| /packages/modules/NeuralNetworks/common/operations/ |
| D | Split.cpp | 29 bool splitGeneric(const Scalar* inputData, const Shape& inputShape, int32_t axis, in splitGeneric()
55 bool splitFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis, in splitFloat16()
62 bool splitFloat32(const float* inputData, const Shape& inputShape, int32_t axis, in splitFloat32()
69 bool splitQuant8(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in splitQuant8()
76 bool splitQuant8Signed(const int8_t* inputData, const Shape& inputShape, int32_t axis, in splitQuant8Signed()
83 bool splitInt32(const int32_t* inputData, const Shape& inputShape, int32_t axis, in splitInt32()
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| D | L2Normalization.cpp | 48 inline bool l2normFloat32Impl(const float* inputData, const Shape& inputShape, int32_t axis, in l2normFloat32Impl()
76 inline bool l2normQuant8Impl(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8Impl()
108 inline bool l2normQuant8SignedImpl(const int8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8SignedImpl()
139 bool l2normFloat32(const float* inputData, const Shape& inputShape, int32_t axis, float* outputData, in l2normFloat32()
155 bool l2normFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis, in l2normFloat16()
168 bool l2normQuant8(const uint8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8()
184 bool l2normQuant8Signed(const int8_t* inputData, const Shape& inputShape, int32_t axis, in l2normQuant8Signed()
240 int32_t axis = context->getNumInputs() == kNumInputs in prepare() local
263 int32_t axis = context->getNumInputs() == kNumInputs in execute() local
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| D | ExpandDims.cpp | 27 bool prepare(const Shape& input, int32_t axis, Shape* output) { in prepare()
40 bool eval(const uint8_t* inputData, const Shape& inputShape, int32_t axis, uint8_t* outputData, in eval()
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| D | Gather.cpp | 40 inline bool eval(const T* inputData, const Shape& inputShape, int32_t axis, in eval()
84 int32_t axis = context->getInputValue<int32_t>(kInputAxis); in prepare() local
102 int32_t axis = context->getInputValue<int32_t>(kInputAxis); in execute() local
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| D | ChannelShuffle.cpp | 38 inline bool eval(const T* inputData, const Shape& inputShape, int32_t numGroups, int32_t axis, in eval()
85 int32_t axis = context->getInputValue<int32_t>(kInputAxis); in prepare() local
94 int32_t axis = context->getInputValue<int32_t>(kInputAxis); in execute() local
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| D | LocalResponseNormalization.cpp | 53 int32_t axis, float* outputData, in localResponseNormFloat32Impl()
87 float bias, float alpha, float beta, int32_t axis, float* outputData, in localResponseNorm()
109 _Float16 bias, _Float16 alpha, _Float16 beta, int32_t axis, in localResponseNorm()
125 int32_t axis = context->getNumInputs() == kNumInputs in executeTyped() local
186 int32_t axis = context->getNumInputs() == kNumInputs in prepare() local
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| D | SimpleMath.cpp | 33 bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis, in meanFloat16()
48 bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape, in meanGeneric()
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| D | Softmax.cpp | 54 int32_t axis, float* outputData, const Shape& outputShape) { in softmaxSlowFloat32()
85 bool softmaxFloat32(const float* inputData, const Shape& inputShape, const float beta, int32_t axis, in softmaxFloat32()
102 int32_t axis, _Float16* outputData, const Shape& outputShape) { in softmaxFloat16()
116 bool softmaxQuant8Impl(const T* inputData, const Shape& inputShape, const float beta, int32_t axis, in softmaxQuant8Impl()
203 bool softmaxQuant8(const T* inputData, const Shape& inputShape, const float beta, int32_t axis, in softmaxQuant8()
287 int32_t axis = (context->getNumInputs() == kNumInputs) in execute() local
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| /packages/modules/NeuralNetworks/runtime/test/specs/V1_3/ |
| D | argmin_quant8_signed.mod.py | 19 axis = Int32Scalar("axis", -1) variable
37 axis = Int32Scalar("axis", 0) variable
55 axis = Int32Scalar("axis", 1) variable
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| D | argmax_quant8_signed.mod.py | 18 axis = Int32Scalar("axis", 1) variable
36 axis = Int32Scalar("axis", 0) variable
56 axis = Int32Scalar("axis", -1) variable
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| D | split_quant8_signed.mod.py | 18 axis = Int32Scalar("axis", 0) variable
41 axis = Int32Scalar("axis", 0) variable
62 axis = Int32Scalar("axis", 1) variable
85 axis = Int32Scalar("axis", 1) variable
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| D | l2_normalization_quant8_signed.mod.py | 19 axis = Int32Scalar("axis", -1) # last axis variable
53 axis = Int32Scalar("axis", -1) # last axis variable
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| D | gather_quant8_signed.mod.py | 18 axis = 1 variable
46 def test(input0, axis, indices, output0, input_data, output_data): argument
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| D | mean_quant8_signed.mod.py | 18 axis = Parameter("axis", "TENSOR_INT32", "{4}", [1, 0, -3, -3]) variable
42 axis = Parameter("axis", "TENSOR_INT32", "{2}", [0, 2]) variable
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| D | channel_shuffle_quant8_signed.mod.py | 19 axis = Int32Scalar("axis", -1) # last axis variable
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| D | l2_normalization_zeros.mod.py | 20 axis = Int32Scalar("axis", -1) # last axis variable
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| /packages/modules/NeuralNetworks/runtime/test/specs/V1_2/ |
| D | l2_normalization_axis.mod.py | 20 axis = Int32Scalar("axis", -1) # last axis variable
54 axis = Int32Scalar("axis", -1) # last axis variable
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| D | local_response_normalization_v1_2.mod.py | 19 axis = Int32Scalar("axis", -1) # last axis variable
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| D | channel_shuffle.mod.py | 19 axis = Int32Scalar("axis", -1) # last axis variable
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| D | argmin_2.mod.py | 18 axis = Int32Scalar("axis", 0) variable
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| D | l2_normalization_v1_2.mod.py | 19 axis = Int32Scalar("axis", -1) # last axis variable
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| D | argmin_1.mod.py | 18 axis = Int32Scalar("axis", 1) variable
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| D | argmax_2.mod.py | 18 axis = Int32Scalar("axis", 0) variable
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| D | argmax_3.mod.py | 20 axis = Int32Scalar("axis", -1) variable
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| D | argmax_1.mod.py | 18 axis = Int32Scalar("axis", 1) variable
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