doc/overview/OUTPUT_FILES.md

- remove/rename changed filenames
- format table

HypVINN/config/checkpoint_paths.yaml
- add config

HypVINN/data_loader/data_utils.py
- fix typing and formatting

HypVINN/utils/checkpoint.py
- fix YAML_DEFAULT

HypVINN/utils/mode_config.py
- set default values for get_hypvinn_mode

HypVINN/inference.py
- fix inclusion of ModalityMode

HypVINN/run_prediction.py
- move HypVINN/run_pipeline.py into run_prediction.py
- fix typing, e.g. FileBasedHeader
- fix function parameters
- add help text to hypo_segfile argument
- fix passing of t1_path and t2_path
- various other changes

HypVINN/README.md

@@ -18,7 +18,7 @@ Hypothalamic subfields segmentation pipeline
 2. Hypothalamus Segmentation
 
 ### Running the tool
-Run the HypVINN/run_pipeline.py which has the following arguments:
+Run the HypVINN/run_prediction.py which has the following arguments:
 ### Input and output arguments
  * `--sid <name>` :  Subject ID, the subject data upon which to operate
  * `--sd <name>` : Directory in which evaluation results should be written.
@@ -54,7 +54,7 @@ The pipeline can do all pre-processing by itself (step 1). This step can be skip
 
 1. Run full pipeline
     ```
-    python HypVINN/run_pipeline.py  --sid test_subject --sd /output \
+    python HypVINN/run_prediction.py  --sid test_subject --sd /output \
                                      --t1 /data/test_subject_t1.nii.gz \
                                      --t2 /data/test_subject_t2.nii.gz \
                                      --reg_mode coreg \
@@ -63,7 +63,7 @@ The pipeline can do all pre-processing by itself (step 1). This step can be skip
    ```
 2. Run full pipeline only using a t1 
     ```
-    python HypVINN/run_pipeline.py  --sid test_subject --sd /output \
+    python HypVINN/run_prediction.py  --sid test_subject --sd /output \
                                      --t1 /data/test_subject_t1.nii.gz \
                                      --reg_mode coreg \
                                      --seg_log /outdir/test_subject.log \
@@ -72,7 +72,7 @@ The pipeline can do all pre-processing by itself (step 1). This step can be skip
 
 3. Run pipeline without the registration step
     ```
-    python HypVINN/run_pipeline.py  --sid test_subject --sd /output \
+    python HypVINN/run_prediction.py  --sid test_subject --sd /output \
                                      --t1 /data/test_subject_t1.nii.gz \
                                      --t2 /data/test_subject_t2.nii.gz \
                                      --reg_mode coreg \
@@ -82,7 +82,7 @@ The pipeline can do all pre-processing by itself (step 1). This step can be skip
 
 4. Run pipeline with creation of qc snapshots
     ```
-    python HypVINN/run_pipeline.py  --sid test_subject --sd /output \
+    python HypVINN/run_prediction.py  --sid test_subject --sd /output \
                                      --t1 /data/test_subject_t1.nii.gz \
                                      --t2 /data/test_subject_t2.nii.gz \
                                      --reg_mode coreg \

HypVINN/__init__.py

@@ -19,5 +19,4 @@
     "utils",
     "inference",
     "run_prediction",
-    "run_pipeline",
 ]

HypVINN/config/checkpoint_paths.yaml

@@ -5,3 +5,8 @@ checkpoint:
   axial: "checkpoints/HypVINN_axial_v1.0.0.pkl"
   coronal: "checkpoints/HypVINN_coronal_v1.0.0.pkl"
   sagittal: "checkpoints/HypVINN_sagittal_v1.0.0.pkl"
+
+config:
+  axial: "HypVINN/config/HypVINN_axial_v1.0.0.yaml"
+  coronal: "HypVINN/config/HypVINN_coronal_v1.0.0.yaml"
+  sagittal: "HypVINN/config/HypVINN_sagittal_v1.0.0.yaml"

HypVINN/data_loader/data_utils.py

@@ -13,19 +13,25 @@
 # limitations under the License.
 
 # IMPORTS
+import nibabel as nib
 import numpy as np
+from numpy import typing as npt
+
 from FastSurferCNN.data_loader.conform import getscale, scalecrop
-import nibabel as nib
-import sys
 from HypVINN.config.hypvinn_global_var import hyposubseg_labels, SAG2FULL_MAP, HYPVINN_CLASS_NAMES, FS_CLASS_NAMES
+
+
 ##
 # Helper Functions
 ##
-def calculate_flip_orientation(iornt,base_ornt):
+
+
+def calculate_flip_orientation(iornt, base_ornt):
     """
     Compute the flip orientation transform.
 
     ornt[N, 1] is flip of axis N, where 1 means no flip and -1 means flip.
+
     Parameters
     ----------
     iornt
@@ -47,9 +53,9 @@ def calculate_flip_orientation(iornt,base_ornt):
 
     return new_iornt
 
-# reorient image based on base image
-def reorient_img(img,ref_img):
-    '''
+
+def reorient_img(img, ref_img):
+    """
     Function to reorient a Nibabel image based on the orientation of a reference nibabel image
     The orientation transform. ornt[N,1]` is flip of axis N of the array implied by `shape`, where 1 means no flip and -1 means flip.
     For example, if ``N==0 and ornt[0,1] == -1, and there’s an array arr of shape shape, the flip would correspond to the effect of
@@ -63,7 +69,7 @@ def reorient_img(img,ref_img):
     Returns
     -------
 
-    '''
+    """
 
     ref_ornt =nib.io_orientation(ref_img.affine)
     iornt=nib.io_orientation(img.affine)
@@ -79,8 +85,7 @@ def reorient_img(img,ref_img):
 
     return img
 
-# Transformation for mapping
-#TODO check compatibility with axis transform from CerebNet
+
 def transform_axial2coronal(vol, axial2coronal=True):
     """
     Function to transform volume into coronal axis and back
@@ -89,11 +94,13 @@ def transform_axial2coronal(vol, axial2coronal=True):
                                 transform from coronal to axial = False
     :return:
     """
+    # TODO check compatibility with axis transform from CerebNet
     if axial2coronal:
         return np.moveaxis(vol, [0, 1, 2], [0, 2, 1])
     else:
         return np.moveaxis(vol, [0, 1, 2], [0, 2, 1])
-#TODO check compatibility with axis transform from CerebNet
+
+
 def transform_axial2sagittal(vol, axial2sagittal=True):
     """
     Function to transform volume into Sagittal axis and back
@@ -102,15 +109,16 @@ def transform_axial2sagittal(vol, axial2sagittal=True):
                                 transform from sagittal to coronal = False
     :return:
     """
+    # TODO check compatibility with axis transform from CerebNet
     if axial2sagittal:
         return np.moveaxis(vol, [0, 1, 2], [2, 0, 1])
     else:
         return np.moveaxis(vol, [0, 1, 2], [1, 2, 0])
 
 
-# Same as CerebNet.datasets.utils.rescale_image
 def rescale_image(img_data):
     # Conform intensities
+    # TODO move function into FastSurferCNN, same: CerebNet.datasets.utils.rescale_image
     src_min, scale = getscale(img_data, 0, 255)
     mapped_data = img_data
     if not img_data.dtype == np.dtype(np.uint8):
@@ -121,80 +129,39 @@ def rescale_image(img_data):
     return new_data
 
 
-
-def hypo_map_subseg2label(subseg):
-    '''
-          Function to perform look-up table mapping from subseg space to label space
-
-
-    Parameters
-    ----------
-    subseg
-
-    Returns
-    -------
-
-    '''
-
-    h, w, d = subseg.shape
-    lbls, lbls_sag = hyposubseg_labels
-
-    lut_subseg = np.zeros(max(lbls) + 1, dtype='int')
-    for idx, value in enumerate(lbls):
-        lut_subseg[value] = idx
-
-    mapped_subseg = lut_subseg.ravel()[subseg.ravel()]
-    mapped_subseg = mapped_subseg.reshape((h, w, d))
-
-
-    # mapping left labels to right labels for sagittal view
-    subseg[subseg == 2] = 1
-    subseg[subseg == 5] = 4
-    subseg[subseg == 6] = 3
-    subseg[subseg == 8] = 7
-    subseg[subseg == 12] = 11
-    subseg[subseg == 20] = 13
-    subseg[subseg == 24] = 23
-
-    subseg[subseg == 126] = 226
-    subseg[subseg == 127] = 227
-    subseg[subseg == 128] = 228
-    subseg[subseg == 129] = 229
-
-    lut_subseg_sag = np.zeros(max(lbls_sag) + 1, dtype='int')
-    for idx, value in enumerate(lbls_sag):
-        lut_subseg_sag[value] = idx
-
-    mapped_subseg_sag = lut_subseg_sag.ravel()[subseg.ravel()]
-
-    mapped_subseg_sag = mapped_subseg_sag.reshape((h, w, d))
-
-    return mapped_subseg,mapped_subseg_sag
-def hypo_map_label2subseg(mapped_subseg):
-    '''
-       Function to perform look-up table mapping from label space to subseg space
-    '''
+def hypo_map_label2subseg(mapped_subseg: npt.NDArray[int]) -> npt.NDArray[int]:
+    """
+    Function to perform look-up table mapping from label space to subseg space
+    """
+    # TODO can this function be replaced by a Mapper and a mapping file?
     labels, _ = hyposubseg_labels
     subseg = np.zeros_like(mapped_subseg)
     h, w, d = subseg.shape
     subseg = labels[mapped_subseg.ravel()]
 
     return subseg.reshape((h, w, d))
 
-def hypo_map_prediction_sagittal2full(prediction_sag):
+
+def hypo_map_prediction_sagittal2full(
+        prediction_sag: npt.NDArray[int],
+) -> npt.NDArray[int]:
     """
     Function to remap the prediction on the sagittal network to full label space used by coronal and axial networks
     :param prediction_sag: sagittal prediction (labels)
     :param lbl_type: type of label
     :return: Remapped prediction
     """
+    # TODO can this function be replaced by a Mapper and a mapping file?
 
     idx_list = list(SAG2FULL_MAP.values())
     prediction_full = prediction_sag[:, idx_list, :, :]
     return prediction_full
 
 
-def hypo_map_subseg_2_fsseg(subseg,reverse=False):
+def hypo_map_subseg_2_fsseg(
+        subseg: npt.NDArray[int],
+        reverse: bool = False,
+) -> npt.NDArray[int]:
     """
     Function to remap HypVINN internal labels to FastSurfer Labels and viceversa
     Parameters
@@ -206,7 +173,9 @@ def hypo_map_subseg_2_fsseg(subseg,reverse=False):
     -------
 
     """
-    fsseg = np.zeros_like(subseg,dtype=np.int16)
+    # TODO can this function be replaced by a Mapper and a mapping file?
+
+    fsseg = np.zeros_like(subseg, dtype=np.int16)
 
     if not reverse:
         for value, name in HYPVINN_CLASS_NAMES.items():

HypVINN/inference.py

@@ -27,17 +27,24 @@
 from HypVINN.models.networks import build_model
 from HypVINN.data_loader.data_utils import hypo_map_prediction_sagittal2full
 from HypVINN.data_loader.dataset import HypoVINN_dataset
-from HypVINN.run_prediction import ModalityMode
+from HypVINN.utils import ModalityMode
 
 logger = logging.get_logger(__name__)
 
 
 class Inference:
-    def __init__(self, cfg, args):
+    def __init__(
+            self,
+            cfg,
+            threads: int = -1,
+            async_io: bool = False,
+            device: str = "auto",
+            viewagg_device: str = "auto",
+    ):
 
-        self._threads = getattr(args, "threads", 1)
+        self._threads = threads
         torch.set_num_threads(self._threads)
-        self._async_io = getattr(args, "async_io", False)
+        self._async_io = async_io
 
         # Set random seed from configs.
         np.random.seed(cfg.RNG_SEED)
@@ -49,16 +56,16 @@ def __init__(self, cfg, args):
         torch.set_flush_denormal(True)
 
         # Define device and transfer model
-        self.device = find_device(args.device)
+        self.device = find_device(device)
 
-        if self.device.type == "cpu" and args.viewagg_device == "auto":
+        if self.device.type == "cpu" and viewagg_device == "auto":
             self.viewagg_device = self.device
         else:
             # check, if GPU is big enough to run view agg on it
             # (this currently takes the memory of the passed device)
             self.viewagg_device = torch.device(
                 find_device(
-                    args.viewagg_device,
+                    viewagg_device,
                     flag_name="viewagg_device",
                     min_memory=4 * (2 ** 30),
                 )