AutoShape explicit arguments fix (#9443)

models/common.py

@@ -633,7 +633,7 @@ class AutoShape(nn.Module):
             autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
             if isinstance(ims, torch.Tensor):  # torch
                 with amp.autocast(autocast):
-                    return self.model(ims.to(p.device).type_as(p), augment, profile)  # inference
+                    return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference
 
             # Pre-process
             n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
@@ -662,7 +662,7 @@ class AutoShape(nn.Module):
         with amp.autocast(autocast):
             # Inference
             with dt[1]:
-                y = self.model(x, augment, profile)  # forward
+                y = self.model(x, augment=augment)  # forward
 
             # Post-process
             with dt[2]:
@@ -696,7 +696,7 @@ class Detections:
         self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
         self.n = len(self.pred)  # number of images (batch size)
         self.t = tuple(x.t / self.n * 1E3 for x in times)  # timestamps (ms)
-        self.s = shape  # inference BCHW shape
+        self.s = tuple(shape)  # inference BCHW shape
 
     def display(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
         crops = []
@@ -726,7 +726,7 @@ class Detections:
 
             im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
             if pprint:
-                print(s.rstrip(', '))
+                LOGGER.info(s.rstrip(', '))
             if show:
                 im.show(self.files[i])  # show
             if save:
@@ -743,7 +743,7 @@ class Detections:
 
     def print(self):
         self.display(pprint=True)  # print results
-        print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
+        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t)
 
     def show(self, labels=True):
         self.display(show=True, labels=labels)  # show results