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}
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