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