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android / platform / packages / modules / NeuralNetworks / c49850065cf80a64f1a99307fdd68f1301fe2582 / . / runtime / test / generated / spec_V1_2 / cts / instance_normalization.model.cpp
blob: 74a9b23e403022adab405378946229af89d6abd6 [file] [log] [blame]
// Generated from instance_normalization.mod.py
// DO NOT EDIT
// clang-format off
#include "TestGenerated.h"
namespace generated_tests::instance_normalization {
void CreateModel_nhwc(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type1);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
assert(model->isValid());
}
bool is_ignored_nhwc(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type3);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
assert(model->isValid());
}
bool is_ignored_nhwc_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_all_inputs_as_internal(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type1);
auto in_tmp = model->addOperand(&type1);
auto dummy = model->addOperand(&type4);
auto param6 = model->addOperand(&type5);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy_init[] = {0.0f};
model->setOperandValue(dummy, dummy_init, sizeof(float) * 1);
static int32_t param6_init[] = {0};
model->setOperandValue(param6, param6_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy, param6}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
assert(model->isValid());
}
bool is_ignored_nhwc_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_all_inputs_as_internal_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type3);
auto in_tmp = model->addOperand(&type1);
auto dummy1 = model->addOperand(&type4);
auto param7 = model->addOperand(&type5);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy1_init[] = {0.0f};
model->setOperandValue(dummy1, dummy1_init, sizeof(float) * 1);
static int32_t param7_init[] = {0};
model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy1, param7}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
assert(model->isValid());
}
bool is_ignored_nhwc_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_relaxed(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type1);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nhwc_relaxed(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_relaxed_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type3);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nhwc_relaxed_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_relaxed_all_inputs_as_internal(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type1);
auto in_tmp = model->addOperand(&type1);
auto dummy2 = model->addOperand(&type4);
auto param8 = model->addOperand(&type5);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy2_init[] = {0.0f};
model->setOperandValue(dummy2, dummy2_init, sizeof(float) * 1);
static int32_t param8_init[] = {0};
model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy2, param8}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nhwc_relaxed_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_relaxed_all_inputs_as_internal_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type3);
auto in_tmp = model->addOperand(&type1);
auto dummy3 = model->addOperand(&type4);
auto param9 = model->addOperand(&type5);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy3_init[] = {0.0f};
model->setOperandValue(dummy3, dummy3_init, sizeof(float) * 1);
static int32_t param9_init[] = {0};
model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy3, param9}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nhwc_relaxed_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_float16(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
// Phase 1, operands
auto in = model->addOperand(&type6);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type7);
auto param2 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type6);
// Phase 2, operations
static _Float16 param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(_Float16) * 1);
static _Float16 param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1);
static _Float16 param2_init[] = {9.999999747378752e-05f};
model->setOperandValue(param2, param2_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
assert(model->isValid());
}
bool is_ignored_nhwc_float16(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_float16_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
OperandType type8(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
// Phase 1, operands
auto in = model->addOperand(&type6);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type7);
auto param2 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type8);
// Phase 2, operations
static _Float16 param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(_Float16) * 1);
static _Float16 param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1);
static _Float16 param2_init[] = {9.999999747378752e-05f};
model->setOperandValue(param2, param2_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
assert(model->isValid());
}
bool is_ignored_nhwc_float16_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_float16_all_inputs_as_internal(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type10(Type::TENSOR_FLOAT16, {1});
OperandType type5(Type::INT32, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
OperandType type9(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
// Phase 1, operands
auto in = model->addOperand(&type9);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type7);
auto param2 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type6);
auto in_tmp = model->addOperand(&type9);
auto dummy4 = model->addOperand(&type10);
auto param10 = model->addOperand(&type5);
// Phase 2, operations
static _Float16 param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(_Float16) * 1);
static _Float16 param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1);
static _Float16 param2_init[] = {9.999999747378752e-05f};
model->setOperandValue(param2, param2_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static _Float16 dummy4_init[] = {0.0f};
model->setOperandValue(dummy4, dummy4_init, sizeof(_Float16) * 1);
static int32_t param10_init[] = {0};
model->setOperandValue(param10, param10_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy4, param10}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
assert(model->isValid());
}
bool is_ignored_nhwc_float16_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_float16_all_inputs_as_internal_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type10(Type::TENSOR_FLOAT16, {1});
