def timestamp(self) -> int:
timestamp = self.__DEFAULT_TIMESTAMP
if exists(self._checkout_info_file):
checkout_info = read_yaml(self._checkout_info_file)
timestamp = checkout_info.get(ProjectCheckout.__KEY_TIMESTAMP, self.__DEFAULT_TIMESTAMP)
return timestamp
def timestamp(self) -> int:
timestamp = VersionCompile.__DEFAULT_TIMESTAMP
if exists(self._compile_info_file):
checkout_info = read_yaml(self._compile_info_file)
timestamp = checkout_info.get(VersionCompile.__KEY_TIMESTAMP,
VersionCompile.__DEFAULT_TIMESTAMP)
return timestamp
def get_white_list(datasets_file_path: str,
dataset_key: Optional[str]) -> List[str]:
if dataset_key is None:
return []
datasets = read_yaml(datasets_file_path)
if dataset_key not in datasets:
raise ValueError("Invalid dataset: '{}'".format(dataset_key))
return datasets[dataset_key]
def __load_data(run_file_path: str):
data = {
"result": None,
"runtime": None,
"message": "",
"md5": None
}
data.update(read_yaml(run_file_path) if exists(run_file_path) else {})
return data
def timestamp(self):
timestamp = self.__DEFAULT_TIMESTAMP
if exists(self._misuse_compile_file):
compile_info = read_yaml(self._misuse_compile_file)
timestamp = compile_info.get(self.__TIMESTAMP_KEY,
self.__DEFAULT_TIMESTAMP)
return timestamp
def __init__(self, run_mode: DetectorMode, detector: Detector,
version: ProjectVersion, findings_base_path: str,
findings_filter: FindingsFilter):
self.run_mode = run_mode
self.detector = detector
self.version = version
self._findings_base_path = findings_base_path
self._findings_file_path = join(self._get_findings_path(),
self.FINDINGS_FILE)
self.__FINDINGS = None
self.__POTENTIAL_HITS = None
data = {"result": None, "runtime": 0, "message": "", "md5": None}
data.update(
read_yaml(self._run_file_path) if exists(self._run_file_path
) else {})
self.result = Result[data["result"]] if data["result"] else None
self.runtime = data["runtime"]
self.message = data["message"]
self._detector_md5 = data["md5"]
self.findings_filter = findings_filter
action='store_true')
__add_check_subprocess(available_datasets, subparsers)
__add_info_subprocess(available_datasets, subparsers)
__add_checkout_subprocess(available_datasets, subparsers)
__add_compile_subprocess(available_datasets, subparsers)
__add_run_subprocess(available_detectors, available_datasets, subparsers)
__add_publish_subprocess(available_detectors, available_datasets,
subparsers)
__add_stats_subprocess(available_scripts, available_datasets, subparsers)
return parser
try:
__default_config = read_yaml("./default.config")
except FileNotFoundError:
__default_config = None
def __get_default(parameter: str, default):
if __default_config is not None and parameter in __default_config:
return __default_config[parameter]
return default
def __add_check_subprocess(available_datasets: List[str], subparsers) -> None:
check_parser = subparsers.add_parser(
'check',
formatter_class=SortingHelpFormatter,
help="Check MUBench runtime requirements or dataset consistency.",
self.optimizer.step()
self.optimizer.zero_grad()
if idx % self.opts['print_freq'] == 0:
print(f'[Dist] Step: {idx} MMD: {mmd.item()}')
if self.opts['use_task_loss']:
print(f'[Task] Reward: {acc}, Baseline: {baseline}')
# debug information
print(
f'[Feat] Step: {idx} {dec_act[0, 2, 15:].tolist()} {x[0, 2, 15:].tolist()}'
)
# To debug, this index is the loc_x, loc_y, yaw of the
# digit in MNIST
if self.opts['use_task_loss']:
self.optimizer.step()
self.optimizer.zero_grad()
# LR scheduler step
self.lr_sched.step()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--exp', required=True, type=str)
opts = parser.parse_args()
opts = io.read_yaml(opts.exp)
trainer = Trainer(opts)
trainer.train()
def _yaml(self) -> Dict[str, Any]:
if self._YAML is None:
self._YAML = read_yaml(self.version_file)
return self._YAML
def get_available_datasets(datasets_file_path: str) -> Dict[str, List[str]]:
return read_yaml(datasets_file_path)
def __get_lowercase_datasets(datasets_file_path: str) -> Dict[str, List[str]]:
datasets = read_yaml(datasets_file_path)
return {k.lower(): [e.lower() for e in v] for k, v in datasets.items()}
def get_available_dataset_ids(datasets_file_path: str) -> List[str]:
datasets = read_yaml(datasets_file_path)
return list(datasets.keys())
