| // clang-format off |
| // Generated file (from: conv2d_per_channel.mod.py). Do not edit |
| void CreateModel(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 2}, 0.5f, 128); |
| OperandType type1(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 3}, 1.0f, 128); |
| OperandType type4(Type::INT32, {}); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type1); |
| auto op3 = model->addOperand(&type2); |
| auto param = model->addOperand(&type4); |
| auto param1 = model->addOperand(&type4); |
| auto param2 = model->addOperand(&type4); |
| auto param3 = model->addOperand(&type4); |
| auto param4 = model->addOperand(&type4); |
| auto param5 = model->addOperand(&type4); |
| auto param6 = model->addOperand(&type4); |
| auto op4 = model->addOperand(&type3); |
| // Phase 2, operations |
| static int8_t op2_init[] = {1, 2, 1, 2, 1, 2}; |
| model->setOperandValue(op2, op2_init, sizeof(int8_t) * 6); |
| static int32_t op3_init[] = {4, 4, 4}; |
| model->setOperandValue(op3, op3_init, sizeof(int32_t) * 3); |
| static int32_t param_init[] = {0}; |
| model->setOperandValue(param, param_init, sizeof(int32_t) * 1); |
| static int32_t param1_init[] = {0}; |
| model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); |
| static int32_t param2_init[] = {0}; |
| model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); |
| static int32_t param3_init[] = {0}; |
| model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); |
| static int32_t param4_init[] = {1}; |
| model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1); |
| static int32_t param5_init[] = {1}; |
| model->setOperandValue(param5, param5_init, sizeof(int32_t) * 1); |
| static int32_t param6_init[] = {0}; |
| model->setOperandValue(param6, param6_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, param3, param4, param5, param6}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_weight_as_input(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 2}, 0.5f, 128); |
| OperandType type18(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 3}, 1.0f, 128); |
| OperandType type4(Type::INT32, {}); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type18); |
| auto op3 = model->addOperand(&type2); |
| auto param = model->addOperand(&type4); |
| auto param1 = model->addOperand(&type4); |
| auto param2 = model->addOperand(&type4); |
| auto param3 = model->addOperand(&type4); |
| auto param4 = model->addOperand(&type4); |
| auto param5 = model->addOperand(&type4); |
| auto param6 = model->addOperand(&type4); |
| auto op4 = model->addOperand(&type3); |
| // Phase 2, operations |
| static int32_t param_init[] = {0}; |
| model->setOperandValue(param, param_init, sizeof(int32_t) * 1); |
| static int32_t param1_init[] = {0}; |
| model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); |
| static int32_t param2_init[] = {0}; |
| model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); |
| static int32_t param3_init[] = {0}; |
| model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); |
| static int32_t param4_init[] = {1}; |
| model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1); |
| static int32_t param5_init[] = {1}; |
| model->setOperandValue(param5, param5_init, sizeof(int32_t) * 1); |
| static int32_t param6_init[] = {0}; |
| model->setOperandValue(param6, param6_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, param3, param4, param5, param6}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1, op2, op3}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_weight_as_input(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_dynamic_output_shape(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 2}, 0.5f, 128); |
| OperandType type1(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type4(Type::INT32, {}); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type1); |
| auto op3 = model->addOperand(&type2); |
| auto param = model->addOperand(&type4); |
| auto param1 = model->addOperand(&type4); |
| auto param2 = model->addOperand(&type4); |
| auto param3 = model->addOperand(&type4); |
| auto param4 = model->addOperand(&type4); |
| auto param5 = model->addOperand(&type4); |
| auto param6 = model->addOperand(&type4); |
| auto op4 = model->addOperand(&type19); |
| // Phase 2, operations |
| static int8_t op2_init[] = {1, 2, 1, 2, 1, 2}; |
| model->setOperandValue(op2, op2_init, sizeof(int8_t) * 6); |
| static int32_t op3_init[] = {4, 4, 4}; |
| model->setOperandValue(op3, op3_init, sizeof(int32_t) * 3); |
| static int32_t param_init[] = {0}; |
| model->setOperandValue(param, param_init, sizeof(int32_t) * 1); |
| static int32_t param1_init[] = {0}; |
| model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); |
| static int32_t param2_init[] = {0}; |
| model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); |
| static int32_t param3_init[] = {0}; |
| model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); |
| static int32_t param4_init[] = {1}; |
| model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1); |
| static int32_t param5_init[] = {1}; |
| model->setOperandValue(param5, param5_init, sizeof(int32_t) * 1); |
| static int32_t param6_init[] = {0}; |
| model->setOperandValue(param6, param6_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, param3, param4, param5, param6}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_dynamic_output_shape(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_dynamic_output_shape_weight_as_input(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 2}, 0.5f, 128); |
| OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type20(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type4(Type::INT32, {}); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type0); |
| auto op2 = model->addOperand(&type20); |
| auto op3 = model->addOperand(&type2); |
| auto param = model->addOperand(&type4); |
| auto param1 = model->addOperand(&type4); |
| auto param2 = model->addOperand(&type4); |
| auto param3 = model->addOperand(&type4); |
| auto param4 = model->addOperand(&type4); |
| auto param5 = model->addOperand(&type4); |
| auto param6 = model->addOperand(&type4); |
| auto op4 = model->addOperand(&type19); |
| // Phase 2, operations |
| static int32_t param_init[] = {0}; |
| model->setOperandValue(param, param_init, sizeof(int32_t) * 1); |
| static int32_t param1_init[] = {0}; |
| model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); |
| static int32_t param2_init[] = {0}; |
| model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); |
| static int32_t param3_init[] = {0}; |
| model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); |
| static int32_t param4_init[] = {1}; |
| model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1); |
| static int32_t param5_init[] = {1}; |
| model->setOperandValue(param5, param5_init, sizeof(int32_t) * 1); |
| static int32_t param6_init[] = {0}; |
| model->setOperandValue(param6, param6_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, param3, param4, param5, param6}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1, op2, op3}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_dynamic_output_shape_weight_as_input(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_layouts_nhwc(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 2}, 0.5f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 3}, 1.0f, 128); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| OperandType type6(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| // Phase 1, operands |
| auto op11 = model->addOperand(&type0); |
| auto op21 = model->addOperand(&type6); |
| auto op31 = model->addOperand(&type2); |
| auto param7 = model->addOperand(&type4); |
| auto param8 = model->addOperand(&type4); |
| auto param9 = model->addOperand(&type4); |
| auto param10 = model->addOperand(&type4); |
| auto param11 = model->addOperand(&type4); |
| auto param12 = model->addOperand(&type4); |
| auto param13 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto op41 = model->addOperand(&type3); |
| // Phase 2, operations |
| static int8_t op21_init[] = {1, 2, 1, 2, 1, 2}; |
| model->setOperandValue(op21, op21_init, sizeof(int8_t) * 6); |
| static int32_t op31_init[] = {4, 4, 4}; |
| model->setOperandValue(op31, op31_init, sizeof(int32_t) * 3); |
| static int32_t param7_init[] = {0}; |
| model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); |
| static int32_t param8_init[] = {0}; |
| model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); |
| static int32_t param9_init[] = {0}; |
| model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1); |
| static int32_t param10_init[] = {0}; |
| model->setOperandValue(param10, param10_init, sizeof(int32_t) * 1); |
| static int32_t param11_init[] = {1}; |
| model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); |
| static int32_t param12_init[] = {1}; |
| model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); |
| static int32_t param13_init[] = {0}; |
| model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {false}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op11, op21, op31, param7, param8, param9, param10, param11, param12, param13, layout}, {op41}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op11}, |
| {op41}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_layouts_nhwc(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_layouts_nhwc_weight_as_input(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 2}, 0.5f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type21(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 3}, 1.0f, 128); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| // Phase 1, operands |
| auto op11 = model->addOperand(&type0); |
| auto op21 = model->addOperand(&type21); |
| auto op31 = model->addOperand(&type2); |
| auto param7 = model->addOperand(&type4); |
| auto param8 = model->addOperand(&type4); |
| auto param9 = model->addOperand(&type4); |
| auto param10 = model->addOperand(&type4); |
| auto param11 = model->addOperand(&type4); |
| auto param12 = model->addOperand(&type4); |
| auto param13 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto op41 = model->addOperand(&type3); |
| // Phase 2, operations |
| static int32_t param7_init[] = {0}; |
| model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); |
| static int32_t param8_init[] = {0}; |
| model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); |
| static int32_t param9_init[] = {0}; |
| model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1); |
| static int32_t param10_init[] = {0}; |
| model->setOperandValue(param10, param10_init, sizeof(int32_t) * 1); |
| static int32_t param11_init[] = {1}; |
| model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); |
| static int32_t param12_init[] = {1}; |
| model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); |
| static int32_t param13_init[] = {0}; |
| model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {false}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op11, op21, op31, param7, param8, param9, param10, param11, param12, param13, layout}, {op41}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op11, op21, op31}, |
| {op41}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_layouts_nhwc_weight_as_input(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_layouts_nchw(Model *model) { |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type22(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 1}, 0.5f, 128); |
| OperandType type23(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 1}, 1.0f, 128); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| OperandType type6(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| // Phase 1, operands |
| auto op11 = model->addOperand(&type22); |
