def main():
parser = get_argument_parser()
parser.add_argument(
"-s",
"--split",
type=str,
default="test",
choices={"train", "valid", "test"},
required=False,
help="which data to use",
)
parser.add_argument(
"-o",
"--output-path",
type=str,
required=True,
help="where to store the predictions",
)
parser.add_argument(
"--filelist-train",
help=
"name of filelist used for training (default, the same as `filelist`)",
)
parser.add_argument(
"-e",
"--embedding",
# choices={"mean", None},
help="what embedding to use",
)
args = parser.parse_args()
args.filelist_train = args.filelist_train or args.filelist
predict(args)
def main():
parser = get_argument_parser()
args = parser.parse_args()
args.batch_size = BATCH_SIZE
args.max_epochs = MAX_EPOCHS
trial = SimpleNamespace(**{
"parameters": {
"lr": 5e-4,
},
})
print(args)
print(trial)
train(args, trial)
def main():
parser = get_argument_parser()
parser.add_argument(
"-s",
"--split",
type=str,
default="test",
choices={"train", "valid", "test"},
required=False,
help="which data to use",
)
parser.add_argument(
"-o",
"--output-path",
type=str,
help="where to store the visual features",
)
args = parser.parse_args()
print(args)
extract_visual_features(args)
def main():
parser = get_argument_parser()
args = parser.parse_args()
args.batch_size = 8
args.max_epochs = 64
study = get_study()
for i, trial in enumerate(study):
print("trial id: {}".format(trial.id))
pprint.pprint(trial.parameters)
loss = train(args, trial, is_train=False, study=study)
study.add_observation(trial=trial, objective=loss)
study.finalize(trial)
print()
print(study.get_best_result())
# save study
model_name = f"{DATASET}_{args.filelist}_{args.model_type}"
path = os.path.join("output/models/sherpa", model_name)
os.makedirs(path, exist_ok=True)
study.save(path)
initialize([hidden_to_output])
linear_output_e1 = hidden_to_output.apply(before_out_e1)
linear_output_e2 = hidden_to_output.apply(before_out_e2)
linear_output_e1.name = 'linear_output_e1'
linear_output_e2.name = 'linear_output_e2'
y_hat_e1 = Logistic(name='logistic1').apply(linear_output_e1)
y_hat_e2 = Logistic(name='logistic2').apply(linear_output_e2)
y_hat_e1.name = 'y_hat_e1'
y_hat_e2.name = 'y_hat_e2'
y_hat_e1 = debug_print(y_hat_e1, 'y_1', DEBUG)
return y_hat_e1, y_hat_e2, before_out_e1, before_out_e2
if __name__ == '__main__':
parser = get_argument_parser()
args = parser.parse_args()
logger.info('args: %s', args)
if not args.config and (not args.samples or not args.embeddings):
print(
"Please provide either a config file (--config) or the paths "
"to the samples and embeddings files. "
"(--samples, --embeddings).")
sys.exit(1)
trainer = JointUnaryBinaryOldEntityTyping.from_config(args.config)
trainer._config[
'hidden_units'] = args.hidden_units if args.hidden_units else trainer._config[
'hidden_units']
if args.max_len:
trainer._config['max_len'] = args.max_len
if args.apply: