| // Generated from conv_quant8_2.mod.py |
| // DO NOT EDIT |
| // clang-format off |
| #include "TestGenerated.h" |
| |
| namespace generated_tests::conv_quant8_2 { |
| |
| void CreateModel(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 6, 1}, 0.5f, 127); |
| OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 127); |
| OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); |
| OperandType type3(Type::INT32, {}); |
| OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 1.0f, 127); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type1); |
| auto op3 = model->addOperand(&type2); |
| auto pad_valid = model->addOperand(&type3); |
| auto stride3 = model->addOperand(&type3); |
| auto stride1 = model->addOperand(&type3); |
| auto act_none = model->addOperand(&type3); |
| auto op4 = model->addOperand(&type4); |
| // Phase 2, operations |
| static uint8_t op2_init[] = {129, 131, 133, 135}; |
| model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 4); |
| static int32_t op3_init[] = {-4}; |
| model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); |
| static int32_t pad_valid_init[] = {2}; |
| model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); |
| static int32_t stride3_init[] = {3}; |
| model->setOperandValue(stride3, stride3_init, sizeof(int32_t) * 1); |
| static int32_t stride1_init[] = {1}; |
| model->setOperandValue(stride1, stride1_init, sizeof(int32_t) * 1); |
| static int32_t act_none_init[] = {0}; |
| model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad_valid, stride3, stride1, act_none}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::conv_quant8_2 |
| namespace generated_tests::conv_quant8_2 { |
| |
| void CreateModel_dynamic_output_shape(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 6, 1}, 0.5f, 127); |
| OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 127); |
| OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); |
| OperandType type3(Type::INT32, {}); |
| OperandType type5(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 127); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type1); |
| auto op3 = model->addOperand(&type2); |
| auto pad_valid = model->addOperand(&type3); |
| auto stride3 = model->addOperand(&type3); |
| auto stride1 = model->addOperand(&type3); |
| auto act_none = model->addOperand(&type3); |
| auto op4 = model->addOperand(&type5); |
| // Phase 2, operations |
| static uint8_t op2_init[] = {129, 131, 133, 135}; |
| model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 4); |
| static int32_t op3_init[] = {-4}; |
| model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); |
| static int32_t pad_valid_init[] = {2}; |
| model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); |
| static int32_t stride3_init[] = {3}; |
| model->setOperandValue(stride3, stride3_init, sizeof(int32_t) * 1); |
| static int32_t stride1_init[] = {1}; |
| model->setOperandValue(stride1, stride1_init, sizeof(int32_t) * 1); |
| static int32_t act_none_init[] = {0}; |
| model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad_valid, stride3, stride1, act_none}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::conv_quant8_2 |
| namespace generated_tests::conv_quant8_2 { |
| |
| void CreateModel_all_inputs_as_internal(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 6, 1}, 0.5f, 127); |
| OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 127); |
| OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); |
| OperandType type3(Type::INT32, {}); |
| OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 1.0f, 127); |
| OperandType type6(Type::TENSOR_QUANT8_ASYMM, {1}, 0.5f, 127); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type1); |
| auto op3 = model->addOperand(&type2); |
| auto pad_valid = model->addOperand(&type3); |
| auto stride3 = model->addOperand(&type3); |
| auto stride1 = model->addOperand(&type3); |
| auto act_none = model->addOperand(&type3); |
| auto op4 = model->addOperand(&type4); |
| auto op1_tmp = model->addOperand(&type0); |
| auto dummy = model->addOperand(&type6); |
| auto param = model->addOperand(&type3); |
| // Phase 2, operations |
| static uint8_t op2_init[] = {129, 131, 133, 135}; |
| model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 4); |
| static int32_t op3_init[] = {-4}; |
| model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); |
| static int32_t pad_valid_init[] = {2}; |
| model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); |
| static int32_t stride3_init[] = {3}; |
| model->setOperandValue(stride3, stride3_init, sizeof(int32_t) * 1); |
| static int32_t stride1_init[] = {1}; |
| model->setOperandValue(stride1, stride1_init, sizeof(int32_t) * 1); |
| static int32_t act_none_init[] = {0}; |
| model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); |
| static uint8_t dummy_init[] = {127}; |
| model->setOperandValue(dummy, dummy_init, sizeof(uint8_t) * 1); |
| static int32_t param_init[] = {0}; |
| model->setOperandValue(param, param_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {op1_tmp, dummy, param}, {op1}); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad_valid, stride3, stride1, act_none}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1_tmp}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_all_inputs_as_internal(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::conv_quant8_2 |
