W&B: Improve documentation of the logger & use wandb assigned run names by default

utils/loggers/__init__.py

@@ -57,7 +57,7 @@ def start(self):
             assert 'wandb' in self.include and wandb
             run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume else None
             self.opt.hyp = self.hyp  # add hyperparameters
-            self.wandb = WandbLogger(self.opt, s.stem, run_id, self.data_dict)
+            self.wandb = WandbLogger(self.opt, run_id, self.data_dict)
         except:
             self.wandb = None
 

utils/loggers/wandb/wandb_utils.py

@@ -99,7 +99,19 @@ class WandbLogger():
     https://docs.wandb.com/guides/integrations/yolov5
     """
 
-    def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
+    def __init__(self, opt, run_id, data_dict, job_type='Training'):
+        '''
+        - Initialize WandbLogger instance
+        - Upload dataset if opt.upload_dataset is True
+        - Setup trainig processes if job_type is 'Training'
+        
+        arguments:
+        opt (namespace) -- Commandline arguments for this run
+        run_id (str) -- Run ID of W&B run to be resumed
+        data_dict (Dict) -- Dictionary conataining info about the dataset to be used
+        job_type (str) -- To set the job_type for this run 
+
+       '''
         # Pre-training routine --
         self.job_type = job_type
         self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
@@ -129,7 +141,7 @@ def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
                                         resume="allow",
                                         project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
                                         entity=opt.entity,
-                                        name=name,
+                                        name=opt.name if opt.name != 'exp' else None,
                                         job_type=job_type,
                                         id=run_id,
                                         allow_val_change=True) if not wandb.run else wandb.run
@@ -145,6 +157,15 @@ def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
                 self.data_dict = self.check_and_upload_dataset(opt)
 
     def check_and_upload_dataset(self, opt):
+        '''
+        Check if the dataset format is compatible and upload it as W&B artifact
+        
+        arguments:
+        opt (namespace)-- Commandline arguments for current run
+        
+        returns:
+        Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
+        '''
         assert wandb, 'Install wandb to upload dataset'
         config_path = self.log_dataset_artifact(check_file(opt.data),
                                                 opt.single_cls,
@@ -155,6 +176,19 @@ def check_and_upload_dataset(self, opt):
         return wandb_data_dict
 
     def setup_training(self, opt, data_dict):
+        '''
+        Setup the necessary processes for training YOLO models:
+          - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
+          - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
+          - Setup log_dict, initialize bbox_interval 
+        
+        arguments:
+        opt (namespace) -- commandline arguments for this run
+        data_dict (Dict) -- Dataset dictionary for this run
+        
+        returns:
+        data_dict (Dict) -- contains the updated info about the dataset to be used for training
+        '''
         self.log_dict, self.current_epoch = {}, 0
         self.bbox_interval = opt.bbox_interval
         if isinstance(opt.resume, str):
@@ -185,12 +219,22 @@ def setup_training(self, opt, data_dict):
             self.val_table = self.val_artifact.get("val")
             if self.val_table_path_map is None:
                 self.map_val_table_path()
-            wandb.log({"validation dataset": self.val_table})
         if opt.bbox_interval == -1:
             self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
         return data_dict
 
     def download_dataset_artifact(self, path, alias):
+        '''
+        download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX
+        
+        arguments:
+        path -- path of the dataset to be used for training
+        alias (str)-- alias of the artifact to be download/used for training
+        
+        returns:
+        (str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
+        is found otherwise returns (None, None)
+        '''
         if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
             artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
             dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
@@ -200,6 +244,12 @@ def download_dataset_artifact(self, path, alias):
         return None, None
 
     def download_model_artifact(self, opt):
+        '''
+        download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX
+        
+        arguments:
+        opt (namespace) -- Commandline arguments for this run
+        '''
         if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
             model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
             assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
@@ -212,6 +262,16 @@ def download_model_artifact(self, opt):
         return None, None
 
     def log_model(self, path, opt, epoch, fitness_score, best_model=False):
+        '''
+        Log the model checkpoint as W&B artifact
+        
+        arguments:
+        path (Path)   -- Path of directory containing the checkpoints
+        opt (namespace) -- Command line arguments for this run
+        epoch (int)  -- Current epoch number
+        fitness_score (float) -- fitness score for current epoch 
+        best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
+        '''
         model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
             'original_url': str(path),
             'epochs_trained': epoch + 1,
@@ -226,6 +286,19 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
         print("Saving model artifact on epoch ", epoch + 1)
 
