File size: 3,854 Bytes
fe8202e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 |
import argparse
import glob
import os
import re
import sys
from typing import List, Sequence, Tuple
import torch
from torch.utils.data import DataLoader
sys.path.append(os.path.join(os.path.dirname(__file__), "src"))
from gliomasam3_moe.data.brats_dataset import SegMambaNPZDataset, split_npz_paths
from gliomasam3_moe.models.gliomasam3_moe import GliomaSAM3_MoE
from train import evaluate_test, load_config
def _find_latest_ckpt(ckpt_dir: str) -> str:
pattern = os.path.join(ckpt_dir, "ckpt_step*.pt")
matches = []
for path in glob.glob(pattern):
m = re.search(r"ckpt_step(\d+)\.pt$", path)
if m:
matches.append((int(m.group(1)), path))
if not matches:
raise FileNotFoundError(f"No checkpoints found under {ckpt_dir}.")
matches.sort(key=lambda x: x[0])
return matches[-1][1]
def _select_train_subset(
data_dir: str,
train_rate: float,
val_rate: float,
test_rate: float,
seed: int,
) -> Tuple[Sequence[str], int, int]:
train_paths, _, test_paths = split_npz_paths(
data_dir, train_rate=train_rate, val_rate=val_rate, test_rate=test_rate, seed=seed
)
test_n = len(test_paths)
if test_n == 0:
raise ValueError("Test split size is 0; cannot match train subset size.")
subset_n = min(len(train_paths), test_n)
rng = torch.Generator().manual_seed(seed)
perm = torch.randperm(len(train_paths), generator=rng).tolist()
subset_paths = [train_paths[i] for i in perm[:subset_n]]
return subset_paths, test_n, len(train_paths)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="configs/train.yaml")
parser.add_argument("--checkpoint", type=str, default=None, help="Path to ckpt_step*.pt (default: latest in ckpt_dir).")
parser.add_argument("--max_cases", type=int, default=0, help="Optional cap on subset size.")
args = parser.parse_args()
cfg = load_config(args.config)
data_dir = cfg.data.root_dir
if not os.path.isdir(data_dir):
raise FileNotFoundError(f"data.root_dir does not exist: {data_dir}")
if getattr(cfg.data, "format", "nifti") != "segmamba_npz":
raise ValueError("Only segmamba_npz format is supported for this evaluation script.")
ckpt_path = args.checkpoint or _find_latest_ckpt(cfg.train.ckpt_dir)
if not os.path.isfile(ckpt_path):
raise FileNotFoundError(f"Checkpoint not found: {ckpt_path}")
subset_paths, test_n, train_n = _select_train_subset(
data_dir,
train_rate=getattr(cfg.data, "train_rate", 0.7),
val_rate=getattr(cfg.data, "val_rate", 0.1),
test_rate=getattr(cfg.data, "test_rate", 0.2),
seed=cfg.seed,
)
if args.max_cases and args.max_cases > 0:
subset_paths = subset_paths[: min(len(subset_paths), args.max_cases)]
device = torch.device(cfg.device if torch.cuda.is_available() else "cpu")
model = GliomaSAM3_MoE(**cfg.model.__dict__).to(device)
ckpt = torch.load(ckpt_path, map_location=device)
model.load_state_dict(ckpt["model"], strict=True)
ensure_npy = bool(getattr(cfg.data, "segmamba_unpack", True))
dataset = SegMambaNPZDataset(
data_dir=data_dir,
npz_paths=subset_paths,
test=False,
ensure_npy=ensure_npy,
map_et_to_4=True,
)
loader = DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=max(0, int(cfg.train.num_workers)),
)
metrics = evaluate_test(model, loader, cfg, device)
print(f"[TRAIN-SUBSET] ckpt={ckpt_path}")
print(f"[TRAIN-SUBSET] total_train={train_n} test_count={test_n} subset={len(subset_paths)}")
print(
f"[TRAIN-SUBSET] dice[WT,TC,ET]={metrics['dice']} "
f"hd95[WT,TC,ET]={metrics['hd95']}"
)
if __name__ == "__main__":
main()
|