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}
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