| // clang-format off | |
| // Generated file (from: minimum.mod.py). Do not edit | |
| void CreateModel(Model *model) { | |
| OperandType type0(Type::TENSOR_FLOAT32, {3, 1, 2}); | |
| // Phase 1, operands | |
| auto input0 = model->addOperand(&type0); | |
| auto input1 = model->addOperand(&type0); | |
| auto output0 = model->addOperand(&type0); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input0, input1}, | |
| {output0}); | |
| 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, {3, 1, 2}); | |
| // Phase 1, operands | |
| auto input0 = model->addOperand(&type0); | |
| auto input1 = model->addOperand(&type0); | |
| auto output0 = model->addOperand(&type0); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input0, input1}, | |
| {output0}); | |
| // 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 type4(Type::TENSOR_FLOAT16, {3, 1, 2}); | |
| // Phase 1, operands | |
| auto input0 = model->addOperand(&type4); | |
| auto input1 = model->addOperand(&type4); | |
| auto output0 = model->addOperand(&type4); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input0, input1}, | |
| {output0}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_float16(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_int32(Model *model) { | |
| OperandType type5(Type::TENSOR_INT32, {3, 1, 2}); | |
| // Phase 1, operands | |
| auto input0 = model->addOperand(&type5); | |
| auto input1 = model->addOperand(&type5); | |
| auto output0 = model->addOperand(&type5); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input0, input1}, | |
| {output0}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_int32(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_quant8(Model *model) { | |
| OperandType type6(Type::TENSOR_QUANT8_ASYMM, {3, 1, 2}, 0.5f, 127); | |
| OperandType type7(Type::TENSOR_QUANT8_ASYMM, {3, 1, 2}, 1.0f, 100); | |
| OperandType type8(Type::TENSOR_QUANT8_ASYMM, {3, 1, 2}, 2.0f, 80); | |
| // Phase 1, operands | |
| auto input0 = model->addOperand(&type6); | |
| auto input1 = model->addOperand(&type7); | |
| auto output0 = model->addOperand(&type8); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input0, input1}, | |
| {output0}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_quant8(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape(Model *model) { | |
| OperandType type0(Type::TENSOR_FLOAT32, {3, 1, 2}); | |
| OperandType type9(Type::TENSOR_FLOAT32, {0, 0, 0}); | |
| // Phase 1, operands | |
| auto input0 = model->addOperand(&type0); | |
| auto input1 = model->addOperand(&type0); | |
| auto output0 = model->addOperand(&type9); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input0, input1}, | |
| {output0}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape_relaxed(Model *model) { | |
| OperandType type0(Type::TENSOR_FLOAT32, {3, 1, 2}); | |
| OperandType type9(Type::TENSOR_FLOAT32, {0, 0, 0}); | |
| // Phase 1, operands | |
| auto input0 = model->addOperand(&type0); | |
| auto input1 = model->addOperand(&type0); | |
| auto output0 = model->addOperand(&type9); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input0, input1}, | |
| {output0}); | |
| // Phase 4: set relaxed execution | |
| model->relaxComputationFloat32toFloat16(true); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape_relaxed(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape_float16(Model *model) { | |
| OperandType type10(Type::TENSOR_FLOAT16, {0, 0, 0}); | |
| OperandType type4(Type::TENSOR_FLOAT16, {3, 1, 2}); | |
| // Phase 1, operands | |
| auto input0 = model->addOperand(&type4); | |
| auto input1 = model->addOperand(&type4); | |
| auto output0 = model->addOperand(&type10); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input0, input1}, | |
| {output0}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape_float16(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape_int32(Model *model) { | |
| OperandType type11(Type::TENSOR_INT32, {0, 0, 0}); | |
| OperandType type5(Type::TENSOR_INT32, {3, 1, 2}); | |
| // Phase 1, operands | |
| auto input0 = model->addOperand(&type5); | |
| auto input1 = model->addOperand(&type5); | |
| auto output0 = model->addOperand(&type11); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input0, input1}, | |
| {output0}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape_int32(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape_quant8(Model *model) { | |
| OperandType type12(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0}, 2.0f, 80); | |
| OperandType type6(Type::TENSOR_QUANT8_ASYMM, {3, 1, 2}, 0.5f, 127); | |
| OperandType type7(Type::TENSOR_QUANT8_ASYMM, {3, 1, 2}, 1.0f, 100); | |
| // Phase 1, operands | |
| auto input0 = model->addOperand(&type6); | |
| auto input1 = model->addOperand(&type7); | |
| auto output0 = model->addOperand(&type12); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input0, input1}, {output0}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input0, input1}, | |
| {output0}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape_quant8(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_2(Model *model) { | |
| OperandType type0(Type::TENSOR_FLOAT32, {3, 1, 2}); | |
| OperandType type1(Type::TENSOR_FLOAT32, {2}); | |
| // Phase 1, operands | |
| auto input01 = model->addOperand(&type0); | |
| auto input11 = model->addOperand(&type1); | |
| auto output01 = model->addOperand(&type0); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input01, input11}, {output01}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input01, input11}, | |
| {output01}); | |
| 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, {3, 1, 2}); | |
| OperandType type1(Type::TENSOR_FLOAT32, {2}); | |
| // Phase 1, operands | |
| auto input01 = model->addOperand(&type0); | |
| auto input11 = model->addOperand(&type1); | |
| auto output01 = model->addOperand(&type0); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input01, input11}, {output01}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input01, input11}, | |
| {output01}); | |
| // 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 type13(Type::TENSOR_FLOAT16, {2}); | |
| OperandType type4(Type::TENSOR_FLOAT16, {3, 1, 2}); | |
| // Phase 1, operands | |
| auto input01 = model->addOperand(&type4); | |
| auto input11 = model->addOperand(&type13); | |
| auto output01 = model->addOperand(&type4); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input01, input11}, {output01}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input01, input11}, | |
| {output01}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_float16_2(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_int32_2(Model *model) { | |
| OperandType type14(Type::TENSOR_INT32, {2}); | |
| OperandType type5(Type::TENSOR_INT32, {3, 1, 2}); | |
| // Phase 1, operands | |
| auto input01 = model->addOperand(&type5); | |
| auto input11 = model->addOperand(&type14); | |
| auto output01 = model->addOperand(&type5); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input01, input11}, {output01}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input01, input11}, | |
| {output01}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_int32_2(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_quant8_2(Model *model) { | |
| OperandType type15(Type::TENSOR_QUANT8_ASYMM, {2}, 1.0f, 100); | |
| OperandType type6(Type::TENSOR_QUANT8_ASYMM, {3, 1, 2}, 0.5f, 127); | |
| OperandType type8(Type::TENSOR_QUANT8_ASYMM, {3, 1, 2}, 2.0f, 80); | |
| // Phase 1, operands | |
| auto input01 = model->addOperand(&type6); | |
| auto input11 = model->addOperand(&type15); | |
| auto output01 = model->addOperand(&type8); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input01, input11}, {output01}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input01, input11}, | |
| {output01}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_quant8_2(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape_2(Model *model) { | |
| OperandType type0(Type::TENSOR_FLOAT32, {3, 1, 2}); | |
| OperandType type1(Type::TENSOR_FLOAT32, {2}); | |
| OperandType type9(Type::TENSOR_FLOAT32, {0, 0, 0}); | |
| // Phase 1, operands | |
| auto input01 = model->addOperand(&type0); | |
| auto input11 = model->addOperand(&type1); | |
| auto output01 = model->addOperand(&type9); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input01, input11}, {output01}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input01, input11}, | |
