1 // clang-format off
2 // Generated file (from: svdf2.mod.py). Do not edit
CreateModel(Model * model)3 void CreateModel(Model *model) {
4 OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
5 OperandType type1(Type::TENSOR_FLOAT32, {8, 3});
6 OperandType type2(Type::TENSOR_FLOAT32, {8, 10});
7 OperandType type3(Type::TENSOR_FLOAT32, {4});
8 OperandType type4(Type::TENSOR_FLOAT32, {2, 80});
9 OperandType type5(Type::INT32, {});
10 OperandType type6(Type::TENSOR_FLOAT32, {2, 4});
11 // Phase 1, operands
12 auto input = model->addOperand(&type0);
13 auto weights_feature = model->addOperand(&type1);
14 auto weights_time = model->addOperand(&type2);
15 auto bias = model->addOperand(&type3);
16 auto state_in = model->addOperand(&type4);
17 auto rank_param = model->addOperand(&type5);
18 auto activation_param = model->addOperand(&type5);
19 auto state_out = model->addOperand(&type4);
20 auto output = model->addOperand(&type6);
21 // Phase 2, operations
22 static int32_t rank_param_init[] = {2};
23 model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
24 static int32_t activation_param_init[] = {0};
25 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
26 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
27 // Phase 3, inputs and outputs
28 model->identifyInputsAndOutputs(
29 {input, weights_feature, weights_time, bias, state_in},
30 {state_out, output});
31 assert(model->isValid());
32 }
33
is_ignored(int i)34 inline bool is_ignored(int i) {
35 static std::set<int> ignore = {0};
36 return ignore.find(i) != ignore.end();
37 }
38
CreateModel_dynamic_output_shape(Model * model)39 void CreateModel_dynamic_output_shape(Model *model) {
40 OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
41 OperandType type1(Type::TENSOR_FLOAT32, {8, 3});
42 OperandType type2(Type::TENSOR_FLOAT32, {8, 10});
43 OperandType type3(Type::TENSOR_FLOAT32, {4});
44 OperandType type4(Type::TENSOR_FLOAT32, {2, 80});
45 OperandType type5(Type::INT32, {});
46 OperandType type7(Type::TENSOR_FLOAT32, {0, 0});
47 // Phase 1, operands
48 auto input = model->addOperand(&type0);
49 auto weights_feature = model->addOperand(&type1);
50 auto weights_time = model->addOperand(&type2);
51 auto bias = model->addOperand(&type3);
52 auto state_in = model->addOperand(&type4);
53 auto rank_param = model->addOperand(&type5);
54 auto activation_param = model->addOperand(&type5);
55 auto state_out = model->addOperand(&type7);
56 auto output = model->addOperand(&type7);
57 // Phase 2, operations
58 static int32_t rank_param_init[] = {2};
59 model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
60 static int32_t activation_param_init[] = {0};
61 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
62 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
63 // Phase 3, inputs and outputs
64 model->identifyInputsAndOutputs(
65 {input, weights_feature, weights_time, bias, state_in},
66 {state_out, output});
67 assert(model->isValid());
68 }
69
is_ignored_dynamic_output_shape(int i)70 inline bool is_ignored_dynamic_output_shape(int i) {
71 static std::set<int> ignore = {0};
72 return ignore.find(i) != ignore.end();
73 }
74
75