| auto op21 = model->addOperand(&type6); |
| auto op31 = model->addOperand(&type2); |
| auto param7 = model->addOperand(&type4); |
| auto param8 = model->addOperand(&type4); |
| auto param9 = model->addOperand(&type4); |
| auto param10 = model->addOperand(&type4); |
| auto param11 = model->addOperand(&type4); |
| auto param12 = model->addOperand(&type4); |
| auto param13 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto op41 = model->addOperand(&type23); |
| // Phase 2, operations |
| static int8_t op21_init[] = {1, 2, 1, 2, 1, 2}; |
| model->setOperandValue(op21, op21_init, sizeof(int8_t) * 6); |
| static int32_t op31_init[] = {4, 4, 4}; |
| model->setOperandValue(op31, op31_init, sizeof(int32_t) * 3); |
| static int32_t param7_init[] = {0}; |
| model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); |
| static int32_t param8_init[] = {0}; |
| model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); |
| static int32_t param9_init[] = {0}; |
| model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1); |
| static int32_t param10_init[] = {0}; |
| model->setOperandValue(param10, param10_init, sizeof(int32_t) * 1); |
| static int32_t param11_init[] = {1}; |
| model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); |
| static int32_t param12_init[] = {1}; |
| model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); |
| static int32_t param13_init[] = {0}; |
| model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {true}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op11, op21, op31, param7, param8, param9, param10, param11, param12, param13, layout}, {op41}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op11}, |
| {op41}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_layouts_nchw(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_layouts_nchw_weight_as_input(Model *model) { |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type22(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 1}, 0.5f, 128); |
| OperandType type23(Type::TENSOR_QUANT8_ASYMM, {1, 3, 3, 1}, 1.0f, 128); |
| OperandType type24(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| // Phase 1, operands |
| auto op11 = model->addOperand(&type22); |
| auto op21 = model->addOperand(&type24); |
| auto op31 = model->addOperand(&type2); |
| auto param7 = model->addOperand(&type4); |
| auto param8 = model->addOperand(&type4); |
| auto param9 = model->addOperand(&type4); |
| auto param10 = model->addOperand(&type4); |
| auto param11 = model->addOperand(&type4); |
| auto param12 = model->addOperand(&type4); |
| auto param13 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto op41 = model->addOperand(&type23); |
| // Phase 2, operations |
| static int32_t param7_init[] = {0}; |
| model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); |
| static int32_t param8_init[] = {0}; |
| model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); |
| static int32_t param9_init[] = {0}; |
| model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1); |
| static int32_t param10_init[] = {0}; |
| model->setOperandValue(param10, param10_init, sizeof(int32_t) * 1); |
| static int32_t param11_init[] = {1}; |
| model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); |
| static int32_t param12_init[] = {1}; |
| model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); |
| static int32_t param13_init[] = {0}; |
| model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {true}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op11, op21, op31, param7, param8, param9, param10, param11, param12, param13, layout}, {op41}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op11, op21, op31}, |
| {op41}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_layouts_nchw_weight_as_input(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_layouts_dynamic_output_shape_nhwc(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 2}, 0.5f, 128); |
| OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| OperandType type6(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| // Phase 1, operands |
| auto op11 = model->addOperand(&type0); |
| auto op21 = model->addOperand(&type6); |
| auto op31 = model->addOperand(&type2); |
| auto param7 = model->addOperand(&type4); |
| auto param8 = model->addOperand(&type4); |
| auto param9 = model->addOperand(&type4); |
| auto param10 = model->addOperand(&type4); |
| auto param11 = model->addOperand(&type4); |
| auto param12 = model->addOperand(&type4); |
| auto param13 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto op41 = model->addOperand(&type19); |
| // Phase 2, operations |
| static int8_t op21_init[] = {1, 2, 1, 2, 1, 2}; |
| model->setOperandValue(op21, op21_init, sizeof(int8_t) * 6); |
| static int32_t op31_init[] = {4, 4, 4}; |
| model->setOperandValue(op31, op31_init, sizeof(int32_t) * 3); |
| static int32_t param7_init[] = {0}; |
| model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); |
| static int32_t param8_init[] = {0}; |
| model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); |
| static int32_t param9_init[] = {0}; |
| model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1); |
| static int32_t param10_init[] = {0}; |
| model->setOperandValue(param10, param10_init, sizeof(int32_t) * 1); |
| static int32_t param11_init[] = {1}; |
| model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); |
| static int32_t param12_init[] = {1}; |
| model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); |
| static int32_t param13_init[] = {0}; |
| model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {false}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op11, op21, op31, param7, param8, param9, param10, param11, param12, param13, layout}, {op41}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op11}, |
| {op41}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_layouts_dynamic_output_shape_nhwc(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_layouts_dynamic_output_shape_nhwc_weight_as_input(Model *model) { |
| OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 3, 1, 2}, 0.5f, 128); |
| OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type25(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| // Phase 1, operands |
| auto op11 = model->addOperand(&type0); |
| auto op21 = model->addOperand(&type25); |
| auto op31 = model->addOperand(&type2); |
| auto param7 = model->addOperand(&type4); |
| auto param8 = model->addOperand(&type4); |
| auto param9 = model->addOperand(&type4); |
| auto param10 = model->addOperand(&type4); |
| auto param11 = model->addOperand(&type4); |
| auto param12 = model->addOperand(&type4); |
| auto param13 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto op41 = model->addOperand(&type19); |
| // Phase 2, operations |
| static int32_t param7_init[] = {0}; |
| model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); |
| static int32_t param8_init[] = {0}; |
| model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); |
| static int32_t param9_init[] = {0}; |
| model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1); |
| static int32_t param10_init[] = {0}; |
| model->setOperandValue(param10, param10_init, sizeof(int32_t) * 1); |
| static int32_t param11_init[] = {1}; |
| model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); |
| static int32_t param12_init[] = {1}; |
| model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); |
| static int32_t param13_init[] = {0}; |
| model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {false}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op11, op21, op31, param7, param8, param9, param10, param11, param12, param13, layout}, {op41}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op11, op21, op31}, |
| {op41}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_layouts_dynamic_output_shape_nhwc_weight_as_input(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_layouts_dynamic_output_shape_nchw(Model *model) { |
| OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type22(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 1}, 0.5f, 128); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| OperandType type6(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| // Phase 1, operands |
| auto op11 = model->addOperand(&type22); |
| auto op21 = model->addOperand(&type6); |
| auto op31 = model->addOperand(&type2); |
| auto param7 = model->addOperand(&type4); |
| auto param8 = model->addOperand(&type4); |
| auto param9 = model->addOperand(&type4); |
| auto param10 = model->addOperand(&type4); |
| auto param11 = model->addOperand(&type4); |
| auto param12 = model->addOperand(&type4); |
| auto param13 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto op41 = model->addOperand(&type19); |
| // Phase 2, operations |
| static int8_t op21_init[] = {1, 2, 1, 2, 1, 2}; |
| model->setOperandValue(op21, op21_init, sizeof(int8_t) * 6); |
| static int32_t op31_init[] = {4, 4, 4}; |
| model->setOperandValue(op31, op31_init, sizeof(int32_t) * 3); |
| static int32_t param7_init[] = {0}; |
| model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); |
| static int32_t param8_init[] = {0}; |
| model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); |
| static int32_t param9_init[] = {0}; |
| model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1); |
| static int32_t param10_init[] = {0}; |
| model->setOperandValue(param10, param10_init, sizeof(int32_t) * 1); |
| static int32_t param11_init[] = {1}; |
| model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); |
| static int32_t param12_init[] = {1}; |
| model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); |
| static int32_t param13_init[] = {0}; |
| model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {true}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op11, op21, op31, param7, param8, param9, param10, param11, param12, param13, layout}, {op41}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op11}, |
| {op41}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_layouts_dynamic_output_shape_nchw(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_layouts_dynamic_output_shape_nchw_weight_as_input(Model *model) { |
| OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type22(Type::TENSOR_QUANT8_ASYMM, {1, 2, 3, 1}, 0.5f, 128); |
| OperandType type26(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| // Phase 1, operands |
| auto op11 = model->addOperand(&type22); |
| auto op21 = model->addOperand(&type26); |
| auto op31 = model->addOperand(&type2); |
| auto param7 = model->addOperand(&type4); |
| auto param8 = model->addOperand(&type4); |
| auto param9 = model->addOperand(&type4); |
| auto param10 = model->addOperand(&type4); |
| auto param11 = model->addOperand(&type4); |
| auto param12 = model->addOperand(&type4); |
| auto param13 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto op41 = model->addOperand(&type19); |
| // Phase 2, operations |
| static int32_t param7_init[] = {0}; |
| model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); |
| static int32_t param8_init[] = {0}; |
| model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); |
| static int32_t param9_init[] = {0}; |
| model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1); |
| static int32_t param10_init[] = {0}; |
| model->setOperandValue(param10, param10_init, sizeof(int32_t) * 1); |
| static int32_t param11_init[] = {1}; |
| model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); |
| static int32_t param12_init[] = {1}; |
| model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); |
| static int32_t param13_init[] = {0}; |
| model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {true}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {op11, op21, op31, param7, param8, param9, param10, param11, param12, param13, layout}, {op41}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op11, op21, op31}, |
