1 // clang-format off
2 // Generated file (from: depthwise_conv2d_float_large_2_weights_as_inputs.mod.py). Do not edit
CreateModel(Model * model)3 void CreateModel(Model *model) {
4 OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 4});
5 OperandType type1(Type::TENSOR_FLOAT32, {4});
6 OperandType type2(Type::INT32, {});
7 OperandType type3(Type::TENSOR_FLOAT32, {1, 1, 1, 4});
8 // Phase 1, operands
9 auto op1 = model->addOperand(&type0);
10 auto op2 = model->addOperand(&type0);
11 auto op3 = model->addOperand(&type1);
12 auto pad0 = model->addOperand(&type2);
13 auto stride = model->addOperand(&type2);
14 auto channelMultiplier = model->addOperand(&type2);
15 auto act = model->addOperand(&type2);
16 auto op4 = model->addOperand(&type3);
17 // Phase 2, operations
18 static int32_t pad0_init[] = {0};
19 model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1);
20 static int32_t stride_init[] = {1};
21 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1);
22 static int32_t channelMultiplier_init[] = {1};
23 model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1);
24 static int32_t act_init[] = {0};
25 model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
26 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
27 // Phase 3, inputs and outputs
28 model->identifyInputsAndOutputs(
29 {op1, op2, op3},
30 {op4});
31 assert(model->isValid());
32 }
33
is_ignored(int i)34 inline bool is_ignored(int i) {
35 static std::set<int> ignore = {};
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, {1, 2, 2, 4});
41 OperandType type1(Type::TENSOR_FLOAT32, {4});
42 OperandType type2(Type::INT32, {});
43 OperandType type4(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
44 // Phase 1, operands
45 auto op1 = model->addOperand(&type0);
46 auto op2 = model->addOperand(&type0);
47 auto op3 = model->addOperand(&type1);
48 auto pad0 = model->addOperand(&type2);
49 auto stride = model->addOperand(&type2);
50 auto channelMultiplier = model->addOperand(&type2);
51 auto act = model->addOperand(&type2);
52 auto op4 = model->addOperand(&type4);
53 // Phase 2, operations
54 static int32_t pad0_init[] = {0};
55 model->setOperandValue(pad0, pad0_init, sizeof(int32_t) * 1);
56 static int32_t stride_init[] = {1};
57 model->setOperandValue(stride, stride_init, sizeof(int32_t) * 1);
58 static int32_t channelMultiplier_init[] = {1};
59 model->setOperandValue(channelMultiplier, channelMultiplier_init, sizeof(int32_t) * 1);
60 static int32_t act_init[] = {0};
61 model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
62 model->addOperation(ANEURALNETWORKS_DEPTHWISE_CONV_2D, {op1, op2, op3, pad0, pad0, pad0, pad0, stride, stride, channelMultiplier, act}, {op4});
63 // Phase 3, inputs and outputs
64 model->identifyInputsAndOutputs(
65 {op1, op2, op3},
66 {op4});
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 = {};
72 return ignore.find(i) != ignore.end();
73 }
74
75