| namespace generated_tests::conv_quant8_2 { |
| |
| void CreateModel_all_inputs_as_internal_dynamic_output_shape(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 6, 1}, 0.5f, 127); |
| OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 127); |
| OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); |
| OperandType type3(Type::INT32, {}); |
| OperandType type5(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 127); |
| OperandType type6(Type::TENSOR_QUANT8_ASYMM, {1}, 0.5f, 127); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type1); |
| auto op3 = model->addOperand(&type2); |
| auto pad_valid = model->addOperand(&type3); |
| auto stride3 = model->addOperand(&type3); |
| auto stride1 = model->addOperand(&type3); |
| auto act_none = model->addOperand(&type3); |
| auto op4 = model->addOperand(&type5); |
| auto op1_tmp = model->addOperand(&type0); |
| auto dummy1 = model->addOperand(&type6); |
| auto param1 = model->addOperand(&type3); |
| // Phase 2, operations |
| static uint8_t op2_init[] = {129, 131, 133, 135}; |
| model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 4); |
| static int32_t op3_init[] = {-4}; |
| model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); |
| static int32_t pad_valid_init[] = {2}; |
| model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); |
| static int32_t stride3_init[] = {3}; |
| model->setOperandValue(stride3, stride3_init, sizeof(int32_t) * 1); |
| static int32_t stride1_init[] = {1}; |
| model->setOperandValue(stride1, stride1_init, sizeof(int32_t) * 1); |
| static int32_t act_none_init[] = {0}; |
| model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); |
| static uint8_t dummy1_init[] = {127}; |
| model->setOperandValue(dummy1, dummy1_init, sizeof(uint8_t) * 1); |
| static int32_t param1_init[] = {0}; |
| model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {op1_tmp, dummy1, param1}, {op1}); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad_valid, stride3, stride1, act_none}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1_tmp}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_all_inputs_as_internal_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::conv_quant8_2 |
| namespace generated_tests::conv_quant8_2 { |
| |
| void CreateModel_all_tensors_as_inputs(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 6, 1}, 0.5f, 127); |
| OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 127); |
| OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); |
| OperandType type3(Type::INT32, {}); |
| OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 1.0f, 127); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type1); |
| auto op3 = model->addOperand(&type2); |
| auto pad_valid = model->addOperand(&type3); |
| auto stride3 = model->addOperand(&type3); |
| auto stride1 = model->addOperand(&type3); |
| auto act_none = model->addOperand(&type3); |
| auto op4 = model->addOperand(&type4); |
| // Phase 2, operations |
| static int32_t pad_valid_init[] = {2}; |
| model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); |
| static int32_t stride3_init[] = {3}; |
| model->setOperandValue(stride3, stride3_init, sizeof(int32_t) * 1); |
| static int32_t stride1_init[] = {1}; |
| model->setOperandValue(stride1, stride1_init, sizeof(int32_t) * 1); |
| static int32_t act_none_init[] = {0}; |
| model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad_valid, stride3, stride1, act_none}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1, op2, op3}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_all_tensors_as_inputs(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::conv_quant8_2 |
| namespace generated_tests::conv_quant8_2 { |
| |
| void CreateModel_all_tensors_as_inputs_dynamic_output_shape(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 6, 1}, 0.5f, 127); |
| OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 127); |
| OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); |
| OperandType type3(Type::INT32, {}); |
| OperandType type5(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 127); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type1); |
| auto op3 = model->addOperand(&type2); |
| auto pad_valid = model->addOperand(&type3); |
| auto stride3 = model->addOperand(&type3); |
| auto stride1 = model->addOperand(&type3); |
| auto act_none = model->addOperand(&type3); |
| auto op4 = model->addOperand(&type5); |
| // Phase 2, operations |
| static int32_t pad_valid_init[] = {2}; |
| model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); |
| static int32_t stride3_init[] = {3}; |
| model->setOperandValue(stride3, stride3_init, sizeof(int32_t) * 1); |
| static int32_t stride1_init[] = {1}; |
| model->setOperandValue(stride1, stride1_init, sizeof(int32_t) * 1); |
| static int32_t act_none_init[] = {0}; |
