diff --git a/src/mednet/config/data/tbx11k/datamodule.py b/src/mednet/config/data/tbx11k/datamodule.py
index dfb3038b24c6097e37edd230f43de29468415f51..9593d23d6de35d5ab874d8aaaecaf0c1cf61262c 100644
--- a/src/mednet/config/data/tbx11k/datamodule.py
+++ b/src/mednet/config/data/tbx11k/datamodule.py
@@ -119,7 +119,12 @@ class BoundingBoxes(collections.abc.Sequence[BoundingBox]):
# explained at:
# https://pytorch.org/docs/stable/data.html#torch.utils.data.default_collate
def _collate_boundingboxes_fn(batch, *, collate_fn_map=None):
- """Custom collate_fn() for pytorch dataloaders that ignores BoundingBoxes objects."""
+ """Custom collate_fn() for pytorch dataloaders that ignores BoundingBoxes objects.
+
+ Returns
+ -------
+ The given batch.
+ """
return batch
diff --git a/src/mednet/config/data/tbx11k/make_splits_from_database.py b/src/mednet/config/data/tbx11k/make_splits_from_database.py
index 4c09c375da3e385cd2845351db64d3d8e24b7975..48b0b90bccb96e8c0c81126f47684197222ecd75 100644
--- a/src/mednet/config/data/tbx11k/make_splits_from_database.py
+++ b/src/mednet/config/data/tbx11k/make_splits_from_database.py
@@ -59,7 +59,18 @@ from sklearn.model_selection import StratifiedKFold, train_test_split
def reorder(data: dict) -> list:
- """Reorder data from TBX11K into a sample-based organisation."""
+ """Reorder data from TBX11K into a sample-based organisation.
+
+ Parameters
+ ----------
+ data
+ A dictionary containing the loaded data.
+
+ Returns
+ -------
+ list
+ The reordered data.
+ """
categories = {k["id"]: k["name"] for k in data["categories"]}
assert len(set(categories.values())) == len(
@@ -112,6 +123,11 @@ def normalize_labels(data: list) -> list:
bounding boxes with label 0 and no bounding box with label 1
4: sick (but no tb), comes from the imgs/sick subdir, does not have any
annotated bounding box.
+
+ Returns
+ -------
+ list
+ A list of labels per sample.
"""
def _set_label(s: list) -> int:
@@ -213,6 +229,11 @@ def create_v1_default_split(d: dict, seed: int, validation_size: float) -> dict:
validation_size
The proportion of data when we split the training set to make a
train and validation sets.
+
+ Returns
+ -------
+ dict
+ A dict containing the various v1 splits.
"""
# filter cases (only interested in labels 0:healthy or 1:active-tb)
@@ -251,6 +272,11 @@ def create_v2_default_split(d: dict, seed: int, validation_size) -> dict:
2. The original training set is split into new training and validation
sets. The selection of samples is stratified (respects class
proportions in Özgür's way - see comments)
+
+ Returns
+ -------
+ dict
+ A dict containing the various v2 splits.
"""
# filter cases (only interested in labels 0:healthy or 1:active-tb)
diff --git a/src/mednet/data/augmentations.py b/src/mednet/data/augmentations.py
index c985b01e39ea5c85946dc3c8a5868e0888862ba8..10c4ed384685ca93bc771f1e880facf940b18e6e 100644
--- a/src/mednet/data/augmentations.py
+++ b/src/mednet/data/augmentations.py
@@ -239,7 +239,7 @@ class ElasticDeformation:
self.parallel = parallel
@property
- def parallel(self):
+ def parallel(self) -> int:
"""Use multiprocessing for data augmentation.
If set to -1 (default), disables multiprocessing. If set to -2,
@@ -247,6 +247,11 @@ class ElasticDeformation:
batch size and total number of processing cores). Set to 0 to
enable as many processes as processing cores available in the
system. Set to >= 1 to enable that many processes.
+
+ Returns
+ -------
+ int
+ The multiprocessing type.
"""
return self._parallel
diff --git a/src/mednet/data/datamodule.py b/src/mednet/data/datamodule.py
index 62e07b9871dbc02e18b2e80c5ec91b281b707473..9dd094a27a38f0e547d5a2e66e5a177514903c06 100644
--- a/src/mednet/data/datamodule.py
+++ b/src/mednet/data/datamodule.py
@@ -46,7 +46,18 @@ def _sample_size_bytes(s: Sample) -> int:
"""
def _tensor_size_bytes(t: torch.Tensor) -> int:
- """Return a tensor size in bytes."""
+ """Return a tensor size in bytes.
+
+ Parameters
+ ----------
+ t
+ A torch Tensor.
+
+ Returns
+ -------
+ int
+ The size of the Tensor in bytes.
+ """
return int(t.element_size() * torch.prod(torch.tensor(t.shape)))
size = sys.getsizeof(s[0]) # tensor metadata
@@ -100,7 +111,13 @@ class _DelayedLoadingDataset(Dataset):
logger.info(f"Estimated sample size: {sample_size_mb:.1f} Mb")
def labels(self) -> list[int | list[int]]:
- """Return the integer labels for all samples in the dataset."""
