File size: 3,981 Bytes
8d3311c | 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 99 100 101 102 103 104 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import torch
import yaml
from torch.utils.data import DataLoader
from src.metrics import (
assd_voxels,
binary_classification_metrics,
concordance_index,
dice_score,
expected_calibration_error,
iou_score,
)
from src.models import CohortAwareCTModel
from src.train import build_dataset, collate_optional
def main() -> None:
parser = argparse.ArgumentParser(description="Evaluate a trained checkpoint.")
parser.add_argument("--config", required=True)
parser.add_argument("--checkpoint", required=True)
parser.add_argument("--output-json")
args = parser.parse_args()
cfg = yaml.safe_load(Path(args.config).read_text())
dataset = build_dataset(cfg)
loader = DataLoader(dataset, batch_size=cfg["train"].get("batch_size", 2), shuffle=False, collate_fn=collate_optional)
model_cfg = cfg["model"]
model = CohortAwareCTModel(
in_channels=model_cfg.get("in_channels", 1),
feature_dim=model_cfg.get("feature_dim", 256),
clinical_dim=model_cfg.get("clinical_dim", 0),
survival_bins=model_cfg.get("survival_bins", 0),
use_pet=model_cfg.get("use_pet", True),
use_support=model_cfg.get("use_support", True),
clinical_use_missingness=model_cfg.get("clinical_use_missingness", True),
gate_mode=model_cfg.get("gate_mode", "selective"),
)
try:
state = torch.load(args.checkpoint, map_location="cpu", weights_only=True)
except TypeError:
state = torch.load(args.checkpoint, map_location="cpu")
model.load_state_dict(state["model"])
model.eval()
logits = []
ct_logits = []
pet_logits = []
utilities = []
labels = []
dice = []
ious = []
assds = []
risks = []
times = []
events = []
with torch.no_grad():
for batch in loader:
outputs = model(batch)
if "label" in batch and not torch.isnan(batch["label"]).all():
logits.append(outputs["fused_logit"])
ct_logits.append(outputs["ct_logit"])
pet_logits.append(outputs["pet_logit"])
utilities.append(outputs["pet_utility"])
labels.append(batch["label"])
if "mask" in batch:
dice.append(dice_score(outputs["support_logits"], batch["mask"]))
ious.append(iou_score(outputs["support_logits"], batch["mask"]))
assds.append(assd_voxels(outputs["support_logits"], batch["mask"]))
if "hazard_logits" in outputs and "time" in batch and "event" in batch:
hazard = torch.sigmoid(outputs["hazard_logits"])
risks.append(hazard.sum(dim=1))
times.append(batch["time"])
events.append(batch["event"])
metrics: dict[str, float] = {}
if logits:
all_logits = torch.cat(logits)
all_labels = torch.cat(labels)
metrics.update(binary_classification_metrics(all_logits, all_labels))
metrics["ece"] = expected_calibration_error(all_logits, all_labels)
metrics["ct_only_brier"] = binary_classification_metrics(torch.cat(ct_logits), all_labels)["brier"]
metrics["pet_fusion_brier"] = binary_classification_metrics(torch.cat(pet_logits), all_labels)["brier"]
metrics["selected_pet_ratio"] = float((torch.cat(utilities) >= 0.5).float().mean())
if dice:
metrics["dice"] = sum(dice) / len(dice)
metrics["iou"] = sum(ious) / len(ious)
metrics["assd"] = sum(assds) / len(assds)
if risks:
metrics["c_index"] = concordance_index(torch.cat(risks), torch.cat(times), torch.cat(events))
if args.output_json:
out = Path(args.output_json)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_text(json.dumps(metrics, indent=2, sort_keys=True) + "\n")
print(metrics)
if __name__ == "__main__":
main()
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