Improved Profile() inference timing (#9024)

* Improved `Profile()` class

* Update predict.py

* Update val.py

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

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

* Update val.py

* Update AutoShape

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

classify/predict.py

@@ -22,8 +22,8 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 from models.common import DetectMultiBackend
 from utils.augmentations import classify_transforms
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages
-from utils.general import LOGGER, check_file, check_requirements, colorstr, increment_path, print_args
-from utils.torch_utils import select_device, smart_inference_mode, time_sync
+from utils.general import LOGGER, Profile, check_file, check_requirements, colorstr, increment_path, print_args
+from utils.torch_utils import select_device, smart_inference_mode
 
 
 @smart_inference_mode()
@@ -44,7 +44,7 @@ def run(
     if is_url and is_file:
         source = check_file(source)  # download
 
-    seen, dt = 1, [0.0, 0.0, 0.0]
+    dt = Profile(), Profile(), Profile()
     device = select_device(device)
 
     # Directories
@@ -55,30 +55,27 @@ def run(
     model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
     model.warmup(imgsz=(1, 3, imgsz, imgsz))  # warmup
     dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz))
-    for path, im, im0s, vid_cap, s in dataset:
+    for seen, (path, im, im0s, vid_cap, s) in enumerate(dataset):
         # Image
-        t1 = time_sync()
-        im = im.unsqueeze(0).to(device)
-        im = im.half() if model.fp16 else im.float()
-        t2 = time_sync()
-        dt[0] += t2 - t1
+        with dt[0]:
+            im = im.unsqueeze(0).to(device)
+            im = im.half() if model.fp16 else im.float()
 
         # Inference
-        results = model(im)
-        t3 = time_sync()
-        dt[1] += t3 - t2
+        with dt[1]:
+            results = model(im)
 
         # Post-process
-        p = F.softmax(results, dim=1)  # probabilities
-        i = p.argsort(1, descending=True)[:, :5].squeeze().tolist()  # top 5 indices
-        dt[2] += time_sync() - t3
-        # if save:
-        #    imshow_cls(im, f=save_dir / Path(path).name, verbose=True)
-        seen += 1
-        LOGGER.info(f"{s}{imgsz}x{imgsz} {', '.join(f'{model.names[j]} {p[0, j]:.2f}' for j in i)}")
+        with dt[2]:
+            p = F.softmax(results, dim=1)  # probabilities
+            i = p.argsort(1, descending=True)[:, :5].squeeze().tolist()  # top 5 indices
+            # if save:
+            #    imshow_cls(im, f=save_dir / Path(path).name, verbose=True)
+            LOGGER.info(
+                f"{s}{imgsz}x{imgsz} {', '.join(f'{model.names[j]} {p[0, j]:.2f}' for j in i)}, {dt[1].dt * 1E3:.1f}ms")
 
     # Print results
-    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
+    t = tuple(x.t / (seen + 1) * 1E3 for x in dt)  # speeds per image
     shape = (1, 3, imgsz, imgsz)
     LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)
     LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")