1 // clang-format off 2 // Generated file (from: svdf2_relaxed.mod.py). Do not edit 3 void CreateModel(Model *model) { 4 OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); 5 OperandType type1(Type::TENSOR_FLOAT32, {8, 3}); 6 OperandType type2(Type::TENSOR_FLOAT32, {8, 10}); 7 OperandType type3(Type::TENSOR_FLOAT32, {4}); 8 OperandType type4(Type::TENSOR_FLOAT32, {2, 80}); 9 OperandType type5(Type::INT32, {}); 10 OperandType type6(Type::TENSOR_FLOAT32, {2, 4}); 11 // Phase 1, operands 12 auto input = model->addOperand(&type0); 13 auto weights_feature = model->addOperand(&type1); 14 auto weights_time = model->addOperand(&type2); 15 auto bias = model->addOperand(&type3); 16 auto state_in = model->addOperand(&type4); 17 auto rank_param = model->addOperand(&type5); 18 auto activation_param = model->addOperand(&type5); 19 auto state_out = model->addOperand(&type4); 20 auto output = model->addOperand(&type6); 21 // Phase 2, operations 22 static int32_t rank_param_init[] = {2}; 23 model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1); 24 static int32_t activation_param_init[] = {0}; 25 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); 26 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output}); 27 // Phase 3, inputs and outputs 28 model->identifyInputsAndOutputs( 29 {input, weights_feature, weights_time, bias, state_in}, 30 {state_out, output}); 31 // Phase 4: set relaxed execution 32 model->relaxComputationFloat32toFloat16(true); 33 assert(model->isValid()); 34 } 35 36 inline bool is_ignored(int i) { 37 static std::set<int> ignore = {0}; 38 return ignore.find(i) != ignore.end(); 39 } 40 41 void CreateModel_dynamic_output_shape(Model *model) { 42 OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); 43 OperandType type1(Type::TENSOR_FLOAT32, {8, 3}); 44 OperandType type2(Type::TENSOR_FLOAT32, {8, 10}); 45 OperandType type3(Type::TENSOR_FLOAT32, {4}); 46 OperandType type4(Type::TENSOR_FLOAT32, {2, 80}); 47 OperandType type5(Type::INT32, {}); 48 OperandType type7(Type::TENSOR_FLOAT32, {0, 0}); 49 // Phase 1, operands 50 auto input = model->addOperand(&type0); 51 auto weights_feature = model->addOperand(&type1); 52 auto weights_time = model->addOperand(&type2); 53 auto bias = model->addOperand(&type3); 54 auto state_in = model->addOperand(&type4); 55 auto rank_param = model->addOperand(&type5); 56 auto activation_param = model->addOperand(&type5); 57 auto state_out = model->addOperand(&type7); 58 auto output = model->addOperand(&type7); 59 // Phase 2, operations 60 static int32_t rank_param_init[] = {2}; 61 model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1); 62 static int32_t activation_param_init[] = {0}; 63 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); 64 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output}); 65 // Phase 3, inputs and outputs 66 model->identifyInputsAndOutputs( 67 {input, weights_feature, weights_time, bias, state_in}, 68 {state_out, output}); 69 // Phase 4: set relaxed execution 70 model->relaxComputationFloat32toFloat16(true); 71 assert(model->isValid()); 72 } 73 74 inline bool is_ignored_dynamic_output_shape(int i) { 75 static std::set<int> ignore = {0}; 76 return ignore.find(i) != ignore.end(); 77 } 78 79
