Simplify val.py benchmark mode with speed mode (#9674)

Update

benchmarks.py

@@ -81,10 +81,10 @@ def run(
 
             # Validate
             if model_type == SegmentationModel:
-                result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='benchmark', half=half)
+                result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
                 metric = result[0][7]  # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))
             else:  # DetectionModel:
-                result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='benchmark', half=half)
+                result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
                 metric = result[0][3]  # (p, r, map50, map, *loss(box, obj, cls))
             speed = result[2][1]  # times (preprocess, inference, postprocess)
             y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)])  # MB, mAP, t_inference

segment/val.py

@@ -210,8 +210,7 @@ def run(
             assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
                               f'classes). Pass correct combination of --weights and --data that are trained together.'
         model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz))  # warmup
-        pad = 0.0 if task in ('speed', 'benchmark') else 0.5
-        rect = False if task == 'benchmark' else pt  # square inference for benchmarks
+        pad, rect = (0.0, False) if task == 'speed' else (0.5, pt)  # square inference for benchmarks
         task = task if task in ('train', 'val', 'test') else 'val'  # path to train/val/test images
         dataloader = create_dataloader(data[task],
                                        imgsz,

val.py

@@ -169,8 +169,7 @@ def run(
             assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
                               f'classes). Pass correct combination of --weights and --data that are trained together.'
         model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz))  # warmup
-        pad = 0.0 if task in ('speed', 'benchmark') else 0.5
-        rect = False if task == 'benchmark' else pt  # square inference for benchmarks
+        pad, rect = (0.0, False) if task == 'speed' else (0.5, pt)  # square inference for benchmarks
         task = task if task in ('train', 'val', 'test') else 'val'  # path to train/val/test images
         dataloader = create_dataloader(data[task],
                                        imgsz,