source_code/SegMamba/4_predict.py

import argparse
import glob
import os
import re
import sys

import numpy as np
import torch
import SimpleITK as sitk

# Prefer pip-installed MONAI over the local monai/ folder.
os.environ.setdefault("MONAI_SKIP_SUBMODULES", "1")
_repo_root = os.path.abspath(os.path.dirname(__file__))
if "" in sys.path:
    sys.path.remove("")
if _repo_root in sys.path:
    sys.path.remove(_repo_root)
import monai  # noqa: E402
sys.path.insert(0, _repo_root)

from monai.inferers import SlidingWindowInferer
from monai.utils import set_determinism

from light_training.dataloading.dataset import MedicalDataset, get_train_val_test_loader_from_train
from light_training.evaluation.metric import dice
from light_training.prediction import Predictor
from light_training.trainer import Trainer

set_determinism(123)


def _parse_csv_ints(s: str, n: int):
    parts = [p.strip() for p in str(s).split(",") if p.strip()]
    if len(parts) != n:
        raise ValueError(f"expect {n} integers like '128,128,128', got: {s}")
    return [int(x) for x in parts]


def _parse_csv_floats(s: str, n: int):
    parts = [p.strip() for p in str(s).split(",") if p.strip()]
    if len(parts) != n:
        raise ValueError(f"expect {n} floats like '1,1,1', got: {s}")
    return [float(x) for x in parts]


def _find_ckpt_from_logdir(logdir: str, prefer: str = "best") -> str:
    model_dir = os.path.join(logdir, "model")
    if not os.path.isdir(model_dir):
        raise FileNotFoundError(f"model dir not found: {model_dir}")

    best = sorted(glob.glob(os.path.join(model_dir, "best_model_*.pt")))
    final = sorted(glob.glob(os.path.join(model_dir, "final_model_*.pt")))
    tmp = sorted(glob.glob(os.path.join(model_dir, "tmp_model_ep*.pt")))
    any_pt = sorted(glob.glob(os.path.join(model_dir, "*.pt")))

    def pick_by_score(paths):
        # filenames like best_model_0.9038.pt / final_model_0.9038.pt
        scored = []
        for p in paths:
            m = re.search(r"_(\d+\\.?\\d*)\\.pt$", os.path.basename(p))
            if m is None:
                continue
            try:
                scored.append((float(m.group(1)), p))
            except ValueError:
                continue
        if scored:
            scored.sort(key=lambda x: x[0], reverse=True)
            return scored[0][1]
        return None

    if prefer == "best":
        picked = pick_by_score(best) or (best[-1] if best else None)
        if picked:
            return picked
    if prefer in {"best", "final"}:
        picked = pick_by_score(final) or (final[-1] if final else None)
        if picked:
            return picked
    if prefer in {"best", "final", "latest"}:
        if tmp:
            tmp.sort(key=lambda p: os.path.getmtime(p), reverse=True)
            return tmp[0]
        if any_pt:
            any_pt.sort(key=lambda p: os.path.getmtime(p), reverse=True)
            return any_pt[0]

    raise FileNotFoundError(f"no checkpoint found under: {model_dir}")


class BraTSTrainer(Trainer):
    def __init__(
        self,
        ckpt_path: str,
        save_path: str,
        patch_size,
        sw_batch_size: int = 2,
        overlap: float = 0.5,
        mirror_axes=(0, 1, 2),
        raw_spacing=(1.0, 1.0, 1.0),
        device="cuda:0",
        print_dice: bool = False,
    ):
        super().__init__(
            env_type="pytorch",
            max_epochs=1,
            batch_size=1,
            device=device,
            val_every=1,
            num_gpus=1,
            logdir="",
            master_port=17751,
            training_script=__file__,
        )

        self.patch_size = patch_size
        self.augmentation = False
        self.print_dice = print_dice
        self.save_path = save_path
        self.raw_spacing = raw_spacing

        from model_segmamba.segmamba import SegMamba

        self.model = SegMamba(
            in_chans=4,
            out_chans=4,
            depths=[2, 2, 2, 2],
            feat_size=[48, 96, 192, 384],
        )
        self.load_state_dict(ckpt_path, strict=True)
        self.model.eval()

        window_infer = SlidingWindowInferer(
            roi_size=patch_size,
            sw_batch_size=sw_batch_size,
            overlap=overlap,
            progress=True,
            mode="gaussian",
        )

        self.predictor = Predictor(
            window_infer=window_infer,
            mirror_axes=list(mirror_axes) if mirror_axes is not None else None,
        )

        os.makedirs(self.save_path, exist_ok=True)
    
    def convert_labels(self, labels):
        ## TC, WT and ET
        result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3]
        
        return torch.cat(result, dim=1).float()

    def get_input(self, batch):
        image = batch["data"]
        label = batch["seg"]
        properties = batch["properties"]
        label = self.convert_labels(label)

        return image, label, properties 
    
    def validation_step(self, batch):
        image, label, properties = self.get_input(batch)
        # The preprocessed datasets used in many setups (including /data/yty/brats23_processed)
        # do NOT contain cropping/resample metadata (shape_before_cropping, bbox_used_for_cropping, ...),
        # so we directly save predictions in the same (D,H,W) space as the inputs.
        #
        # We save as a TRUE 4D NIfTI (t,z,y,x) with t=3 (TC/WT/ET) so that
        # `sitk.GetArrayFromImage` returns shape (3, D, H, W), matching `5_compute_metrics.py`.

        logits = self.predictor.maybe_mirror_and_predict(image, self.model, device=self.device)  # (1,4,D,H,W) on CPU
        pred_lbl = logits.argmax(dim=1)  # (1,D,H,W)
        pred_3c = self.convert_labels(pred_lbl[:, None])[0].cpu().numpy().astype(np.uint8)  # (3,D,H,W)

        if self.print_dice:
            gt_3c = label[0].cpu().numpy()
            dices = [dice(pred_3c[i], gt_3c[i]) for i in range(3)]
            print(dices)

        case_name = properties.get("name", "")
        if isinstance(case_name, (list, tuple)) and len(case_name) > 0:
            case_name = case_name[0]

        out_path = os.path.join(self.save_path, f"{case_name}.nii.gz")
        pred_itk = sitk.GetImageFromArray(pred_3c, isVector=False)
        pred_itk.SetSpacing((float(self.raw_spacing[0]), float(self.raw_spacing[1]), float(self.raw_spacing[2]), 1.0))
        sitk.WriteImage(pred_itk, out_path)
        print(f"saved: {out_path}")
        
        return 0

    def convert_labels_dim0(self, labels):
        ## TC, WT and ET
        result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3]
        
        return torch.cat(result, dim=0).float()


def main():
    parser = argparse.ArgumentParser(description="SegMamba inference/prediction for BraTS2023.")
    parser.add_argument("--data_dir", type=str, default="./data/fullres/train", help="Preprocessed data directory (contains *.npz).")
    parser.add_argument("--split", type=str, default="test", choices=["train", "val", "test", "all"])
    parser.add_argument("--train_rate", type=float, default=0.7)
    parser.add_argument("--val_rate", type=float, default=0.1)
    parser.add_argument("--test_rate", type=float, default=0.2)
    parser.add_argument("--seed", type=int, default=42)

    parser.add_argument("--ckpt", type=str, default="", help="Checkpoint path (*.pt). If empty, will search under --logdir/model.")
    parser.add_argument("--logdir", type=str, default="./logs/segmamba", help="Training logdir to locate checkpoints when --ckpt is empty.")
    parser.add_argument("--ckpt_prefer", type=str, default="best", choices=["best", "final", "latest"])

    parser.add_argument("--save_dir", type=str, default="./prediction_results/segmamba", help="Directory to save prediction nii.gz.")
    parser.add_argument("--device", type=str, default="cuda:0")
    parser.add_argument("--patch_size", type=str, default="128,128,128")
    parser.add_argument("--sw_batch_size", type=int, default=2)
    parser.add_argument("--overlap", type=float, default=0.5)
    parser.add_argument("--raw_spacing", type=str, default="1,1,1", help="Spacing used when saving NIfTI, e.g. '1,1,1'.")
    parser.add_argument("--no_mirror", action="store_true", help="Disable mirror TTA.")
    parser.add_argument("--print_dice", action="store_true", help="Print dice against preprocessed seg (if available).")
    args = parser.parse_args()

    patch_size = _parse_csv_ints(args.patch_size, 3)
    raw_spacing = _parse_csv_floats(args.raw_spacing, 3)

    ckpt_path = args.ckpt.strip()
    if ckpt_path == "":
        ckpt_path = _find_ckpt_from_logdir(args.logdir, prefer=args.ckpt_prefer)
    if not os.path.isfile(ckpt_path):
        raise FileNotFoundError(f"checkpoint not found: {ckpt_path}")
    print(f"Using checkpoint: {ckpt_path}")

    trainer = BraTSTrainer(
        ckpt_path=ckpt_path,
        save_path=args.save_dir,
        patch_size=patch_size,
        sw_batch_size=args.sw_batch_size,
        overlap=args.overlap,
        mirror_axes=None if args.no_mirror else (0, 1, 2),
        raw_spacing=raw_spacing,
        device=args.device,
        print_dice=args.print_dice,
    )

    if args.split == "all":
        all_paths = sorted(glob.glob(os.path.join(args.data_dir, "*.npz")))
        ds = MedicalDataset(all_paths, test=False)
    else:
        train_ds, val_ds, test_ds = get_train_val_test_loader_from_train(
            args.data_dir,
            train_rate=args.train_rate,
            val_rate=args.val_rate,
            test_rate=args.test_rate,
            seed=args.seed,
        )
        ds = {"train": train_ds, "val": val_ds, "test": test_ds}[args.split]

    trainer.validation_single_gpu(ds)


if __name__ == "__main__":
    main()