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android / platform / packages / modules / NeuralNetworks / c49850065cf80a64f1a99307fdd68f1301fe2582 / . / runtime / test / generated / spec_V1_2 / cts / quantize.model.cpp
blob: e1784d0629b72aa6788a5c55616711ae86063d6c [file] [log] [blame]
// Generated from quantize.mod.py
// DO NOT EDIT
// clang-format off
#include "TestGenerated.h"
namespace generated_tests::quantize {
void CreateModel_quant8(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type14(Type::TENSOR_QUANT8_ASYMM, {300}, 1.0f, 0);
// Phase 1, operands
auto input0 = model->addOperand(&type0);
auto output0 = model->addOperand(&type14);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input0}, {output0});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input0},
{output0});
assert(model->isValid());
}
bool is_ignored_quant8(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_dynamic_output_shape(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type15(Type::TENSOR_QUANT8_ASYMM, {0}, 1.0f, 0);
// Phase 1, operands
auto input0 = model->addOperand(&type0);
auto output0 = model->addOperand(&type15);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input0}, {output0});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input0},
{output0});
assert(model->isValid());
}
bool is_ignored_quant8_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type14(Type::TENSOR_QUANT8_ASYMM, {300}, 1.0f, 0);
OperandType type16(Type::TENSOR_FLOAT32, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input0 = model->addOperand(&type0);
auto output0 = model->addOperand(&type14);
auto input0_tmp = model->addOperand(&type0);
auto dummy = model->addOperand(&type16);
auto param13 = model->addOperand(&type9);
// Phase 2, operations
static float dummy_init[] = {0.0f};
model->setOperandValue(dummy, dummy_init, sizeof(float) * 1);
static int32_t param13_init[] = {0};
model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy, param13}, {input0});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input0}, {output0});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input0_tmp},
{output0});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_dynamic_output_shape(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type15(Type::TENSOR_QUANT8_ASYMM, {0}, 1.0f, 0);
OperandType type16(Type::TENSOR_FLOAT32, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input0 = model->addOperand(&type0);
auto output0 = model->addOperand(&type15);
auto input0_tmp = model->addOperand(&type0);
auto dummy1 = model->addOperand(&type16);
auto param14 = model->addOperand(&type9);
// Phase 2, operations
static float dummy1_init[] = {0.0f};
model->setOperandValue(dummy1, dummy1_init, sizeof(float) * 1);
static int32_t param14_init[] = {0};
model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy1, param14}, {input0});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input0}, {output0});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input0_tmp},
{output0});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_2(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type17(Type::TENSOR_QUANT8_ASYMM, {300}, 1.0f, 1);
// Phase 1, operands
auto input01 = model->addOperand(&type0);
auto output01 = model->addOperand(&type17);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input01}, {output01});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input01},
{output01});
assert(model->isValid());
}
bool is_ignored_quant8_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type18(Type::TENSOR_QUANT8_ASYMM, {0}, 1.0f, 1);
// Phase 1, operands
auto input01 = model->addOperand(&type0);
auto output01 = model->addOperand(&type18);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input01}, {output01});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input01},
{output01});
assert(model->isValid());
}
bool is_ignored_quant8_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_2(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type16(Type::TENSOR_FLOAT32, {1});
OperandType type17(Type::TENSOR_QUANT8_ASYMM, {300}, 1.0f, 1);
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input01 = model->addOperand(&type0);
auto output01 = model->addOperand(&type17);
auto input01_tmp = model->addOperand(&type0);
auto dummy2 = model->addOperand(&type16);
auto param15 = model->addOperand(&type9);
// Phase 2, operations
static float dummy2_init[] = {0.0f};
model->setOperandValue(dummy2, dummy2_init, sizeof(float) * 1);
static int32_t param15_init[] = {0};
model->setOperandValue(param15, param15_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input01_tmp, dummy2, param15}, {input01});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input01}, {output01});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input01_tmp},
{output01});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type16(Type::TENSOR_FLOAT32, {1});
OperandType type18(Type::TENSOR_QUANT8_ASYMM, {0}, 1.0f, 1);
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input01 = model->addOperand(&type0);
auto output01 = model->addOperand(&type18);
auto input01_tmp = model->addOperand(&type0);
auto dummy3 = model->addOperand(&type16);
auto param16 = model->addOperand(&type9);
// Phase 2, operations
static float dummy3_init[] = {0.0f};
model->setOperandValue(dummy3, dummy3_init, sizeof(float) * 1);
static int32_t param16_init[] = {0};
model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input01_tmp, dummy3, param16}, {input01});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input01}, {output01});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input01_tmp},
{output01});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_3(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type19(Type::TENSOR_QUANT8_ASYMM, {300}, 0.01f, 120);
// Phase 1, operands
auto input02 = model->addOperand(&type0);
auto output02 = model->addOperand(&type19);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input02}, {output02});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input02},
{output02});
assert(model->isValid());
}
bool is_ignored_quant8_3(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_dynamic_output_shape_3(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type20(Type::TENSOR_QUANT8_ASYMM, {0}, 0.01f, 120);
// Phase 1, operands
auto input02 = model->addOperand(&type0);
auto output02 = model->addOperand(&type20);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input02}, {output02});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input02},
{output02});
assert(model->isValid());
}
bool is_ignored_quant8_dynamic_output_shape_3(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_3(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type16(Type::TENSOR_FLOAT32, {1});
OperandType type19(Type::TENSOR_QUANT8_ASYMM, {300}, 0.01f, 120);
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input02 = model->addOperand(&type0);
auto output02 = model->addOperand(&type19);
auto input02_tmp = model->addOperand(&type0);
auto dummy4 = model->addOperand(&type16);
auto param17 = model->addOperand(&type9);
// Phase 2, operations
static float dummy4_init[] = {0.0f};
model->setOperandValue(dummy4, dummy4_init, sizeof(float) * 1);
static int32_t param17_init[] = {0};
model->setOperandValue(param17, param17_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input02_tmp, dummy4, param17}, {input02});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input02}, {output02});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input02_tmp},
{output02});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_3(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_dynamic_output_shape_3(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type16(Type::TENSOR_FLOAT32, {1});
OperandType type20(Type::TENSOR_QUANT8_ASYMM, {0}, 0.01f, 120);
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input02 = model->addOperand(&type0);
auto output02 = model->addOperand(&type20);
auto input02_tmp = model->addOperand(&type0);
auto dummy5 = model->addOperand(&type16);
auto param18 = model->addOperand(&type9);
// Phase 2, operations
static float dummy5_init[] = {0.0f};
model->setOperandValue(dummy5, dummy5_init, sizeof(float) * 1);
static int32_t param18_init[] = {0};
model->setOperandValue(param18, param18_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input02_tmp, dummy5, param18}, {input02});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input02}, {output02});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input02_tmp},
{output02});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_dynamic_output_shape_3(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_4(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type21(Type::TENSOR_QUANT8_ASYMM, {300}, 10.0f, 120);
// Phase 1, operands
auto input03 = model->addOperand(&type0);
auto output03 = model->addOperand(&type21);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input03}, {output03});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input03},
{output03});
assert(model->isValid());
}
bool is_ignored_quant8_4(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_dynamic_output_shape_4(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type22(Type::TENSOR_QUANT8_ASYMM, {0}, 10.0f, 120);
// Phase 1, operands
auto input03 = model->addOperand(&type0);
auto output03 = model->addOperand(&type22);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input03}, {output03});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input03},
{output03});
assert(model->isValid());
}
bool is_ignored_quant8_dynamic_output_shape_4(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_4(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type16(Type::TENSOR_FLOAT32, {1});
OperandType type21(Type::TENSOR_QUANT8_ASYMM, {300}, 10.0f, 120);
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input03 = model->addOperand(&type0);
auto output03 = model->addOperand(&type21);
auto input03_tmp = model->addOperand(&type0);
auto dummy6 = model->addOperand(&type16);
auto param19 = model->addOperand(&type9);
// Phase 2, operations
static float dummy6_init[] = {0.0f};
model->setOperandValue(dummy6, dummy6_init, sizeof(float) * 1);
static int32_t param19_init[] = {0};
model->setOperandValue(param19, param19_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input03_tmp, dummy6, param19}, {input03});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input03}, {output03});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input03_tmp},
{output03});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_4(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_dynamic_output_shape_4(Model *model) {
OperandType type0(Type::TENSOR_FLOAT32, {300});
OperandType type16(Type::TENSOR_FLOAT32, {1});
OperandType type22(Type::TENSOR_QUANT8_ASYMM, {0}, 10.0f, 120);
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input03 = model->addOperand(&type0);
auto output03 = model->addOperand(&type22);
auto input03_tmp = model->addOperand(&type0);
auto dummy7 = model->addOperand(&type16);
auto param20 = model->addOperand(&type9);
// Phase 2, operations
static float dummy7_init[] = {0.0f};
model->setOperandValue(dummy7, dummy7_init, sizeof(float) * 1);
static int32_t param20_init[] = {0};
model->setOperandValue(param20, param20_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input03_tmp, dummy7, param20}, {input03});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input03}, {output03});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input03_tmp},
{output03});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_dynamic_output_shape_4(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_5(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type14(Type::TENSOR_QUANT8_ASYMM, {300}, 1.0f, 0);
// Phase 1, operands
auto input04 = model->addOperand(&type1);
auto output04 = model->addOperand(&type14);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input04}, {output04});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input04},
{output04});
assert(model->isValid());
}
bool is_ignored_quant8_5(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_dynamic_output_shape_5(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type15(Type::TENSOR_QUANT8_ASYMM, {0}, 1.0f, 0);
// Phase 1, operands
auto input04 = model->addOperand(&type1);
auto output04 = model->addOperand(&type15);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input04}, {output04});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input04},
{output04});
assert(model->isValid());
}
bool is_ignored_quant8_dynamic_output_shape_5(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_5(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type14(Type::TENSOR_QUANT8_ASYMM, {300}, 1.0f, 0);
OperandType type23(Type::TENSOR_FLOAT16, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input04 = model->addOperand(&type1);
auto output04 = model->addOperand(&type14);
auto input04_tmp = model->addOperand(&type1);
auto dummy8 = model->addOperand(&type23);
auto param21 = model->addOperand(&type9);
// Phase 2, operations
static _Float16 dummy8_init[] = {0.0f};
model->setOperandValue(dummy8, dummy8_init, sizeof(_Float16) * 1);
static int32_t param21_init[] = {0};
model->setOperandValue(param21, param21_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input04_tmp, dummy8, param21}, {input04});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input04}, {output04});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input04_tmp},
{output04});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_5(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_dynamic_output_shape_5(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type15(Type::TENSOR_QUANT8_ASYMM, {0}, 1.0f, 0);
OperandType type23(Type::TENSOR_FLOAT16, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input04 = model->addOperand(&type1);
auto output04 = model->addOperand(&type15);
auto input04_tmp = model->addOperand(&type1);
auto dummy9 = model->addOperand(&type23);
auto param22 = model->addOperand(&type9);
// Phase 2, operations
static _Float16 dummy9_init[] = {0.0f};
model->setOperandValue(dummy9, dummy9_init, sizeof(_Float16) * 1);
static int32_t param22_init[] = {0};
model->setOperandValue(param22, param22_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input04_tmp, dummy9, param22}, {input04});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input04}, {output04});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input04_tmp},
{output04});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_dynamic_output_shape_5(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_6(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type17(Type::TENSOR_QUANT8_ASYMM, {300}, 1.0f, 1);
// Phase 1, operands
auto input05 = model->addOperand(&type1);
auto output05 = model->addOperand(&type17);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input05}, {output05});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input05},
{output05});
assert(model->isValid());
}
bool is_ignored_quant8_6(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_dynamic_output_shape_6(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type18(Type::TENSOR_QUANT8_ASYMM, {0}, 1.0f, 1);
// Phase 1, operands
auto input05 = model->addOperand(&type1);
auto output05 = model->addOperand(&type18);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input05}, {output05});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input05},
{output05});
assert(model->isValid());
}
bool is_ignored_quant8_dynamic_output_shape_6(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_6(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type17(Type::TENSOR_QUANT8_ASYMM, {300}, 1.0f, 1);
OperandType type23(Type::TENSOR_FLOAT16, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input05 = model->addOperand(&type1);
auto output05 = model->addOperand(&type17);
auto input05_tmp = model->addOperand(&type1);
auto dummy10 = model->addOperand(&type23);
auto param23 = model->addOperand(&type9);
// Phase 2, operations
static _Float16 dummy10_init[] = {0.0f};
model->setOperandValue(dummy10, dummy10_init, sizeof(_Float16) * 1);
static int32_t param23_init[] = {0};
model->setOperandValue(param23, param23_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input05_tmp, dummy10, param23}, {input05});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input05}, {output05});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input05_tmp},
{output05});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_6(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_dynamic_output_shape_6(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type18(Type::TENSOR_QUANT8_ASYMM, {0}, 1.0f, 1);
OperandType type23(Type::TENSOR_FLOAT16, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input05 = model->addOperand(&type1);
auto output05 = model->addOperand(&type18);
auto input05_tmp = model->addOperand(&type1);
auto dummy11 = model->addOperand(&type23);
auto param24 = model->addOperand(&type9);
// Phase 2, operations
static _Float16 dummy11_init[] = {0.0f};
model->setOperandValue(dummy11, dummy11_init, sizeof(_Float16) * 1);
static int32_t param24_init[] = {0};
model->setOperandValue(param24, param24_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input05_tmp, dummy11, param24}, {input05});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input05}, {output05});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input05_tmp},
{output05});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_dynamic_output_shape_6(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_7(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type19(Type::TENSOR_QUANT8_ASYMM, {300}, 0.01f, 120);
// Phase 1, operands
auto input06 = model->addOperand(&type1);
auto output06 = model->addOperand(&type19);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input06}, {output06});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input06},
{output06});
assert(model->isValid());
}
bool is_ignored_quant8_7(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_dynamic_output_shape_7(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type20(Type::TENSOR_QUANT8_ASYMM, {0}, 0.01f, 120);
// Phase 1, operands
auto input06 = model->addOperand(&type1);
auto output06 = model->addOperand(&type20);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input06}, {output06});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input06},
{output06});
assert(model->isValid());
}
bool is_ignored_quant8_dynamic_output_shape_7(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_7(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type19(Type::TENSOR_QUANT8_ASYMM, {300}, 0.01f, 120);
OperandType type23(Type::TENSOR_FLOAT16, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input06 = model->addOperand(&type1);
auto output06 = model->addOperand(&type19);
auto input06_tmp = model->addOperand(&type1);
auto dummy12 = model->addOperand(&type23);
auto param25 = model->addOperand(&type9);
// Phase 2, operations
static _Float16 dummy12_init[] = {0.0f};
model->setOperandValue(dummy12, dummy12_init, sizeof(_Float16) * 1);
static int32_t param25_init[] = {0};
model->setOperandValue(param25, param25_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input06_tmp, dummy12, param25}, {input06});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input06}, {output06});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input06_tmp},
{output06});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_7(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_dynamic_output_shape_7(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type20(Type::TENSOR_QUANT8_ASYMM, {0}, 0.01f, 120);
OperandType type23(Type::TENSOR_FLOAT16, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input06 = model->addOperand(&type1);
auto output06 = model->addOperand(&type20);
auto input06_tmp = model->addOperand(&type1);
auto dummy13 = model->addOperand(&type23);
auto param26 = model->addOperand(&type9);
// Phase 2, operations
static _Float16 dummy13_init[] = {0.0f};
model->setOperandValue(dummy13, dummy13_init, sizeof(_Float16) * 1);
static int32_t param26_init[] = {0};
model->setOperandValue(param26, param26_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input06_tmp, dummy13, param26}, {input06});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input06}, {output06});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input06_tmp},
{output06});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_dynamic_output_shape_7(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_8(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type21(Type::TENSOR_QUANT8_ASYMM, {300}, 10.0f, 120);
// Phase 1, operands
auto input07 = model->addOperand(&type1);
auto output07 = model->addOperand(&type21);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input07}, {output07});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input07},
{output07});
assert(model->isValid());
}
bool is_ignored_quant8_8(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_dynamic_output_shape_8(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type22(Type::TENSOR_QUANT8_ASYMM, {0}, 10.0f, 120);
// Phase 1, operands
auto input07 = model->addOperand(&type1);
auto output07 = model->addOperand(&type22);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input07}, {output07});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input07},
{output07});
assert(model->isValid());
}
bool is_ignored_quant8_dynamic_output_shape_8(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_8(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type21(Type::TENSOR_QUANT8_ASYMM, {300}, 10.0f, 120);
OperandType type23(Type::TENSOR_FLOAT16, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input07 = model->addOperand(&type1);
auto output07 = model->addOperand(&type21);
auto input07_tmp = model->addOperand(&type1);
auto dummy14 = model->addOperand(&type23);
auto param27 = model->addOperand(&type9);
// Phase 2, operations
static _Float16 dummy14_init[] = {0.0f};
model->setOperandValue(dummy14, dummy14_init, sizeof(_Float16) * 1);
static int32_t param27_init[] = {0};
model->setOperandValue(param27, param27_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input07_tmp, dummy14, param27}, {input07});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input07}, {output07});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input07_tmp},
{output07});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_8(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_quant8_all_inputs_as_internal_dynamic_output_shape_8(Model *model) {
OperandType type1(Type::TENSOR_FLOAT16, {300});
OperandType type22(Type::TENSOR_QUANT8_ASYMM, {0}, 10.0f, 120);
OperandType type23(Type::TENSOR_FLOAT16, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto input07 = model->addOperand(&type1);
auto output07 = model->addOperand(&type22);
auto input07_tmp = model->addOperand(&type1);
auto dummy15 = model->addOperand(&type23);
auto param28 = model->addOperand(&type9);
// Phase 2, operations
static _Float16 dummy15_init[] = {0.0f};
model->setOperandValue(dummy15, dummy15_init, sizeof(_Float16) * 1);
static int32_t param28_init[] = {0};
model->setOperandValue(param28, param28_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input07_tmp, dummy15, param28}, {input07});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {input07}, {output07});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input07_tmp},
{output07});
assert(model->isValid());
}
bool is_ignored_quant8_all_inputs_as_internal_dynamic_output_shape_8(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_zero_sized(Model *model) {
OperandType type10(Type::BOOL, {});
OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 1});
OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 1});
OperandType type13(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 1}, 0.1f, 128);
OperandType type2(Type::TENSOR_FLOAT32, {1, 2});
OperandType type3(Type::TENSOR_FLOAT32, {1, 8});
OperandType type4(Type::TENSOR_FLOAT32, {0});
OperandType type5(Type::TENSOR_INT32, {0});
OperandType type6(Type::TENSOR_FLOAT32, {0, 4});
OperandType type7(Type::TENSOR_INT32, {1});
OperandType type8(Type::FLOAT32, {});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto scores = model->addOperand(&type2);
auto roi = model->addOperand(&type3);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type8);
auto param2 = model->addOperand(&type9);
auto param3 = model->addOperand(&type9);
auto param4 = model->addOperand(&type8);
auto param5 = model->addOperand(&type8);
auto param6 = model->addOperand(&type8);
auto scoresOut = model->addOperand(&type4);
auto roiOut = model->addOperand(&type6);
auto classesOut = model->addOperand(&type5);
auto batchSplitOut = model->addOperand(&type5);
auto in = model->addOperand(&type11);
auto param7 = model->addOperand(&type9);
auto param8 = model->addOperand(&type9);
auto param9 = model->addOperand(&type8);
auto param10 = model->addOperand(&type8);
auto param11 = model->addOperand(&type9);
auto param12 = model->addOperand(&type9);
auto layout = model->addOperand(&type10);
auto featureMap = model->addOperand(&type12);
auto out = model->addOperand(&type13);
// Phase 2, operations
static float scores_init[] = {0.9f, 0.1f};
model->setOperandValue(scores, scores_init, sizeof(float) * 2);
static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
model->setOperandValue(roi, roi_init, sizeof(float) * 8);
static int32_t param_init[] = {0};
model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
static float param1_init[] = {0.3f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static int32_t param2_init[] = {-1};
model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
static int32_t param3_init[] = {0};
model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
static float param4_init[] = {0.4f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {1.0f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static float param6_init[] = {0.3f};
model->setOperandValue(param6, param6_init, sizeof(float) * 1);
static int32_t param7_init[] = {2};
model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1);
static int32_t param8_init[] = {2};
model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
static float param9_init[] = {2.0f};
model->setOperandValue(param9, param9_init, sizeof(float) * 1);
static float param10_init[] = {2.0f};
model->setOperandValue(param10, param10_init, sizeof(float) * 1);
static int32_t param11_init[] = {4};
model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1);
static int32_t param12_init[] = {4};
model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut});
model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {featureMap}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{scoresOut, classesOut, out});
assert(model->isValid());
}
bool is_ignored_zero_sized(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_zero_sized_dynamic_output_shape(Model *model) {
OperandType type10(Type::BOOL, {});
OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 1});
OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 1});
OperandType type2(Type::TENSOR_FLOAT32, {1, 2});
OperandType type24(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.1f, 128);
OperandType type3(Type::TENSOR_FLOAT32, {1, 8});
OperandType type4(Type::TENSOR_FLOAT32, {0});
OperandType type5(Type::TENSOR_INT32, {0});
OperandType type6(Type::TENSOR_FLOAT32, {0, 4});
OperandType type7(Type::TENSOR_INT32, {1});
OperandType type8(Type::FLOAT32, {});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto scores = model->addOperand(&type2);
auto roi = model->addOperand(&type3);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type8);
auto param2 = model->addOperand(&type9);
auto param3 = model->addOperand(&type9);
auto param4 = model->addOperand(&type8);
auto param5 = model->addOperand(&type8);
auto param6 = model->addOperand(&type8);
auto scoresOut = model->addOperand(&type4);
auto roiOut = model->addOperand(&type6);
auto classesOut = model->addOperand(&type5);
auto batchSplitOut = model->addOperand(&type5);
auto in = model->addOperand(&type11);
auto param7 = model->addOperand(&type9);
auto param8 = model->addOperand(&type9);
auto param9 = model->addOperand(&type8);
auto param10 = model->addOperand(&type8);
auto param11 = model->addOperand(&type9);
auto param12 = model->addOperand(&type9);
auto layout = model->addOperand(&type10);
auto featureMap = model->addOperand(&type12);
auto out = model->addOperand(&type24);
// Phase 2, operations
static float scores_init[] = {0.9f, 0.1f};
model->setOperandValue(scores, scores_init, sizeof(float) * 2);
static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
model->setOperandValue(roi, roi_init, sizeof(float) * 8);
static int32_t param_init[] = {0};
model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
static float param1_init[] = {0.3f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static int32_t param2_init[] = {-1};
model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
static int32_t param3_init[] = {0};
model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
static float param4_init[] = {0.4f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {1.0f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static float param6_init[] = {0.3f};
model->setOperandValue(param6, param6_init, sizeof(float) * 1);
static int32_t param7_init[] = {2};
model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1);
static int32_t param8_init[] = {2};
model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
static float param9_init[] = {2.0f};
model->setOperandValue(param9, param9_init, sizeof(float) * 1);
static float param10_init[] = {2.0f};
model->setOperandValue(param10, param10_init, sizeof(float) * 1);
static int32_t param11_init[] = {4};
model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1);
static int32_t param12_init[] = {4};
model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut});
model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {featureMap}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{scoresOut, classesOut, out});
assert(model->isValid());
}
bool is_ignored_zero_sized_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_zero_sized_relaxed(Model *model) {
OperandType type10(Type::BOOL, {});
OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 1});
OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 1});
OperandType type13(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 1}, 0.1f, 128);
OperandType type2(Type::TENSOR_FLOAT32, {1, 2});
OperandType type3(Type::TENSOR_FLOAT32, {1, 8});
OperandType type4(Type::TENSOR_FLOAT32, {0});
OperandType type5(Type::TENSOR_INT32, {0});
OperandType type6(Type::TENSOR_FLOAT32, {0, 4});
OperandType type7(Type::TENSOR_INT32, {1});
OperandType type8(Type::FLOAT32, {});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto scores = model->addOperand(&type2);
auto roi = model->addOperand(&type3);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type8);
auto param2 = model->addOperand(&type9);
auto param3 = model->addOperand(&type9);
auto param4 = model->addOperand(&type8);
auto param5 = model->addOperand(&type8);
auto param6 = model->addOperand(&type8);
auto scoresOut = model->addOperand(&type4);
auto roiOut = model->addOperand(&type6);
auto classesOut = model->addOperand(&type5);
auto batchSplitOut = model->addOperand(&type5);
auto in = model->addOperand(&type11);
auto param7 = model->addOperand(&type9);
auto param8 = model->addOperand(&type9);
auto param9 = model->addOperand(&type8);
auto param10 = model->addOperand(&type8);
auto param11 = model->addOperand(&type9);
auto param12 = model->addOperand(&type9);
auto layout = model->addOperand(&type10);
auto featureMap = model->addOperand(&type12);
auto out = model->addOperand(&type13);
// Phase 2, operations
static float scores_init[] = {0.9f, 0.1f};
model->setOperandValue(scores, scores_init, sizeof(float) * 2);
static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
model->setOperandValue(roi, roi_init, sizeof(float) * 8);
static int32_t param_init[] = {0};
model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
static float param1_init[] = {0.3f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static int32_t param2_init[] = {-1};
model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
static int32_t param3_init[] = {0};
model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
static float param4_init[] = {0.4f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {1.0f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static float param6_init[] = {0.3f};
model->setOperandValue(param6, param6_init, sizeof(float) * 1);
static int32_t param7_init[] = {2};
model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1);
static int32_t param8_init[] = {2};
model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
static float param9_init[] = {2.0f};
model->setOperandValue(param9, param9_init, sizeof(float) * 1);
static float param10_init[] = {2.0f};
model->setOperandValue(param10, param10_init, sizeof(float) * 1);
static int32_t param11_init[] = {4};
model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1);
static int32_t param12_init[] = {4};
model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut});
model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {featureMap}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{scoresOut, classesOut, out});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_zero_sized_relaxed(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_zero_sized_relaxed_dynamic_output_shape(Model *model) {
OperandType type10(Type::BOOL, {});
OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 1});
OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 1});
OperandType type2(Type::TENSOR_FLOAT32, {1, 2});
OperandType type24(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.1f, 128);
OperandType type3(Type::TENSOR_FLOAT32, {1, 8});
OperandType type4(Type::TENSOR_FLOAT32, {0});
OperandType type5(Type::TENSOR_INT32, {0});
OperandType type6(Type::TENSOR_FLOAT32, {0, 4});
OperandType type7(Type::TENSOR_INT32, {1});
OperandType type8(Type::FLOAT32, {});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto scores = model->addOperand(&type2);
auto roi = model->addOperand(&type3);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type8);
auto param2 = model->addOperand(&type9);
auto param3 = model->addOperand(&type9);
auto param4 = model->addOperand(&type8);
auto param5 = model->addOperand(&type8);
auto param6 = model->addOperand(&type8);
auto scoresOut = model->addOperand(&type4);
auto roiOut = model->addOperand(&type6);
auto classesOut = model->addOperand(&type5);
auto batchSplitOut = model->addOperand(&type5);
auto in = model->addOperand(&type11);
auto param7 = model->addOperand(&type9);
auto param8 = model->addOperand(&type9);
auto param9 = model->addOperand(&type8);
auto param10 = model->addOperand(&type8);
auto param11 = model->addOperand(&type9);
auto param12 = model->addOperand(&type9);
auto layout = model->addOperand(&type10);
auto featureMap = model->addOperand(&type12);
auto out = model->addOperand(&type24);
// Phase 2, operations
static float scores_init[] = {0.9f, 0.1f};
model->setOperandValue(scores, scores_init, sizeof(float) * 2);
static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
model->setOperandValue(roi, roi_init, sizeof(float) * 8);
static int32_t param_init[] = {0};
model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
static float param1_init[] = {0.3f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static int32_t param2_init[] = {-1};
model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
static int32_t param3_init[] = {0};
model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
static float param4_init[] = {0.4f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {1.0f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static float param6_init[] = {0.3f};
model->setOperandValue(param6, param6_init, sizeof(float) * 1);
static int32_t param7_init[] = {2};
model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1);
static int32_t param8_init[] = {2};
model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
static float param9_init[] = {2.0f};
model->setOperandValue(param9, param9_init, sizeof(float) * 1);
static float param10_init[] = {2.0f};
model->setOperandValue(param10, param10_init, sizeof(float) * 1);
static int32_t param11_init[] = {4};
model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1);
static int32_t param12_init[] = {4};
model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut});
model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {featureMap}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{scoresOut, classesOut, out});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_zero_sized_relaxed_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_zero_sized_float16(Model *model) {
OperandType type10(Type::BOOL, {});
OperandType type13(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 1}, 0.1f, 128);
OperandType type25(Type::TENSOR_FLOAT16, {0, 2, 2, 1});
OperandType type26(Type::TENSOR_FLOAT16, {1, 1, 1, 1});
OperandType type27(Type::FLOAT16, {});
OperandType type28(Type::TENSOR_FLOAT16, {1, 8});
OperandType type29(Type::TENSOR_FLOAT16, {0, 4});
OperandType type30(Type::TENSOR_FLOAT16, {1, 2});
OperandType type31(Type::TENSOR_FLOAT16, {0});
OperandType type5(Type::TENSOR_INT32, {0});
OperandType type7(Type::TENSOR_INT32, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto scores = model->addOperand(&type30);
auto roi = model->addOperand(&type28);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type27);
auto param2 = model->addOperand(&type9);
auto param3 = model->addOperand(&type9);
auto param4 = model->addOperand(&type27);
auto param5 = model->addOperand(&type27);
auto param6 = model->addOperand(&type27);
auto scoresOut = model->addOperand(&type31);
auto roiOut = model->addOperand(&type29);
auto classesOut = model->addOperand(&type5);
auto batchSplitOut = model->addOperand(&type5);
auto in = model->addOperand(&type26);
auto param7 = model->addOperand(&type9);
auto param8 = model->addOperand(&type9);
auto param9 = model->addOperand(&type27);
auto param10 = model->addOperand(&type27);
auto param11 = model->addOperand(&type9);
auto param12 = model->addOperand(&type9);
auto layout = model->addOperand(&type10);
auto featureMap = model->addOperand(&type25);
auto out = model->addOperand(&type13);
// Phase 2, operations
