New smart_inference_mode() conditional decorator

detect.py

@@ -44,10 +44,10 @@
 from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                            increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
 from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import select_device, time_sync
+from utils.torch_utils import select_device, smart_inference_mode, time_sync
 
 
-@torch.no_grad()
+@smart_inference_mode()
 def run(
         weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
         source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam

export.py

@@ -69,7 +69,7 @@
 from utils.dataloaders import LoadImages
 from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_version, check_yaml,
                            colorstr, file_size, print_args, url2file)
-from utils.torch_utils import select_device
+from utils.torch_utils import select_device, smart_inference_mode
 
 
 def export_formats():
@@ -455,7 +455,7 @@ def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
         LOGGER.info(f'\n{prefix} export failure: {e}')
 
 
-@torch.no_grad()
+@smart_inference_mode()
 def run(
         data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
         weights=ROOT / 'yolov5s.pt',  # weights path

models/common.py

@@ -25,7 +25,7 @@
 from utils.general import (LOGGER, check_requirements, check_suffix, check_version, colorstr, increment_path,
                            make_divisible, non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh)
 from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import copy_attr, time_sync
+from utils.torch_utils import copy_attr, smart_inference_mode, time_sync
 
 
 def autopad(k, p=None):  # kernel, padding
@@ -578,7 +578,7 @@ def _apply(self, fn):
                 m.anchor_grid = list(map(fn, m.anchor_grid))
         return self
 
-    @torch.no_grad()
+    @smart_inference_mode()
     def forward(self, imgs, size=640, augment=False, profile=False):
         # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
         #   file:       imgs = 'data/images/zidane.jpg'  # str or PosixPath

models/yolo.py

@@ -76,12 +76,12 @@ def forward(self, x):
 
         return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
 
-    def _make_grid(self, nx=20, ny=20, i=0):
+    def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
         d = self.anchors[i].device
         t = self.anchors[i].dtype
         shape = 1, self.na, ny, nx, 2  # grid shape
         y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
-        if check_version(torch.__version__, '1.10.0'):  # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
+        if torch_1_10:  # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
             yv, xv = torch.meshgrid(y, x, indexing='ij')
         else:
             yv, xv = torch.meshgrid(y, x)

utils/torch_utils.py

@@ -34,6 +34,14 @@
 warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling')
 
 
+def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):
+    # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator
+    def decorate(fn):
+        return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)
+
+    return decorate
+
+
 def smart_DDP(model):
     # Model DDP creation with checks
     assert not check_version(torch.__version__, '1.12.0', pinned=True), \
@@ -364,17 +372,17 @@ def __init__(self, model, decay=0.9999, tau=2000, updates=0):
         for p in self.ema.parameters():
             p.requires_grad_(False)
 
+    @smart_inference_mode()
     def update(self, model):
         # Update EMA parameters
-        with torch.no_grad():
-            self.updates += 1
-            d = self.decay(self.updates)
-
-            msd = de_parallel(model).state_dict()  # model state_dict
-            for k, v in self.ema.state_dict().items():
-                if v.dtype.is_floating_point:
-                    v *= d
-                    v += (1 - d) * msd[k].detach()
+        self.updates += 1
+        d = self.decay(self.updates)
+
+        msd = de_parallel(model).state_dict()  # model state_dict
+        for k, v in self.ema.state_dict().items():
+            if v.dtype.is_floating_point:
+                v *= d
+                v += (1 - d) * msd[k].detach()
 
     def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
         # Update EMA attributes

val.py

@@ -42,7 +42,7 @@
                            scale_coords, xywh2xyxy, xyxy2xywh)
 from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
 from utils.plots import output_to_target, plot_images, plot_val_study
-from utils.torch_utils import select_device, time_sync
+from utils.torch_utils import select_device, smart_inference_mode, time_sync
 
 
 def save_one_txt(predn, save_conf, shape, file):
@@ -93,7 +93,7 @@ def process_batch(detections, labels, iouv):
     return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
 
 
-@torch.no_grad()
+@smart_inference_mode()
 def run(
         data,
         weights=None,  # model.pt path(s)