Merge branch 'main' into crop-kspace-transforms-merged

direct/__init__.py

@@ -2,4 +2,4 @@
 # Copyright (c) DIRECT Contributors
 
 __author__ = """direct contributors"""
-__version__ = "1.0.1"
+__version__ = "1.0.2"

direct/cli/utils.py

@@ -19,12 +19,10 @@ def is_file(path):
 def file_or_url(path: PathOrString) -> FileOrUrl:
     if check_is_valid_url(path):
         return FileOrUrl(path)
-    else:
-        path = pathlib.Path(path)
-        if path.is_file():
-            return FileOrUrl(path)
-        else:
-            raise argparse.ArgumentTypeError(f"{path} is not a valid file or url.")
+    path = pathlib.Path(path)
+    if path.is_file():
+        return FileOrUrl(path)
+    raise argparse.ArgumentTypeError(f"{path} is not a valid file or url.")
 
 
 def check_train_val(key, name):

direct/common/subsample.py

@@ -126,8 +126,11 @@ def mask_func(self, shape, return_acs=False, seed=None):
         The mask selects a subset of columns from the input k-space data. If the k-space data has N
         columns, the mask picks out:
 
-            #.  :math:`N_{\text{low\_freqs}} = (N \times \text{center_fraction})`  columns in the center corresponding to low-frequencies.  # pylint: disable=line-too-long
-            #.  The other columns are selected uniformly at random with a probability equal to: :math:`\text{prob} = (N / \text{acceleration} - N_{\text{low\_freqs}}) / (N - N_{\text{low\_freqs}})`. This ensures that the expected number of columns selected is equal to (N / acceleration)  # pylint: disable=line-too-long
+            #.  :math:`N_{\text{low freqs}} = (N \times \text{center_fraction})`  columns in the center corresponding
+                to low-frequencies.
+            #.  The other columns are selected uniformly at random with a probability equal to:
+                :math:`\text{prob} = (N / \text{acceleration} - N_{\text{low freqs}}) / (N - N_{\text{low freqs}})`.
+                This ensures that the expected number of columns selected is equal to (N / acceleration).
 
         It is possible to use multiple center_fractions and accelerations, in which case one possible
         (center_fraction, acceleration) is chosen uniformly at random each time the MaskFunc object is
@@ -204,15 +207,18 @@ def mask_func(self, shape, return_acs=False, seed=None):
         FastMRIEquispacedMaskFunc creates a sub-sampling mask of a given shape. The mask selects a subset of columns
         from the input k-space data. If the k-space data has N columns, the mask picks out:
 
-            #.  :math:`N_{\text{low\_freqs}} = (N \times \text{center_fraction})` columns in the center corresponding to low-frequencies.  # pylint: disable=line-too-long
-            #.  The other columns are selected with equal spacing at a proportion that reaches the desired acceleration rate taking into consideration the number of low frequencies. This ensures that the expected number of columns selected is equal to :math:`\frac{N}{\text{acceleration}}`.  # pylint: disable=line-too-long
+            #.  :math:`N_{\text{low freqs}} = (N \times \text{center_fraction})` columns in the center corresponding
+                to low-frequencies.
+            #.  The other columns are selected with equal spacing at a proportion that reaches the desired acceleration
+                rate taking into consideration the number of low frequencies. This ensures that the expected number of
+                columns selected is equal to :math:`\frac{N}{\text{acceleration}}`.
 
         It is possible to use multiple center_fractions and accelerations, in which case one possible
         (center_fraction, acceleration) is chosen uniformly at random each time the EquispacedMaskFunc object is called.
 
-        Note that this function may not give equispaced samples (documented in https://github.com/facebookresearch/fastMRI/issues/54),
-        which will require modifications to standard GRAPPA approaches. Nonetheless, this aspect of the function has
-        been preserved to match the public multicoil data.
+        Note that this function may not give equispaced samples (documented in
+        https://github.com/facebookresearch/fastMRI/issues/54), which will require modifications to standard GRAPPA
+        approaches. Nonetheless, this aspect of the function has been preserved to match the public multicoil data.
 
