Refactor collections and fstrings (#7821)

* Update torch_utils.py

* Additional code refactoring

* tuples to sets

* Cleanup

utils/torch_utils.py

@@ -50,9 +50,9 @@ def device_count():
 
 
 def select_device(device='', batch_size=0, newline=True):
-    # device = 'cpu' or '0' or '0,1,2,3'
+    # device = None or 'cpu' or 0 or '0' or '0,1,2,3'
     s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '
-    device = str(device).strip().lower().replace('cuda:', '')  # to string, 'cuda:0' to '0'
+    device = str(device).strip().lower().replace('cuda:', '').replace('none', '')  # to string, 'cuda:0' to '0'
     cpu = device == 'cpu'
     if cpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
@@ -97,7 +97,8 @@ def profile(input, ops, n=10, device=None):
     #     profile(input, [m1, m2], n=100)  # profile over 100 iterations
 
     results = []
-    device = device or select_device()
+    if not isinstance(device, torch.device):
+        device = select_device(device)
     print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
           f"{'input':>24s}{'output':>24s}")
 
@@ -127,9 +128,8 @@ def profile(input, ops, n=10, device=None):
                     tf += (t[1] - t[0]) * 1000 / n  # ms per op forward
                     tb += (t[2] - t[1]) * 1000 / n  # ms per op backward
                 mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0  # (GB)
-                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
+                s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y))  # shapes
+                p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0  # parameters
                 print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
                 results.append([p, flops, mem, tf, tb, s_in, s_out])
             except Exception as e:
@@ -227,7 +227,7 @@ def model_info(model, verbose=False, img_size=640):
         flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2  # stride GFLOPs
         img_size = img_size if isinstance(img_size, list) else [img_size, img_size]  # expand if int/float
         fs = ', %.1f GFLOPs' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 GFLOPs
-    except (ImportError, Exception):
+    except Exception:
         fs = ''
 
     name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model'
@@ -238,13 +238,12 @@ def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
     # Scales img(bs,3,y,x) by ratio constrained to gs-multiple
     if ratio == 1.0:
         return img
-    else:
-        h, w = img.shape[2:]
-        s = (int(h * ratio), int(w * ratio))  # new size
-        img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
-        if not same_shape:  # pad/crop img
-            h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
-        return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
+    h, w = img.shape[2:]
+    s = (int(h * ratio), int(w * ratio))  # new size
+    img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
+    if not same_shape:  # pad/crop img
+        h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
+    return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
 
 
 def copy_attr(a, b, include=(), exclude=()):