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eebd638 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | """Evaluate a trained checkpoint on the validation or test split.
Usage::
python scripts/evaluate.py \\
--checkpoint checkpoints/best.pt \\
--prototypes checkpoints/prototypes.pt \\
--split val
"""
from __future__ import annotations
import argparse
import logging
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
import torch
from omegaconf import OmegaConf
from torch.utils.data import DataLoader
from data.dataset import ConstructionChangeDataset
from evaluation.metrics import SegmentationEvaluator
from models.detector import ConstructionChangeDetector
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
)
logger = logging.getLogger(__name__)
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description="Evaluate construction change detector.")
p.add_argument("--checkpoint", required=True)
p.add_argument("--prototypes", required=True)
p.add_argument("--config", default="configs/base.yaml")
p.add_argument("--split", default="val", choices=["train", "val"])
p.add_argument("--threshold", type=float, default=0.5)
return p.parse_args()
def main() -> None:
args = parse_args()
cfg = OmegaConf.load(args.config)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load model
model = ConstructionChangeDetector(k_prototypes=cfg.model.k_prototypes)
model.load_sam2(cfg.model.sam2_hf_id)
state = torch.load(args.checkpoint, map_location=device)
model.load_trainable_state_dict(state)
model.to(device).eval()
# Load prototypes
proto_ckpt = torch.load(args.prototypes, map_location=device)
prototypes = proto_ckpt["prototypes"].to(device)
# Dataset
dataset = ConstructionChangeDataset(
tile_dir=cfg.data.tile_dir,
image_size=cfg.data.image_size,
augment=False,
min_fg_pixels=cfg.data.min_fg_pixels,
split=args.split,
val_split=cfg.training.val_split,
seed=cfg.training.seed,
)
loader = DataLoader(
dataset,
batch_size=cfg.training.batch_size,
shuffle=False,
num_workers=cfg.data.num_workers,
)
evaluator = SegmentationEvaluator(threshold=args.threshold)
with torch.no_grad():
for batch in loader:
tile_2022 = batch["tile_2022"].to(device)
tile_2024 = batch["tile_2024"].to(device)
mask_2022 = batch["mask_2022"].to(device)
mask_2024 = batch["mask_2024"].to(device)
out = model(tile_2022, tile_2024, mask_2022, prototypes)
evaluator.update(out["prob_map"].cpu(), mask_2024.cpu())
metrics = evaluator.compute()
logger.info("=== Evaluation results (%s split) ===", args.split)
for k, v in metrics.items():
logger.info(" %-12s %.4f", k, v)
if __name__ == "__main__":
main()
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