         Parameters
         ----------
@@ -374,7 +380,9 @@ class CIRCUSMaskFunc(BaseMaskFunc):
     References
     ----------
 
-    .. [1] Liu J, Saloner D. Accelerated MRI with CIRcular Cartesian UnderSampling (CIRCUS): a variable density Cartesian sampling strategy for compressed sensing and parallel imaging. Quant Imaging Med Surg. 2014 Feb;4(1):57-67. doi: 10.3978/j.issn.2223-4292.2014.02.01. PMID: 24649436; PMCID: PMC3947985.
+    .. [1] Liu J, Saloner D. Accelerated MRI with CIRcular Cartesian UnderSampling (CIRCUS): a variable density
+        Cartesian sampling strategy for compressed sensing and parallel imaging. Quant Imaging Med Surg.
+        2014 Feb;4(1):57-67. doi: 10.3978/j.issn.2223-4292.2014.02.01. PMID: 24649436; PMCID: PMC3947985.
     """
 
     def __init__(

direct/data/datasets.py

@@ -138,7 +138,7 @@ def __init__(
         self.pass_attrs = pass_attrs if pass_attrs is not None else True
         self.text_description = text_description
         if self.text_description:
-            self.logger.info(f"Dataset description: {self.text_description}.")
+            self.logger.info("Dataset description: %s.", self.text_description)
 
         self.fake_data: Callable = FakeMRIData(
             ndim=len(self.spatial_shape),
@@ -178,7 +178,8 @@ def parse_filenames_data(self, filenames):
         current_slice_number = 0
         for idx, filename in enumerate(filenames):
             if len(filenames) < 5 or idx % (len(filenames) // 5) == 0 or len(filenames) == (idx + 1):
-                self.logger.info("Parsing: {(idx + 1) / len(filenames) * 100:.2f}%.")
+                # pylint: disable=logging-fstring-interpolation
+                self.logger.info(f"Parsing: {(idx + 1) / len(filenames) * 100:.2f}%.")
 
             num_slices = self.spatial_shape[0] if len(self.spatial_shape) == 3 else 1
             self.volume_indices[pathlib.PosixPath(filename)] = range(
@@ -514,14 +515,19 @@ class ImageIntensityMode(str, Enum):
 class SheppLoganDataset(Dataset):
     """Shepp Logan Dataset for MRI as implemented in [1]_. Code was adapted from [2]_.
 
-    References
-    ----------
-    .. [1] Gach, H. Michael, Costin Tanase, and Fernando Boada. "2D & 3D Shepp-Logan phantom standards for MRI." 2008 19th International Conference on Systems Engineering. IEEE, 2008.
-    .. [2] https://github.com/mckib2/phantominator/blob/master/phantominator/mr_shepp_logan.py
-
-    Notes
-    -----
-    This dataset reconstructs into a single volume.
+        References
+        ----------
+    <<<<<<< HEAD
+        .. [1] Gach, H. Michael, Costin Tanase, and Fernando Boada. "2D & 3D Shepp-Logan phantom standards for MRI." 2008 19th International Conference on Systems Engineering. IEEE, 2008.
+    =======
+        .. [1] Gach, H. Michael, Costin Tanase, and Fernando Boada. "2D & 3D Shepp-Logan phantom standards for MRI."
+            2008 19th International Conference on Systems Engineering. IEEE, 2008.
+    >>>>>>> main
+        .. [2] https://github.com/mckib2/phantominator/blob/master/phantominator/mr_shepp_logan.py
+
+        Notes
+        -----
+        This dataset reconstructs into a single volume.
     """
 
     GYROMAGNETIC_RATIO: float = 267.52219
@@ -593,7 +599,7 @@ def __init__(
             self.seed = list(self.rng.choice(a=range(int(1e5)), size=self.nz, replace=False))
         self.text_description = text_description
         if self.text_description:
-            self.logger.info(f"Dataset description: {self.text_description}.")
+            self.logger.info("Dataset description: %s.", self.text_description)
 
         self.name = "shepp_loggan" + "_" + self.intensity
         self.ndim = 2
@@ -623,7 +629,7 @@ def _set_params(self, ellipsoids=None) -> None:
 
         self.ellipsoids = ellipsoids
 
-    def sample_image(self, idx: int) -> np.ndarray:
+    def sample_image(self, idx: int) -> np.ndarray:  # pylint: disable=too-many-locals
         # meshgrid does X, Y backwards
         X, Y, Z = np.meshgrid(
             np.linspace(-1, 1, self.ny),
@@ -708,7 +714,8 @@ def default_mr_ellipsoid_parameters() -> np.ndarray:
 
         References
         ----------
-        .. [1] Gach, H. Michael, Costin Tanase, and Fernando Boada. "2D & 3D Shepp-Logan phantom standards for MRI." 2008 19th International Conference on Systems Engineering. IEEE, 2008.
+        .. [1] Gach, H. Michael, Costin Tanase, and Fernando Boada. "2D & 3D Shepp-Logan phantom standards for MRI."
+            2008 19th International Conference on Systems Engineering. IEEE, 2008.
         """
         params = _mr_relaxation_parameters()
 
@@ -780,11 +787,12 @@ def _mr_relaxation_parameters():
 
     References
     ----------
-    .. [1] Gach, H. Michael, Costin Tanase, and Fernando Boada. "2D & 3D Shepp-Logan phantom standards for MRI." 2008 19th International Conference on Systems Engineering. IEEE, 2008.
+    .. [1] Gach, H. Michael, Costin Tanase, and Fernando Boada. "2D & 3D Shepp-Logan phantom standards for MRI."
+        2008 19th International Conference on Systems Engineering. IEEE, 2008.
     """
 
     # params['tissue-name'] = [A, C, (t1 value if explicit), t2, chi]
-    params = dict()
+    params = {}
     params["scalp"] = [0.324, 0.137, np.nan, 0.07, -7.5e-6]
     params["marrow"] = [0.533, 0.088, np.nan, 0.05, -8.85e-6]
     params["csf"] = [np.nan, np.nan, 4.2, 1.99, -9e-6]

direct/data/fake.py

@@ -108,7 +108,8 @@ def make_blobs(
 
         return samples
 
-    def _get_image_from_samples(self, samples, spatial_shape):
+    @staticmethod
+    def _get_image_from_samples(samples, spatial_shape):
         image = np.zeros(list(spatial_shape))
         image[tuple(np.split(samples, len(spatial_shape), axis=-1))] = 1
 
@@ -140,11 +141,11 @@ def __call__(
 
         Returns:
         --------
-            sample: dict or list of dicts
-                Contains:
-                    "kspace": np.array of shape (slice, num_coils, height, width)
-                    "reconstruction_rss": np. array of shape (slice, height, width)
-                    If spatial_shape is of shape 2 (height, width), slice=1.
+        sample: dict or list of dicts
+            Contains:
+                "kspace": np.array of shape (slice, num_coils, height, width)
+                "reconstruction_rss": np. array of shape (slice, height, width)
+                If spatial_shape is of shape 2 (height, width), slice=1.
         """
 
         if len(spatial_shape) != self.ndim:

direct/data/h5_data.py

@@ -100,14 +100,13 @@ def __init__(
         if filenames_filter is None:
             if filenames_lists is not None:
                 if filenames_lists_root is None:
-                    e = f"`filenames_lists` is passed but `filenames_lists_root` is None."
+                    e = "`filenames_lists` is passed but `filenames_lists_root` is None."
                     self.logger.error(e)
                     raise ValueError(e)
-                else:
-                    filenames = get_filenames_for_datasets(
-                        lists=filenames_lists, files_root=filenames_lists_root, data_root=root
-                    )
-                    self.logger.info("Attempting to load %s filenames from list(s).", len(filenames))
+                filenames = get_filenames_for_datasets(
+                    lists=filenames_lists, files_root=filenames_lists_root, data_root=root
+                )
+                self.logger.info("Attempting to load %s filenames from list(s).", len(filenames))
             else:
                 self.logger.info("Parsing directory %s for h5 files.", self.root)
                 filenames = list(self.root.glob("*.h5"))

direct/train.py

@@ -116,11 +116,14 @@ def build_training_datasets_from_environment(
             dataset_args.update({"pass_dictionaries": pass_dictionaries})
         dataset = build_dataset_from_input(**dataset_args)
 
-        logger.debug(f"Transforms {idx + 1} / {len(datasets_config)} :\n{transforms}")
+        logger.debug("Transforms %s / %s :\n%s", idx + 1, len(datasets_config), transforms)
         datasets.append(dataset)
         logger.info(
-            f"Data size for {dataset_config.text_description}"
-            f" ({idx + 1}/{len(datasets_config)}): {len(dataset)}."  # type: ignore
+            "Data size for %s (%s/%s): %s.",
+            dataset_config.text_description,  # type: ignore
+            idx + 1,
+            len(datasets_config),
+            len(dataset),
         )
 
     return datasets
@@ -188,7 +191,7 @@ def setup_train(
     # Build training datasets
     training_datasets = build_training_datasets_from_environment(**training_dataset_args)
     training_data_sizes = [len(_) for _ in training_datasets]
-    logger.info(f"Training data sizes: {training_data_sizes} (sum={sum(training_data_sizes)}).")
+    logger.info("Training data sizes: %s (sum=%s).", training_data_sizes, sum(training_data_sizes))
 
     # Create validation data
     if "validation" in env.cfg:
@@ -219,7 +222,7 @@ def setup_train(
     for curr_model_name in env.engine.models:
         # TODO(jt): Can get learning rate from the config per additional model too.
         curr_learning_rate = env.cfg.training.lr
-        logger.info(f"Adding model parameters of {curr_model_name} with learning rate {curr_learning_rate}.")
+        logger.info("Adding model parameters of %s with learning rate %s.", curr_model_name, curr_learning_rate)
         optimizer_params.append(
             {
                 "params": env.engine.models[curr_model_name].parameters(),
@@ -257,9 +260,10 @@ def setup_train(
     if env.cfg.training.model_checkpoint:
         if initialization_checkpoint:
             logger.warning(
-                f"`--initialization-checkpoint is set, and config has a set `training.model_checkpoint`: "
-                f"{env.cfg.training.model_checkpoint}. Will overwrite config variable with the command line: "
-                f"{initialization_checkpoint}."
+                "`--initialization-checkpoint is set, and config has a set `training.model_checkpoint`: %s. "
+                "Will overwrite config variable with the command line: %s.",
+                env.cfg.training.model_checkpoint,
+                initialization_checkpoint,
             )
             # Now overwrite this in the configuration, so the correct value is dumped.
             env.cfg.training.model_checkpoint = str(initialization_checkpoint)

direct/utils/dataset.py

@@ -24,9 +24,8 @@ def get_filenames_for_datasets_from_config(cfg, files_root: PathOrString, data_r
     """
     if "filenames_lists" not in cfg:
         return None
-    else:
-        lists = cfg.filenames_lists
-        return get_filenames_for_datasets(lists, files_root, data_root)
+    lists = cfg.filenames_lists
+    return get_filenames_for_datasets(lists, files_root, data_root)
 
 
 def get_filenames_for_datasets(lists: List[PathOrString], files_root: PathOrString, data_root: pathlib.Path):

docker/Dockerfile

@@ -1,10 +1,9 @@
-ARG CUDA="10.2"
-ARG CUDNN="8"
-ARG PYTORCH="1.7"
+ARG CUDA="11.3.0"
+ARG PYTORCH="1.10"
 ARG PYTHON="3.8"
 
 # TODO: conda installs its own version of cuda
-FROM nvidia/cuda:${CUDA}-cudnn${CUDNN}-devel-ubuntu18.04
+FROM nvidia/cuda:${CUDA}-devel-ubuntu18.04
 
 ENV CUDA_PATH /usr/local/cuda
 ENV CUDA_ROOT /usr/local/cuda/bin
@@ -17,10 +16,10 @@ RUN apt-get -qq update && apt-get install -y --no-install-recommends libxext6 li
     && rm -rf /var/lib/apt/lists/* \
     && ldconfig
 
-RUN git clone https://github.com/mrirecon/bart.git /tmp/bart
-    && cd /tmp/bart
-    && make -j4
-    && make install
+RUN git clone https://github.com/mrirecon/bart.git /tmp/bart \
+    && cd /tmp/bart \
+    && make -j4 \
+    && make install \
     && rm -rf /tmp/bart
 
 # Make a user
@@ -41,27 +40,29 @@ ENV PATH "/users/direct/miniconda3/bin:/tmp/bart/:$PATH:$CUDA_ROOT"
 # Setup python packages
 RUN conda update -n base conda -yq \
  && conda install python=${PYTHON} \
+ && conda install jupyter \
  && conda install cudatoolkit=${CUDA} torchvision -c pytorch
 
 RUN if [ "nightly$PYTORCH" = "nightly" ] ; then echo "Installing pytorch nightly" && \
     conda install pytorch -c pytorch-nightly; else conda install pytorch=${PYTORCH} -c pytorch ; fi
 
 USER root
-RUN mkdir /direct && chmod 777 /direct
+RUN mkdir direct:direct /direct && chown direct:direct /direct && chmod 777 /direct
 
 USER direct
 
 RUN jupyter notebook --generate-config
 ENV CONFIG_PATH "/users/direct/.jupyter/jupyter_notebook_config.py"
-COPY "jupyter_notebook_config.py" ${CONFIG_PATH}
+COPY "docker/jupyter_notebook_config.py" ${CONFIG_PATH}
 
 # Copy files into the docker
 COPY [".", "/direct"]
 WORKDIR /direct
+USER root
 RUN python -m pip install -e ".[dev]"
+USER direct
 
 ENV PYTHONPATH /tmp/bart/python:/direct
-WORKDIR /direct
 
 # Provide an open entrypoint for the docker
 ENTRYPOINT $0 $@

docker/README.rst

@@ -1,20 +1,25 @@
 Docker Installation
 -------------------
 
-Use the container (docker ≥ 19.03 required)
--------------------------------------------
+Use the container
+~~~~~~~~~~~~~~~~~
 
-To build:
+To build the image:
 
 .. code-block:: bash
 
-    cd docker/
-    docker build -t direct:latest .
+    cd direct/
+    docker build -t direct:latest -f docker/Dockerfile .
 
-To run using all GPUs:
+To run `DIRECT` using all GPUs:
 
 .. code-block:: bash
 
     docker run --gpus all -it \
         --shm-size=24gb --volume=<source_to_data>:/data --volume=<source_to_results>:/output \
 	    --name=direct direct:latest /bin/bash
+
+Requirements
+~~~~~~~~~~~~
+
+* docker ≥ 19.03

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.0.1
+current_version = 1.0.2
 commit = True
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?