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import argparse
from pprint import pprint
from typing import Optional
from relik.reader.relik_reader_predictor import RelikReaderPredictor
from relik.reader.utils.strong_matching_eval import StrongMatching
from relik.reader.relik_reader_core import RelikReaderCoreModel
from relik.reader.pytorch_modules.span import RelikReaderForSpanExtraction
import hydra
from omegaconf import DictConfig
from relik.reader.data.relik_reader_sample import load_relik_reader_samples
import json
# @hydra.main(config_path="config.yaml", config_name="") # Specify your config path and name here
def predict(
model_path: str,
dataset_path: str,
token_batch_size: int,
is_eval: bool,
output_path: Optional[str],
) -> None:
relik_reader = RelikReaderForSpanExtraction(model_path,training=False, device="cuda")
samples = list(load_relik_reader_samples(dataset_path))
predicted_samples = relik_reader.read(
samples=samples, progress_bar=True
)
if True:
eval_dict = StrongMatching()(predicted_samples)
pprint(eval_dict)
if output_path is not None:
with open(output_path, "w") as f:
gold_text = ""
for sample in predicted_samples:
text = sample.to_jsons()
# json.dump(text, f)
# f.write("\n")
gold_text += str(text["window_labels"]) + "\t" + str(text["predicted_window_labels"]) + "\n"
f.write(gold_text)
def parse_arg() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
"--model-path",
required=True,
)
parser.add_argument("--dataset-path", "-i", required=True)
parser.add_argument("--is-eval", action="store_true")
parser.add_argument(
"--output-path",
"-o",
)
parser.add_argument("--token-batch-size", default=4096)
return parser.parse_args()
def main():
args = parse_arg()
predict(
args.model_path,
args.dataset_path,
token_batch_size=args.token_batch_size,
is_eval=args.is_eval,
output_path=args.output_path,
)
if __name__ == "__main__":
main()
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