Fix neural networks docs

Author: dshabin

superannotate/ml/ml_funcs.py

@@ -33,14 +33,15 @@
 @model_metadata
 def run_prediction(project, images_list, model):
     """This function runs smart prediction on given list of images from a given project using the neural network of your choice
+
     :param project: the project in which the target images are uploaded.
     :type project: str or dict
     :param images_list: the list of image names on which smart prediction has to be run
     :type images_list: list of str
     :param model: the name of the model that should be used for running smart prediction
     :type model: str or dict
-    :out res: tupe of two lists, list of images on which the prediction has succeded and failed respectively
-    :rtype res: tuple
+    :return: tupe of two lists, list of images on which the prediction has succeded and failed respectively
+    :rtype: tuple
     """
 
     if not isinstance(project, dict):
@@ -114,7 +115,7 @@ def run_segmentation(project, images_list, model):
     :type  project: str or dict
     :param model  : The model name or metadata of the model
     :type  model  : str or dict
-    :out res: tupe of two lists, list of images on which the prediction has succeded and failed respectively
+    :return: tupe of two lists, list of images on which the prediction has succeded and failed respectively
     :rtype res: tuple
     """
 
@@ -194,22 +195,23 @@ def run_training(
     log=False
 ):
     """Runs neural network training
+
     :param project: project or list of projects that contain the training images
     :type  project: str, dict or list of dict
-    :param base_model  : base model on which the new network will be trained
-    :type  base_model  : str or dict
-    :param model_name   : name of the new model
-    :type  model_name   : str
+    :param base_model: base model on which the new network will be trained
+    :type  base_model: str or dict
+    :param model_name: name of the new model
+    :type  model_name: str
     :param model_description: description of the new model
     :type  model_description: str
-    :param task   : The model training task
-    :type  task   : str
+    :param task: The model training task
+    :type  task: str
     :param hyperparameters: hyperparameters that should be used in training
     :type  hyperparameters: dict
     :param log: If true will log training metrics in the stdout
     :type log: boolean
-    :out new_model: the metadata of the newly created model
-    :rtype new_model: dict
+    :return: the metadata of the newly created model
+    :rtype: dict
     """
 
     project_ids = None
@@ -350,12 +352,13 @@ def run_training(
 @Trackable
 @model_metadata
 def stop_model_training(model):
-    '''This function will stop training model provided by either name or metadata, and return the ID
+    """This function will stop training model provided by either name or metadata, and return the ID
+
     :param model: The name or the metadata of the model the training of which the user needs to terminate
-    :type model: str or dict
-    :out model: the metadata of the now, stopped model
-    :rtype out: dict
-    '''
+    :type model: dict
+    :return: the metadata of the now, stopped model
+    :rtype: dict
+    """
     params = {"team_id": _api.team_id}
     response = _api.send_request(
         req_type="POST",
@@ -373,6 +376,7 @@ def stop_model_training(model):
 @Trackable
 def plot_model_metrics(metric_json_list):
     """plots the metrics generated by neural network using plotly
+
        :param metric_json_list: list of <model_name>.json files
        :type  metric_json_list: list of str
     """
@@ -445,13 +449,15 @@ def get_plottable_cols(df):
 def download_model(model, output_dir):
     """Downloads the neural network and related files
     which are the <model_name>.pth/pkl. <model_name>.json, <model_name>.yaml, classes_mapper.json
+
     :param model: the model that needs to be downloaded
     :type  model: str
     :param output_dir: the directiory in which the files will be saved
     :type output_dir: str
-    :out model: the metadata of the model that was deleted
-    :rtype model: dict
+    :return: the metadata of the model
+    :rtype: dict
     """
+
     if not os.path.isdir(output_dir):
         os.makedirs(output_dir, exist_ok=True)
 
@@ -506,11 +512,13 @@ def download_model(model, output_dir):
 @Trackable
 @model_metadata
 def delete_model(model):
-    '''This function deletes the provided model
+    """This function deletes the provided model
+
        :param model: the model to be deleted
-       :out model: the metadata of the model that was deleted
-       :rtype out: dict
-    '''
+       :type model: dict
+       :return: the metadata of the model that was deleted
+       :rtype: dict
+    """
     params = {"team_id": _api.team_id}
     response = _api.send_request(
         req_type="DELETE", path=f'/ml_model/{model["id"]}', params=params

superannotate/ml/ml_models.py

@@ -16,6 +16,22 @@ def search_models(
     task=None,
     include_global=True,
 ):
+    """Search for ML models.
+
+    :param name: search string
+    :type name: str
+    :param type_: ml model type string
+    :type type_: str
+    :param project_id: project id
+    :type project_id: str
+    :param task: training task
+    :type task: str
+    :param include_global: include global ml models
+    :type include_global: bool
+
+    :return: ml model metadata
+    :rtype: list of dicts
+    """
     params = {
         "name": name,
         "team_id": _api.team_id,