Merge branch 'Deep-MI:dev' into dev

.github/workflows/code-style.yml

@@ -27,12 +27,6 @@ jobs:
           python -m pip install --progress-bar off .[style]
       - name: Run Ruff
         run: ruff check .
-      - name: Run isort
-        uses: isort/isort-action@master
-      - name: Run black
-        uses: psf/black@stable
-        with:
-          options: "--check --verbose"
       - name: Run codespell
         uses: codespell-project/actions-codespell@master
         with:

Docker/Dockerfile

@@ -72,7 +72,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
 
 ARG PYTHON_VERSION=3.10
-ARG FORGE_VERSION=23.11.0-0
+ARG FORGE_VERSION=24.3.0-0
 
 # Install conda
 RUN wget --no-check-certificate -qO ~/miniforge.sh \
@@ -189,6 +189,9 @@ SHELL ["/bin/bash", "--login", "-c"]
 COPY --from=selected_freesurfer_build_image /opt/freesurfer /opt/freesurfer
 COPY --from=selected_conda_build_image /venv /venv
 
+# Fix for cuda11.8+cudnn8.7 bug+warning: https://github.com/pytorch/pytorch/issues/97041
+RUN if [[ "$DEVICE" == "cu118" ]] ; then cd /venv/python3.10/site-packages/torch/lib && ln -s libnvrtc-*.so.11.2 libnvrtc.so ; fi
+
 # Copy fastsurfer over from the build context and add PYTHONPATH
 COPY . /fastsurfer/
 ENV PYTHONPATH=/fastsurfer:/opt/freesurfer/python/packages \

Docker/README.md

@@ -139,7 +139,7 @@ As you can see, only the tag of the image is changed from gpu to cpu and the sta
 
 Here we build an experimental image to test performance when running on AMD GPUs. Note that you need a supported OS and Kernel version and supported GPU for the RocM to work correctly. You need to install the Kernel drivers into 
 your host machine kernel (amdgpu-install --usecase=dkms) for the amd docker to work. For this follow:
-https://docs.amd.com/en/latest/deploy/linux/quick_start.html
+https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html#rocm-install-quick, https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/amdgpu-install.html#amdgpu-install-dkms and https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html
 
 ```bash
 PYTHONPATH=<FastSurferRoot>
@@ -149,22 +149,22 @@ python build.py --device rocm --tag my_fastsurfer:rocm
 and run segmentation only:
 
 ```bash
-docker run --rm --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
-           --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host \
-	   --shm-size 8G \
+docker run --rm --security-opt seccomp=unconfined \
+           --device=/dev/kfd --device=/dev/dri --group-add video \
 	   -v /home/user/my_mri_data:/data \
 	   -v /home/user/my_fastsurfer_analysis:/output \
 	   my_fastsurfer:rocm \
 	   --t1 /data/subjectX/t1-weighted.nii.gz \
 	   --sid subjectX --sd /output 
 ```
 
-Note, we tested on an AMD Radeon Pro W6600, which is [not officially supported](https://docs.amd.com/en/latest/release/gpu_os_support.html), but setting `HSA_OVERRIDE_GFX_VERSION=10.3.0` [inside docker did the trick](https://en.opensuse.org/AMD_OpenCL#ROCm_-_Running_on_unsupported_hardware):
+In conflict with the official ROCm documentation (above), we also needed to add the group render `--group-add render` (in addition to `--group-add video`).
+
+Note, we tested on an AMD Radeon Pro W6600, which is [not officially supported](https://docs.amd.com/en/latest/release/gpu_os_support.html), but setting `HSA_OVERRIDE_GFX_VERSION=10.3.0` [inside docker did the trick](https://en.opensuse.org/SDB:AMD_GPGPU#Using_CUDA_code_with_ZLUDA_and_ROCm):
 
 ```bash
-docker run --rm --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
-           --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host \
-	   --shm-size 8G \
+docker run --rm --security-opt seccomp=unconfined \
+           --device=/dev/kfd --device=/dev/dri --group-add video --group-add render \
 	   -v /home/user/my_mri_data:/data \
 	   -v /home/user/my_fastsurfer_analysis:/output \
 	   -e HSA_OVERRIDE_GFX_VERSION=10.3.0 \

Docker/build.py

@@ -30,9 +30,8 @@
 Target = Literal['runtime', 'build_common', 'build_conda', 'build_freesurfer',
                  'build_base', 'runtime_cuda']
 CacheType = Literal["inline", "registry", "local", "gha", "s3", "azblob"]
-AllDeviceType = Literal["cpu", "cuda", "cu116", "cu117", "cu118", "rocm", "rocm5.1.1",
-                        "rocm5.4.2"]
-DeviceType = Literal["cpu", "cu116", "cu117", "cu118", "rocm5.1.1", "rocm5.4.2"]
+AllDeviceType = Literal["cpu", "cuda", "cu118", "cu121", "cu124", "rocm", "rocm6.1"]
+DeviceType = Literal["cpu", "cu118", "cu121", "cu124", "rocm6.1"]
 
