Update inference default to multi_label=False (#2252)

* Update inference default to multi_label=False

* bug fix

* Update plots.py

* Update plots.py

test.py

@@ -106,7 +106,7 @@ def test(data,
         with torch.no_grad():
             # Run model
             t = time_synchronized()
-            inf_out, train_out = model(img, augment=augment)  # inference and training outputs
+            out, train_out = model(img, augment=augment)  # inference and training outputs
             t0 += time_synchronized() - t
 
             # Compute loss
@@ -117,11 +117,11 @@ def test(data,
             targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
             lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
             t = time_synchronized()
-            output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb)
+            out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)
             t1 += time_synchronized() - t
 
         # Statistics per image
-        for si, pred in enumerate(output):
+        for si, pred in enumerate(out):
             labels = targets[targets[:, 0] == si, 1:]
             nl = len(labels)
             tcls = labels[:, 0].tolist() if nl else []  # target class
@@ -209,7 +209,7 @@ def test(data,
             f = save_dir / f'test_batch{batch_i}_labels.jpg'  # labels
             Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
             f = save_dir / f'test_batch{batch_i}_pred.jpg'  # predictions
-            Thread(target=plot_images, args=(img, output_to_target(output), paths, f, names), daemon=True).start()
+            Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
 
     # Compute statistics
     stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy