| // clang-format off |
| // Generated file (from: topk_v2.mod.py). Do not edit |
| void CreateModel(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 2}); |
| OperandType type1(Type::INT32, {}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| // Phase 1, operands |
| auto input = model->addOperand(&type0); |
| auto k = model->addOperand(&type1); |
| auto out_values = model->addOperand(&type0); |
| auto out_indices = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k_init[] = {2}; |
| model->setOperandValue(k, k_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input, k}, {out_values, out_indices}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input}, |
| {out_values, out_indices}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_relaxed(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 2}); |
| OperandType type1(Type::INT32, {}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| // Phase 1, operands |
| auto input = model->addOperand(&type0); |
| auto k = model->addOperand(&type1); |
| auto out_values = model->addOperand(&type0); |
| auto out_indices = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k_init[] = {2}; |
| model->setOperandValue(k, k_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input, k}, {out_values, out_indices}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input}, |
| {out_values, out_indices}); |
| // Phase 4: set relaxed execution |
| model->relaxComputationFloat32toFloat16(true); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_relaxed(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_float16(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type11(Type::TENSOR_FLOAT16, {2, 2}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| // Phase 1, operands |
| auto input = model->addOperand(&type11); |
| auto k = model->addOperand(&type1); |
| auto out_values = model->addOperand(&type11); |
| auto out_indices = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k_init[] = {2}; |
| model->setOperandValue(k, k_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input, k}, {out_values, out_indices}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input}, |
| {out_values, out_indices}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_float16(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_2(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 2}); |
| OperandType type1(Type::INT32, {}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| OperandType type3(Type::TENSOR_FLOAT32, {2, 3}); |
| // Phase 1, operands |
| auto input1 = model->addOperand(&type3); |
| auto k1 = model->addOperand(&type1); |
| auto out_values1 = model->addOperand(&type0); |
| auto out_indices1 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k1_init[] = {2}; |
| model->setOperandValue(k1, k1_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input1, k1}, {out_values1, out_indices1}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input1}, |
| {out_values1, out_indices1}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_2(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_relaxed_2(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 2}); |
| OperandType type1(Type::INT32, {}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| OperandType type3(Type::TENSOR_FLOAT32, {2, 3}); |
| // Phase 1, operands |
| auto input1 = model->addOperand(&type3); |
| auto k1 = model->addOperand(&type1); |
| auto out_values1 = model->addOperand(&type0); |
| auto out_indices1 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k1_init[] = {2}; |
| model->setOperandValue(k1, k1_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input1, k1}, {out_values1, out_indices1}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input1}, |
| {out_values1, out_indices1}); |
| // Phase 4: set relaxed execution |
| model->relaxComputationFloat32toFloat16(true); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_relaxed_2(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_float16_2(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type11(Type::TENSOR_FLOAT16, {2, 2}); |
| OperandType type12(Type::TENSOR_FLOAT16, {2, 3}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| // Phase 1, operands |
| auto input1 = model->addOperand(&type12); |
| auto k1 = model->addOperand(&type1); |
| auto out_values1 = model->addOperand(&type11); |
| auto out_indices1 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k1_init[] = {2}; |
| model->setOperandValue(k1, k1_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input1, k1}, {out_values1, out_indices1}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input1}, |
| {out_values1, out_indices1}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_float16_2(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_3(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 2}); |
| OperandType type1(Type::INT32, {}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| OperandType type4(Type::TENSOR_FLOAT32, {2, 4}); |
| // Phase 1, operands |
| auto input2 = model->addOperand(&type4); |
| auto k2 = model->addOperand(&type1); |
| auto out_values2 = model->addOperand(&type0); |
| auto out_indices2 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k2_init[] = {2}; |
| model->setOperandValue(k2, k2_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input2, k2}, {out_values2, out_indices2}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input2}, |
| {out_values2, out_indices2}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_3(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_relaxed_3(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 2}); |
| OperandType type1(Type::INT32, {}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| OperandType type4(Type::TENSOR_FLOAT32, {2, 4}); |
| // Phase 1, operands |
| auto input2 = model->addOperand(&type4); |
| auto k2 = model->addOperand(&type1); |
| auto out_values2 = model->addOperand(&type0); |
| auto out_indices2 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k2_init[] = {2}; |
| model->setOperandValue(k2, k2_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input2, k2}, {out_values2, out_indices2}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input2}, |
| {out_values2, out_indices2}); |
| // Phase 4: set relaxed execution |
| model->relaxComputationFloat32toFloat16(true); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_relaxed_3(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_float16_3(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type11(Type::TENSOR_FLOAT16, {2, 2}); |
| OperandType type13(Type::TENSOR_FLOAT16, {2, 4}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| // Phase 1, operands |
| auto input2 = model->addOperand(&type13); |
| auto k2 = model->addOperand(&type1); |
| auto out_values2 = model->addOperand(&type11); |
| auto out_indices2 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k2_init[] = {2}; |
| model->setOperandValue(k2, k2_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input2, k2}, {out_values2, out_indices2}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input2}, |
| {out_values2, out_indices2}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_float16_3(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_4(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type5(Type::TENSOR_FLOAT32, {8}); |
