Writing docs to summary.py

ammico/summary.py

@@ -10,6 +10,15 @@ class SummaryDetector(AnalysisMethod):
         self.summary_device = device("cuda" if cuda.is_available() else "cpu")
 
     def load_model_base(self):
+        """
+        Load base_coco blip_caption model and preprocessors for visual inputs from lavis.models.
+
+        Args:
+
+        Returns:
+            model (torch.nn.Module): model.
+            vis_processors (dict): preprocessors for visual inputs.
+        """
         summary_model, summary_vis_processors, _ = load_model_and_preprocess(
             name="blip_caption",
             model_type="base_coco",
@@ -19,6 +28,15 @@ class SummaryDetector(AnalysisMethod):
         return summary_model, summary_vis_processors
 
     def load_model_large(self):
+        """
+        Load large_coco blip_caption model and preprocessors for visual inputs from lavis.models.
+
+        Args:
+
+        Returns:
+            model (torch.nn.Module): model.
+            vis_processors (dict): preprocessors for visual inputs.
+        """
         summary_model, summary_vis_processors, _ = load_model_and_preprocess(
             name="blip_caption",
             model_type="large_coco",
@@ -27,7 +45,17 @@ class SummaryDetector(AnalysisMethod):
         )
         return summary_model, summary_vis_processors
 
-    def load_model(self, model_type):
+    def load_model(self, model_type: str):
+        """
+        Load blip_caption model and preprocessors for visual inputs from lavis.models.
+
+        Args:
+            model_type (str): type of the model.
+
+        Returns:
+            model (torch.nn.Module): model.
+            vis_processors (dict): preprocessors for visual inputs.
+        """
         select_model = {
             "base": SummaryDetector.load_model_base,
             "large": SummaryDetector.load_model_large,
@@ -36,6 +64,16 @@ class SummaryDetector(AnalysisMethod):
         return summary_model, summary_vis_processors
 
     def analyse_image(self, summary_model=None, summary_vis_processors=None):
+        """
+        Create 1 constant and 3 non deterministic captions for image.
+
+        Args:
+            summary_model (str): model.
+            summary_vis_processors (str): preprocessors for visual inputs.
+
+        Returns:
+            self.subdict (dict): dictionary with constant image summary and 3 non deterministic summary.
+        """
         if summary_model is None and summary_vis_processors is None:
             summary_model, summary_vis_processors = self.load_model_base()
 
@@ -55,7 +93,16 @@ class SummaryDetector(AnalysisMethod):
             )
         return self.subdict
 
-    def analyse_questions(self, list_of_questions):
+    def analyse_questions(self, list_of_questions: list[str]) -> dict:
+        """
+        Generate answers to free-form questions about image written in natural language.
+
+        Args:
+            list_of_questions (list[str]): list of questions.
+
+        Returns:
+            self.subdict (dict): dictionary with answers to questions.
+        """
         (
             summary_vqa_model,
             summary_vqa_vis_processors,