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Diffstat (limited to 'tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/stdout.txt.expect')
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1 files changed, 3688 insertions, 0 deletions
diff --git a/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/stdout.txt.expect b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/stdout.txt.expect new file mode 100644 index 000000000..87e74f722 --- /dev/null +++ b/tests/nnapi/nnapi_test_generator/android-q-beta/tests/P_variation/stdout.txt.expect @@ -0,0 +1,3688 @@ +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +// clang-format off +// Generated file (from: conv_float.mod.py). Do not edit +#include "../../TestGenerated.h" + +namespace conv_float { +// Generated conv_float test +#include "-" +// Generated model constructor +#include "-" +} // namespace conv_float + +void CreateModel_none(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_none(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none) { + execute(conv_float::CreateModel_none, + conv_float::is_ignored_none, + conv_float::examples_none); +} + +void CreateModel_none_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_none_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none_relaxed) { + execute(conv_float::CreateModel_none_relaxed, + conv_float::is_ignored_none_relaxed, + conv_float::examples_none_relaxed); +} + +void CreateModel_none_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_none_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none_quant8) { + execute(conv_float::CreateModel_none_quant8, + conv_float::is_ignored_none_quant8, + conv_float::examples_none_quant8); +} + +void CreateModel_none_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_none_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none_weight_as_input) { + execute(conv_float::CreateModel_none_weight_as_input, + conv_float::is_ignored_none_weight_as_input, + conv_float::examples_none_weight_as_input); +} + +void CreateModel_none_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_none_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none_weight_as_input_relaxed) { + execute(conv_float::CreateModel_none_weight_as_input_relaxed, + conv_float::is_ignored_none_weight_as_input_relaxed, + conv_float::examples_none_weight_as_input_relaxed); +} + +void CreateModel_none_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_none_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_none_weight_as_input_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_none_weight_as_input_quant8) { + execute(conv_float::CreateModel_none_weight_as_input_quant8, + conv_float::is_ignored_none_weight_as_input_quant8, + conv_float::examples_none_weight_as_input_quant8); +} + +void CreateModel_relu(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu) { + execute(conv_float::CreateModel_relu, + conv_float::is_ignored_relu, + conv_float::examples_relu); +} + +void CreateModel_relu_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_relaxed) { + execute(conv_float::CreateModel_relu_relaxed, + conv_float::is_ignored_relu_relaxed, + conv_float::examples_relu_relaxed); +} + +void CreateModel_relu_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_quant8) { + execute(conv_float::CreateModel_relu_quant8, + conv_float::is_ignored_relu_quant8, + conv_float::examples_relu_quant8); +} + +void CreateModel_relu_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_weight_as_input) { + execute(conv_float::CreateModel_relu_weight_as_input, + conv_float::is_ignored_relu_weight_as_input, + conv_float::examples_relu_weight_as_input); +} + +void CreateModel_relu_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 120.0f, 94.0f, 104.0f, 70.0f, 164.0f, 23.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_weight_as_input_relaxed) { + execute(conv_float::CreateModel_relu_weight_as_input_relaxed, + conv_float::is_ignored_relu_weight_as_input_relaxed, + conv_float::examples_relu_weight_as_input_relaxed); +} + +void CreateModel_relu_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu_weight_as_input_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 160, 147, 152, 135, 182, 112, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu_weight_as_input_quant8) { + execute(conv_float::CreateModel_relu_weight_as_input_quant8, + conv_float::is_ignored_relu_weight_as_input_quant8, + conv_float::examples_relu_weight_as_input_quant8); +} + +void CreateModel_relu1(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu1(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1) { + execute(conv_float::CreateModel_relu1, + conv_float::is_ignored_relu1, + conv_float::examples_relu1); +} + +void CreateModel_relu1_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu1_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1_relaxed) { + execute(conv_float::CreateModel_relu1_relaxed, + conv_float::is_ignored_relu1_relaxed, + conv_float::examples_relu1_relaxed); +} + +void CreateModel_relu1_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu1_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1_quant8) { + execute(conv_float::CreateModel_relu1_quant8, + conv_float::is_ignored_relu1_quant8, + conv_float::examples_relu1_quant8); +} + +void CreateModel_relu1_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu1_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1_weight_as_input) { + execute(conv_float::CreateModel_relu1_weight_as_input, + conv_float::is_ignored_relu1_weight_as_input, + conv_float::examples_relu1_weight_as_input); +} + +void CreateModel_relu1_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu1_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1_weight_as_input_relaxed) { + execute(conv_float::CreateModel_relu1_weight_as_input_relaxed, + conv_float::is_ignored_relu1_weight_as_input_relaxed, + conv_float::examples_relu1_weight_as_input_relaxed); +} + +void CreateModel_relu1_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu1_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu1_weight_as_input_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu1_weight_as_input_quant8) { + execute(conv_float::CreateModel_relu1_weight_as_input_quant8, + conv_float::is_ignored_relu1_weight_as_input_quant8, + conv_float::examples_relu1_weight_as_input_quant8); +} + +void CreateModel_relu6(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6) { + execute(conv_float::CreateModel_relu6, + conv_float::is_ignored_relu6, + conv_float::examples_relu6); +} + +void CreateModel_relu6_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu6_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_relaxed) { + execute(conv_float::CreateModel_relu6_relaxed, + conv_float::is_ignored_relu6_relaxed, + conv_float::examples_relu6_relaxed); +} + +void CreateModel_relu6_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 132, 134, 136, 138, 140, 142, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_quant8) { + execute(conv_float::CreateModel_relu6_quant8, + conv_float::is_ignored_relu6_quant8, + conv_float::examples_relu6_quant8); +} + +void CreateModel_relu6_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_weight_as_input) { + execute(conv_float::CreateModel_relu6_weight_as_input, + conv_float::is_ignored_relu6_weight_as_input, + conv_float::examples_relu6_weight_as_input); +} + +void CreateModel_relu6_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_relu6_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f}}, {1, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_weight_as_input_relaxed) { + execute(conv_float::CreateModel_relu6_weight_as_input_relaxed, + conv_float::is_ignored_relu6_weight_as_input_relaxed, + conv_float::examples_relu6_weight_as_input_relaxed); +} + +void CreateModel_relu6_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {0}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_relu6_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_relu6_weight_as_input_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 132, 134, 136, 138, 140, 142, 144}}, {1, {132, 136, 140, 144, 148, 152, 156, 160, 160, 156, 152, 148, 144, 140, 136, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_relu6_weight_as_input_quant8) { + execute(conv_float::CreateModel_relu6_weight_as_input_quant8, + conv_float::is_ignored_relu6_weight_as_input_quant8, + conv_float::examples_relu6_weight_as_input_quant8); +} + +void CreateModel_nchw_none(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_none(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none) { + execute(conv_float::CreateModel_nchw_none, + conv_float::is_ignored_nchw_none, + conv_float::examples_nchw_none); +} + +void CreateModel_nchw_none_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_none_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none_relaxed) { + execute(conv_float::CreateModel_nchw_none_relaxed, + conv_float::is_ignored_nchw_none_relaxed, + conv_float::examples_nchw_none_relaxed); +} + +void CreateModel_nchw_none_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_none_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none_quant8) { + execute(conv_float::CreateModel_nchw_none_quant8, + conv_float::is_ignored_nchw_none_quant8, + conv_float::examples_nchw_none_quant8); +} + +void CreateModel_nchw_none_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_none_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none_weight_as_input) { + execute(conv_float::CreateModel_nchw_none_weight_as_input, + conv_float::is_ignored_nchw_none_weight_as_input, + conv_float::examples_nchw_none_weight_as_input); +} + +void CreateModel_nchw_none_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_none_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none_weight_as_input_relaxed) { + execute(conv_float::CreateModel_nchw_none_weight_as_input_relaxed, + conv_float::is_ignored_nchw_none_weight_as_input_relaxed, + conv_float::examples_nchw_none_weight_as_input_relaxed); +} + +void CreateModel_nchw_none_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + 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 int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_none_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_none_weight_as_input_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_none_weight_as_input_quant8) { + execute(conv_float::CreateModel_nchw_none_weight_as_input_quant8, + conv_float::is_ignored_nchw_none_weight_as_input_quant8, + conv_float::examples_nchw_none_weight_as_input_quant8); +} + +void CreateModel_nchw_relu(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu) { + execute(conv_float::CreateModel_nchw_relu, + conv_float::is_ignored_nchw_relu, + conv_float::examples_nchw_relu); +} + +void CreateModel_nchw_relu_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_relaxed) { + execute(conv_float::CreateModel_nchw_relu_relaxed, + conv_float::is_ignored_nchw_relu_relaxed, + conv_float::examples_nchw_relu_relaxed); +} + +void CreateModel_nchw_relu_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_quant8) { + execute(conv_float::CreateModel_nchw_relu_quant8, + conv_float::is_ignored_nchw_relu_quant8, + conv_float::examples_nchw_relu_quant8); +} + +void CreateModel_nchw_relu_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_weight_as_input) { + execute(conv_float::CreateModel_nchw_relu_weight_as_input, + conv_float::is_ignored_nchw_relu_weight_as_input, + conv_float::examples_nchw_relu_weight_as_input); +} + +void CreateModel_nchw_relu_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {204.0f, 94.0f, 70.0f, 23.0f, 120.0f, 104.0f, 164.0f, 112.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_weight_as_input_relaxed) { + execute(conv_float::CreateModel_nchw_relu_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu_weight_as_input_relaxed, + conv_float::examples_nchw_relu_weight_as_input_relaxed); +} + +void CreateModel_nchw_relu_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {1}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu_weight_as_input_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {202, 147, 135, 112, 160, 152, 182, 156}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu_weight_as_input_quant8) { + execute(conv_float::CreateModel_nchw_relu_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu_weight_as_input_quant8, + conv_float::examples_nchw_relu_weight_as_input_quant8); +} + +void CreateModel_nchw_relu1(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1) { + execute(conv_float::CreateModel_nchw_relu1, + conv_float::is_ignored_nchw_relu1, + conv_float::examples_nchw_relu1); +} + +void CreateModel_nchw_relu1_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1_relaxed) { + execute(conv_float::CreateModel_nchw_relu1_relaxed, + conv_float::is_ignored_nchw_relu1_relaxed, + conv_float::examples_nchw_relu1_relaxed); +} + +void CreateModel_nchw_relu1_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1_quant8) { + execute(conv_float::CreateModel_nchw_relu1_quant8, + conv_float::is_ignored_nchw_relu1_quant8, + conv_float::examples_nchw_relu1_quant8); +} + +void CreateModel_nchw_relu1_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1_weight_as_input) { + execute(conv_float::CreateModel_nchw_relu1_weight_as_input, + conv_float::is_ignored_nchw_relu1_weight_as_input, + conv_float::examples_nchw_relu1_weight_as_input); +} + +void CreateModel_nchw_relu1_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1_weight_as_input_relaxed) { + execute(conv_float::CreateModel_nchw_relu1_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu1_weight_as_input_relaxed, + conv_float::examples_nchw_relu1_weight_as_input_relaxed); +} + +void CreateModel_nchw_relu1_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {2}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu1_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu1_weight_as_input_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {100, 100, 100, 100, 100, 100, 100, 100}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu1_weight_as_input_quant8) { + execute(conv_float::CreateModel_nchw_relu1_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu1_weight_as_input_quant8, + conv_float::examples_nchw_relu1_weight_as_input_quant8); +} + +void CreateModel_nchw_relu6(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6) { + execute(conv_float::CreateModel_nchw_relu6, + conv_float::is_ignored_nchw_relu6, + conv_float::examples_nchw_relu6); +} + +void CreateModel_nchw_relu6_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op2_init[] = {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}; + model->setOperandValue(op2, op2_init, sizeof(float) * 16); + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_relaxed) { + execute(conv_float::CreateModel_nchw_relu6_relaxed, + conv_float::is_ignored_nchw_relu6_relaxed, + conv_float::examples_nchw_relu6_relaxed); +} + +void CreateModel_nchw_relu6_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static uint8_t op2_init[] = {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}; + model->setOperandValue(op2, op2_init, sizeof(uint8_t) * 16); + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 134, 138, 142, 132, 136, 140, 144}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_quant8) { + execute(conv_float::CreateModel_nchw_relu6_quant8, + conv_float::is_ignored_nchw_relu6_quant8, + conv_float::examples_nchw_relu6_quant8); +} + +void CreateModel_nchw_relu6_weight_as_input(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_weight_as_input(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_weight_as_input) { + execute(conv_float::CreateModel_nchw_relu6_weight_as_input, + conv_float::is_ignored_nchw_relu6_weight_as_input, + conv_float::examples_nchw_relu6_weight_as_input); +} + +void CreateModel_nchw_relu6_weight_as_input_relaxed(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type0); + auto op2 = model->addOperand(&type1); + auto op3 = model->addOperand(&type2); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type0); + // Phase 2, operations + static float op3_init[] = {-200.0f}; + model->setOperandValue(op3, op3_init, sizeof(float) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + // Phase 4: set relaxed execution + model->relaxComputationFloat32toFloat16(true); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_weight_as_input_relaxed(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input_relaxed = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f}}, {1, {1.0f, 3.0f, 5.0f, 7.0f, 2.0f, 4.0f, 6.0f, 8.0f, 8.0f, 6.0f, 4.0f, 2.0f, 7.0f, 5.0f, 3.0f, 1.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {{0, {6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}}}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_weight_as_input_relaxed) { + execute(conv_float::CreateModel_nchw_relu6_weight_as_input_relaxed, + conv_float::is_ignored_nchw_relu6_weight_as_input_relaxed, + conv_float::examples_nchw_relu6_weight_as_input_relaxed); +} + +void CreateModel_nchw_relu6_weight_as_input_quant8(Model *model) { + OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 2}); + OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2}); + OperandType type2(Type::TENSOR_FLOAT32, {1}); + OperandType type3(Type::INT32, {}); + OperandType type4(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 0.5f, 128); + OperandType type5(Type::TENSOR_QUANT8_ASYMM, {2, 2, 2, 2}, 0.25f, 128); + OperandType type6(Type::TENSOR_INT32, {1}, 0.125f, 0); + OperandType type7(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 2}, 2.0f, 100); + // Phase 1, operands + auto op1 = model->addOperand(&type4); + auto op2 = model->addOperand(&type5); + auto op3 = model->addOperand(&type6); + auto param = model->addOperand(&type3); + auto param1 = model->addOperand(&type3); + auto param2 = model->addOperand(&type3); + auto act = model->addOperand(&type3); + auto layout = model->addOperand(&type3); + auto op4 = model->addOperand(&type7); + // Phase 2, operations + static int32_t op3_init[] = {-1600}; + model->setOperandValue(op3, op3_init, sizeof(int32_t) * 1); + static int32_t param_init[] = {1}; + model->setOperandValue(param, param_init, sizeof(int32_t) * 1); + static int32_t param1_init[] = {1}; + model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1); + static int32_t param2_init[] = {1}; + model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); + static int32_t act_init[] = {3}; + model->setOperandValue(act, act_init, sizeof(int32_t) * 1); + static int32_t layout_init[] = {1}; + model->setOperandValue(layout, layout_init, sizeof(int32_t) * 1); + model->addOperation(ANEURALNETWORKS_CONV_2D, {op1, op2, op3, param, param1, param2, act, layout}, {op4}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {op1, op2}, + {op4}); + assert(model->isValid()); +} + +bool is_ignored_nchw_relu6_weight_as_input_quant8(int i) { + static std::set<int> ignore = {}; + return ignore.find(i) != ignore.end(); +} + +std::vector<MixedTypedExample> examples_nchw_relu6_weight_as_input_quant8 = { +// Begin of an example +{ +//Input(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {130, 134, 138, 142, 132, 136, 140, 144}}, {1, {132, 140, 148, 156, 136, 144, 152, 160, 160, 152, 144, 136, 156, 148, 140, 132}}} +}, +//Output(s) +{ // See tools/test_generator/include/TestHarness.h:MixedTyped + // int -> FLOAT32 map + {}, + // int -> INT32 map + {}, + // int -> QUANT8_ASYMM map + {{0, {103, 103, 103, 103, 103, 103, 103, 103}}} +} +}, // End of an example +}; + +TEST_F(GeneratedTests, conv_float_nchw_relu6_weight_as_input_quant8) { + execute(conv_float::CreateModel_nchw_relu6_weight_as_input_quant8, + conv_float::is_ignored_nchw_relu6_weight_as_input_quant8, + conv_float::examples_nchw_relu6_weight_as_input_quant8); +} + +#include "../generated/tests/conv_float.mod.py.cpp" |