     def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
+        '''
+        Log the dataset as W&B artifact and return the new data file with W&B links
+        
+        arguments:
+        data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
+        single_class (boolean)  -- train multi-class data as single-class
+        project (str) -- project name. Used to construct the artifact path
+        overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new 
+        file with _wandb postfix. Eg -> data_wandb.yaml
+        
+        returns:
+        the new .yaml file with artifact links. it can be used to start training directly from artifacts
+        '''
         with open(data_file, encoding='ascii', errors='ignore') as f:
             data = yaml.safe_load(f)  # data dict
         check_dataset(data)
@@ -257,12 +330,27 @@ def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=
         return path
 
     def map_val_table_path(self):
+        '''
+        Map the validation dataset Table like name of file -> it's id in the W&B Table.
+        Useful for - referencing artifacts for evaluation.
+        '''
         self.val_table_path_map = {}
         print("Mapping dataset")
         for i, data in enumerate(tqdm(self.val_table.data)):
             self.val_table_path_map[data[3]] = data[0]
 
     def create_dataset_table(self, dataset, class_to_id, name='dataset'):
+        '''
+        Create and return W&B artifact containing W&B Table of the dataset.
+        
+        arguments:
+        dataset (LoadImagesAndLabels) -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
+        class_to_id (dict(int, str)) -- hash map that maps class ids to labels
+        name (str) -- name of the artifact
+        
+        returns:
+        dataset artifact to be logged or used
+        '''
         # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
         artifact = wandb.Artifact(name=name, type="dataset")
         img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
@@ -294,6 +382,14 @@ def create_dataset_table(self, dataset, class_to_id, name='dataset'):
         return artifact
 
     def log_training_progress(self, predn, path, names):
+        '''
+        Build evaluation Table. Uses reference from validation dataset table.
+        
+        arguments:
+        predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
+        path (str): local path of the current evaluation image 
+        names (dict(int, str)): hash map that maps class ids to labels
+        '''
         class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
         box_data = []
         total_conf = 0
@@ -316,25 +412,45 @@ def log_training_progress(self, predn, path, names):
                                    )
 
     def val_one_image(self, pred, predn, path, names, im):
+        '''
+        Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel
+        
+        arguments:
+        pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
+        predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
+        path (str): local path of the current evaluation image 
+        '''
         if self.val_table and self.result_table:  # Log Table if Val dataset is uploaded as artifact
             self.log_training_progress(predn, path, names)
-        else:  # Default to bbox media panelif Val artifact not found
-            if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
-                if self.current_epoch % self.bbox_interval == 0:
-                    box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
-                                 "class_id": int(cls),
-                                 "box_caption": "%s %.3f" % (names[cls], conf),
-                                 "scores": {"class_score": conf},
-                                 "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
-                    boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
-                    self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))
+
+        if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
+            if self.current_epoch % self.bbox_interval == 0:
+                box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
+                             "class_id": int(cls),
+                             "box_caption": "%s %.3f" % (names[cls], conf),
+                             "scores": {"class_score": conf},
+                             "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
+                boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
+                self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))
 
     def log(self, log_dict):
+        '''
+        save the metrics to the logging dictionary
+        
+        arguments:
+        log_dict (Dict) -- metrics/media to be logged in current step
+        '''
         if self.wandb_run:
             for key, value in log_dict.items():
                 self.log_dict[key] = value
 
     def end_epoch(self, best_result=False):
+        '''
+        commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
+        
+        arguments:
+        best_result (boolean): Boolean representing if the result of this evaluation is best or not
+        '''
         if self.wandb_run:
             with all_logging_disabled():
                 if self.bbox_media_panel_images:
@@ -352,6 +468,9 @@ def end_epoch(self, best_result=False):
                 self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
 
     def finish_run(self):
+        '''
+        Log metrics if any and finish the current W&B run
+        '''
         if self.wandb_run:
             if self.log_dict:
                 with all_logging_disabled():