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from typing import List
import argparse
import os
from atoms_detection.cv_detection import CVDetection
from atoms_detection.evaluation import Evaluation
from utils.paths import LOGS_PATH, DETECTION_PATH, PREDS_PATH
def cv_full_pipeline(
extension_name: str,
coords_csv: str,
thresholds_list: List[float],
force: bool = False
):
# DL Detection & Evaluation
for threshold in thresholds_list:
inference_cache_path = os.path.join(PREDS_PATH, f"cv_detection_{extension_name}")
detections_path = os.path.join(DETECTION_PATH, f"cv_detection_{extension_name}_{threshold}")
if force or not os.path.exists(detections_path):
print(f"Detecting atoms on test data with threshold={threshold}...")
detection = CVDetection(
dataset_csv=coords_csv,
threshold=threshold,
detections_path=detections_path,
inference_cache_path=inference_cache_path
)
detection.run()
logging_filename = os.path.join(LOGS_PATH, f"cv_detection_{extension_name}_{threshold}.csv")
if force or not os.path.exists(logging_filename):
evaluation = Evaluation(
coords_csv=coords_csv,
predictions_path=detections_path,
logging_filename=logging_filename
)
evaluation.run()
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"extension_name",
type=str,
help="Experiment extension name"
)
parser.add_argument(
"coords_csv",
type=str,
help="Coordinates CSV file to use as input"
)
parser.add_argument(
"-t"
"--thresholds",
nargs="+",
type=float,
help="Coordinates CSV file to use as input"
)
parser.add_argument(
"--force",
action="store_true"
)
return parser.parse_args()
if __name__ == "__main__":
args = get_args()
print(args)
cv_full_pipeline(args.extension_name, args.coords_csv, args.t__thresholds, args.force)
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