 CREATE_BUILDER = "Create builder with 'docker buildx create --name fastsurfer'."
 CONTAINERD_MESSAGE = (
@@ -59,10 +58,11 @@ class DEFAULTS:
     # and rocm versions, if pytorch comes with new versions.
     # torch 1.12.0 comes compiled with cu113, cu116, rocm5.0 and rocm5.1.1
     # torch 2.0.1 comes compiled with cu117, cu118, and rocm5.4.2
+    # torch 2.4 comes compiled with cu118, cu121, cu124 and rocm6.1
     MapDeviceType: Dict[AllDeviceType, DeviceType] = dict(
         ((d, d) for d in get_args(DeviceType)),
-        rocm="rocm5.1.1",
-        cuda="cu117",
+        rocm="rocm6.1",
+        cuda="cu124",
     )
     BUILD_BASE_IMAGE = "ubuntu:22.04"
     RUNTIME_BASE_IMAGE = "ubuntu:22.04"
@@ -185,12 +185,12 @@ def make_parser() -> argparse.ArgumentParser:
 
     parser.add_argument(
         "--device",
-        choices=["cpu", "cuda", "cu117", "cu118", "rocm", "rocm5.4.2"],
+        choices=["cpu", "cuda", "cu118", "cu121", "cu124", "rocm", "rocm6.1"],
         required=True,
         help="""selection of internal build stages to build for a specific platform.<br>
-                - cuda: defaults to cu118, cuda 11.8<br>
+                - cuda: defaults to cu124, cuda 12.4<br>
                 - cpu: only cpu support<br>
-                - rocm: defaults to rocm5.4.2 (experimental)""",
+                - rocm: defaults to rocm6.1 (experimental)""",
     )
     parser.add_argument(
         "--tag",
@@ -231,6 +231,7 @@ def make_parser() -> argparse.ArgumentParser:
                  --cache type=registry,ref=server/fastbuild,mode=max.
                  Will default to the environment variable FASTSURFER_BUILD_CACHE: 
                  {cache_kwargs.get('default', 'N/A')}""",
+        metavar="type={inline,local,...}[,<param>=<value>[,...]]",
         **cache_kwargs,
     )
     parser.add_argument(

Docker/install_env.py

@@ -19,7 +19,7 @@
 def mode(arg: str) -> str:
     if arg in ["base", "cpu"] or \
             re.match("^cu\\d+$", arg) or \
-            re.match("^rocm\\d+\\.\\d+(\\.\\d+)?$"):
+            re.match("^rocm\\d+\\.\\d+(\\.\\d+)?$", arg):
         return arg
     else:
         raise argparse.ArgumentTypeError(f"The mode was '{arg}', but should be "

FastSurferCNN/data_loader/conform.py

@@ -635,7 +635,8 @@ def conform(
     # Pxyz is the center of the image in world coords
 
     # target scalar type and dtype
-    sctype = np.uint8 if dtype is None else np.obj2sctype(dtype, default=np.uint8)
+    #sctype = np.uint8 if dtype is None else np.obj2sctype(dtype, default=np.uint8)
+    sctype = np.uint8 if dtype is None else np.dtype(dtype).type
     target_dtype = np.dtype(sctype)
 
     src_min, scale = 0, 1.0
@@ -761,7 +762,7 @@ def is_conform(
         raise ValueError(f"ERROR: Multiple input frames ({ishape[3]}) not supported!")
 
     checks = {
-        f"Number of Dimensions 3": (len(ishape) == 3, f"image ndim {img.ndim}")
+        "Number of Dimensions 3": (len(ishape) == 3, f"image ndim {img.ndim}")
     }
     # check dimensions
     if Criteria.FORCE_IMG_SIZE in criteria:
@@ -775,7 +776,7 @@ def is_conform(
     _vox_sizes = conformed_vox_size if is_correct_vox_size else izoom[:3]
     if Criteria.FORCE_ISO_VOX in criteria:
         vox_size_criteria = f"Voxel Size {'x'.join([str(conformed_vox_size)] * 3)}"
-        image_vox_size = f"image " + "x".join(map(str, izoom))
+        image_vox_size = "image " + "x".join(map(str, izoom))
         checks[vox_size_criteria] = (is_correct_vox_size, image_vox_size)
 
     # check orientation LIA
@@ -795,7 +796,8 @@ def is_conform(
         if dtype is None or (isinstance(dtype, str) and dtype.lower() == "uchar"):
             dtype = "uint8"
         else:  # assume obj
-            dtype = np.dtype(np.obj2sctype(dtype)).name
+            #dtype = np.dtype(np.obj2sctype(dtype)).name
+            dtype = np.dtype(dtype).type.__name__
         is_correct_dtype = img.get_data_dtype() == dtype
         checks[f"Dtype {dtype}"] = (is_correct_dtype, f"dtype {img.get_data_dtype()}")
 

