fix: resolve Python 3.12+ syntax errors in escape sequences and type annotations (Fixes #228, #216)

guided_diffusion/dpm_solver.py

@@ -13,7 +13,7 @@ def __init__(
             continuous_beta_1=20.,
             dtype=torch.float32,
         ):
-        """Create a wrapper class for the forward SDE (VP type).
+        r"""Create a wrapper class for the forward SDE (VP type).
         ***
         Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
                 We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.

guided_diffusion/unet.py

@@ -1761,7 +1761,7 @@ def _internal_predict_3D_3Dconv(self, x: np.ndarray, min_size: Tuple[int, ...],
 
     def _internal_maybe_mirror_and_pred_3D(self, x: Union[np.ndarray, torch.tensor], mirror_axes: tuple,
                                            do_mirroring: bool = True,
-                                           mult: np.ndarray or torch.tensor = None) -> torch.tensor:
+                                           mult: Union[np.ndarray, torch.tensor] = None) -> torch.tensor:
         assert len(x.shape) == 5, 'x must be (b, c, x, y, z)'
 
         # if cuda available:
@@ -1828,7 +1828,7 @@ def _internal_maybe_mirror_and_pred_3D(self, x: Union[np.ndarray, torch.tensor],
 
     def _internal_maybe_mirror_and_pred_2D(self, x: Union[np.ndarray, torch.tensor], mirror_axes: tuple,
                                            do_mirroring: bool = True,
-                                           mult: np.ndarray or torch.tensor = None) -> torch.tensor:
+                                           mult: Union[np.ndarray, torch.tensor] = None) -> torch.tensor:
         # if cuda available:
         #   everything in here takes place on the GPU. If x and mult are not yet on GPU this will be taken care of here
         #   we now return a cuda tensor! Not numpy array!

scripts/segmentation_train.py

@@ -41,7 +41,7 @@ def main():
 
         ds = BRATSDataset3D(args.data_dir, transform_train, test_flag=False)
         args.in_ch = 5
-    elif any(Path(args.data_dir).glob("*\*.nii.gz")):
+    elif any(Path(args.data_dir).glob("*/*.nii.gz")):
         tran_list = [transforms.Resize((args.image_size,args.image_size)),]
         transform_train = transforms.Compose(tran_list)
         print("Your current directory : ",args.data_dir)