| {output01}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape_2(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape_relaxed_2(Model *model) { | |
| OperandType type0(Type::TENSOR_FLOAT32, {3, 1, 2}); | |
| OperandType type1(Type::TENSOR_FLOAT32, {2}); | |
| OperandType type9(Type::TENSOR_FLOAT32, {0, 0, 0}); | |
| // Phase 1, operands | |
| auto input01 = model->addOperand(&type0); | |
| auto input11 = model->addOperand(&type1); | |
| auto output01 = model->addOperand(&type9); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input01, input11}, {output01}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input01, input11}, | |
| {output01}); | |
| // Phase 4: set relaxed execution | |
| model->relaxComputationFloat32toFloat16(true); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape_relaxed_2(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape_float16_2(Model *model) { | |
| OperandType type10(Type::TENSOR_FLOAT16, {0, 0, 0}); | |
| OperandType type13(Type::TENSOR_FLOAT16, {2}); | |
| OperandType type4(Type::TENSOR_FLOAT16, {3, 1, 2}); | |
| // Phase 1, operands | |
| auto input01 = model->addOperand(&type4); | |
| auto input11 = model->addOperand(&type13); | |
| auto output01 = model->addOperand(&type10); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input01, input11}, {output01}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input01, input11}, | |
| {output01}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape_float16_2(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape_int32_2(Model *model) { | |
| OperandType type11(Type::TENSOR_INT32, {0, 0, 0}); | |
| OperandType type14(Type::TENSOR_INT32, {2}); | |
| OperandType type5(Type::TENSOR_INT32, {3, 1, 2}); | |
| // Phase 1, operands | |
| auto input01 = model->addOperand(&type5); | |
| auto input11 = model->addOperand(&type14); | |
| auto output01 = model->addOperand(&type11); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input01, input11}, {output01}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input01, input11}, | |
| {output01}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape_int32_2(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape_quant8_2(Model *model) { | |
| OperandType type12(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0}, 2.0f, 80); | |
| OperandType type15(Type::TENSOR_QUANT8_ASYMM, {2}, 1.0f, 100); | |
| OperandType type6(Type::TENSOR_QUANT8_ASYMM, {3, 1, 2}, 0.5f, 127); | |
| // Phase 1, operands | |
| auto input01 = model->addOperand(&type6); | |
| auto input11 = model->addOperand(&type15); | |
| auto output01 = model->addOperand(&type12); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input01, input11}, {output01}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input01, input11}, | |
| {output01}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape_quant8_2(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_3(Model *model) { | |
| OperandType type2(Type::TENSOR_QUANT8_ASYMM, {2}, 1.0f, 128); | |
| OperandType type3(Type::TENSOR_QUANT8_ASYMM, {2}, 0.5f, 128); | |
| // Phase 1, operands | |
| auto input02 = model->addOperand(&type2); | |
| auto input12 = model->addOperand(&type2); | |
| auto output02 = model->addOperand(&type3); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input02, input12}, {output02}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input02, input12}, | |
| {output02}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_3(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |
| void CreateModel_dynamic_output_shape_3(Model *model) { | |
| OperandType type16(Type::TENSOR_QUANT8_ASYMM, {0}, 0.5f, 128); | |
| OperandType type2(Type::TENSOR_QUANT8_ASYMM, {2}, 1.0f, 128); | |
| // Phase 1, operands | |
| auto input02 = model->addOperand(&type2); | |
| auto input12 = model->addOperand(&type2); | |
| auto output02 = model->addOperand(&type16); | |
| // Phase 2, operations | |
| model->addOperation(ANEURALNETWORKS_MINIMUM, {input02, input12}, {output02}); | |
| // Phase 3, inputs and outputs | |
| model->identifyInputsAndOutputs( | |
| {input02, input12}, | |
| {output02}); | |
| assert(model->isValid()); | |
| } | |
| inline bool is_ignored_dynamic_output_shape_3(int i) { | |
| static std::set<int> ignore = {}; | |
| return ignore.find(i) != ignore.end(); | |
| } | |