FastSurferCNN/data_loader/data_utils.py

@@ -623,7 +623,7 @@ def read_classes_from_lut(lut_file: str | Path):
     if lut_file.suffix == ".csv":
         kwargs["sep"] = ","
     elif lut_file.suffix == ".txt":
-        kwargs["delim_whitespace"] = True
+        kwargs["sep"] = "\\s+"
     else:
         raise RuntimeError(
             f"Unknown LUT file extension {lut_file}, must be csv, txt or tsv."

FastSurferCNN/inference.py

@@ -213,7 +213,9 @@ def load_checkpoint(self, ckpt: Union[str, os.PathLike]):
             # make sure the model is, where it is supposed to be
             self.model.to(self.device)
 
-        model_state = torch.load(ckpt, map_location=device)
+        # WARNING: weights_only=False can cause unsafe code execution, but here the
+        # checkpoint can be considered to be from a safe source
+        model_state = torch.load(ckpt, map_location=device, weights_only=False)
         self.model.load_state_dict(model_state["model_state"])
 
         # workaround for mps (move the model back to mps)

FastSurferCNN/utils/checkpoint.py

@@ -228,7 +228,9 @@ def load_from_checkpoint(
     loaded_epoch : int
         Epoch number.
     """
-    checkpoint = torch.load(checkpoint_path, map_location="cpu")
+    # WARNING: weights_only=False can cause unsafe code execution, but here the
+    # checkpoint can be considered to be from a safe source
+    checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
 
     if drop_classifier:
         classifier_conv = ["classifier.conv.weight", "classifier.conv.bias"]

HypVINN/inference.py

@@ -181,7 +181,9 @@ def load_checkpoint(self, ckpt: str):
             of a model.
         """
         logger.info("Loading checkpoint {}".format(ckpt))
-        model_state = torch.load(ckpt, map_location=self.device)
+        # WARNING: weights_only=False can cause unsafe code execution, but here the
+        # checkpoint can be considered to be from a safe source
+        model_state = torch.load(ckpt, map_location=self.device, weights_only=False)
         self.model.load_state_dict(model_state["model_state"])
 
     def get_modelname(self):

Tutorial/Tutorial_FastSurferCNN_QuickSeg.ipynb

@@ -218,7 +218,7 @@
     "#@title Here we first setup the environment by downloading the open source deep-mi/fastsurfer project and the required packages\n",
     "import os\n",
     "import sys\n",
-    "from os.path import exists, join, basename, splitext\n",
+    "from os.path import exists, basename, splitext\n",
     "\n",
     "print(\"Starting setup. This could take a few minutes\")\n",
     "print(\"----------------------------------------------\")\n",
@@ -474,12 +474,12 @@
    "source": [
     "#@title Click this run button, if you would prefer to download the segmentation in nifti-format\n",
     "import nibabel as nib\n",
+    "from google.colab import files\n",
     "# conversion to nifti\n",
     "data = nib.load(f'{SETUP_DIR}fastsurfer_seg/Tutorial/mri/aparc.DKTatlas+aseg.deep.mgz')\n",
     "img_nifti = nib.Nifti1Image(data.get_fdata(), data.affine, header=nib.Nifti1Header())\n",
     "nib.nifti1.save(img_nifti, f'{SETUP_DIR}fastsurfer_seg/Tutorial/mri/aparc.DKTatlas+aseg.deep.nii.gz')\n",
     "\n",
-    "from google.colab import files\n",
     "files.download(f'{SETUP_DIR}fastsurfer_seg/Tutorial/mri/aparc.DKTatlas+aseg.deep.nii.gz')\n"
    ]
   },
@@ -519,12 +519,12 @@
    "source": [
     "#@title Click this run button, if you would prefer to download the image in nifti-format\n",
     "import nibabel as nib\n",
+    "from google.colab import files\n",
     "# conversion to nifti\n",
     "data = nib.load(f\"{SETUP_DIR}140_orig.mgz\")\n",
     "img_nifti = nib.Nifti1Image(data.get_fdata(), data.affine, header=nib.Nifti1Header())\n",
     "nib.nifti1.save(img_nifti, f\"{SETUP_DIR}140_orig.nii.gz\")\n",
     "\n",
-    "from google.colab import files\n",
     "files.download(f\"{SETUP_DIR}140_orig.nii.gz\")\n"
    ]
   },
@@ -612,11 +612,11 @@
     "%matplotlib inline\n",
     "import nibabel as nib\n",
     "import matplotlib.pyplot as plt\n",
-    "plt.style.use('seaborn-v0_8-whitegrid')\n",
-    "from skimage import color\n",
     "import torch\n",
     "import numpy as np\n",
+    "from skimage import color\n",
     "from torchvision import utils\n",
+    "plt.style.use('seaborn-v0_8-whitegrid')\n",
     "\n",
     "def plot_predictions(image, pred):\n",
     "    \"\"\"\n",
@@ -676,7 +676,6 @@
     "from ipywidgets import widgets\n",
     "import matplotlib.pyplot as plt\n",
     "import nibabel as nib\n",
-    "import numpy as np\n",
     "#from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
     "from skimage import measure\n",
     "\n",
@@ -853,7 +852,7 @@
     "def plot_3d_plotly_shape(structure, hemisphere, show_mesh=True, crop=True, grid=True):\n",
     "  import plotly.graph_objects as go\n",
     "  label = label_lookups(structure, hemisphere)\n",
-    "  test_cond = np.in1d(pred_data, label).reshape(pred_data.shape)\n",
+    "  test_cond = np.isin(pred_data, label).reshape(pred_data.shape)\n",
     "  roi = np.where(test_cond, 1, 0)\n",
     "  vert_p, faces_p, normals_p, values_p = measure.marching_cubes(roi, 0, spacing=(1, 1, 1))\n",
     "\n",

env/export_pip-r.sh

@@ -48,21 +48,23 @@ echo "Exporting versions from $2..."
   echo "#"
 } > $1
 
-pip_cmd="python --version && pip list --format=freeze --no-color --all --disable-pip-version-check --no-input"
+pip_cmd="python --version && pip list --format=freeze --no-color --disable-pip-version-check --no-input"
 if [ "${2/#.sif}" != "$2" ]
 then
   # singularity
-  cmd="singularity exec $2 /bin/bash -c '$pip_cmd'"
+  cmd=("singularity" "exec" "$2" "/bin/bash" -c "$pip_cmd")
+  clean_cmd="singularity exec $2 /bin/bash -c '$pip_cmd'"
 else
   # docker
-  cmd="docker run --entrypoint /bin/bash $2 -c '$pip_cmd'"
+  clean_cmd="docker run --rm -u <user_id>:<group_id> --entrypoint /bin/bash $2 -c '$pip_cmd'"
+  cmd=("docker" "run" --rm -u "$(id -u):$(id -g)" --entrypoint /bin/bash "$2" -c "$pip_cmd")
 fi
 {
   echo "# Which ran the following command:"
-  echo "#    $cmd"
+  echo "#    $clean_cmd"
   echo "#"
 } >> $1
-out=$($cmd)
+out=$("${cmd[@]}")
 hardware=$(echo "$out" | grep "torch==" | cut -d"+" -f2)
 pyversion=$(echo "$out" | head -n 1 | cut -d" " -f2)
 {
@@ -73,5 +75,3 @@ pyversion=$(echo "$out" | head -n 1 | cut -d" " -f2)
   echo ""
   echo "# $out"
 } >> $1
-
-}

env/fastsurfer.yml

@@ -5,28 +5,28 @@ channels:
   - defaults
 
 dependencies:
-  - h5py=3.7.0
-  - lapy=1.0.1
-  - matplotlib=3.7.1
-  - nibabel=5.1.0
-  - numpy=1.25.0
-  - pandas=1.5.3
-  - pillow=10.0.1
-  - pip=23.1.2
-  - python=3.10
-  - python-dateutil=2.8.2
-  - pyyaml=6.0
-  - scikit-image=0.19.3
-  - scikit-learn=1.2.2
-  - scipy=1.10.1
-  - setuptools=67.8.0
-  - tensorboard=2.12.1
-  - tqdm=4.66
-  - yacs=0.1.8
-  - pip
-  - pip:
-    - --extra-index-url https://download.pytorch.org/whl/cu117
-    - simpleitk==2.2.1
-    - torch==2.0.1
-    - torchio==0.18.83
-    - torchvision==0.15.2
+- h5py=3.11.0
+- lapy=1.1.0
+- matplotlib=3.9.2
+- nibabel=5.2.1
+- numpy=1.26.4
+- pandas=2.2.2
+- pillow=10.4.0
+- pip=24.2
+- python=3.10
+- python-dateutil=2.9.0
+- pyyaml=6.0.2
+- requests=2.32.3
+- scikit-image=0.24.0
+- scikit-learn=1.5.1
+- scipy=1.14.1
+- setuptools=72.2.0
+- tensorboard=2.17.1
+- tqdm=4.66.5
+- yacs=0.1.8
+- pip:
+  - --extra-index-url https://download.pytorch.org/whl/cu124
+  - simpleitk==2.4.0
+  - torch==2.4.0+cu124
+  - torchio==0.19.9
+  - torchvision==0.19.0+cu124