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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()
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