| // Generated from tile_2.mod.py |
| // DO NOT EDIT |
| // clang-format off |
| #include "TestGenerated.h" |
| |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type2(Type::TENSOR_FLOAT32, {4, 3}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type0); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type2); |
| // Phase 2, operations |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input0, multipliers}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_dynamic_output_shape(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type3(Type::TENSOR_FLOAT32, {0, 0}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type0); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type3); |
| // Phase 2, operations |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input0, multipliers}, |
| {output0}); |
| 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::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_all_inputs_as_internal(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type2(Type::TENSOR_FLOAT32, {4, 3}); |
| OperandType type4(Type::TENSOR_FLOAT32, {1}); |
| OperandType type5(Type::INT32, {}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type0); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type2); |
| auto input0_tmp = model->addOperand(&type0); |
| auto dummy = model->addOperand(&type4); |
| auto param = model->addOperand(&type5); |
| // Phase 2, operations |
| static float dummy_init[] = {0.0f}; |
| model->setOperandValue(dummy, dummy_init, sizeof(float) * 1); |
| static int32_t param_init[] = {0}; |
| model->setOperandValue(param, param_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy, param}, {input0}); |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {multipliers, input0_tmp}, |
| {output0}); |
| 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::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_all_inputs_as_internal_dynamic_output_shape(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type3(Type::TENSOR_FLOAT32, {0, 0}); |
| OperandType type4(Type::TENSOR_FLOAT32, {1}); |
| OperandType type5(Type::INT32, {}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type0); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type3); |
| auto input0_tmp = model->addOperand(&type0); |
| auto dummy1 = model->addOperand(&type4); |
| auto param1 = model->addOperand(&type5); |
| // Phase 2, operations |
| static float dummy1_init[] = {0.0f}; |
| model->setOperandValue(dummy1, dummy1_init, sizeof(float) * 1); |
| static int32_t param1_init[] = {0}; |
| model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy1, param1}, {input0}); |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {multipliers, input0_tmp}, |
| {output0}); |
| 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::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_relaxed(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type2(Type::TENSOR_FLOAT32, {4, 3}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type0); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type2); |
| // Phase 2, operations |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input0, multipliers}, |
| {output0}); |
| // Phase 4: set relaxed execution |
| model->relaxComputationFloat32toFloat16(true); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_relaxed(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_relaxed_dynamic_output_shape(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type3(Type::TENSOR_FLOAT32, {0, 0}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type0); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type3); |
| // Phase 2, operations |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input0, multipliers}, |
| {output0}); |
| // Phase 4: set relaxed execution |
| model->relaxComputationFloat32toFloat16(true); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_relaxed_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_relaxed_all_inputs_as_internal(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type2(Type::TENSOR_FLOAT32, {4, 3}); |
| OperandType type4(Type::TENSOR_FLOAT32, {1}); |
| OperandType type5(Type::INT32, {}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type0); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type2); |
| auto input0_tmp = model->addOperand(&type0); |
| auto dummy2 = model->addOperand(&type4); |
| auto param2 = model->addOperand(&type5); |
| // Phase 2, operations |
| static float dummy2_init[] = {0.0f}; |
| model->setOperandValue(dummy2, dummy2_init, sizeof(float) * 1); |
| static int32_t param2_init[] = {0}; |
| model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy2, param2}, {input0}); |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {multipliers, input0_tmp}, |
| {output0}); |
| // Phase 4: set relaxed execution |
| model->relaxComputationFloat32toFloat16(true); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_relaxed_all_inputs_as_internal(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_relaxed_all_inputs_as_internal_dynamic_output_shape(Model *model) { |
| OperandType type0(Type::TENSOR_FLOAT32, {2, 3}); |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type3(Type::TENSOR_FLOAT32, {0, 0}); |
| OperandType type4(Type::TENSOR_FLOAT32, {1}); |
| OperandType type5(Type::INT32, {}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type0); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type3); |
| auto input0_tmp = model->addOperand(&type0); |
| auto dummy3 = model->addOperand(&type4); |
| auto param3 = model->addOperand(&type5); |
| // Phase 2, operations |
| static float dummy3_init[] = {0.0f}; |
| model->setOperandValue(dummy3, dummy3_init, sizeof(float) * 1); |
| static int32_t param3_init[] = {0}; |
| model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy3, param3}, {input0}); |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {multipliers, input0_tmp}, |
| {output0}); |
| // Phase 4: set relaxed execution |
| model->relaxComputationFloat32toFloat16(true); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_relaxed_all_inputs_as_internal_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_float16(Model *model) { |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type6(Type::TENSOR_FLOAT16, {2, 3}); |
| OperandType type7(Type::TENSOR_FLOAT16, {4, 3}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type6); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type7); |
| // Phase 2, operations |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input0, multipliers}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_float16(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_float16_dynamic_output_shape(Model *model) { |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type6(Type::TENSOR_FLOAT16, {2, 3}); |
| OperandType type8(Type::TENSOR_FLOAT16, {0, 0}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type6); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type8); |
