1e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang// Generated file (from: local_response_norm_float_1_relaxed.mod.py). Do not edit 2e8e5d34c4159532eb324df393c2c752a508bced1Miao Wangvoid CreateModel(Model *model) { 3e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang OperandType type2(Type::FLOAT32, {}); 4e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang OperandType type1(Type::INT32, {}); 5e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 6}); 6e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 1, operands 7e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto input = model->addOperand(&type0); 8e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto radius = model->addOperand(&type1); 9e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto bias = model->addOperand(&type2); 10e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto alpha = model->addOperand(&type2); 11e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto beta = model->addOperand(&type2); 12e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto output = model->addOperand(&type0); 13e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 2, operations 14e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static int32_t radius_init[] = {20}; 15e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1); 16e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static float bias_init[] = {9.0f}; 17e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(bias, bias_init, sizeof(float) * 1); 18e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static float alpha_init[] = {4.0f}; 19e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(alpha, alpha_init, sizeof(float) * 1); 20e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static float beta_init[] = {0.5f}; 21e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(beta, beta_init, sizeof(float) * 1); 22e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output}); 23e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 3, inputs and outputs 24e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->identifyInputsAndOutputs( 25e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang {input}, 26e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang {output}); 27e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 4: set relaxed execution 28e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->relaxComputationFloat32toFloat16(true); 29e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang assert(model->isValid()); 30e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang} 31e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang 32e8e5d34c4159532eb324df393c2c752a508bced1Miao Wangbool is_ignored(int i) { 33e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static std::set<int> ignore = {}; 34e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang return ignore.find(i) != ignore.end(); 35e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang} 36