Improved yolo.py profiling (#2940)

* Improved yolo.py profiling

Improved column order and labelling.

* Update yolo.py

models/yolo.py

@@ -134,7 +134,9 @@ class Model(nn.Module):
                 for _ in range(10):
                     _ = m(x)
                 dt.append((time_synchronized() - t) * 100)
-                logger.info('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
+                if m == self.model[0]:
+                    logger.info(f"{'time (ms)':>10s} {'GFLOPS':>10s} {'params':>10s}  {'module'}")
+                logger.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
 
             x = m(x)  # run
             y.append(x if m.i in self.save else None)  # save output
@@ -157,7 +159,8 @@ class Model(nn.Module):
         m = self.model[-1]  # Detect() module
         for mi in m.m:  # from
             b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
-            logger.info(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
+            logger.info(
+                ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
 
     # def _print_weights(self):
     #     for m in self.model.modules():