWithLayout<T_Input> output(useNchw); 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()
231 bool useNchw = context->getInputValue<bool>(kLayoutScalar); in prepare() local
239 uint32_t inHeight = getSizeOfDimension(input, useNchw ? 2 : 1); in prepare()
240 uint32_t inWidth = getSizeOfDimension(input, useNchw ? 3 : 2); in prepare()
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| D | Conv2D.cpp | 69 bool useNchw = false; member
81 useNchw = context->getInputValue<bool>(7); in initialize()
97 useNchw = context->getInputValue<bool>(10); in initialize()
109 int32_t input_width = getSizeOfDimension(inputShape, useNchw ? 3 : 2); in initialize()
110 int32_t input_height = getSizeOfDimension(inputShape, useNchw ? 2 : 1); in initialize()
337 int32_t dilation_height_factor, int32_t activation, bool useNchw, T_Input* outputData, in conv() argument
339 InputWithLayout<T_Input> input(useNchw); in conv()
340 OutputWithLayout<T_Input> output(useNchw); in conv()
514 int32_t dilationHeightFactor, int32_t activation, bool useNchw, in convQuant8PerChannel() argument
516 InputWithLayout<T> input(useNchw); in convQuant8PerChannel()
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| D | TransposeConv2D.cpp | 64 bool useNchw = false; member
74 useNchw = context->getInputValue<bool>(8); in initialize()
81 int32_t outputWidth = useNchw ? outputShapeData[3] : outputShapeData[2]; in initialize()
82 int32_t outputHeight = useNchw ? outputShapeData[2] : outputShapeData[1]; in initialize()
95 useNchw = context->getInputValue<bool>(10); in initialize()
305 InputWithLayout<T_Input> input(param.useNchw); in transposeConv()
306 OutputWithLayout<T_Input> output(param.useNchw); in transposeConv()
425 InputWithLayout<T> input(param.useNchw); in transposeConvQuant8PerChannel()
426 OutputWithLayout<T> output(param.useNchw); in transposeConvQuant8PerChannel()
513 uint32_t height = getSizeOfDimension(input, param.useNchw ? 2 : 1); in prepare()
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| D | RoiAlign.cpp | 320 int32_t heightSamplingRatio, int32_t widthSamplingRatio, bool useNchw, in roiAlign() argument
322 InputWithLayout<T_Input> input(useNchw); in roiAlign()
323 OutputWithLayout<T_Input> output(useNchw); in roiAlign()
388 bool useNchw = context->getInputValue<bool>(kLayoutScalar); in prepare() local
396 uint32_t inHeight = getSizeOfDimension(input, useNchw ? 2 : 1); in prepare()
397 uint32_t inWidth = getSizeOfDimension(input, useNchw ? 3 : 2); in prepare()
398 uint32_t inDepth = getSizeOfDimension(input, useNchw ? 1 : 3); in prepare()
435 if (useNchw) { in prepare()
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| D | InstanceNormalization.cpp | 93 bool useNchw, T* outputData, const Shape& outputShape) { in instanceNorm() argument
94 InputWithLayout<T> input(useNchw); in instanceNorm()
95 OutputWithLayout<T> output(useNchw); in instanceNorm()
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| D | GenerateProposals.cpp | 1067 bool useNchw, std::vector<float>* scoresOutData, in generateProposalsFloat32Compute() argument
1070 InputWithLayout<float> score_nhwc(useNchw), delta_nhwc(useNchw); in generateProposalsFloat32Compute()
1085 int32_t postNmsTopN, float iouThreshold, float minSize, bool useNchw, in generateProposalsFloat32() argument
1092 iouThreshold, minSize, useNchw, &scoresOut_float32, &roiOut_float32, &batchesOut)); in generateProposalsFloat32()
1128 int32_t postNmsTopN, float iouThreshold, float minSize, bool useNchw, in generateProposalsFloat16() argument
1143 heightStride, widthStride, preNmsTopN, postNmsTopN, iouThreshold, minSize, useNchw, in generateProposalsFloat16()
1177 int32_t postNmsTopN, float iouThreshold, float minSize, bool useNchw, in generateProposalsQuant() argument
1195 heightStride, widthStride, preNmsTopN, postNmsTopN, iouThreshold, minSize, useNchw, in generateProposalsQuant()
1290 bool useNchw = context->getInputValue<bool>(kLayoutScalar); in prepare() local
1306 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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