Add pre-commit CI actions (#4982)

* define pre-commit

* add CI code

* configure

* apply pre-commit

* fstring

* apply MD

* pre-commit

* Update torch_utils.py

* Update print strings

* notes

* Cleanup code-format.yml

* Update setup.cfg

* Update .pre-commit-config.yaml

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

utils/loggers/wandb/wandb_utils.py

@@ -5,6 +5,7 @@ import os
 import sys
 from contextlib import contextmanager
 from pathlib import Path
+from typing import Dict
 
 import pkg_resources as pkg
 import yaml
@@ -25,7 +26,7 @@ try:
     assert hasattr(wandb, '__version__')  # verify package import not local dir
 except (ImportError, AssertionError):
     wandb = None
-    
+
 RANK = int(os.getenv('RANK', -1))
 WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
 
@@ -127,7 +128,7 @@ class WandbLogger():
         arguments:
         opt (namespace) -- Commandline arguments for this run
         run_id (str) -- Run ID of W&B run to be resumed
-        job_type (str) -- To set the job_type for this run 
+        job_type (str) -- To set the job_type for this run
 
        """
         # Pre-training routine --
@@ -142,7 +143,8 @@ class WandbLogger():
         self.max_imgs_to_log = 16
         self.wandb_artifact_data_dict = None
         self.data_dict = None
-        # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call
+        # It's more elegant to stick to 1 wandb.init call,
+        #  but useful config data is overwritten in the WandbLogger's wandb.init call
         if isinstance(opt.resume, str):  # checks resume from artifact
             if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
                 entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
@@ -212,7 +214,7 @@ class WandbLogger():
         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 
+          - Setup log_dict, initialize bbox_interval
 
         arguments:
         opt (namespace) -- commandline arguments for this run
@@ -301,7 +303,7 @@ class WandbLogger():
         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 
+        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={
@@ -325,7 +327,7 @@ class WandbLogger():
         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 
+        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:
@@ -371,14 +373,14 @@ class WandbLogger():
         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'):
+    def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int,str], name: str = '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
+        dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
+        class_to_id -- hash map that maps class ids to labels
+        name -- name of the artifact
 
         returns:
         dataset artifact to be logged or used
@@ -419,7 +421,7 @@ class WandbLogger():
 
         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 
+        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()])
@@ -430,7 +432,7 @@ class WandbLogger():
                 box_data.append(
                     {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                      "class_id": int(cls),
-                     "box_caption": "%s %.3f" % (names[cls], conf),
+                     "box_caption": f"{names[cls]} {conf:.3f}",
                      "scores": {"class_score": conf},
                      "domain": "pixel"})
                 total_conf += conf
@@ -450,7 +452,7 @@ class WandbLogger():
         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 
+        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)
@@ -459,7 +461,7 @@ class WandbLogger():
             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),
+                             "box_caption": f"{names[cls]} {conf:.3f}",
                              "scores": {"class_score": conf},
                              "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
                 boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space