def test_get_predefined_config_path_domain_failure(self) -> None:
"""Test getting predefined config path for onnx NLP models."""
with self.assertRaises(Exception):
get_predefined_config_path(
framework="onnxrt",
domain="object_detection",
)
def test_get_predefined_config_path_framework_failure(self) -> None:
"""Test getting predefined config path for onnx NLP models."""
with self.assertRaises(Exception):
get_predefined_config_path(
framework="onnx",
domain="image_recognition",
)
def get_predefined_configuration(
data: Dict[str, Any], ) -> Union[Dict[str, Any], List[Dict[str, Any]]]:
"""Get configuration."""
from lpot.ux.utils.utils import get_framework_from_path, get_predefined_config_path
from lpot.ux.utils.workload.config import Config
model_path = data.get("model_path", "")
if not os.path.isfile(model_path):
raise ClientErrorException(
f"Could not find model in specified path: {model_path}.", )
model_name = Path(model_path).stem
domain = data.get("domain", None)
if not domain:
raise ClientErrorException("Domain is not defined!")
framework = get_framework_from_path(model_path)
if framework is None:
raise ClientErrorException(
f"Could not find framework for specified model {model_name} in path {model_path}.",
)
config = Config()
predefined_config_path = get_predefined_config_path(framework, domain)
config.load(predefined_config_path)
return {
"config": config.serialize(),
"framework": framework,
"name": model_name,
"domain": domain,
}
def _assert_predefined_config_path(self, framework: str,
domain: str) -> None:
"""Assert predefined config path."""
result = get_predefined_config_path(framework, domain)
expected = os.path.join(
os.path.abspath(
os.path.dirname(
inspect.getfile(get_predefined_config_path), ), ),
"configs",
"predefined_configs",
f"{framework}",
f"{domain}.yaml",
)
self.assertEqual(result, expected)
self.assertEqual(os.path.isfile(result), True)
def __init__(self, data: Dict[str, Any]):
"""Initialize Workload class."""
super().__init__()
self.config: Config = Config()
self.id: str = str(data.get("id", ""))
if not self.id:
raise ClientErrorException("Workload ID not specified.")
self.model_path: str = data.get("model_path", "")
if not self.model_path:
raise ClientErrorException("Model path is not defined!")
self.model_name = Path(self.model_path).stem
self.domain: str = data.get("domain", None)
if not self.domain:
raise ClientErrorException("Domain is not defined!")
self.framework: str = data.get(
"framework",
get_framework_from_path(self.model_path),
)
self.predefined_config_path = data.get(
"config_path",
get_predefined_config_path(self.framework, self.domain),
)
self.workspace_path = data.get(
"workspace_path",
os.path.dirname(self.model_path),
)
self.workload_path = data.get(
"workload_path",
os.path.join(
self.workspace_path,
"workloads",
f"{self.model_name}_{self.id}",
),
)
self.set_workspace()
self.config_name = "config.yaml"
self.config_path = os.path.join(
self.workload_path,
self.config_name,
)
model_output_name = (self.model_name + "_int8." +
get_file_extension(self.model_path))
self.model_output_path = os.path.join(
self.workload_path,
model_output_name,
)
self.eval_dataset_path: str = data.get("eval_dataset_path", "")
self.calib_dataset_path: str = data.get("eval_dataset_path", "")
self.set_dataset_paths(data)
for dataset_path in [self.eval_dataset_path, self.calib_dataset_path]:
if dataset_path != "no_dataset_location" and not os.path.exists(
dataset_path, ):
raise ClientErrorException(
f'Could not found dataset in specified location: "{dataset_path}".',
)
if not os.path.isfile(self.model_path):
raise ClientErrorException(
f'Could not found model in specified location: "{self.model_path}".',
)
self.accuracy_goal: float = data.get("accuracy_goal", 0.01)
if not os.path.isfile(self.config_path):
self.config.load(self.predefined_config_path)
else:
self.config.load(self.config_path)
self.config.model.name = self.model_name
self.config.set_evaluation_dataset_path(self.eval_dataset_path)
self.config.set_quantization_dataset_path(self.calib_dataset_path)
self.config.set_workspace(self.workload_path)
self.config.set_accuracy_goal(self.accuracy_goal)
def __init__(self, data: Dict[str, Any]):
"""Initialize Workload class."""
super().__init__()
self.config: Config = Config()
self.id: str = str(data.get("id", ""))
if not self.id:
raise ClientErrorException("Workload ID not specified.")
self.model_path: str = data.get("model_path", "")
if not self.model_path:
raise ClientErrorException("Model path is not defined!")
self.model_name = Path(self.model_path).stem
self.domain: str = data.get("domain", None)
if not self.domain:
raise ClientErrorException("Domain is not defined!")
self.framework: str = data.get(
"framework",
get_framework_from_path(self.model_path),
)
self.workspace_path = data.get(
"workspace_path",
os.path.dirname(self.model_path),
)
self.workload_path = data.get(
"workload_path",
os.path.join(
self.workspace_path,
"workloads",
f"{self.model_name}_{self.id}",
),
)
self.set_workspace()
self.eval_dataset_path: str = data.get("eval_dataset_path", "")
self.calib_dataset_path: str = data.get("eval_dataset_path", "")
self.set_dataset_paths(data)
for dataset_path in [self.eval_dataset_path, self.calib_dataset_path]:
if dataset_path != "no_dataset_location" and not os.path.exists(
dataset_path, ):
raise ClientErrorException(
f'Could not found dataset in specified location: "{dataset_path}".',
)
if not ModelRepository.is_model_path(self.model_path):
raise ClientErrorException(
f'Could not found model in specified location: "{self.model_path}".',
)
self.accuracy_goal: float = data.get("accuracy_goal", 0.01)
self.config_name = "config.yaml"
self.predefined_config_path = data.get(
"config_path",
get_predefined_config_path(self.framework, self.domain),
)
self.config_path = os.path.join(
self.workload_path,
self.config_name,
)
self.input_precision = Precisions.FP32 # TODO: Detect input model precision
self.output_precision = data.get("precision",
data.get("output_precision"))
self.mode = self.get_optimization_mode()
self.tune = data.get("tune", self.is_tuning_enabled(data))
self.initialize_config(data)
self.input_nodes: Optional[str] = data.get("inputs",
data.get("input_nodes"))
self.output_nodes: Optional[str] = data.get("outputs",
data.get("output_nodes"))
self.model_output_path = os.path.join(
self.workload_path,
self.model_output_name,
)
self.version = "2.0"
