def main() -> None:
parser = argparse.ArgumentParser()
utils.add_dynet_argparse(parser)
parser.add_argument("--src",
help=f"Path of source file to read from.",
required=True)
parser.add_argument("--hyp",
help="Path of file to write hypothesis to.",
required=True)
parser.add_argument("--mod",
help="Path of model file to read.",
required=True)
args = parser.parse_args()
exp_dir = os.path.dirname(__file__)
exp = "{EXP}"
param_collections.ParamManager.init_param_col()
# TODO: can we avoid the LoadSerialized proxy and load stuff directly?
load_experiment = LoadSerialized(filename=args.mod)
uninitialized_experiment = YamlPreloader.preload_obj(load_experiment,
exp_dir=exp_dir,
exp_name=exp)
loaded_experiment = initialize_if_needed(uninitialized_experiment)
model = loaded_experiment.model
inference = model.inference
param_collections.ParamManager.populate()
decoding_task = tasks.DecodingEvalTask(args.src, args.hyp, model,
inference)
decoding_task.eval()
def main() -> None:
parser = argparse.ArgumentParser()
utils.add_dynet_argparse(parser)
parser.add_argument(
"--metric",
help=f"Scoring metric(s), a string. "
f"Accepted metrics are {', '.join(eval_shortcuts.keys())}."
f"Alternatively, metrics with non-default settings can by used by specifying a Python "
f"Evaluator object to be parsed using eval(). Example: 'WEREvaluator(case_sensitive=True)'",
nargs="+")
parser.add_argument("--hyp", help="Path to read hypothesis file from")
parser.add_argument("--ref",
help="Path to read reference file from",
nargs="+")
args = parser.parse_args()
evaluators = args.metric
evaluators = [
eval_shortcuts[shortcut]()
if shortcut in eval_shortcuts else eval(shortcut)
for shortcut in evaluators
]
scores = xnmt_evaluate(args.ref, args.hyp, evaluators)
for score in scores:
print(score)
def main(overwrite_args: Optional[Sequence[str]] = None) -> None:
with tee.Tee(), tee.Tee(error=True):
argparser = argparse.ArgumentParser()
utils.add_dynet_argparse(argparser)
argparser.add_argument("--settings",
type=str,
default="standard",
help="settings (standard, debug, or unittest)"
"must be given in '=' syntax, e.g."
" --settings=standard")
argparser.add_argument(
"--resume",
action='store_true',
help="whether a saved experiment is being resumed, and"
"locations of output files should be re-used.")
argparser.add_argument("experiments_file")
argparser.add_argument("experiment_name",
nargs='*',
help="Run only the specified experiments")
argparser.set_defaults(generate_doc=False)
args = argparser.parse_args(overwrite_args)
if args.dynet_seed:
random.seed(args.dynet_seed)
np.random.seed(args.dynet_seed)
if args.dynet_gpu:
if settings.CHECK_VALIDITY:
settings.CHECK_VALIDITY = False
log_preamble(
"disabling CHECK_VALIDITY because it is not supported on GPU currently",
logging.WARNING)
config_experiment_names = YamlPreloader.experiment_names_from_file(
args.experiments_file)
results = []
# Check ahead of time that all experiments exist, to avoid bad surprises
experiment_names = args.experiment_name or config_experiment_names
if args.experiment_name:
nonexistent = set(experiment_names).difference(
config_experiment_names)
if len(nonexistent) != 0:
raise Exception("Experiments {} do not exist".format(",".join(
list(nonexistent))))
log_preamble(
f"running XNMT revision {tee.get_git_revision()} on {socket.gethostname()} on {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
)
for experiment_name in experiment_names:
ParamManager.init_param_col()
uninitialized_exp_args = YamlPreloader.preload_experiment_from_file(
args.experiments_file, experiment_name, resume=args.resume)
logger.info(f"=> Running {experiment_name}")
glob_args = uninitialized_exp_args.data.exp_global
log_file = glob_args.log_file
if os.path.isfile(log_file) and not settings.OVERWRITE_LOG:
logger.warning(
f"log file {log_file} already exists, skipping experiment; please delete log file by hand if you want to overwrite it "
f"(or activate OVERWRITE_LOG, by either specifying an environment variable as OVERWRITE_LOG=1, "
f"or specifying --settings=debug, or changing xnmt.settings.Standard.OVERWRITE_LOG manually)"
)
continue
tee.set_out_file(log_file, exp_name=experiment_name)
try:
model_file = glob_args.model_file
uninitialized_exp_args.data.exp_global.commandline_args = vars(
args)
# Create the model
experiment = initialize_if_needed(uninitialized_exp_args)
ParamManager.param_col.model_file = experiment.exp_global.model_file
ParamManager.param_col.save_num_checkpoints = experiment.exp_global.save_num_checkpoints
ParamManager.populate()
# Run the experiment
eval_scores = experiment(
save_fct=lambda: save_to_file(model_file, experiment))
results.append((experiment_name, eval_scores))
print_results(results)
except Exception as e:
file_logger.error(traceback.format_exc())
raise e
finally:
tee.unset_out_file()