📝 Update augmentations

InnerEye/ML/augmentations/augmentation_for_segmentation_utils.py

@@ -20,9 +20,9 @@ class among the available classes then samples a center point among the pixels o
  class.
 
  :param sample: A set of Image channels, ground truth labels and mask to randomly crop.
- :param class_weights: A weighting vector with values [0, 1] to influence the class the center crop
-  voxel belongs to (must sum to 1), uniform distribution assumed if none provided.
- :return numpy int array (3x1) containing patch center spatial coordinates
+ :param class_weights: A weighting vector with values [0, 1] to influence the class the center crop voxel belongs
+ to (must sum to 1), uniform distribution assumed if none provided.
+ :return: numpy int array (3x1) containing patch center spatial coordinates
  """
  num_classes = sample.labels.shape[0]
 
@@ -70,9 +70,9 @@ def slicers_for_random_crop(sample: Sample,
  :param sample: A set of Image channels, ground truth labels and mask to randomly crop.
  :param crop_size: The size of the crop expressed as a list of 3 ints, one per spatial dimension.
  :param class_weights: A weighting vector with values [0, 1] to influence the class the center crop
-  voxel belongs to (must sum to 1), uniform distribution assumed if none provided.
+ voxel belongs to (must sum to 1), uniform distribution assumed if none provided.
  :return: Tuple element 1: The slicers that convert the input image to the chosen crop. Tuple element 2: The
- indices of the center point of the crop.
+  indices of the center point of the crop.
  :raises ValueError: If there are shape mismatches among the arguments or if the crop size is larger than the image.
  """
  shape = sample.image.shape[1:]

InnerEye/ML/configs/classification/CovidModel.py

@@ -246,6 +246,7 @@ def generate_custom_report(self, report_dir: Path, model_proc: ModelProcessing)
  :param report_dir: Directory report is to be written to
  :param model_proc: Whether this is a single or ensemble model (model_output.csv will be located in different
  paths for single vs ensemble runs.)
+
  """
 
  label_prefix = LoggingColumns.Label.value

docs/source/rst/api/ML/augmentations/index.rst

@@ -1,8 +1,17 @@
 Data augmentation
 =================
 
-.. automodule:: InnerEye.ML.augmentations.augmentation_for_segmentation_utils
+Image Transformations
+----------------------
 
 .. automodule:: InnerEye.ML.augmentations.image_transforms
 
+Transformation Pipeline
+-----------------------
+
 .. automodule:: InnerEye.ML.augmentations.transform_pipeline
+
+Utils
+-----
+
+.. automodule:: InnerEye.ML.augmentations.augmentation_for_segmentation_utils