| model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad_valid, stride3, stride1, act_none}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1, op2, op3}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_all_tensors_as_inputs_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::conv_quant8_2 |
| namespace generated_tests::conv_quant8_2 { |
| |
| void CreateModel_all_tensors_as_inputs_all_inputs_as_internal(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 6, 1}, 0.5f, 127); |
| OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 127); |
| OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); |
| OperandType type3(Type::INT32, {}); |
| OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 1.0f, 127); |
| OperandType type6(Type::TENSOR_QUANT8_ASYMM, {1}, 0.5f, 127); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type1); |
| auto op3 = model->addOperand(&type2); |
| auto pad_valid = model->addOperand(&type3); |
| auto stride3 = model->addOperand(&type3); |
| auto stride1 = model->addOperand(&type3); |
| auto act_none = model->addOperand(&type3); |
| auto op4 = model->addOperand(&type4); |
| auto op1_tmp = model->addOperand(&type0); |
| auto dummy2 = model->addOperand(&type6); |
| auto param2 = model->addOperand(&type3); |
| auto op2_tmp = model->addOperand(&type1); |
| auto dummy3 = model->addOperand(&type6); |
| auto param3 = model->addOperand(&type3); |
| // Phase 2, operations |
| static int32_t pad_valid_init[] = {2}; |
| model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); |
| static int32_t stride3_init[] = {3}; |
| model->setOperandValue(stride3, stride3_init, sizeof(int32_t) * 1); |
| static int32_t stride1_init[] = {1}; |
| model->setOperandValue(stride1, stride1_init, sizeof(int32_t) * 1); |
| static int32_t act_none_init[] = {0}; |
| model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); |
| static uint8_t dummy2_init[] = {127}; |
| model->setOperandValue(dummy2, dummy2_init, sizeof(uint8_t) * 1); |
| static int32_t param2_init[] = {0}; |
| model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); |
| static uint8_t dummy3_init[] = {127}; |
| model->setOperandValue(dummy3, dummy3_init, sizeof(uint8_t) * 1); |
| static int32_t param3_init[] = {0}; |
| model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {op1_tmp, dummy2, param2}, {op1}); |
| model->addOperation(ANEURALNETWORKS_ADD, {op2_tmp, dummy3, param3}, {op2}); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad_valid, stride3, stride1, act_none}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op3, op1_tmp, op2_tmp}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_all_tensors_as_inputs_all_inputs_as_internal(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::conv_quant8_2 |
| namespace generated_tests::conv_quant8_2 { |
| |
| void CreateModel_all_tensors_as_inputs_all_inputs_as_internal_dynamic_output_shape(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 6, 1}, 0.5f, 127); |
| OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.5f, 127); |
| OperandType type2(Type::TENSOR_INT32, {1}, 0.25f, 0); |
| OperandType type3(Type::INT32, {}); |
| OperandType type5(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 127); |
| OperandType type6(Type::TENSOR_QUANT8_ASYMM, {1}, 0.5f, 127); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type1); |
| auto op3 = model->addOperand(&type2); |
| auto pad_valid = model->addOperand(&type3); |
| auto stride3 = model->addOperand(&type3); |
| auto stride1 = model->addOperand(&type3); |
| auto act_none = model->addOperand(&type3); |
| auto op4 = model->addOperand(&type5); |
| auto op1_tmp = model->addOperand(&type0); |
| auto dummy4 = model->addOperand(&type6); |
| auto param4 = model->addOperand(&type3); |
| auto op2_tmp = model->addOperand(&type1); |
| auto dummy5 = model->addOperand(&type6); |
| auto param5 = model->addOperand(&type3); |
| // Phase 2, operations |
| static int32_t pad_valid_init[] = {2}; |
| model->setOperandValue(pad_valid, pad_valid_init, sizeof(int32_t) * 1); |
| static int32_t stride3_init[] = {3}; |
| model->setOperandValue(stride3, stride3_init, sizeof(int32_t) * 1); |
| static int32_t stride1_init[] = {1}; |
| model->setOperandValue(stride1, stride1_init, sizeof(int32_t) * 1); |
| static int32_t act_none_init[] = {0}; |
| model->setOperandValue(act_none, act_none_init, sizeof(int32_t) * 1); |
| static uint8_t dummy4_init[] = {127}; |
| model->setOperandValue(dummy4, dummy4_init, sizeof(uint8_t) * 1); |
| static int32_t param4_init[] = {0}; |
| model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1); |
| static uint8_t dummy5_init[] = {127}; |
| model->setOperandValue(dummy5, dummy5_init, sizeof(uint8_t) * 1); |
| static int32_t param5_init[] = {0}; |
| model->setOperandValue(param5, param5_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {op1_tmp, dummy4, param4}, {op1}); |
| model->addOperation(ANEURALNETWORKS_ADD, {op2_tmp, dummy5, param5}, {op2}); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, pad_valid, stride3, stride1, act_none}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op3, op1_tmp, op2_tmp}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_all_tensors_as_inputs_all_inputs_as_internal_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::conv_quant8_2 |