import argparse import os import sys from types import SimpleNamespace from typing import Any import yaml import numpy as np import torch import nibabel as nib from torch.utils.data import DataLoader # Avoid heavy MONAI import side effects. os.environ.setdefault("MONAI_SKIP_SUBMODULES", "1") from monai.inferers import sliding_window_inference sys.path.append(os.path.join(os.path.dirname(__file__), "src")) from gliomasam3_moe.models.gliomasam3_moe import GliomaSAM3_MoE from gliomasam3_moe.data.brats_dataset import BraTSDataset, SegMambaNPZDataset from gliomasam3_moe.data.transforms_segmamba_like import get_infer_transforms from gliomasam3_moe.utils.brats_regions import regions_to_label from gliomasam3_moe.utils.postprocess import remove_small_components def _to_namespace(obj: Any): if isinstance(obj, dict): return SimpleNamespace(**{k: _to_namespace(v) for k, v in obj.items()}) return obj def load_config(path: str) -> SimpleNamespace: with open(path, "r") as f: cfg = yaml.safe_load(f) return _to_namespace(cfg) def _get_affine(meta_dict): if meta_dict is None: return np.eye(4) affine = meta_dict.get("affine", None) if isinstance(affine, torch.Tensor): affine = affine.detach().cpu().numpy() if isinstance(affine, np.ndarray) and affine.ndim == 3: affine = affine[0] if affine is None: affine = np.eye(4) return affine def save_nifti(path: str, arr: np.ndarray, affine: np.ndarray): img = nib.Nifti1Image(arr, affine) nib.save(img, path) def save_segmamba_3c(path: str, arr_3c: np.ndarray, affine: np.ndarray | None = None): """Save 3-channel mask for SegMamba metrics. Expected input: [3, D, H, W], saved as 4D NIfTI (D,H,W,3). """ if affine is None: affine = np.eye(4) if arr_3c.ndim != 4 or arr_3c.shape[0] != 3: raise ValueError(f"expected (3,D,H,W), got {arr_3c.shape}") arr = arr_3c.transpose(1, 2, 3, 0) # (D,H,W,3) save_nifti(path, arr.astype(np.uint8), affine) def main(): parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default="configs/train.yaml") parser.add_argument("--input", type=str, required=True, help="Case folder or root folder.") parser.add_argument("--output", type=str, default="./prediction_results/segmamba") parser.add_argument("--checkpoint", type=str, required=True) args = parser.parse_args() cfg = load_config(args.config) device = torch.device(cfg.device if torch.cuda.is_available() else "cpu") model = GliomaSAM3_MoE(**cfg.model.__dict__).to(device) ckpt = torch.load(args.checkpoint, map_location="cpu") model.load_state_dict(ckpt["model"], strict=True) model.eval() data_format = getattr(cfg.data, "format", "nifti") input_path = args.input if data_format == "segmamba_npz": if not os.path.isdir(input_path): raise ValueError("Input must be a directory containing *.npz files.") ensure_npy = bool(getattr(cfg.data, "segmamba_unpack", True)) dataset = SegMambaNPZDataset( data_dir=input_path, test=True, ensure_npy=ensure_npy, map_et_to_4=True, ) loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0) else: if os.path.isdir(input_path): has_modalities = any( os.path.isfile(os.path.join(input_path, m + ".nii.gz")) or os.path.isfile(os.path.join(input_path, m + ".nii")) for m in cfg.data.modalities ) if has_modalities: root_dir = os.path.dirname(input_path) case_ids = [os.path.basename(input_path)] else: root_dir = input_path case_ids = None else: raise ValueError("Input must be a directory.") image_keys = [f"image{i}" for i in range(len(cfg.data.modalities))] transforms = get_infer_transforms(cfg, image_keys=image_keys) dataset = BraTSDataset( root_dir=root_dir, modalities=cfg.data.modalities, seg_name=cfg.data.seg_name, transforms=transforms, include_label=False, case_ids=case_ids, image_keys=image_keys, ) loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0) os.makedirs(args.output, exist_ok=True) with torch.no_grad(): for batch in loader: image = batch["image"].to(device) case_id = batch["case_id"][0] if isinstance(batch["case_id"], (list, tuple)) else batch["case_id"] # Sliding window for logits only (aux is computed from full pass). logits = sliding_window_inference( inputs=image, roi_size=tuple(cfg.infer.roi_size), sw_batch_size=cfg.infer.sw_batch_size, predictor=lambda x: model(x)[0], overlap=cfg.infer.overlap, ) _, aux = model(image) probs = torch.sigmoid(logits) pi_et = aux["pi_et"].view(probs.shape[0], 1, 1, 1, 1) probs[:, 2:3] = probs[:, 2:3] * pi_et regions_bin = (probs > cfg.infer.threshold).float() # ET postprocess (remove small components) et_pp = remove_small_components(regions_bin[:, 2], cfg.infer.et_cc_min_size) regions_bin[:, 2] = et_pp label_map = regions_to_label(regions_bin) meta = batch.get("image_meta_dict", None) affine = _get_affine(meta) prob_np = probs[0].detach().cpu().numpy().transpose(1, 2, 3, 0) # (D,H,W,3) bin_np = regions_bin[0].detach().cpu().numpy().transpose(1, 2, 3, 0) lbl_np = label_map[0, 0].detach().cpu().numpy().astype(np.int16) save_nifti(os.path.join(args.output, f"{case_id}_regions_prob.nii.gz"), prob_np, affine) save_nifti(os.path.join(args.output, f"{case_id}_regions_bin.nii.gz"), bin_np, affine) save_nifti(os.path.join(args.output, f"{case_id}_label.nii.gz"), lbl_np, affine) if data_format == "segmamba_npz": seg_path = os.path.join(args.output, f"{case_id}.nii.gz") seg_arr = regions_bin[0].detach().cpu().numpy().astype(np.uint8) save_segmamba_3c(seg_path, seg_arr, affine) if __name__ == "__main__": main()