'_z_') if args.el_datasets != "" else None
args.ed_val_datasets = [int(x) for x in args.ed_val_datasets.split('_')]
args.el_val_datasets = [int(x) for x in args.el_val_datasets.split('_')]
args.gm_bucketing_pempos = [
int(x) for x in args.gm_bucketing_pempos.split('_')
] if args.gm_bucketing_pempos else []
print(args)
return args, train_args
if __name__ == "__main__":
args, train_args = _parse_args()
# TODO do this argument transfering in the load_train_args instead of train.py, evaluate.py, ensemble_evaluate.py
train_args.checkpoint_model_num = args.checkpoint_model_num
train_args.entity_extension = args.entity_extension
train.args = args
args.batch_size = train_args.batch_size
printPredictions = None
if args.print_predictions:
from evaluation.print_predictions import PrintPredictions
printPredictions = PrintPredictions(
config.base_folder + "data/tfrecords/" + args.experiment_name +
"/", args.predictions_folder, args.entity_extension,
args.gm_bucketing_pempos, args.print_global_voters,
args.print_global_pairwise_scores)
from model.util import Tee
tee = Tee(args.output_folder + 'evaluate-log.txt', 'a')
print("train_args:\n", train_args)
evaluate()
print(train_args)
return args, train_args
def terminate():
tee.close()
if args.build_entity_universe:
buildEntityUniverse.flush_entity_universe()
else:
print("from_myspans_to_given_spans_map_errors:",
nnprocessing.from_myspans_to_given_spans_map_errors)
if __name__ == "__main__":
args, train_args = _parse_args()
print(args)
print(train_args)
if args.build_entity_universe:
buildEntityUniverse = BuildEntityUniverse()
else:
nnprocessing = NNProcessing(train_args, args)
server = HTTPServer(('localhost', 5555), GetHandler)
print('Starting server at http://localhost:5555')
from model.util import Tee
tee = Tee('server.txt', 'w')
try:
server.serve_forever()
except KeyboardInterrupt:
terminate()
exit(0)
args.el_datasets = None
return args
def log_args(filepath):
with open(filepath, "w") as fout:
attrs = vars(args)
# {'kids': 0, 'name': 'Dog', 'color': 'Spotted', 'age': 10, 'legs': 2, 'smell': 'Alot'}
fout.write('\n'.join("%s: %s" % item for item in attrs.items()))
with open(args.output_folder + "train_args.pickle", 'wb') as handle:
pickle.dump(args, handle)
def terminate():
tee.close()
with open(args.output_folder + "train_args.pickle", 'wb') as handle:
pickle.dump(args, handle)
if __name__ == "__main__":
args = _parse_args()
print(args)
log_args(args.output_folder + "train_args.txt")
from model.util import Tee
tee = Tee(args.output_folder + 'log.txt', 'a')
try:
train()
except KeyboardInterrupt:
terminate()
def log_args(filepath):
with open(filepath, "w") as fout:
attrs = vars(args)
# {'kids': 0, 'name': 'Dog', 'color': 'Spotted', 'age': 10, 'legs': 2, 'smell': 'Alot'}
fout.write('\n'.join("%s: %s" % item for item in attrs.items()))
with open(args.output_folder+"train_args.pickle", 'wb') as handle:
pickle.dump(args, handle)
def terminate():
tee.close()
with open(args.output_folder+"train_args.pickle", 'wb') as handle:
pickle.dump(args, handle)
if __name__ == "__main__":
args = _parse_args()
log_args(args.output_folder+"train_args.txt")
from model.util import Tee
tee = Tee(args.output_folder+'train-log.txt', 'a')
print(args)
try:
train()
except KeyboardInterrupt:
terminate()