1224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen// Generated file (from: max_pool_float_3.mod.py). Do not edit 2224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chenvoid CreateModel(Model *model) { 3224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen OperandType type1(Type::INT32, {}); 4e68d924a02511200be6186c947359dee7bb58f23Jean-Luc Brouillet OperandType type2(Type::TENSOR_FLOAT32, {5, 2, 3, 3}); 5e68d924a02511200be6186c947359dee7bb58f23Jean-Luc Brouillet OperandType type0(Type::TENSOR_FLOAT32, {5, 50, 70, 3}); 6224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen // Phase 1, operands 7224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen auto i0 = model->addOperand(&type0); 8224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen auto stride = model->addOperand(&type1); 9224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen auto filter = model->addOperand(&type1); 10224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen auto padding = model->addOperand(&type1); 11224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen auto relu6_activation = model->addOperand(&type1); 12224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen auto output = model->addOperand(&type2); 13224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen // Phase 2, operations 14224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen static int32_t stride_init[] = {20}; 15224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1); 16224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen static int32_t filter_init[] = {20}; 17224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen model->setOperandValue(filter, filter_init, sizeof(int32_t) * 1); 18224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen static int32_t padding_init[] = {0}; 19224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen model->setOperandValue(padding, padding_init, sizeof(int32_t) * 1); 20224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen static int32_t relu6_activation_init[] = {3}; 21224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen model->setOperandValue(relu6_activation, relu6_activation_init, sizeof(int32_t) * 1); 22224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen model->addOperation(ANEURALNETWORKS_MAX_POOL_2D, {i0, padding, padding, padding, padding, stride, stride, filter, filter, relu6_activation}, {output}); 23224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen // Phase 3, inputs and outputs 2466d5cb6e3a90aefc8d545f6369080ab88de9d667Jean-Luc Brouillet model->identifyInputsAndOutputs( 25224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen {i0}, 26224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen {output}); 27224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen assert(model->isValid()); 28224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen} 29224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen 30224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chenbool is_ignored(int i) { 31224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen static std::set<int> ignore = {}; 32224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen return ignore.find(i) != ignore.end(); 33224c01eb06d3dc496b99b0827fdcc9e65bfc4f9aDong Chen} 34