| {op41}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_layouts_dynamic_output_shape_nchw_weight_as_input(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_zero_sized_nhwc(Model *model) { |
| OperandType type10(Type::TENSOR_INT32, {0}); |
| OperandType type11(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0); |
| OperandType type12(Type::TENSOR_INT32, {1}); |
| OperandType type13(Type::FLOAT32, {}); |
| OperandType type14(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 2}, 0.5f, 128); |
| OperandType type15(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 2}, 0.5f, 128); |
| OperandType type16(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type17(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 3}, 1.0f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128); |
| OperandType type8(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0); |
| OperandType type9(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128); |
| // Phase 1, operands |
| auto scores = model->addOperand(&type7); |
| auto roi = model->addOperand(&type8); |
| auto param14 = model->addOperand(&type12); |
| auto param15 = model->addOperand(&type13); |
| auto param16 = model->addOperand(&type4); |
| auto param17 = model->addOperand(&type4); |
| auto param18 = model->addOperand(&type13); |
| auto param19 = model->addOperand(&type13); |
| auto param20 = model->addOperand(&type13); |
| auto scoresOut = model->addOperand(&type9); |
| auto roiOut = model->addOperand(&type11); |
| auto classesOut = model->addOperand(&type10); |
| auto batchSplitOut = model->addOperand(&type10); |
| auto in = model->addOperand(&type14); |
| auto param21 = model->addOperand(&type4); |
| auto param22 = model->addOperand(&type4); |
| auto param23 = model->addOperand(&type13); |
| auto param24 = model->addOperand(&type13); |
| auto param25 = model->addOperand(&type4); |
| auto param26 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto featureMap = model->addOperand(&type15); |
| auto weights = model->addOperand(&type16); |
| auto bias = model->addOperand(&type2); |
| auto param27 = model->addOperand(&type4); |
| auto param28 = model->addOperand(&type4); |
| auto param29 = model->addOperand(&type4); |
| auto param30 = model->addOperand(&type4); |
| auto param31 = model->addOperand(&type4); |
| auto param32 = model->addOperand(&type4); |
| auto param33 = model->addOperand(&type4); |
| auto out = model->addOperand(&type17); |
| // Phase 2, operations |
| static uint8_t scores_init[] = {137, 129}; |
| model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2); |
| static uint16_t roi_init[] = {1, 1, 10, 10, 0, 0, 10, 10}; |
| model->setOperandValue(roi, roi_init, sizeof(uint16_t) * 8); |
| static int32_t param14_init[] = {0}; |
| model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1); |
| static float param15_init[] = {0.3f}; |
| model->setOperandValue(param15, param15_init, sizeof(float) * 1); |
| static int32_t param16_init[] = {-1}; |
| model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1); |
| static int32_t param17_init[] = {0}; |
| model->setOperandValue(param17, param17_init, sizeof(int32_t) * 1); |
| static float param18_init[] = {0.4f}; |
| model->setOperandValue(param18, param18_init, sizeof(float) * 1); |
| static float param19_init[] = {1.0f}; |
| model->setOperandValue(param19, param19_init, sizeof(float) * 1); |
| static float param20_init[] = {0.3f}; |
| model->setOperandValue(param20, param20_init, sizeof(float) * 1); |
| static int32_t param21_init[] = {2}; |
| model->setOperandValue(param21, param21_init, sizeof(int32_t) * 1); |
| static int32_t param22_init[] = {2}; |
| model->setOperandValue(param22, param22_init, sizeof(int32_t) * 1); |
| static float param23_init[] = {2.0f}; |
| model->setOperandValue(param23, param23_init, sizeof(float) * 1); |
| static float param24_init[] = {2.0f}; |
| model->setOperandValue(param24, param24_init, sizeof(float) * 1); |
| static int32_t param25_init[] = {4}; |
| model->setOperandValue(param25, param25_init, sizeof(int32_t) * 1); |
| static int32_t param26_init[] = {4}; |
| model->setOperandValue(param26, param26_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {false}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| static int8_t weights_init[] = {1, 2, 1, 2, 1, 2}; |
| model->setOperandValue(weights, weights_init, sizeof(int8_t) * 6); |
| static int32_t bias_init[] = {4, 4, 4}; |
| model->setOperandValue(bias, bias_init, sizeof(int32_t) * 3); |
| static int32_t param27_init[] = {0}; |
| model->setOperandValue(param27, param27_init, sizeof(int32_t) * 1); |
| static int32_t param28_init[] = {0}; |
| model->setOperandValue(param28, param28_init, sizeof(int32_t) * 1); |
| static int32_t param29_init[] = {0}; |
| model->setOperandValue(param29, param29_init, sizeof(int32_t) * 1); |
| static int32_t param30_init[] = {0}; |
| model->setOperandValue(param30, param30_init, sizeof(int32_t) * 1); |
| static int32_t param31_init[] = {1}; |
| model->setOperandValue(param31, param31_init, sizeof(int32_t) * 1); |
| static int32_t param32_init[] = {1}; |
| model->setOperandValue(param32, param32_init, sizeof(int32_t) * 1); |
| static int32_t param33_init[] = {0}; |
| model->setOperandValue(param33, param33_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param14, param15, param16, param17, param18, param19, param20}, {scoresOut, roiOut, classesOut, batchSplitOut}); |
| model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param21, param22, param23, param24, param25, param26, layout}, {featureMap}); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {featureMap, weights, bias, param27, param28, param29, param30, param31, param32, param33, layout}, {out}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {in}, |
