Update selectable device Profile (#12353)

* Update selectable device Profile

* [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>

classify/predict.py

@@ -106,7 +106,7 @@ def run(
 
     # Run inference
     model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
-    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
     for path, im, im0s, vid_cap, s in dataset:
         with dt[0]:
             im = torch.Tensor(im).to(model.device)

classify/val.py

@@ -97,7 +97,7 @@ def run(
                                                       workers=workers)
 
     model.eval()
-    pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())
+    pred, targets, loss, dt = [], [], 0, (Profile(device=device), Profile(device=device), Profile(device=device))
     n = len(dataloader)  # number of batches
     action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
     desc = f'{pbar.desc[:-36]}{action:>36}' if pbar else f'{action}'

detect.py

@@ -116,7 +116,7 @@ def run(
 
     # Run inference
     model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
-    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
     for path, im, im0s, vid_cap, s in dataset:
         with dt[0]:
             im = torch.from_numpy(im).to(model.device)

segment/predict.py

@@ -117,7 +117,7 @@ def run(
 
     # Run inference
     model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
-    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
     for path, im, im0s, vid_cap, s in dataset:
         with dt[0]:
             im = torch.from_numpy(im).to(model.device)

segment/val.py

@@ -233,7 +233,7 @@ def run(
     class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
     s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P', 'R',
                                   'mAP50', 'mAP50-95)')
-    dt = Profile(), Profile(), Profile()
+    dt = Profile(device=device), Profile(device=device), Profile(device=device)
     metrics = Metrics()
     loss = torch.zeros(4, device=device)
     jdict, stats = [], []

utils/general.py

@@ -182,9 +182,10 @@ CONFIG_DIR = user_config_dir()  # Ultralytics settings dir
 
 class Profile(contextlib.ContextDecorator):
     # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager
-    def __init__(self, t=0.0):
+    def __init__(self, t=0.0, device: torch.device = None):
         self.t = t
-        self.cuda = torch.cuda.is_available()
+        self.device = device
+        self.cuda = True if (device and str(device)[:4] == 'cuda') else False
 
     def __enter__(self):
         self.start = self.time()
@@ -196,7 +197,7 @@ class Profile(contextlib.ContextDecorator):
 
     def time(self):
         if self.cuda:
-            torch.cuda.synchronize()
+            torch.cuda.synchronize(self.device)
         return time.time()
 
 

val.py

@@ -191,7 +191,7 @@ def run(
     class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
     s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP50', 'mAP50-95')
     tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
-    dt = Profile(), Profile(), Profile()  # profiling times
+    dt = Profile(device=device), Profile(device=device), Profile(device=device)  # profiling times
     loss = torch.zeros(3, device=device)
     jdict, stats, ap, ap_class = [], [], [], []
     callbacks.run('on_val_start')