Add @threaded decorator (#7813)

* Add `@threaded` decorator

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

utils/loggers/__init__.py

@@ -5,7 +5,6 @@ Logging utils
 
 import os
 import warnings
-from threading import Thread
 
 import pkg_resources as pkg
 import torch
@@ -109,7 +108,7 @@ class Loggers():
                         self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
             if ni < 3:
                 f = self.save_dir / f'train_batch{ni}.jpg'  # filename
-                Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
+                plot_images(imgs, targets, paths, f)
             if self.wandb and ni == 10:
                 files = sorted(self.save_dir.glob('train*.jpg'))
                 self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
@@ -132,7 +131,7 @@ class Loggers():
 
     def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
         # Callback runs at the end of each fit (train+val) epoch
-        x = {k: v for k, v in zip(self.keys, vals)}  # dict
+        x = dict(zip(self.keys, vals))
         if self.csv:
             file = self.save_dir / 'results.csv'
             n = len(x) + 1  # number of cols
@@ -171,7 +170,7 @@ class Loggers():
                 self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
 
         if self.wandb:
-            self.wandb.log({k: v for k, v in zip(self.keys[3:10], results)})  # log best.pt val results
+            self.wandb.log(dict(zip(self.keys[3:10], results)))
             self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
             # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
             if not self.opt.evolve: