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