| {scoresOut, classesOut, out}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_zero_sized_nhwc(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_zero_sized_nchw(Model *model) { |
| OperandType type10(Type::TENSOR_INT32, {0}); |
| OperandType type11(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0); |
| OperandType type12(Type::TENSOR_INT32, {1}); |
| OperandType type13(Type::FLOAT32, {}); |
| OperandType type15(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 2}, 0.5f, 128); |
| OperandType type16(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type27(Type::TENSOR_QUANT8_ASYMM, {1, 2, 1, 1}, 0.5f, 128); |
| OperandType type28(Type::TENSOR_QUANT8_ASYMM, {0, 3, 2, 2}, 1.0f, 128); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128); |
| OperandType type8(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0); |
| OperandType type9(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128); |
| // Phase 1, operands |
| auto scores = model->addOperand(&type7); |
| auto roi = model->addOperand(&type8); |
| auto param14 = model->addOperand(&type12); |
| auto param15 = model->addOperand(&type13); |
| auto param16 = model->addOperand(&type4); |
| auto param17 = model->addOperand(&type4); |
| auto param18 = model->addOperand(&type13); |
| auto param19 = model->addOperand(&type13); |
| auto param20 = model->addOperand(&type13); |
| auto scoresOut = model->addOperand(&type9); |
| auto roiOut = model->addOperand(&type11); |
| auto classesOut = model->addOperand(&type10); |
| auto batchSplitOut = model->addOperand(&type10); |
| auto in = model->addOperand(&type27); |
| auto param21 = model->addOperand(&type4); |
| auto param22 = model->addOperand(&type4); |
| auto param23 = model->addOperand(&type13); |
| auto param24 = model->addOperand(&type13); |
| auto param25 = model->addOperand(&type4); |
| auto param26 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto featureMap = model->addOperand(&type15); |
| auto weights = model->addOperand(&type16); |
| auto bias = model->addOperand(&type2); |
| auto param27 = model->addOperand(&type4); |
| auto param28 = model->addOperand(&type4); |
| auto param29 = model->addOperand(&type4); |
| auto param30 = model->addOperand(&type4); |
| auto param31 = model->addOperand(&type4); |
| auto param32 = model->addOperand(&type4); |
| auto param33 = model->addOperand(&type4); |
| auto out = model->addOperand(&type28); |
| // Phase 2, operations |
| static uint8_t scores_init[] = {137, 129}; |
| model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2); |
| static uint16_t roi_init[] = {1, 1, 10, 10, 0, 0, 10, 10}; |
| model->setOperandValue(roi, roi_init, sizeof(uint16_t) * 8); |
| static int32_t param14_init[] = {0}; |
| model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1); |
| static float param15_init[] = {0.3f}; |
| model->setOperandValue(param15, param15_init, sizeof(float) * 1); |
| static int32_t param16_init[] = {-1}; |
| model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1); |
| static int32_t param17_init[] = {0}; |
| model->setOperandValue(param17, param17_init, sizeof(int32_t) * 1); |
| static float param18_init[] = {0.4f}; |
| model->setOperandValue(param18, param18_init, sizeof(float) * 1); |
| static float param19_init[] = {1.0f}; |
| model->setOperandValue(param19, param19_init, sizeof(float) * 1); |
| static float param20_init[] = {0.3f}; |
| model->setOperandValue(param20, param20_init, sizeof(float) * 1); |
| static int32_t param21_init[] = {2}; |
| model->setOperandValue(param21, param21_init, sizeof(int32_t) * 1); |
| static int32_t param22_init[] = {2}; |
| model->setOperandValue(param22, param22_init, sizeof(int32_t) * 1); |
| static float param23_init[] = {2.0f}; |
| model->setOperandValue(param23, param23_init, sizeof(float) * 1); |
| static float param24_init[] = {2.0f}; |
| model->setOperandValue(param24, param24_init, sizeof(float) * 1); |
| static int32_t param25_init[] = {4}; |
| model->setOperandValue(param25, param25_init, sizeof(int32_t) * 1); |
| static int32_t param26_init[] = {4}; |
| model->setOperandValue(param26, param26_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {true}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| static int8_t weights_init[] = {1, 2, 1, 2, 1, 2}; |
| model->setOperandValue(weights, weights_init, sizeof(int8_t) * 6); |
| static int32_t bias_init[] = {4, 4, 4}; |
| model->setOperandValue(bias, bias_init, sizeof(int32_t) * 3); |
| static int32_t param27_init[] = {0}; |
| model->setOperandValue(param27, param27_init, sizeof(int32_t) * 1); |
| static int32_t param28_init[] = {0}; |
| model->setOperandValue(param28, param28_init, sizeof(int32_t) * 1); |
| static int32_t param29_init[] = {0}; |
| model->setOperandValue(param29, param29_init, sizeof(int32_t) * 1); |
| static int32_t param30_init[] = {0}; |
| model->setOperandValue(param30, param30_init, sizeof(int32_t) * 1); |
| static int32_t param31_init[] = {1}; |
| model->setOperandValue(param31, param31_init, sizeof(int32_t) * 1); |
| static int32_t param32_init[] = {1}; |
| model->setOperandValue(param32, param32_init, sizeof(int32_t) * 1); |
| static int32_t param33_init[] = {0}; |
| model->setOperandValue(param33, param33_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param14, param15, param16, param17, param18, param19, param20}, {scoresOut, roiOut, classesOut, batchSplitOut}); |
| model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param21, param22, param23, param24, param25, param26, layout}, {featureMap}); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {featureMap, weights, bias, param27, param28, param29, param30, param31, param32, param33, layout}, {out}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {in}, |
| {scoresOut, classesOut, out}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_zero_sized_nchw(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_zero_sized_dynamic_output_shape_nhwc(Model *model) { |
