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e51b27b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | from __future__ import annotations
import argparse
import csv
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader
from pet_vlm_dataset import PETSUVRDataset, collate_pet_suvr
from train_pet_foundation import PETSUVRFoundationModel, build_encoder
def _pearson(pred: np.ndarray, target: np.ndarray) -> float:
pred_flat = pred.reshape(-1)
target_flat = target.reshape(-1)
if pred_flat.std() < 1e-8 or target_flat.std() < 1e-8:
return float("nan")
return float(np.corrcoef(pred_flat, target_flat)[0, 1])
def _rankdata(values: np.ndarray) -> np.ndarray:
order = np.argsort(values)
ranks = np.empty_like(order, dtype=np.float64)
ranks[order] = np.arange(len(values), dtype=np.float64)
return ranks
def _spearman(pred: np.ndarray, target: np.ndarray) -> float:
pred_ranks = np.apply_along_axis(_rankdata, 1, pred)
target_ranks = np.apply_along_axis(_rankdata, 1, target)
return _pearson(pred_ranks, target_ranks)
def _topk_overlap(pred: np.ndarray, target: np.ndarray, k: int, largest: bool) -> float:
pred_idx = np.argsort(pred, axis=1)
target_idx = np.argsort(target, axis=1)
if largest:
pred_idx = pred_idx[:, -k:]
target_idx = target_idx[:, -k:]
else:
pred_idx = pred_idx[:, :k]
target_idx = target_idx[:, :k]
overlaps = []
for p, t in zip(pred_idx, target_idx):
overlaps.append(len(set(p.tolist()) & set(t.tolist())) / k)
return float(np.mean(overlaps))
def _retrieval_metrics(logits: np.ndarray) -> dict[str, float]:
ranks = []
for i in range(logits.shape[0]):
order = np.argsort(-logits[i])
rank = int(np.where(order == i)[0][0]) + 1
ranks.append(rank)
ranks_np = np.asarray(ranks)
return {
"recall@1": float(np.mean(ranks_np <= 1)),
"recall@5": float(np.mean(ranks_np <= 5)),
"recall@10": float(np.mean(ranks_np <= 10)),
"mrr": float(np.mean(1.0 / ranks_np)),
"median_rank": float(np.median(ranks_np)),
}
def evaluate(model: PETSUVRFoundationModel, loader: DataLoader, device: torch.device) -> dict[str, float]:
model.eval()
pred_chunks: list[np.ndarray] = []
target_chunks: list[np.ndarray] = []
pet_z_chunks: list[torch.Tensor] = []
suvr_z_chunks: list[torch.Tensor] = []
with torch.no_grad():
for batch in loader:
image = batch["image"].to(device, non_blocking=True)
suvr = batch["suvr"].to(device, non_blocking=True)
outputs = model(image, suvr)
pred_chunks.append(outputs["pred_suvr"].detach().cpu().numpy())
target_chunks.append(suvr.detach().cpu().numpy())
pet_feat = model.pet_encoder(image)
pet_z = torch.nn.functional.normalize(model.pet_projector(pet_feat), dim=-1)
suvr_z = torch.nn.functional.normalize(model.suvr_encoder(suvr), dim=-1)
pet_z_chunks.append(pet_z.cpu())
suvr_z_chunks.append(suvr_z.cpu())
pred = np.concatenate(pred_chunks, axis=0)
target = np.concatenate(target_chunks, axis=0)
pet_z = torch.cat(pet_z_chunks, dim=0)
suvr_z = torch.cat(suvr_z_chunks, dim=0)
logits = (pet_z @ suvr_z.T).numpy()
diff = pred - target
metrics = {
"samples": float(target.shape[0]),
"mae": float(np.mean(np.abs(diff))),
"rmse": float(np.sqrt(np.mean(diff**2))),
"pearson": _pearson(pred, target),
"spearman": _spearman(pred, target),
"top5_high_overlap": _topk_overlap(pred, target, 5, largest=True),
"top5_low_overlap": _topk_overlap(pred, target, 5, largest=False),
}
metrics.update({f"pet_to_suvr_{k}": v for k, v in _retrieval_metrics(logits).items()})
metrics.update({f"suvr_to_pet_{k}": v for k, v in _retrieval_metrics(logits.T).items()})
return metrics
def main() -> None:
parser = argparse.ArgumentParser(description="Evaluate PET-SUVR foundation checkpoints.")
parser.add_argument("--checkpoint", type=Path, required=True)
parser.add_argument("--manifest", type=Path, default=Path("metadata/splits/test.csv"))
parser.add_argument("--backbone", choices=["small_cnn", "medicalnet", "brainiac", "brainfm", "swinunetr", "sam_med3d"], default=None)
parser.add_argument("--medicalnet-weights", type=Path, default=Path("pretrained/medicalnet/resnet_50_23dataset.pth"))
parser.add_argument("--brainiac-weights", type=Path, default=Path("pretrained/brainiac/backbone.safetensors"))
parser.add_argument("--brainfm-weights", type=Path, default=Path("pretrained/brainfm/assets/brainfm_pretrained.pth"))
parser.add_argument("--brainfm-code-root", type=Path, default=Path("pretrained/brainfm"))
parser.add_argument("--swinunetr-weights", type=Path, default=Path("pretrained/swinunetr/model_swinvit.pt"))
parser.add_argument("--sam-med3d-weights", type=Path, default=Path("pretrained/sam-med3d/sam_med3d_turbo.pth"))
parser.add_argument("--batch-size", type=int, default=4)
parser.add_argument("--num-workers", type=int, default=2)
parser.add_argument("--output-size", type=int, nargs=3, default=None)
parser.add_argument("--embed-dim", type=int, default=None)
parser.add_argument("--freeze-encoder", action=argparse.BooleanOptionalAction, default=None)
parser.add_argument("--csv-out", type=Path, default=None)
args = parser.parse_args()
ckpt = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
saved_args = ckpt.get("args", {})
for name in ("backbone", "embed_dim", "freeze_encoder"):
if getattr(args, name.replace("-", "_"), None) is None and name in saved_args:
setattr(args, name, saved_args[name])
if args.output_size is None:
args.output_size = tuple(saved_args.get("output_size", (96, 96, 96)))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dataset = PETSUVRDataset(args.manifest, output_size=tuple(args.output_size))
loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, collate_fn=collate_pet_suvr)
n_regions = int(dataset[0]["suvr"].numel())
encoder = build_encoder(args)
model = PETSUVRFoundationModel(encoder, n_regions, args.embed_dim or 256, bool(args.freeze_encoder)).to(device)
model.load_state_dict(ckpt["model"], strict=True)
metrics = evaluate(model, loader, device)
print(f"checkpoint={args.checkpoint}")
print(f"manifest={args.manifest}")
for key, value in metrics.items():
print(f"{key}={value:.6f}")
if args.csv_out:
args.csv_out.parent.mkdir(parents=True, exist_ok=True)
write_header = not args.csv_out.exists()
with args.csv_out.open("a", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=["checkpoint", "manifest", *metrics.keys()])
if write_header:
writer.writeheader()
writer.writerow({"checkpoint": str(args.checkpoint), "manifest": str(args.manifest), **metrics})
if __name__ == "__main__":
main()
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