Display correct CUDA devices (#1776)

* Display correct CUDA devices

* cleanup

utils/torch_utils.py

@@ -48,20 +48,20 @@ def select_device(device='', batch_size=None):
     cpu_request = device.lower() == 'cpu'
     if device and not cpu_request:  # if device requested other than 'cpu'
         os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
-        assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity
+        assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'  # check availablity
 
     cuda = False if cpu_request else torch.cuda.is_available()
     if cuda:
         c = 1024 ** 2  # bytes to MB
         ng = torch.cuda.device_count()
         if ng > 1 and batch_size:  # check that batch_size is compatible with device_count
-            assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)
+            assert batch_size % ng == 0, f'batch-size {batch_size} not multiple of GPU count {ng}'
         x = [torch.cuda.get_device_properties(i) for i in range(ng)]
         s = f'Using torch {torch.__version__} '
-        for i in range(0, ng):
+        for i, d in enumerate((device or '0').split(',')):
             if i == 1:
                 s = ' ' * len(s)
-            logger.info("%sCUDA:%g (%s, %dMB)" % (s, i, x[i].name, x[i].total_memory / c))
+            logger.info(f"{s}CUDA:{d} ({x[i].name}, {x[i].total_memory / c}MB)")
     else:
         logger.info(f'Using torch {torch.__version__} CPU')