| OperandType type10(Type::TENSOR_INT32, {0}); |
| OperandType type11(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0); |
| OperandType type12(Type::TENSOR_INT32, {1}); |
| OperandType type13(Type::FLOAT32, {}); |
| OperandType type14(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 2}, 0.5f, 128); |
| OperandType type15(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 2}, 0.5f, 128); |
| OperandType type16(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128); |
| OperandType type8(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0); |
| OperandType type9(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128); |
| // Phase 1, operands |
| auto scores = model->addOperand(&type7); |
| auto roi = model->addOperand(&type8); |
| auto param14 = model->addOperand(&type12); |
| auto param15 = model->addOperand(&type13); |
| auto param16 = model->addOperand(&type4); |
| auto param17 = model->addOperand(&type4); |
| auto param18 = model->addOperand(&type13); |
| auto param19 = model->addOperand(&type13); |
| auto param20 = model->addOperand(&type13); |
| auto scoresOut = model->addOperand(&type9); |
| auto roiOut = model->addOperand(&type11); |
| auto classesOut = model->addOperand(&type10); |
| auto batchSplitOut = model->addOperand(&type10); |
| auto in = model->addOperand(&type14); |
| auto param21 = model->addOperand(&type4); |
| auto param22 = model->addOperand(&type4); |
| auto param23 = model->addOperand(&type13); |
| auto param24 = model->addOperand(&type13); |
| auto param25 = model->addOperand(&type4); |
| auto param26 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto featureMap = model->addOperand(&type15); |
| auto weights = model->addOperand(&type16); |
| auto bias = model->addOperand(&type2); |
| auto param27 = model->addOperand(&type4); |
| auto param28 = model->addOperand(&type4); |
| auto param29 = model->addOperand(&type4); |
| auto param30 = model->addOperand(&type4); |
| auto param31 = model->addOperand(&type4); |
| auto param32 = model->addOperand(&type4); |
| auto param33 = model->addOperand(&type4); |
| auto out = model->addOperand(&type19); |
| // Phase 2, operations |
| static uint8_t scores_init[] = {137, 129}; |
| model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2); |
| static uint16_t roi_init[] = {1, 1, 10, 10, 0, 0, 10, 10}; |
| model->setOperandValue(roi, roi_init, sizeof(uint16_t) * 8); |
| static int32_t param14_init[] = {0}; |
| model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1); |
| static float param15_init[] = {0.3f}; |
| model->setOperandValue(param15, param15_init, sizeof(float) * 1); |
| static int32_t param16_init[] = {-1}; |
| model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1); |
| static int32_t param17_init[] = {0}; |
| model->setOperandValue(param17, param17_init, sizeof(int32_t) * 1); |
| static float param18_init[] = {0.4f}; |
| model->setOperandValue(param18, param18_init, sizeof(float) * 1); |
| static float param19_init[] = {1.0f}; |
| model->setOperandValue(param19, param19_init, sizeof(float) * 1); |
| static float param20_init[] = {0.3f}; |
| model->setOperandValue(param20, param20_init, sizeof(float) * 1); |
| static int32_t param21_init[] = {2}; |
| model->setOperandValue(param21, param21_init, sizeof(int32_t) * 1); |
| static int32_t param22_init[] = {2}; |
| model->setOperandValue(param22, param22_init, sizeof(int32_t) * 1); |
| static float param23_init[] = {2.0f}; |
| model->setOperandValue(param23, param23_init, sizeof(float) * 1); |
| static float param24_init[] = {2.0f}; |
| model->setOperandValue(param24, param24_init, sizeof(float) * 1); |
| static int32_t param25_init[] = {4}; |
| model->setOperandValue(param25, param25_init, sizeof(int32_t) * 1); |
| static int32_t param26_init[] = {4}; |
| model->setOperandValue(param26, param26_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {false}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| static int8_t weights_init[] = {1, 2, 1, 2, 1, 2}; |
| model->setOperandValue(weights, weights_init, sizeof(int8_t) * 6); |
| static int32_t bias_init[] = {4, 4, 4}; |
| model->setOperandValue(bias, bias_init, sizeof(int32_t) * 3); |
| static int32_t param27_init[] = {0}; |
| model->setOperandValue(param27, param27_init, sizeof(int32_t) * 1); |
| static int32_t param28_init[] = {0}; |
| model->setOperandValue(param28, param28_init, sizeof(int32_t) * 1); |
| static int32_t param29_init[] = {0}; |
| model->setOperandValue(param29, param29_init, sizeof(int32_t) * 1); |
| static int32_t param30_init[] = {0}; |
| model->setOperandValue(param30, param30_init, sizeof(int32_t) * 1); |
| static int32_t param31_init[] = {1}; |
| model->setOperandValue(param31, param31_init, sizeof(int32_t) * 1); |
| static int32_t param32_init[] = {1}; |
| model->setOperandValue(param32, param32_init, sizeof(int32_t) * 1); |
| static int32_t param33_init[] = {0}; |
| model->setOperandValue(param33, param33_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param14, param15, param16, param17, param18, param19, param20}, {scoresOut, roiOut, classesOut, batchSplitOut}); |
| model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param21, param22, param23, param24, param25, param26, layout}, {featureMap}); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {featureMap, weights, bias, param27, param28, param29, param30, param31, param32, param33, layout}, {out}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {in}, |
| {scoresOut, classesOut, out}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_zero_sized_dynamic_output_shape_nhwc(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_zero_sized_dynamic_output_shape_nchw(Model *model) { |
| OperandType type10(Type::TENSOR_INT32, {0}); |
| OperandType type11(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0); |