static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f};
model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2);
static _Float16 roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
model->setOperandValue(roi, roi_init, sizeof(_Float16) * 8);
static int32_t param_init[] = {0};
model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
static _Float16 param1_init[] = {0.30000001192092896f};
model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1);
static int32_t param2_init[] = {-1};
model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
static int32_t param3_init[] = {0};
model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
static _Float16 param4_init[] = {0.4000000059604645f};
model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1);
static _Float16 param5_init[] = {1.0f};
model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
static _Float16 param6_init[] = {0.30000001192092896f};
model->setOperandValue(param6, param6_init, sizeof(_Float16) * 1);
static int32_t param7_init[] = {2};
model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1);
static int32_t param8_init[] = {2};
model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
static _Float16 param9_init[] = {2.0f};
model->setOperandValue(param9, param9_init, sizeof(_Float16) * 1);
static _Float16 param10_init[] = {2.0f};
model->setOperandValue(param10, param10_init, sizeof(_Float16) * 1);
static int32_t param11_init[] = {4};
model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1);
static int32_t param12_init[] = {4};
model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut});
model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {featureMap}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{scoresOut, classesOut, out});
assert(model->isValid());
}
bool is_ignored_zero_sized_float16(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize
namespace generated_tests::quantize {
void CreateModel_zero_sized_float16_dynamic_output_shape(Model *model) {
OperandType type10(Type::BOOL, {});
OperandType type24(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.1f, 128);
OperandType type25(Type::TENSOR_FLOAT16, {0, 2, 2, 1});
OperandType type26(Type::TENSOR_FLOAT16, {1, 1, 1, 1});
OperandType type27(Type::FLOAT16, {});
OperandType type28(Type::TENSOR_FLOAT16, {1, 8});
OperandType type29(Type::TENSOR_FLOAT16, {0, 4});
OperandType type30(Type::TENSOR_FLOAT16, {1, 2});
OperandType type32(Type::TENSOR_FLOAT16, {0});
OperandType type5(Type::TENSOR_INT32, {0});
OperandType type7(Type::TENSOR_INT32, {1});
OperandType type9(Type::INT32, {});
// Phase 1, operands
auto scores = model->addOperand(&type30);
auto roi = model->addOperand(&type28);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type27);
auto param2 = model->addOperand(&type9);
auto param3 = model->addOperand(&type9);
auto param4 = model->addOperand(&type27);
auto param5 = model->addOperand(&type27);
auto param6 = model->addOperand(&type27);
auto scoresOut = model->addOperand(&type32);
auto roiOut = model->addOperand(&type29);
auto classesOut = model->addOperand(&type5);
auto batchSplitOut = model->addOperand(&type5);
auto in = model->addOperand(&type26);
auto param7 = model->addOperand(&type9);
auto param8 = model->addOperand(&type9);
auto param9 = model->addOperand(&type27);
auto param10 = model->addOperand(&type27);
auto param11 = model->addOperand(&type9);
auto param12 = model->addOperand(&type9);
auto layout = model->addOperand(&type10);
auto featureMap = model->addOperand(&type25);
auto out = model->addOperand(&type24);
// Phase 2, operations
static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f};
model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2);
static _Float16 roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
model->setOperandValue(roi, roi_init, sizeof(_Float16) * 8);
static int32_t param_init[] = {0};
model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
static _Float16 param1_init[] = {0.30000001192092896f};
model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1);
static int32_t param2_init[] = {-1};
model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1);
static int32_t param3_init[] = {0};
model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
static _Float16 param4_init[] = {0.4000000059604645f};
model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1);
static _Float16 param5_init[] = {1.0f};
model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
static _Float16 param6_init[] = {0.30000001192092896f};
model->setOperandValue(param6, param6_init, sizeof(_Float16) * 1);
static int32_t param7_init[] = {2};
model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1);
static int32_t param8_init[] = {2};
model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
static _Float16 param9_init[] = {2.0f};
model->setOperandValue(param9, param9_init, sizeof(_Float16) * 1);
static _Float16 param10_init[] = {2.0f};
model->setOperandValue(param10, param10_init, sizeof(_Float16) * 1);
static int32_t param11_init[] = {4};
model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1);
static int32_t param12_init[] = {4};
model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut});
model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap});
model->addOperation(ANEURALNETWORKS_QUANTIZE, {featureMap}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{scoresOut, classesOut, out});
assert(model->isValid());
}
bool is_ignored_zero_sized_float16_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::quantize