Reduce val device transfers (ultralytics#7525)

val.py

@@ -220,14 +220,14 @@ def run(
         # Metrics
         for si, pred in enumerate(out):
             labels = targets[targets[:, 0] == si, 1:]
-            nl = len(labels)
-            tcls = labels[:, 0].tolist() if nl else []  # target class
+            nl, npr = labels.shape[0], pred.shape[0]  # number of labels, predictions
             path, shape = Path(paths[si]), shapes[si][0]
+            correct = torch.zeros(npr, niou, dtype=torch.bool, device=device)  # init
             seen += 1
 
-            if len(pred) == 0:
+            if npr == 0:
                 if nl:
-                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
+                    stats.append((correct, *torch.zeros((3, 0))))
                 continue
 
             # Predictions
@@ -244,9 +244,7 @@ def run(
                 correct = process_batch(predn, labelsn, iouv)
                 if plots:
                     confusion_matrix.process_batch(predn, labelsn)
-            else:
-                correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)
-            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))  # (correct, conf, pcls, tcls)
+            stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0]))  # (correct, conf, pcls, tcls)
 
             # Save/log
             if save_txt:
@@ -265,7 +263,7 @@ def run(
         callbacks.run('on_val_batch_end')
 
     # Compute metrics
-    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
+    stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)]  # to numpy
     if len(stats) and stats[0].any():
         tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
         ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95