| OperandType type12(Type::TENSOR_INT32, {1}); |
| OperandType type13(Type::FLOAT32, {}); |
| OperandType type15(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 2}, 0.5f, 128); |
| OperandType type16(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {3, 1, 1, 2}, SymmPerChannelQuantParams({0.5f, 0.75f, 1.0f},0)); |
| OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 1.0f, 128); |
| OperandType type2(Type::TENSOR_INT32, {3}); |
| OperandType type27(Type::TENSOR_QUANT8_ASYMM, {1, 2, 1, 1}, 0.5f, 128); |
| OperandType type4(Type::INT32, {}); |
| OperandType type5(Type::BOOL, {}); |
| OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128); |
| OperandType type8(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0); |
| OperandType type9(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128); |
| // Phase 1, operands |
| auto scores = model->addOperand(&type7); |
| auto roi = model->addOperand(&type8); |
| auto param14 = model->addOperand(&type12); |
| auto param15 = model->addOperand(&type13); |
| auto param16 = model->addOperand(&type4); |
| auto param17 = model->addOperand(&type4); |
| auto param18 = model->addOperand(&type13); |
| auto param19 = model->addOperand(&type13); |
| auto param20 = model->addOperand(&type13); |
| auto scoresOut = model->addOperand(&type9); |
| auto roiOut = model->addOperand(&type11); |
| auto classesOut = model->addOperand(&type10); |
| auto batchSplitOut = model->addOperand(&type10); |
| auto in = model->addOperand(&type27); |
| auto param21 = model->addOperand(&type4); |
| auto param22 = model->addOperand(&type4); |
| auto param23 = model->addOperand(&type13); |
| auto param24 = model->addOperand(&type13); |
| auto param25 = model->addOperand(&type4); |
| auto param26 = model->addOperand(&type4); |
| auto layout = model->addOperand(&type5); |
| auto featureMap = model->addOperand(&type15); |
| auto weights = model->addOperand(&type16); |
| auto bias = model->addOperand(&type2); |
| auto param27 = model->addOperand(&type4); |
| auto param28 = model->addOperand(&type4); |
| auto param29 = model->addOperand(&type4); |
| auto param30 = model->addOperand(&type4); |
| auto param31 = model->addOperand(&type4); |
| auto param32 = model->addOperand(&type4); |
| auto param33 = model->addOperand(&type4); |
| auto out = model->addOperand(&type19); |
| // Phase 2, operations |
| static uint8_t scores_init[] = {137, 129}; |
| model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2); |
| static uint16_t roi_init[] = {1, 1, 10, 10, 0, 0, 10, 10}; |
| model->setOperandValue(roi, roi_init, sizeof(uint16_t) * 8); |
| static int32_t param14_init[] = {0}; |
| model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1); |
| static float param15_init[] = {0.3f}; |
| model->setOperandValue(param15, param15_init, sizeof(float) * 1); |
| static int32_t param16_init[] = {-1}; |
| model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1); |
| static int32_t param17_init[] = {0}; |
| model->setOperandValue(param17, param17_init, sizeof(int32_t) * 1); |
| static float param18_init[] = {0.4f}; |
| model->setOperandValue(param18, param18_init, sizeof(float) * 1); |
| static float param19_init[] = {1.0f}; |
| model->setOperandValue(param19, param19_init, sizeof(float) * 1); |
| static float param20_init[] = {0.3f}; |
| model->setOperandValue(param20, param20_init, sizeof(float) * 1); |
| static int32_t param21_init[] = {2}; |
| model->setOperandValue(param21, param21_init, sizeof(int32_t) * 1); |
| static int32_t param22_init[] = {2}; |
| model->setOperandValue(param22, param22_init, sizeof(int32_t) * 1); |
| static float param23_init[] = {2.0f}; |
| model->setOperandValue(param23, param23_init, sizeof(float) * 1); |
| static float param24_init[] = {2.0f}; |
| model->setOperandValue(param24, param24_init, sizeof(float) * 1); |
| static int32_t param25_init[] = {4}; |
| model->setOperandValue(param25, param25_init, sizeof(int32_t) * 1); |
| static int32_t param26_init[] = {4}; |
| model->setOperandValue(param26, param26_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {true}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| static int8_t weights_init[] = {1, 2, 1, 2, 1, 2}; |
| model->setOperandValue(weights, weights_init, sizeof(int8_t) * 6); |
| static int32_t bias_init[] = {4, 4, 4}; |
| model->setOperandValue(bias, bias_init, sizeof(int32_t) * 3); |
| static int32_t param27_init[] = {0}; |
| model->setOperandValue(param27, param27_init, sizeof(int32_t) * 1); |
| static int32_t param28_init[] = {0}; |
| model->setOperandValue(param28, param28_init, sizeof(int32_t) * 1); |
| static int32_t param29_init[] = {0}; |
| model->setOperandValue(param29, param29_init, sizeof(int32_t) * 1); |
| static int32_t param30_init[] = {0}; |
| model->setOperandValue(param30, param30_init, sizeof(int32_t) * 1); |
| static int32_t param31_init[] = {1}; |
| model->setOperandValue(param31, param31_init, sizeof(int32_t) * 1); |
| static int32_t param32_init[] = {1}; |
| model->setOperandValue(param32, param32_init, sizeof(int32_t) * 1); |
| static int32_t param33_init[] = {0}; |
| model->setOperandValue(param33, param33_init, sizeof(int32_t) * 1); |
| model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param14, param15, param16, param17, param18, param19, param20}, {scoresOut, roiOut, classesOut, batchSplitOut}); |
| model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param21, param22, param23, param24, param25, param26, layout}, {featureMap}); |
| model->addOperation(ANEURALNETWORKS_CONV_2D, {featureMap, weights, bias, param27, param28, param29, param30, param31, param32, param33, layout}, {out}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {in}, |
| {scoresOut, classesOut, out}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_zero_sized_dynamic_output_shape_nchw(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |