Merge branch 'Deep-MI:dev' into dev

.github/workflows/doc.yml

@@ -0,0 +1,63 @@
+name: doc
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.number }}-${{ github.event.ref }}
+  cancel-in-progress: true
+on:
+  pull_request:
+  push:
+    branches: [dev]
+  workflow_dispatch:
+
+jobs:
+  build:
+    timeout-minutes: 10
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          path: ./dev
+      - name: Setup Python 3.10
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+          architecture: 'x64'
+      - name: Install package
+        run: |
+          python -m pip install --progress-bar off --upgrade pip setuptools wheel
+          python -m pip install --progress-bar off dev/.[doc]
+      - name: Build doc
+        run: PYTHONPATH=$PYTHONPATH:./dev TZ=UTC sphinx-build ./dev/doc ./doc-build/dev -W --keep-going
+      - name: Upload documentation
+        uses: actions/upload-artifact@v4
+        with:
+          name: doc-dev
+          path: ./doc-build/dev
+
+  deploy:
+    if: github.event_name == 'push'
+    needs: build
+    timeout-minutes: 10
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Download documentation
+        uses: actions/download-artifact@v4
+        with:
+          name: doc-dev
+          path: ./doc-dev
+      - name: Deploy dev documentation
+        uses: peaceiris/actions-gh-pages@v3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./doc-dev
+          destination_dir: ./dev
+          user_name: 'github-actions[bot]'
+          user_email: 'github-actions[bot]@users.noreply.github.com'

CerebNet/__init__.py

@@ -0,0 +1,10 @@
+__all__ = [
+    "apply_warp",
+    "config",
+    "datasets",
+    "data_loader",
+    "inference",
+    "models",
+    "run_prediction",
+    "utils",
+]

CerebNet/config/__init__.py

@@ -15,3 +15,9 @@
 # IMPORTS
 from CerebNet.config.cerebnet import get_cfg_cerebnet
 from CerebNet.config.dataset import get_cfg_dataset
+__all__ = [
+    "cerebnet",
+    "dataset",
+    "get_cfg_cerebnet",
+    "get_cfg_dataset",
+]

CerebNet/config/checkpoint_paths.yaml

@@ -1,8 +1,8 @@
-URL:
+url:
 - "https://b2share.fz-juelich.de/api/files/c6cf7bc6-2ae5-4d0e-814d-2a3cf0e1a8c5"
 - "https://zenodo.org/records/10390742/files"
 
-CKPT:
-  AXIAL:  "CerebNet/checkpoints/CerebNet_axial_v1.0.0.pkl"
-  CORONAL: "CerebNet/checkpoints/CerebNet_coronal_v1.0.0.pkl"
-  SAGITTAL: "CerebNet/checkpoints/CerebNet_sagittal_v1.0.0.pkl"
+checkpoint:
+  axial:  "checkpoints/CerebNet_axial_v1.0.0.pkl"
+  coronal: "checkpoints/CerebNet_coronal_v1.0.0.pkl"
+  sagittal: "checkpoints/CerebNet_sagittal_v1.0.0.pkl"

CerebNet/data_loader/__init__.py

@@ -0,0 +1,6 @@
+__all__ = [
+    "augmentation",
+    "data_utils",
+    "dataset",
+    "loader",
+]

CerebNet/data_loader/augmentation.py

@@ -333,14 +333,14 @@ def sample_intensity_stats_from_image(
 
     Parameters
     ----------
-    image : np.ndarray
+    image : array_like
         Image from which to evaluate mean intensity and std deviation.
-    segmentation : np.ndarray
+    segmentation : array_like
         Segmentation of the input image. Must have the same size as image.
-    labels_list : np.ndarray
+    labels_list : array_like
         List of labels for which to evaluate mean and std intensity.
         Can be a sequence, a 1d numpy array, or the path to a 1d numpy array.
-    classes_list : np.ndarray, optional
+    classes_list : array_like, optional
         Enables grouping structures into classes of similar intensity statistics.
         The intensities associated with regrouped labels will contribute to the same
         Gaussian during statistics estimation. Can be a sequence, a 1D numpy array,

CerebNet/data_loader/data_utils.py

@@ -15,13 +15,14 @@
 
 
 # IMPORTS
-from typing import Literal, TypeVar
+from typing import TypeVar
 
 import numpy as np
 import torch
 from numpy import typing as npt
 
-Plane = Literal['axial', 'coronal', 'sagittal']
+from FastSurferCNN.utils import Plane
+
 AT = TypeVar('AT', np.ndarray, torch.Tensor)
 
 # CLASSES for final evaluation
@@ -146,6 +147,8 @@
                               14, 15, 14,
                               16, 16
                               ])}
+
+
 # Transformation for mapping
 def transform_axial(vol, coronal2axial=True):
     """

CerebNet/data_loader/dataset.py

@@ -11,10 +11,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import time
 
 # IMPORTS
-from typing import Sequence, Tuple, Literal, get_args as _get_args, TypeVar, Dict
+from typing import Tuple, Literal, TypeVar, Dict
 from numbers import Number
 
 import nibabel as nib
@@ -25,8 +24,7 @@
 from torch.utils.data.dataset import Dataset
 from torchvision.transforms import Compose
 
-from CerebNet.data_loader.data_utils import Plane
-from FastSurferCNN.utils import logging
+from FastSurferCNN.utils import logging, Plane
 from FastSurferCNN.data_loader.data_utils import (
     get_thick_slices,
     transform_axial,
@@ -42,7 +40,6 @@
 LocalizerROI = Dict[ROIKeys, Tuple[int, ...]]
 
 NT = TypeVar("NT", bound=Number)
-PLANES = _get_args(Plane)
 
 logger = logging.get_logger(__name__)
 

CerebNet/datasets/__init__.py

@@ -0,0 +1,6 @@
+__all__ = [
+    "generate_hdf5",
+    "load_data",
+    "utils",
+    "wm_merge_clean",
+]

CerebNet/datasets/utils.py

@@ -695,11 +695,6 @@ def crop_transform(image: AT, offsets=None, target_shape=None, out: Optional[AT]
         Array to store the cropped image in (optional), can be a view on image for memory-efficiency.
     pad :  int, str, default=0
         Padding strategy to use when padding is required, if int, pad with that value (default: zero-pad).
-        
-    See Also
-    ---------
-    numpy.pad
-        For additional information refer to numpy.pad function.
 
     Returns
     -------
@@ -718,6 +713,11 @@ def crop_transform(image: AT, offsets=None, target_shape=None, out: Optional[AT]
     RuntimeError 
         If the dimensionality of image, out, offset or target_shape is invalid or inconsistent.
 
+    See Also
+    --------
+    numpy.pad
+        For additional information refer to numpy.pad function.
+
     Notes
     -----
     Either offsets, target_shape or out must be defined.

CerebNet/inference.py

@@ -24,7 +24,7 @@
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
-from FastSurferCNN.utils import logging
+from FastSurferCNN.utils import logging, Plane, PLANES
 from FastSurferCNN.utils.threads import get_num_threads
 from FastSurferCNN.utils.mapper import JsonColorLookupTable, TSVLookupTable
 from FastSurferCNN.utils.common import (
@@ -34,7 +34,7 @@
     SerialExecutor,
 )
 from CerebNet.data_loader.augmentation import ToTensorTest
-from CerebNet.data_loader.dataset import SubjectDataset, Plane, PLANES
+from CerebNet.data_loader.dataset import SubjectDataset
 from CerebNet.datasets.utils import crop_transform
 from CerebNet.models.networks import build_model
 from CerebNet.utils import checkpoint as cp

CerebNet/models/__init__.py

@@ -0,0 +1,4 @@
+__all__ = [
+    "networks",
+    "sub_module",
+]

CerebNet/models/sub_module.py

@@ -21,8 +21,9 @@
 # Building Blocks
 class CompetitiveDenseBlock(nn.Module):
     """
-    Function to define a competitive dense block comprising
-    of 3 convolutional layers, with BN/ReLU.
+    Define a competitive dense block.
+    
+    A dense block consists of 3 convolutional layers, with BN/ReLU.
 
     Parameters
     ----------
@@ -35,8 +36,10 @@ class CompetitiveDenseBlock(nn.Module):
               'stride_pool' : 2,
               'num_classes' : 44,
               'kernel_c' : 1,
-              'input' : True}.
+              'input' : True
+              }.
     """
+
     def __init__(self, params, outblock=False, discriminator_block=False):
         """
         Constructor to initialize the Competitive Dense Block.
@@ -162,7 +165,8 @@ class CompetitiveDenseBlockInput(nn.Module):
                'stride_pool' : 2,
                'num_classes' : 44,
                'kernel_c' : 1,
-               'input' : True}.
+               'input' : True
+               }.
     """
     def __init__(self, params):
         """