Fix model re-fuse() in inference loops (#466)

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

ultralytics/nn/tasks.py

@@ -63,7 +63,8 @@ class BaseModel(nn.Module):
 
     def _profile_one_layer(self, m, x, dt):
         """
-        Profile the computation time and FLOPs of a single layer of the model on a given input. Appends the results to the provided list.
+        Profile the computation time and FLOPs of a single layer of the model on a given input.
+        Appends the results to the provided list.
 
         Args:
             m (nn.Module): The layer to be profiled.
@@ -74,10 +75,10 @@ class BaseModel(nn.Module):
             None
         """
         c = m == self.model[-1]  # is final layer, copy input as inplace fix
-        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
+        o = thop.profile(m, inputs=(x.clone() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
         t = time_sync()
         for _ in range(10):
-            m(x.copy() if c else x)
+            m(x.clone() if c else x)
         dt.append((time_sync() - t) * 100)
         if m == self.model[0]:
             LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
@@ -87,20 +88,36 @@ class BaseModel(nn.Module):
 
     def fuse(self):
         """
-        Fuse the `Conv2d()` and `BatchNorm2d()` layers of the model into a single layer, in order to improve the computation efficiency.
+        Fuse the `Conv2d()` and `BatchNorm2d()` layers of the model into a single layer, in order to improve the
+        computation efficiency.
 
         Returns:
             (nn.Module): The fused model is returned.
         """
-        LOGGER.info('Fusing layers... ')
-        for m in self.model.modules():
-            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
-                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
-                delattr(m, 'bn')  # remove batchnorm
-                m.forward = m.forward_fuse  # update forward
-        self.info()
+        if not self.is_fused():
+            LOGGER.info('Fusing... ')
+            for m in self.model.modules():
+                if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
+                    m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
+                    delattr(m, 'bn')  # remove batchnorm
+                    m.forward = m.forward_fuse  # update forward
+            self.info()
+
         return self
 
+    def is_fused(self, thresh=10):
+        """
+        Check if the model has less than a certain threshold of BatchNorm layers.
+
+        Args:
+            thresh (int, optional): The threshold number of BatchNorm layers. Default is 10.
+
+        Returns:
+            bool: True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
+        """
+        bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()
+        return sum(isinstance(v, bn) for v in self.modules()) < thresh  # True if < 'thresh' BatchNorm layers in model
+
     def info(self, verbose=False, imgsz=640):
         """
         Prints model information