Rename 'labels' to 'instances' (ultralytics#9066)

* Rename labels to instances

* Rename labels to instances

* align val

train.py

@@ -271,7 +271,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
         if RANK != -1:
             train_loader.sampler.set_epoch(epoch)
         pbar = enumerate(train_loader)
-        LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
+        LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
         if RANK in {-1, 0}:
             pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
         optimizer.zero_grad()
@@ -326,7 +326,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
             if RANK in {-1, 0}:
                 mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
                 mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
-                pbar.set_description(('%10s' * 2 + '%10.4g' * 5) %
+                pbar.set_description(('%11s' * 2 + '%11.4g' * 5) %
                                      (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
                 callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots)
                 if callbacks.stop_training:

val.py

@@ -186,7 +186,7 @@ def run(
     if isinstance(names, (list, tuple)):  # old format
         names = dict(enumerate(names))
     class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
-    s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', '[email protected]', '[email protected]:.95')
+    s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', '[email protected]', '[email protected]:.95')
     dt, p, r, f1, mp, mr, map50, map = (Profile(), Profile(), Profile()), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
     loss = torch.zeros(3, device=device)
     jdict, stats, ap, ap_class = [], [], [], []
@@ -270,7 +270,7 @@ def run(
     nt = np.bincount(stats[3].astype(int), minlength=nc)  # number of targets per class
 
     # Print results
-    pf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print format
+    pf = '%22s' + '%11i' * 2 + '%11.3g' * 4  # print format
     LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
     if nt.sum() == 0:
         LOGGER.warning(f'WARNING: no labels found in {task} set, can not compute metrics without labels ⚠️')