+ """Return the integer labels for all samples in the dataset.
+
+ Returns
+ -------
+ list[int | list[int]]
+ The integer labels for all samples in the dataset.
+ """
return [self.loader.label(k) for k in self.raw_dataset]
def __getitem__(self, key: int) -> Sample:
@@ -207,7 +224,13 @@ class _CachedDataset(Dataset):
)
def labels(self) -> list[int | list[int]]:
- """Return the integer labels for all samples in the dataset."""
+ """Return the integer labels for all samples in the dataset.
+
+ Returns
+ -------
+ list[int | list[int]]
+ The integer labels for all samples in the dataset.
+ """
return [k[1]["label"] for k in self.data]
def __getitem__(self, key: int) -> Sample:
@@ -239,7 +262,13 @@ class _ConcatDataset(Dataset):
]
def labels(self) -> list[int | list[int]]:
- """Return the integer labels for all samples in the dataset."""
+ """Return the integer labels for all samples in the dataset.
+
+ Returns
+ -------
+ list[int | list[int]]
+ The integer labels for all samples in the dataset.
+ """
return list(itertools.chain(*[k.labels() for k in self._datasets]))
def __getitem__(self, key: int) -> Sample:
@@ -550,6 +579,11 @@ class ConcatDataModule(lightning.LightningDataModule):
- ``parallel``
- Runs mini-batch data loading on as many external processes as set on
``parallel``
+
+ Returns
+ -------
+ int
+ The value of self._parallel.
"""
return self._parallel
@@ -588,6 +622,10 @@ class ConcatDataModule(lightning.LightningDataModule):
data is cached, it is cached **after** model-transforms are
applied, as that is a potential memory saver (e.g., if it
contains a resizing operation to smaller images).
+
+ Returns
+ -------
+ A list containing the model tansforms.
"""
return self._model_transforms
@@ -606,7 +644,7 @@ class ConcatDataModule(lightning.LightningDataModule):
self._datasets = {}
@property
- def balance_sampler_by_class(self):
+ def balance_sampler_by_class(self) -> bool:
"""Whether to balance samples across labels/datasets.
If set, then modifies the random sampler used during training
@@ -620,6 +658,11 @@ class ConcatDataModule(lightning.LightningDataModule):
samples acording to their ground-truth (labels). If you'd like to
have samples balanced per dataset, then implement your own data
module inheriting from this one.
+
+ Returns
+ -------
+ bool
+ True if self._train_sample is set, else False.
"""
return self._train_sampler is not None
@@ -733,7 +776,13 @@ class ConcatDataModule(lightning.LightningDataModule):
self._datasets[name] = _ConcatDataset(datasets)
def _val_dataset_keys(self) -> list[str]:
- """Return list of validation dataset names."""
+ """Return list of validation dataset names.
+
+ Returns
+ -------
+ list[str]
+ The list of validation dataset names.
+ """
return ["validation"] + [
k for k in self.splits.keys() if k.startswith("monitor-")
]
@@ -801,7 +850,12 @@ class ConcatDataModule(lightning.LightningDataModule):
super().teardown(stage)
def train_dataloader(self) -> DataLoader:
- """Return the train data loader."""
+ """Return the train data loader.
+
+ Returns
+ -------
+ The train data loader(s).
+ """
return torch.utils.data.DataLoader(
self._datasets["train"],
@@ -814,7 +868,12 @@ class ConcatDataModule(lightning.LightningDataModule):
)
def unshuffled_train_dataloader(self) -> DataLoader:
- """Return the train data loader without shuffling."""
+ """Return the train data loader without shuffling.
+
+ Returns
+ -------
+ The train data loader without shuffling.
+ """
return torch.utils.data.DataLoader(
self._datasets["train"],
@@ -825,7 +884,12 @@ class ConcatDataModule(lightning.LightningDataModule):
)
def val_dataloader(self) -> dict[str, DataLoader]:
- """Return the validation data loader(s)"""
+ """Return the validation data loader(s).
+
+ Returns
+ -------
+ The validation data loader(s).
+ """
validation_loader_opts = {
"batch_size": self._chunk_size,
@@ -843,7 +907,12 @@ class ConcatDataModule(lightning.LightningDataModule):
}
def test_dataloader(self) -> dict[str, DataLoader]:
- """Return the test data loader(s)"""
+ """Return the test data loader(s).
+
+ Returns
+ -------
+ The test data loader(s).
+ """
return dict(
test=torch.utils.data.DataLoader(
@@ -857,7 +926,12 @@ class ConcatDataModule(lightning.LightningDataModule):
)
def predict_dataloader(self) -> dict[str, DataLoader]:
- """Return the prediction data loader(s)"""
+ """Return the prediction data loader(s).
+
+ Returns
+ -------
+ The prediction data loader(s).
+ """
return {
k: torch.utils.data.DataLoader(
diff --git a/src/mednet/data/split.py b/src/mednet/data/split.py
index c6abae451f86be71ee47e54bbdedbe3a382620de..5d70aafd551b18fca856f846d91818442bb63c03 100644
--- a/src/mednet/data/split.py
+++ b/src/mednet/data/split.py
@@ -156,7 +156,6 @@ class CSVDatabaseSplit(DatabaseSplit):
Returns
-------
- datasets : dict
A dictionary mapping dataset names to lists of JSON objects.
"""
diff --git a/src/mednet/data/typing.py b/src/mednet/data/typing.py
index 658cad9d603f8a9e067c5de0089d811648b5d279..f61b423059f165f22fd0f860843f2573fa6be772 100644
--- a/src/mednet/data/typing.py
+++ b/src/mednet/data/typing.py
@@ -34,6 +34,16 @@ class RawDataLoader:
If you do not override this implementation, then, by default,
this method will call :py:meth:`sample` to load the whole sample
and extract the label.
+
+ Parameters
+ ----------
+ k
+ The sample to load. This is implementation-dependent.
+
+ Returns
+ -------
+ int | list[int]
+ The label corresponding to the specified sample.
"""
return self.sample(k)[1]["label"]
diff --git a/src/mednet/engine/device.py b/src/mednet/engine/device.py
index c501f7f2029780239f6169e867b547eccefea77e..b008c4cbe952c33bf4ace294426e1362ff15b205 100644
--- a/src/mednet/engine/device.py
+++ b/src/mednet/engine/device.py
@@ -21,7 +21,18 @@ SupportedPytorchDevice: typing.TypeAlias = typing.Literal[
def _split_int_list(s: str) -> list[int]:
- """Split a list of integers encoded in a string (e.g. "1,2,3") into a Python list of integers (e.g. ``[1, 2, 3]``)."""
+ """Split a list of integers encoded in a string (e.g. "1,2,3") into a Python list of integers (e.g. ``[1, 2, 3]``).
+
+ Parameters
+ ----------
+ s
+ A list of integers encoded in a string.
+
+ Returns
+ -------
+ list[int]
+ A Python list of integers.
+ """
return [int(k.strip()) for k in s.split(",")]
diff --git a/src/mednet/engine/saliency/generator.py b/src/mednet/engine/saliency/generator.py
index 65b4b62d35c7e815242560b9d5f7040f38d1064b..e99f78879f6049b6520c1525ef69b2db4f89143e 100644
--- a/src/mednet/engine/saliency/generator.py
+++ b/src/mednet/engine/saliency/generator.py
@@ -36,6 +36,10 @@ def _create_saliency_map_callable(
The target layers to compute CAM for.
use_cuda
Whether to use cuda or not.
+
+ Returns
+ -------
+ A class activation map (CAM) instance for the given model.
"""
import pytorch_grad_cam
diff --git a/src/mednet/utils/rc.py b/src/mednet/utils/rc.py
index b3657e0292014135cc9eb15a0b29a07dc3c03ab0..fcc1659d1f5304c44e0561a7e1e70c6bd905db7a 100644
--- a/src/mednet/utils/rc.py
+++ b/src/mednet/utils/rc.py
@@ -6,5 +6,10 @@ from clapper.rc import UserDefaults
def load_rc() -> UserDefaults:
- """Return global configuration variables."""
+ """Return global configuration variables.
+
+ Returns
+ -------
+ The user defaults read from the user .toml configuration file.
+ """
return UserDefaults("mednet.toml")
diff --git a/src/mednet/utils/resources.py b/src/mednet/utils/resources.py
index 184b9d39a81d664e00dc689cf3757d1b2fd02d47..a17d3cbd2750e01650e3d7ddc89bb5f379df0157 100644
--- a/src/mednet/utils/resources.py
+++ b/src/mednet/utils/resources.py
@@ -431,7 +431,13 @@ class _InformationGatherer:
self.data[k] = []
def summary(self) -> dict[str, list[int | float]]:
- """Return the current data."""
+ """Return the current data.
+
+ Returns
+ -------
+ dict[str, list[int | float]]
+ A dictionary with a list of resources and their corresponding values.
+ """
if len(next(iter(self.data.values()))) == 0:
self.logger.error("CPU/GPU logger was not able to collect any data")
return self.data
diff --git a/tests/conftest.py b/tests/conftest.py
index bacef95ee8c7193eb684fd71fc2cbe46354cc76f..01223e6b8251f5e9fb9a1f2bb2bb55e8983cf2f9 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -17,7 +17,13 @@ from mednet.data.typing import DatabaseSplit
@pytest.fixture
def datadir(request) -> pathlib.Path:
- """Return the directory in which the test is sitting."""
+ """Return the directory in which the test is sitting.
+
+ Returns
+ -------
+ pathlib.Path
+ The directory in which the test is sitting.
+ """
return pathlib.Path(request.module.__file__).parents[0] / "data"