Add train.py and val.py callbacks (#4220)

* added callbacks

* Update callbacks.py

* Update train.py

* Update val.py

* Fix CamlCase add staticmethod

* Refactor logger into callbacks

* Cleanup

* New callback on_val_image_end()

* Add curves and results images to TensorBoard

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

val.py

@@ -25,7 +25,7 @@ from utils.general import coco80_to_coco91_class, check_dataset, check_file, che
 from utils.metrics import ap_per_class, ConfusionMatrix
 from utils.plots import plot_images, output_to_target, plot_study_txt
 from utils.torch_utils import select_device, time_sync
-from utils.loggers import Loggers
+from utils.callbacks import Callbacks
 
 
 def save_one_txt(predn, save_conf, shape, file):
@@ -97,7 +97,7 @@ def run(data,
         dataloader=None,
         save_dir=Path(''),
         plots=True,
-        loggers=Loggers(),
+        callbacks=Callbacks(),
         compute_loss=None,
         ):
     # Initialize/load model and set device
@@ -213,7 +213,7 @@ def run(data,
                 save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
             if save_json:
                 save_one_json(predn, jdict, path, class_map)  # append to COCO-JSON dictionary
-            loggers.on_val_batch_end(pred, predn, path, names, img[si])
+            callbacks.on_val_image_end(pred, predn, path, names, img[si])
 
         # Plot images
         if plots and batch_i < 3:
@@ -250,7 +250,7 @@ def run(data,
     # Plots
     if plots:
         confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
-        loggers.on_val_end()
+        callbacks.on_val_end()
 
     # Save JSON
     if save_json and len(jdict):
@@ -282,7 +282,7 @@ def run(data,
     model.float()  # for training
     if not training:
         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-        print(f"Results saved to {save_dir}{s}")
+        print(f"Results saved to {colorstr('bold', save_dir)}{s}")
     maps = np.zeros(nc) + map
     for i, c in enumerate(ap_class):
         maps[c] = ap[i]