val.py refactor (#4053)

* val.py refactor

* cleanup

* cleanup

* cleanup

* cleanup

* save after eval

* opt.imgsz bug fix

* wandb refactor

* dataloader to train_loader

* capitalize global variables

* runs/hub/exp to runs/detect/exp

* refactor wandb logging

* Refactor wandb operations (#4061)

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

utils/torch_utils.py

@@ -22,7 +22,7 @@ try:
     import thop  # for FLOPs computation
 except ImportError:
     thop = None
-logger = logging.getLogger(__name__)
+LOGGER = logging.getLogger(__name__)
 
 
 @contextmanager
@@ -85,11 +85,11 @@ def select_device(device='', batch_size=None):
     else:
         s += 'CPU\n'
 
-    logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe
+    LOGGER.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe
     return torch.device('cuda:0' if cuda else 'cpu')
 
 
-def time_synchronized():
+def time_sync():
     # pytorch-accurate time
     if torch.cuda.is_available():
         torch.cuda.synchronize()
@@ -118,12 +118,12 @@ def profile(x, ops, n=100, device=None):
             flops = 0
 
         for _ in range(n):
-            t[0] = time_synchronized()
+            t[0] = time_sync()
             y = m(x)
-            t[1] = time_synchronized()
+            t[1] = time_sync()
             try:
                 _ = y.sum().backward()
-                t[2] = time_synchronized()
+                t[2] = time_sync()
             except:  # no backward method
                 t[2] = float('nan')
             dtf += (t[1] - t[0]) * 1000 / n  # ms per op forward
@@ -231,7 +231,7 @@ def model_info(model, verbose=False, img_size=640):
     except (ImportError, Exception):
         fs = ''
 
-    logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
+    LOGGER.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
 
 
 def load_classifier(name='resnet101', n=2):