OperandType type5(Type::INT32, {});
OperandType type7(Type::FLOAT16, {});
OperandType type8(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
OperandType type9(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
// Phase 1, operands
auto in = model->addOperand(&type9);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type7);
auto param2 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type8);
auto in_tmp = model->addOperand(&type9);
auto dummy5 = model->addOperand(&type10);
auto param11 = model->addOperand(&type5);
// Phase 2, operations
static _Float16 param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(_Float16) * 1);
static _Float16 param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1);
static _Float16 param2_init[] = {9.999999747378752e-05f};
model->setOperandValue(param2, param2_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static _Float16 dummy5_init[] = {0.0f};
model->setOperandValue(dummy5, dummy5_init, sizeof(_Float16) * 1);
static int32_t param11_init[] = {0};
model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy5, param11}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
assert(model->isValid());
}
bool is_ignored_nhwc_float16_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type1);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
assert(model->isValid());
}
bool is_ignored_nchw(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type3);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
assert(model->isValid());
}
bool is_ignored_nchw_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_all_inputs_as_internal(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type1);
auto in_tmp = model->addOperand(&type1);
auto dummy6 = model->addOperand(&type4);
auto param12 = model->addOperand(&type5);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy6_init[] = {0.0f};
model->setOperandValue(dummy6, dummy6_init, sizeof(float) * 1);
static int32_t param12_init[] = {0};
model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy6, param12}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
assert(model->isValid());
}
bool is_ignored_nchw_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_all_inputs_as_internal_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type3);
auto in_tmp = model->addOperand(&type1);
auto dummy7 = model->addOperand(&type4);
auto param13 = model->addOperand(&type5);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy7_init[] = {0.0f};
model->setOperandValue(dummy7, dummy7_init, sizeof(float) * 1);
static int32_t param13_init[] = {0};
model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy7, param13}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
assert(model->isValid());
}
bool is_ignored_nchw_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_relaxed(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type1);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nchw_relaxed(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_relaxed_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type3);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nchw_relaxed_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_relaxed_all_inputs_as_internal(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type1);
auto in_tmp = model->addOperand(&type1);
auto dummy8 = model->addOperand(&type4);
auto param14 = model->addOperand(&type5);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy8_init[] = {0.0f};
model->setOperandValue(dummy8, dummy8_init, sizeof(float) * 1);
static int32_t param14_init[] = {0};
model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy8, param14}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nchw_relaxed_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_relaxed_all_inputs_as_internal_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in = model->addOperand(&type1);
auto param = model->addOperand(&type2);
auto param1 = model->addOperand(&type2);
auto param2 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type3);
auto in_tmp = model->addOperand(&type1);
auto dummy9 = model->addOperand(&type4);
auto param15 = model->addOperand(&type5);
// Phase 2, operations
static float param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(float) * 1);
static float param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(float) * 1);
static float param2_init[] = {0.0001f};
model->setOperandValue(param2, param2_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy9_init[] = {0.0f};
model->setOperandValue(dummy9, dummy9_init, sizeof(float) * 1);
static int32_t param15_init[] = {0};
model->setOperandValue(param15, param15_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy9, param15}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nchw_relaxed_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_float16(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
// Phase 1, operands
auto in = model->addOperand(&type6);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type7);
auto param2 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type6);
// Phase 2, operations
static _Float16 param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(_Float16) * 1);
static _Float16 param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1);
static _Float16 param2_init[] = {9.999999747378752e-05f};
model->setOperandValue(param2, param2_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
assert(model->isValid());
}
bool is_ignored_nchw_float16(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_float16_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
OperandType type8(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
// Phase 1, operands
auto in = model->addOperand(&type6);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type7);
auto param2 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type8);
// Phase 2, operations
static _Float16 param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(_Float16) * 1);
static _Float16 param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1);
static _Float16 param2_init[] = {9.999999747378752e-05f};
model->setOperandValue(param2, param2_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in},
{out});
assert(model->isValid());
}
bool is_ignored_nchw_float16_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_float16_all_inputs_as_internal(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type10(Type::TENSOR_FLOAT16, {1});
