1e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang// Generated file (from: conv_float_large_relaxed.mod.py). Do not edit 2e8e5d34c4159532eb324df393c2c752a508bced1Miao Wangvoid CreateModel(Model *model) { 3e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang OperandType type3(Type::INT32, {}); 4e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 3, 3}); 5e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang OperandType type1(Type::TENSOR_FLOAT32, {3, 1, 1, 3}); 6e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang OperandType type2(Type::TENSOR_FLOAT32, {3}); 7e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 1, operands 8e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto op1 = model->addOperand(&type0); 9e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto op2 = model->addOperand(&type1); 10e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto op3 = model->addOperand(&type2); 11e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto pad0 = model->addOperand(&type3); 12e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto act = model->addOperand(&type3); 13e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto stride = model->addOperand(&type3); 14e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang auto op4 = model->addOperand(&type0); 15e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 2, operations 16e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static float op2_init[] = {1.0f, 4.0f, 7.0f, 2.0f, 5.0f, 8.0f, 3.0f, 6.0f, 9.0f}; 17e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(op2, op2_init, sizeof(float) * 9); 18e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static float op3_init[] = {0.0f, 0.0f, 0.0f}; 19e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(op3, op3_init, sizeof(float) * 3); 20e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static int32_t pad0_init[] = {0}; 21e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1); 22e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static int32_t act_init[] = {0}; 23e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(act, act_init, sizeof(int32_t) * 1); 24e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static int32_t stride_init[] = {1}; 25e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 26e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, act}, {op4}); 27e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 3, inputs and outputs 28e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->identifyInputsAndOutputs( 29e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang {op1}, 30e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang {op4}); 31e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang // Phase 4: set relaxed execution 32e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang model->relaxComputationFloat32toFloat16(true); 33e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang assert(model->isValid()); 34e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang} 35e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang 36e8e5d34c4159532eb324df393c2c752a508bced1Miao Wangbool is_ignored(int i) { 37e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang static std::set<int> ignore = {}; 38e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang return ignore.find(i) != ignore.end(); 39e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang} 40