| // Phase 2, operations |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input0, multipliers}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_float16_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_float16_all_inputs_as_internal(Model *model) { |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type10(Type::TENSOR_FLOAT16, {1}); |
| OperandType type5(Type::INT32, {}); |
| OperandType type7(Type::TENSOR_FLOAT16, {4, 3}); |
| OperandType type9(Type::TENSOR_FLOAT16, {2, 3}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type9); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type7); |
| auto input0_tmp = model->addOperand(&type9); |
| auto dummy4 = model->addOperand(&type10); |
| auto param4 = model->addOperand(&type5); |
| // Phase 2, operations |
| static _Float16 dummy4_init[] = {0.0f}; |
| model->setOperandValue(dummy4, dummy4_init, sizeof(_Float16) * 1); |
| static int32_t param4_init[] = {0}; |
| model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy4, param4}, {input0}); |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {multipliers, input0_tmp}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_float16_all_inputs_as_internal(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_float16_all_inputs_as_internal_dynamic_output_shape(Model *model) { |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type10(Type::TENSOR_FLOAT16, {1}); |
| OperandType type5(Type::INT32, {}); |
| OperandType type8(Type::TENSOR_FLOAT16, {0, 0}); |
| OperandType type9(Type::TENSOR_FLOAT16, {2, 3}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type9); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type8); |
| auto input0_tmp = model->addOperand(&type9); |
| auto dummy5 = model->addOperand(&type10); |
| auto param5 = model->addOperand(&type5); |
| // Phase 2, operations |
| static _Float16 dummy5_init[] = {0.0f}; |
| model->setOperandValue(dummy5, dummy5_init, sizeof(_Float16) * 1); |
| static int32_t param5_init[] = {0}; |
| model->setOperandValue(param5, param5_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy5, param5}, {input0}); |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {multipliers, input0_tmp}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_float16_all_inputs_as_internal_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_quant8(Model *model) { |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type11(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 0.5f, 127); |
| OperandType type12(Type::TENSOR_QUANT8_ASYMM, {4, 3}, 0.5f, 127); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type11); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type12); |
| // Phase 2, operations |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input0, multipliers}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_quant8(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_quant8_dynamic_output_shape(Model *model) { |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type11(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 0.5f, 127); |
| OperandType type13(Type::TENSOR_QUANT8_ASYMM, {0, 0}, 0.5f, 127); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type11); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type13); |
| // Phase 2, operations |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input0, multipliers}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_quant8_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_quant8_all_inputs_as_internal(Model *model) { |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type11(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 0.5f, 127); |
| OperandType type12(Type::TENSOR_QUANT8_ASYMM, {4, 3}, 0.5f, 127); |
| OperandType type14(Type::TENSOR_QUANT8_ASYMM, {1}, 0.5f, 127); |
| OperandType type5(Type::INT32, {}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type11); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type12); |
| auto input0_tmp = model->addOperand(&type11); |
| auto dummy6 = model->addOperand(&type14); |
| auto param6 = model->addOperand(&type5); |
| // Phase 2, operations |
| static uint8_t dummy6_init[] = {127}; |
| model->setOperandValue(dummy6, dummy6_init, sizeof(uint8_t) * 1); |
| static int32_t param6_init[] = {0}; |
| model->setOperandValue(param6, param6_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy6, param6}, {input0}); |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {multipliers, input0_tmp}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_quant8_all_inputs_as_internal(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_quant8_all_inputs_as_internal_dynamic_output_shape(Model *model) { |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type11(Type::TENSOR_QUANT8_ASYMM, {2, 3}, 0.5f, 127); |
| OperandType type13(Type::TENSOR_QUANT8_ASYMM, {0, 0}, 0.5f, 127); |
| OperandType type14(Type::TENSOR_QUANT8_ASYMM, {1}, 0.5f, 127); |
| OperandType type5(Type::INT32, {}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type11); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type13); |
| auto input0_tmp = model->addOperand(&type11); |
| auto dummy7 = model->addOperand(&type14); |
| auto param7 = model->addOperand(&type5); |
| // Phase 2, operations |
| static uint8_t dummy7_init[] = {127}; |
| model->setOperandValue(dummy7, dummy7_init, sizeof(uint8_t) * 1); |
| static int32_t param7_init[] = {0}; |
| model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy7, param7}, {input0}); |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {multipliers, input0_tmp}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_quant8_all_inputs_as_internal_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_int32(Model *model) { |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type15(Type::TENSOR_INT32, {2, 3}); |
| OperandType type16(Type::TENSOR_INT32, {4, 3}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type15); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type16); |
| // Phase 2, operations |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input0, multipliers}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_int32(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |
| namespace generated_tests::tile_2 { |
| |
| void CreateModel_int32_dynamic_output_shape(Model *model) { |
| OperandType type1(Type::TENSOR_INT32, {2}); |
| OperandType type15(Type::TENSOR_INT32, {2, 3}); |
| OperandType type17(Type::TENSOR_INT32, {0, 0}); |
| // Phase 1, operands |
| auto input0 = model->addOperand(&type15); |
| auto multipliers = model->addOperand(&type1); |
| auto output0 = model->addOperand(&type17); |
| // Phase 2, operations |
| model->addOperation(ANEURALNETWORKS_TILE, {input0, multipliers}, {output0}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {input0, multipliers}, |
| {output0}); |
| assert(model->isValid()); |
| } |
| |
| bool is_ignored_int32_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| } // namespace generated_tests::tile_2 |