Integer printout (#2450)

* Integer printout

* test.py 'Labels'

* Update train.py

test.py

@@ -93,7 +93,7 @@ def test(data,
     confusion_matrix = ConfusionMatrix(nc=nc)
     names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
     coco91class = coco80_to_coco91_class()
-    s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
+    s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
     p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
     loss = torch.zeros(3, device=device)
     jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
@@ -223,7 +223,7 @@ def test(data,
         nt = torch.zeros(1)
 
     # Print results
-    pf = '%20s' + '%12.3g' * 6  # print format
+    pf = '%20s' + '%12i' * 2 + '%12.3g' * 4  # print format
     print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
 
     # Print results per class

train.py

@@ -264,7 +264,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         if rank != -1:
             dataloader.sampler.set_epoch(epoch)
         pbar = enumerate(dataloader)
-        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size'))
+        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
         if rank in [-1, 0]:
             pbar = tqdm(pbar, total=nb)  # progress bar
         optimizer.zero_grad()

utils/torch_utils.py

@@ -120,7 +120,7 @@ def profile(x, ops, n=100, device=None):
         s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list'
         s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list'
         p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0  # parameters
-        print(f'{p:12.4g}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}')
+        print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}')
 
 
 def is_parallel(model):