OperandType type5(Type::INT32, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
OperandType type9(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
// Phase 1, operands
auto in = model->addOperand(&type9);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type7);
auto param2 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type6);
auto in_tmp = model->addOperand(&type9);
auto dummy10 = model->addOperand(&type10);
auto param16 = model->addOperand(&type5);
// Phase 2, operations
static _Float16 param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(_Float16) * 1);
static _Float16 param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1);
static _Float16 param2_init[] = {9.999999747378752e-05f};
model->setOperandValue(param2, param2_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static _Float16 dummy10_init[] = {0.0f};
model->setOperandValue(dummy10, dummy10_init, sizeof(_Float16) * 1);
static int32_t param16_init[] = {0};
model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy10, param16}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
assert(model->isValid());
}
bool is_ignored_nchw_float16_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_float16_all_inputs_as_internal_dynamic_output_shape(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type10(Type::TENSOR_FLOAT16, {1});
OperandType type5(Type::INT32, {});
OperandType type7(Type::FLOAT16, {});
OperandType type8(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
OperandType type9(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
// Phase 1, operands
auto in = model->addOperand(&type9);
auto param = model->addOperand(&type7);
auto param1 = model->addOperand(&type7);
auto param2 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out = model->addOperand(&type8);
auto in_tmp = model->addOperand(&type9);
auto dummy11 = model->addOperand(&type10);
auto param17 = model->addOperand(&type5);
// Phase 2, operations
static _Float16 param_init[] = {1.0f};
model->setOperandValue(param, param_init, sizeof(_Float16) * 1);
static _Float16 param1_init[] = {0.0f};
model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1);
static _Float16 param2_init[] = {9.999999747378752e-05f};
model->setOperandValue(param2, param2_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static _Float16 dummy11_init[] = {0.0f};
model->setOperandValue(dummy11, dummy11_init, sizeof(_Float16) * 1);
static int32_t param17_init[] = {0};
model->setOperandValue(param17, param17_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in_tmp, dummy11, param17}, {in});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in, param, param1, param2, layout}, {out});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in_tmp},
{out});
assert(model->isValid());
}
bool is_ignored_nchw_float16_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type1);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
assert(model->isValid());
}
bool is_ignored_nhwc_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type3);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
assert(model->isValid());
}
bool is_ignored_nhwc_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_all_inputs_as_internal_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type1);
auto in1_tmp = model->addOperand(&type1);
auto dummy12 = model->addOperand(&type4);
auto param18 = model->addOperand(&type5);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy12_init[] = {0.0f};
model->setOperandValue(dummy12, dummy12_init, sizeof(float) * 1);
static int32_t param18_init[] = {0};
model->setOperandValue(param18, param18_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy12, param18}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
assert(model->isValid());
}
bool is_ignored_nhwc_all_inputs_as_internal_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_all_inputs_as_internal_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type3);
auto in1_tmp = model->addOperand(&type1);
auto dummy13 = model->addOperand(&type4);
auto param19 = model->addOperand(&type5);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy13_init[] = {0.0f};
model->setOperandValue(dummy13, dummy13_init, sizeof(float) * 1);
static int32_t param19_init[] = {0};
model->setOperandValue(param19, param19_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy13, param19}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
assert(model->isValid());
}
bool is_ignored_nhwc_all_inputs_as_internal_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_relaxed_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type1);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nhwc_relaxed_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_relaxed_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type3);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nhwc_relaxed_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_relaxed_all_inputs_as_internal_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type1);
auto in1_tmp = model->addOperand(&type1);
auto dummy14 = model->addOperand(&type4);
auto param20 = model->addOperand(&type5);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy14_init[] = {0.0f};
model->setOperandValue(dummy14, dummy14_init, sizeof(float) * 1);
static int32_t param20_init[] = {0};
model->setOperandValue(param20, param20_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy14, param20}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nhwc_relaxed_all_inputs_as_internal_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_relaxed_all_inputs_as_internal_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type3);
auto in1_tmp = model->addOperand(&type1);
auto dummy15 = model->addOperand(&type4);
auto param21 = model->addOperand(&type5);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy15_init[] = {0.0f};
model->setOperandValue(dummy15, dummy15_init, sizeof(float) * 1);
static int32_t param21_init[] = {0};
model->setOperandValue(param21, param21_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy15, param21}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nhwc_relaxed_all_inputs_as_internal_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_float16_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
// Phase 1, operands
auto in1 = model->addOperand(&type6);
auto param3 = model->addOperand(&type7);
auto param4 = model->addOperand(&type7);
auto param5 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type6);
// Phase 2, operations
static _Float16 param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(_Float16) * 1);
static _Float16 param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1);
static _Float16 param5_init[] = {9.999999747378752e-05f};
model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
assert(model->isValid());
}
bool is_ignored_nhwc_float16_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_float16_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
OperandType type8(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
// Phase 1, operands
auto in1 = model->addOperand(&type6);
auto param3 = model->addOperand(&type7);
auto param4 = model->addOperand(&type7);
auto param5 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type8);
// Phase 2, operations
static _Float16 param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(_Float16) * 1);
static _Float16 param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1);
static _Float16 param5_init[] = {9.999999747378752e-05f};
model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
assert(model->isValid());
}
bool is_ignored_nhwc_float16_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_float16_all_inputs_as_internal_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type10(Type::TENSOR_FLOAT16, {1});
OperandType type5(Type::INT32, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
OperandType type9(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
// Phase 1, operands
auto in1 = model->addOperand(&type9);
auto param3 = model->addOperand(&type7);
auto param4 = model->addOperand(&type7);
auto param5 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type6);
auto in1_tmp = model->addOperand(&type9);
auto dummy16 = model->addOperand(&type10);
auto param22 = model->addOperand(&type5);
// Phase 2, operations
static _Float16 param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(_Float16) * 1);
static _Float16 param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1);
static _Float16 param5_init[] = {9.999999747378752e-05f};
model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static _Float16 dummy16_init[] = {0.0f};
model->setOperandValue(dummy16, dummy16_init, sizeof(_Float16) * 1);
static int32_t param22_init[] = {0};
model->setOperandValue(param22, param22_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy16, param22}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
assert(model->isValid());
}
bool is_ignored_nhwc_float16_all_inputs_as_internal_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nhwc_float16_all_inputs_as_internal_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type10(Type::TENSOR_FLOAT16, {1});
OperandType type5(Type::INT32, {});
OperandType type7(Type::FLOAT16, {});
OperandType type8(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
OperandType type9(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
// Phase 1, operands
auto in1 = model->addOperand(&type9);
auto param3 = model->addOperand(&type7);
auto param4 = model->addOperand(&type7);
auto param5 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type8);
auto in1_tmp = model->addOperand(&type9);
auto dummy17 = model->addOperand(&type10);
auto param23 = model->addOperand(&type5);
// Phase 2, operations
static _Float16 param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(_Float16) * 1);
static _Float16 param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1);
static _Float16 param5_init[] = {9.999999747378752e-05f};
model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {false};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static _Float16 dummy17_init[] = {0.0f};
model->setOperandValue(dummy17, dummy17_init, sizeof(_Float16) * 1);
static int32_t param23_init[] = {0};
model->setOperandValue(param23, param23_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy17, param23}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
assert(model->isValid());
}
bool is_ignored_nhwc_float16_all_inputs_as_internal_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type1);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
assert(model->isValid());
}
bool is_ignored_nchw_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type3);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
assert(model->isValid());
}
bool is_ignored_nchw_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_all_inputs_as_internal_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type1);
auto in1_tmp = model->addOperand(&type1);
auto dummy18 = model->addOperand(&type4);
auto param24 = model->addOperand(&type5);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy18_init[] = {0.0f};
model->setOperandValue(dummy18, dummy18_init, sizeof(float) * 1);
static int32_t param24_init[] = {0};
model->setOperandValue(param24, param24_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy18, param24}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
assert(model->isValid());
}
bool is_ignored_nchw_all_inputs_as_internal_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_all_inputs_as_internal_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type3);
auto in1_tmp = model->addOperand(&type1);
auto dummy19 = model->addOperand(&type4);
auto param25 = model->addOperand(&type5);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy19_init[] = {0.0f};
model->setOperandValue(dummy19, dummy19_init, sizeof(float) * 1);
static int32_t param25_init[] = {0};
model->setOperandValue(param25, param25_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy19, param25}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
assert(model->isValid());
}
bool is_ignored_nchw_all_inputs_as_internal_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_relaxed_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type1);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nchw_relaxed_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_relaxed_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type3);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nchw_relaxed_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_relaxed_all_inputs_as_internal_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type1);
auto in1_tmp = model->addOperand(&type1);
auto dummy20 = model->addOperand(&type4);
auto param26 = model->addOperand(&type5);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy20_init[] = {0.0f};
model->setOperandValue(dummy20, dummy20_init, sizeof(float) * 1);
static int32_t param26_init[] = {0};
