1e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang// Generated file (from: avg_pool_float_4_relaxed.mod.py). Do not edit 2e8e5d34c4159532eb324df393c2c752a508bced1Miao Wangvoid CreateModel(Model *model) { 3e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang OperandType type1(Type::INT32, {}); 4e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang OperandType type2(Type::TENSOR_FLOAT32, {5, 11, 13, 3}); 5e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang OperandType type0(Type::TENSOR_FLOAT32, {5, 52, 60, 3}); 6e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 1, operands 7e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto i0 = model->addOperand(&type0); 8e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto stride = model->addOperand(&type1); 9e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto filter = model->addOperand(&type1); 10e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto padding = model->addOperand(&type1); 11e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto relu6_activation = model->addOperand(&type1); 12e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto output = model->addOperand(&type2); 13e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 2, operations 14e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static int32_t stride_init[] = {5}; 15e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 16e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static int32_t filter_init[] = {100}; 17e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); 18e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static int32_t padding_init[] = {50}; 19e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 20e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static int32_t relu6_activation_init[] = {3}; 21e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(relu6_activation, relu6_activation_init, sizeof(int32_t) * 1); 22e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->addOperation(ANEURALNETWORKS_AVERAGE_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output}); 23e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 3, inputs and outputs 24e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->identifyInputsAndOutputs( 25e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang {i0}, 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