| // clang-format off |
| // Generated file (from: svdf.mod.py). Do not edit |
| void CreateModel(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); |
| OperandType type1(Type::TENSOR_FLOAT32, {4, 3}); |
| OperandType type2(Type::TENSOR_FLOAT32, {4, 10}); |
| OperandType type3(Type::TENSOR_FLOAT32, {4}); |
| OperandType type4(Type::TENSOR_FLOAT32, {2, 40}); |
| OperandType type5(Type::INT32, {}); |
| OperandType type6(Type::TENSOR_FLOAT32, {2, 4}); |
| // Phase 1, operands |
| auto input = model->addOperand(&type0); |
| auto weights_feature = model->addOperand(&type1); |
| auto weights_time = model->addOperand(&type2); |
| auto bias = model->addOperand(&type3); |
| auto state_in = model->addOperand(&type4); |
| auto rank_param = model->addOperand(&type5); |
| auto activation_param = model->addOperand(&type5); |
| auto state_out = model->addOperand(&type4); |
| auto output = model->addOperand(&type6); |
| // Phase 2, operations |
| static int32_t rank_param_init[] = {1}; |
| model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1); |
| static int32_t activation_param_init[] = {0}; |
| model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input, weights_feature, weights_time, bias, state_in}, |
| {state_out, output}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored(int i) { |
| static std::set<int> ignore = {0}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |