| // clang-format off |
| // Generated file (from: transpose_conv2d_large.mod.py). Do not edit |
| void CreateModel_quant8(Model *model) { |
| OperandType type0(Type::BOOL, {}); |
| OperandType type10(Type::TENSOR_QUANT8_ASYMM, {25, 32, 32, 16}, 0.5f, 0); |
| OperandType type4(Type::TENSOR_INT32, {4}); |
| OperandType type5(Type::INT32, {}); |
| OperandType type7(Type::TENSOR_QUANT8_ASYMM, {25, 1, 1, 1}, 0.5f, 0); |
| OperandType type8(Type::TENSOR_QUANT8_ASYMM, {16, 1, 1, 1}, 0.5f, 0); |
| OperandType type9(Type::TENSOR_INT32, {16}, 0.25f, 0); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type7); |
| auto op2 = model->addOperand(&type8); |
| auto op3 = model->addOperand(&type9); |
| auto shape = model->addOperand(&type4); |
| auto param = model->addOperand(&type5); |
| auto param1 = model->addOperand(&type5); |
| auto param2 = model->addOperand(&type5); |
| auto act = model->addOperand(&type5); |
| auto layout = model->addOperand(&type0); |
| auto op4 = model->addOperand(&type10); |
| // Phase 2, operations |
| static uint8_t op2_init[] = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2}; |
| model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); |
| static int32_t op3_init[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; |
| model->setOperandValue(op3, op3_init, sizeof(int32_t) * 16); |
| static int32_t shape_init[] = {25, 32, 32, 16}; |
| model->setOperandValue(shape, shape_init, sizeof(int32_t) * 4); |
| static int32_t param_init[] = {1}; |
| model->setOperandValue(param, param_init, sizeof(int32_t) * 1); |
| static int32_t param1_init[] = {32}; |
| model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); |
| static int32_t param2_init[] = {32}; |
| model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); |
| static int32_t act_init[] = {0}; |
| model->setOperandValue(act, act_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {false}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_TRANSPOSE_CONV_2D, {op1, op2, op3, shape, param, param1, param2, act, layout}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_quant8(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_channelQuant8(Model *model) { |
| OperandType type0(Type::BOOL, {}); |
| OperandType type11(Type::TENSOR_QUANT8_ASYMM, {25, 1, 1, 1}, 0.25f, 100); |
| OperandType type12(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {16, 1, 1, 1}, SymmPerChannelQuantParams({0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f},0)); |
| OperandType type13(Type::TENSOR_INT32, {16}, 0.0f, 0); |
| OperandType type14(Type::TENSOR_QUANT8_ASYMM, {25, 32, 32, 16}, 0.5f, 80); |
| OperandType type4(Type::TENSOR_INT32, {4}); |
| OperandType type5(Type::INT32, {}); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type11); |
| auto op2 = model->addOperand(&type12); |
| auto op3 = model->addOperand(&type13); |
| auto shape = model->addOperand(&type4); |
| auto param = model->addOperand(&type5); |
| auto param1 = model->addOperand(&type5); |
| auto param2 = model->addOperand(&type5); |
| auto act = model->addOperand(&type5); |
| auto layout = model->addOperand(&type0); |
| auto op4 = model->addOperand(&type14); |
| // Phase 2, operations |
| static int8_t op2_init[] = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2}; |
| model->setOperandValue(op2, op2_init, sizeof(int8_t) * 16); |
| static int32_t op3_init[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; |
| model->setOperandValue(op3, op3_init, sizeof(int32_t) * 16); |
| static int32_t shape_init[] = {25, 32, 32, 16}; |
| model->setOperandValue(shape, shape_init, sizeof(int32_t) * 4); |
| static int32_t param_init[] = {1}; |
| model->setOperandValue(param, param_init, sizeof(int32_t) * 1); |
| static int32_t param1_init[] = {32}; |
| model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); |
| static int32_t param2_init[] = {32}; |
| model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); |
| static int32_t act_init[] = {0}; |
| model->setOperandValue(act, act_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {false}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_TRANSPOSE_CONV_2D, {op1, op2, op3, shape, param, param1, param2, act, layout}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_channelQuant8(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_dynamic_output_shape_quant8(Model *model) { |
| OperandType type0(Type::BOOL, {}); |
| OperandType type15(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.5f, 0); |
| OperandType type4(Type::TENSOR_INT32, {4}); |
| OperandType type5(Type::INT32, {}); |
| OperandType type7(Type::TENSOR_QUANT8_ASYMM, {25, 1, 1, 1}, 0.5f, 0); |
| OperandType type8(Type::TENSOR_QUANT8_ASYMM, {16, 1, 1, 1}, 0.5f, 0); |
| OperandType type9(Type::TENSOR_INT32, {16}, 0.25f, 0); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type7); |
| auto op2 = model->addOperand(&type8); |
| auto op3 = model->addOperand(&type9); |
| auto shape = model->addOperand(&type4); |
| auto param = model->addOperand(&type5); |
| auto param1 = model->addOperand(&type5); |
| auto param2 = model->addOperand(&type5); |
| auto act = model->addOperand(&type5); |
| auto layout = model->addOperand(&type0); |
| auto op4 = model->addOperand(&type15); |
| // Phase 2, operations |
| static uint8_t op2_init[] = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2}; |
| model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); |
| static int32_t op3_init[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; |
| model->setOperandValue(op3, op3_init, sizeof(int32_t) * 16); |
| static int32_t shape_init[] = {25, 32, 32, 16}; |
| model->setOperandValue(shape, shape_init, sizeof(int32_t) * 4); |
| static int32_t param_init[] = {1}; |
| model->setOperandValue(param, param_init, sizeof(int32_t) * 1); |
| static int32_t param1_init[] = {32}; |
| model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); |
| static int32_t param2_init[] = {32}; |
| model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); |
| static int32_t act_init[] = {0}; |
| model->setOperandValue(act, act_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {false}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_TRANSPOSE_CONV_2D, {op1, op2, op3, shape, param, param1, param2, act, layout}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_dynamic_output_shape_quant8(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |
| void CreateModel_dynamic_output_shape_channelQuant8(Model *model) { |
| OperandType type0(Type::BOOL, {}); |
| OperandType type11(Type::TENSOR_QUANT8_ASYMM, {25, 1, 1, 1}, 0.25f, 100); |
| OperandType type12(Type::TENSOR_QUANT8_SYMM_PER_CHANNEL, {16, 1, 1, 1}, SymmPerChannelQuantParams({0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f},0)); |
| OperandType type13(Type::TENSOR_INT32, {16}, 0.0f, 0); |
| OperandType type16(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.5f, 80); |
| OperandType type4(Type::TENSOR_INT32, {4}); |
| OperandType type5(Type::INT32, {}); |
| // Phase 1, operands |
| auto op1 = model->addOperand(&type11); |
| auto op2 = model->addOperand(&type12); |
| auto op3 = model->addOperand(&type13); |
| auto shape = model->addOperand(&type4); |
| auto param = model->addOperand(&type5); |
| auto param1 = model->addOperand(&type5); |
| auto param2 = model->addOperand(&type5); |
| auto act = model->addOperand(&type5); |
| auto layout = model->addOperand(&type0); |
| auto op4 = model->addOperand(&type16); |
| // Phase 2, operations |
| static int8_t op2_init[] = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2}; |
| model->setOperandValue(op2, op2_init, sizeof(int8_t) * 16); |
| static int32_t op3_init[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; |
| model->setOperandValue(op3, op3_init, sizeof(int32_t) * 16); |
| static int32_t shape_init[] = {25, 32, 32, 16}; |
| model->setOperandValue(shape, shape_init, sizeof(int32_t) * 4); |
| static int32_t param_init[] = {1}; |
| model->setOperandValue(param, param_init, sizeof(int32_t) * 1); |
| static int32_t param1_init[] = {32}; |
| model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); |
| static int32_t param2_init[] = {32}; |
| model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); |
| static int32_t act_init[] = {0}; |
| model->setOperandValue(act, act_init, sizeof(int32_t) * 1); |
| static bool8 layout_init[] = {false}; |
| model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); |
| model->addOperation(ANEURALNETWORKS_TRANSPOSE_CONV_2D, {op1, op2, op3, shape, param, param1, param2, act, layout}, {op4}); |
| // Phase 3, inputs and outputs |
| model->identifyInputsAndOutputs( |
| {op1}, |
| {op4}); |
| assert(model->isValid()); |
| } |
| |
| inline bool is_ignored_dynamic_output_shape_channelQuant8(int i) { |
| static std::set<int> ignore = {}; |
| return ignore.find(i) != ignore.end(); |
| } |
| |