blob: 69b5f4b8e9a6dbaf16d329310afd7fac6a6c27cf [file] [log] [blame]
// Generated from logistic_quant8_2.mod.py
// DO NOT EDIT
// clang-format off
#include "TestGenerated.h"
namespace generated_tests::logistic_quant8_2 {
void CreateModel(Model *model) {
OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 16, 16, 1}, 0.5f, 0);
OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 16, 16, 1}, 0.00390625f, 0);
// Phase 1, operands
auto input = model->addOperand(&type0);
auto output = model->addOperand(&type1);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_LOGISTIC, {input}, {output});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input},
{output});
assert(model->isValid());
}
bool is_ignored(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::logistic_quant8_2
namespace generated_tests::logistic_quant8_2 {
void CreateModel_dynamic_output_shape(Model *model) {
OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 16, 16, 1}, 0.5f, 0);
OperandType type2(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.00390625f, 0);
// Phase 1, operands
auto input = model->addOperand(&type0);
auto output = model->addOperand(&type2);
// Phase 2, operations
model->addOperation(ANEURALNETWORKS_LOGISTIC, {input}, {output});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input},
{output});
assert(model->isValid());
}
bool is_ignored_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::logistic_quant8_2
namespace generated_tests::logistic_quant8_2 {
void CreateModel_all_inputs_as_internal(Model *model) {
OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 16, 16, 1}, 0.5f, 0);
OperandType type1(Type::TENSOR_QUANT8_ASYMM, {1, 16, 16, 1}, 0.00390625f, 0);
OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1}, 0.5f, 0);
OperandType type4(Type::INT32, {});
// Phase 1, operands
auto input = model->addOperand(&type0);
auto output = model->addOperand(&type1);
auto input_tmp = model->addOperand(&type0);
auto dummy = model->addOperand(&type3);
auto param = model->addOperand(&type4);
// Phase 2, operations
static uint8_t dummy_init[] = {0};
model->setOperandValue(dummy, dummy_init, sizeof(uint8_t) * 1);
static int32_t param_init[] = {0};
model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input_tmp, dummy, param}, {input});
model->addOperation(ANEURALNETWORKS_LOGISTIC, {input}, {output});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input_tmp},
{output});
assert(model->isValid());
}
bool is_ignored_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::logistic_quant8_2
namespace generated_tests::logistic_quant8_2 {
void CreateModel_all_inputs_as_internal_dynamic_output_shape(Model *model) {
OperandType type0(Type::TENSOR_QUANT8_ASYMM, {1, 16, 16, 1}, 0.5f, 0);
OperandType type2(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.00390625f, 0);
OperandType type3(Type::TENSOR_QUANT8_ASYMM, {1}, 0.5f, 0);
OperandType type4(Type::INT32, {});
// Phase 1, operands
auto input = model->addOperand(&type0);
auto output = model->addOperand(&type2);
auto input_tmp = model->addOperand(&type0);
auto dummy1 = model->addOperand(&type3);
auto param1 = model->addOperand(&type4);
// Phase 2, operations
static uint8_t dummy1_init[] = {0};
model->setOperandValue(dummy1, dummy1_init, sizeof(uint8_t) * 1);
static int32_t param1_init[] = {0};
model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {input_tmp, dummy1, param1}, {input});
model->addOperation(ANEURALNETWORKS_LOGISTIC, {input}, {output});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input_tmp},
{output});
assert(model->isValid());
}
bool is_ignored_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::logistic_quant8_2