model->setOperandValue(param26, param26_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy20, param26}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nchw_relaxed_all_inputs_as_internal_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_relaxed_all_inputs_as_internal_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type1(Type::TENSOR_FLOAT32, {2, 2, 2, 2});
OperandType type2(Type::FLOAT32, {});
OperandType type3(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
OperandType type4(Type::TENSOR_FLOAT32, {1});
OperandType type5(Type::INT32, {});
// Phase 1, operands
auto in1 = model->addOperand(&type1);
auto param3 = model->addOperand(&type2);
auto param4 = model->addOperand(&type2);
auto param5 = model->addOperand(&type2);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type3);
auto in1_tmp = model->addOperand(&type1);
auto dummy21 = model->addOperand(&type4);
auto param27 = model->addOperand(&type5);
// Phase 2, operations
static float param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(float) * 1);
static float param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(float) * 1);
static float param5_init[] = {0.0001f};
model->setOperandValue(param5, param5_init, sizeof(float) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static float dummy21_init[] = {0.0f};
model->setOperandValue(dummy21, dummy21_init, sizeof(float) * 1);
static int32_t param27_init[] = {0};
model->setOperandValue(param27, param27_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy21, param27}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
bool is_ignored_nchw_relaxed_all_inputs_as_internal_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_float16_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
// Phase 1, operands
auto in1 = model->addOperand(&type6);
auto param3 = model->addOperand(&type7);
auto param4 = model->addOperand(&type7);
auto param5 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type6);
// Phase 2, operations
static _Float16 param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(_Float16) * 1);
static _Float16 param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1);
static _Float16 param5_init[] = {9.999999747378752e-05f};
model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
assert(model->isValid());
}
bool is_ignored_nchw_float16_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_float16_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
OperandType type8(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
// Phase 1, operands
auto in1 = model->addOperand(&type6);
auto param3 = model->addOperand(&type7);
auto param4 = model->addOperand(&type7);
auto param5 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type8);
// Phase 2, operations
static _Float16 param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(_Float16) * 1);
static _Float16 param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1);
static _Float16 param5_init[] = {9.999999747378752e-05f};
model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1},
{out1});
assert(model->isValid());
}
bool is_ignored_nchw_float16_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_float16_all_inputs_as_internal_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type10(Type::TENSOR_FLOAT16, {1});
OperandType type5(Type::INT32, {});
OperandType type6(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
OperandType type7(Type::FLOAT16, {});
OperandType type9(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
// Phase 1, operands
auto in1 = model->addOperand(&type9);
auto param3 = model->addOperand(&type7);
auto param4 = model->addOperand(&type7);
auto param5 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type6);
auto in1_tmp = model->addOperand(&type9);
auto dummy22 = model->addOperand(&type10);
auto param28 = model->addOperand(&type5);
// Phase 2, operations
static _Float16 param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(_Float16) * 1);
static _Float16 param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1);
static _Float16 param5_init[] = {9.999999747378752e-05f};
model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static _Float16 dummy22_init[] = {0.0f};
model->setOperandValue(dummy22, dummy22_init, sizeof(_Float16) * 1);
static int32_t param28_init[] = {0};
model->setOperandValue(param28, param28_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy22, param28}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
assert(model->isValid());
}
bool is_ignored_nchw_float16_all_inputs_as_internal_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization
namespace generated_tests::instance_normalization {
void CreateModel_nchw_float16_all_inputs_as_internal_dynamic_output_shape_2(Model *model) {
OperandType type0(Type::BOOL, {});
OperandType type10(Type::TENSOR_FLOAT16, {1});
OperandType type5(Type::INT32, {});
OperandType type7(Type::FLOAT16, {});
OperandType type8(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
OperandType type9(Type::TENSOR_FLOAT16, {2, 2, 2, 2});
// Phase 1, operands
auto in1 = model->addOperand(&type9);
auto param3 = model->addOperand(&type7);
auto param4 = model->addOperand(&type7);
auto param5 = model->addOperand(&type7);
auto layout = model->addOperand(&type0);
auto out1 = model->addOperand(&type8);
auto in1_tmp = model->addOperand(&type9);
auto dummy23 = model->addOperand(&type10);
auto param29 = model->addOperand(&type5);
// Phase 2, operations
static _Float16 param3_init[] = {2.0f};
model->setOperandValue(param3, param3_init, sizeof(_Float16) * 1);
static _Float16 param4_init[] = {10.0f};
model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1);
static _Float16 param5_init[] = {9.999999747378752e-05f};
model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
static bool8 layout_init[] = {true};
model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
static _Float16 dummy23_init[] = {0.0f};
model->setOperandValue(dummy23, dummy23_init, sizeof(_Float16) * 1);
static int32_t param29_init[] = {0};
model->setOperandValue(param29, param29_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_ADD, {in1_tmp, dummy23, param29}, {in1});
model->addOperation(ANEURALNETWORKS_INSTANCE_NORMALIZATION, {in1, param3, param4, param5, layout}, {out1});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{in1_tmp},
{out1});
assert(model->isValid());
}
bool is_ignored_nchw_float16_all_inputs_as_internal_dynamic_output_shape_2(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace generated_tests::instance_normalization