| OperandType type6(Type::TENSOR_FLOAT32, {2}); |
| OperandType type7(Type::TENSOR_INT32, {2}); |
| // Phase 1, operands |
| auto input3 = model->addOperand(&type5); |
| auto k3 = model->addOperand(&type1); |
| auto out_values3 = model->addOperand(&type6); |
| auto out_indices3 = model->addOperand(&type7); |
| // Phase 2, operations |
| static int32_t k3_init[] = {2}; |
| model->setOperandValue(k3, k3_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input3, k3}, {out_values3, out_indices3}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input3}, |
| {out_values3, out_indices3}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_4(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_relaxed_4(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type5(Type::TENSOR_FLOAT32, {8}); |
| OperandType type6(Type::TENSOR_FLOAT32, {2}); |
| OperandType type7(Type::TENSOR_INT32, {2}); |
| // Phase 1, operands |
| auto input3 = model->addOperand(&type5); |
| auto k3 = model->addOperand(&type1); |
| auto out_values3 = model->addOperand(&type6); |
| auto out_indices3 = model->addOperand(&type7); |
| // Phase 2, operations |
| static int32_t k3_init[] = {2}; |
| model->setOperandValue(k3, k3_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input3, k3}, {out_values3, out_indices3}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input3}, |
| {out_values3, out_indices3}); |
| // Phase 4: set relaxed execution |
| model->relaxComputationFloat32toFloat16(true); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_relaxed_4(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_float16_4(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type14(Type::TENSOR_FLOAT16, {8}); |
| OperandType type15(Type::TENSOR_FLOAT16, {2}); |
| OperandType type7(Type::TENSOR_INT32, {2}); |
| // Phase 1, operands |
| auto input3 = model->addOperand(&type14); |
| auto k3 = model->addOperand(&type1); |
| auto out_values3 = model->addOperand(&type15); |
| auto out_indices3 = model->addOperand(&type7); |
| // Phase 2, operations |
| static int32_t k3_init[] = {2}; |
| model->setOperandValue(k3, k3_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input3, k3}, {out_values3, out_indices3}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input3}, |
| {out_values3, out_indices3}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_float16_4(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_5(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| OperandType type8(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 2.0f, 128); |
| OperandType type9(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 2.0f, 128); |
| // Phase 1, operands |
| auto input4 = model->addOperand(&type8); |
| auto k4 = model->addOperand(&type1); |
| auto out_values4 = model->addOperand(&type9); |
| auto out_indices4 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k4_init[] = {2}; |
| model->setOperandValue(k4, k4_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input4, k4}, {out_values4, out_indices4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input4}, |
| {out_values4, out_indices4}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_5(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_relaxed_5(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| OperandType type8(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 2.0f, 128); |
| OperandType type9(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 2.0f, 128); |
| // Phase 1, operands |
| auto input4 = model->addOperand(&type8); |
| auto k4 = model->addOperand(&type1); |
| auto out_values4 = model->addOperand(&type9); |
| auto out_indices4 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k4_init[] = {2}; |
| model->setOperandValue(k4, k4_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input4, k4}, {out_values4, out_indices4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input4}, |
| {out_values4, out_indices4}); |
| // Phase 4: set relaxed execution |
| model->relaxComputationFloat32toFloat16(true); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_relaxed_5(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_float16_5(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| OperandType type8(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 2.0f, 128); |
| OperandType type9(Type::TENSOR_QUANT8_ASYMM, {2, 2}, 2.0f, 128); |
| // Phase 1, operands |
| auto input4 = model->addOperand(&type8); |
| auto k4 = model->addOperand(&type1); |
| auto out_values4 = model->addOperand(&type9); |
| auto out_indices4 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k4_init[] = {2}; |
| model->setOperandValue(k4, k4_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input4, k4}, {out_values4, out_indices4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input4}, |
| {out_values4, out_indices4}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_float16_5(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_6(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type10(Type::TENSOR_INT32, {2, 3}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| // Phase 1, operands |
| auto input5 = model->addOperand(&type10); |
| auto k5 = model->addOperand(&type1); |
| auto out_values5 = model->addOperand(&type2); |
| auto out_indices5 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k5_init[] = {2}; |
| model->setOperandValue(k5, k5_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input5, k5}, {out_values5, out_indices5}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input5}, |
| {out_values5, out_indices5}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_6(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_relaxed_6(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type10(Type::TENSOR_INT32, {2, 3}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| // Phase 1, operands |
| auto input5 = model->addOperand(&type10); |
| auto k5 = model->addOperand(&type1); |
| auto out_values5 = model->addOperand(&type2); |
| auto out_indices5 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k5_init[] = {2}; |
| model->setOperandValue(k5, k5_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input5, k5}, {out_values5, out_indices5}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input5}, |
| {out_values5, out_indices5}); |
| // Phase 4: set relaxed execution |
| model->relaxComputationFloat32toFloat16(true); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_relaxed_6(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_float16_6(Model *model) { |
| OperandType type1(Type::INT32, {}); |
| OperandType type10(Type::TENSOR_INT32, {2, 3}); |
| OperandType type2(Type::TENSOR_INT32, {2, 2}); |
| // Phase 1, operands |
| auto input5 = model->addOperand(&type10); |
| auto k5 = model->addOperand(&type1); |
| auto out_values5 = model->addOperand(&type2); |
| auto out_indices5 = model->addOperand(&type2); |
| // Phase 2, operations |
| static int32_t k5_init[] = {2}; |
| model->setOperandValue(k5, k5_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_TOPK_V2, {input5, k5}, {out_values5, out_indices5}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input5}, |
| {out_values5, out_indices5}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_float16_6(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |