'data_docs_update'

aitlas/base/datasets.py

@@ -1,3 +1,8 @@
+"""Dataset base class.
+
+This is the base class for all datasets. All datasets should subclass it. 
+"""
+
 import torch
 from torch.utils.data import Dataset
 
@@ -7,6 +12,15 @@
 
 
 class BaseDataset(Dataset, Configurable):
+    """This class represents a basic dataset for machine learning tasks. It is a
+    subclass of both :class:Dataset and :class:Configurable.
+    You can use it as a base class to define your own custom datasets.
+
+    :param Dataset: _description_
+    :type Dataset: _type_
+    :param Configurable: _description_
+    :type Configurable: _type_
+    """
 
     schema = BaseDatasetSchema
     labels = None  # need to put the labels here
@@ -38,7 +52,7 @@ def __init__(self, config):
         self.joint_transform = self.load_transforms(self.config.joint_transforms)
 
     def __getitem__(self, index):
-        """ Implement here what you want to return"""
+        """Implement here what you want to return"""
         raise NotImplementedError(
             "Please implement the `__getittem__` method for your dataset"
         )
@@ -108,4 +122,3 @@ def data_distribution_barchart(self):
     def load_transforms(self, class_names):
         """Loads transformation classes and make a composition of them"""
         return load_transforms(class_names, self.config)
-

aitlas/base/models.py

@@ -1,3 +1,8 @@
+"""Models base class.
+
+This is the base class for all models. All models should subclass it. 
+
+"""
 import collections
 import copy
 import logging
@@ -458,7 +463,7 @@ def predict_image(
         # Convert results to dataframe for plotting
         result = pd.DataFrame({"p": y_pred_probs[0]}, index=labels)
         # Show the image
-        plt.rcParams.update({'font.size': 16})
+        plt.rcParams.update({"font.size": 16})
         fig = plt.figure(figsize=(16, 7))
         ax = plt.subplot(1, 2, 1)
         ax.axis("off")