rename benchmark to cudnn_benchmark

configs/pascal_voc/ssd300_voc.py

-benchmark = True
 # model settings
 input_size = 300
 model = dict(
@@ -24,6 +23,7 @@ model = dict(
         anchor_ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]),
         target_means=(.0, .0, .0, .0),
         target_stds=(0.1, 0.1, 0.2, 0.2)))
+cudnn_benchmark = True
 train_cfg = dict(
     assigner=dict(
         type='MaxIoUAssigner',

configs/pascal_voc/ssd512_voc.py

-benchmark = True
 # model settings
 input_size = 512
 model = dict(
@@ -24,6 +23,7 @@ model = dict(
         anchor_ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]),
         target_means=(.0, .0, .0, .0),
         target_stds=(0.1, 0.1, 0.2, 0.2)))
+cudnn_benchmark = True
 train_cfg = dict(
     assigner=dict(
         type='MaxIoUAssigner',

configs/ssd300_coco.py

-benchmark = True
 # model settings
 input_size = 300
 model = dict(
@@ -24,6 +23,7 @@ model = dict(
         anchor_ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]),
         target_means=(.0, .0, .0, .0),
         target_stds=(0.1, 0.1, 0.2, 0.2)))
+cudnn_benchmark = True
 train_cfg = dict(
     assigner=dict(
         type='MaxIoUAssigner',

configs/ssd512_coco.py

-benchmark = True
 # model settings
 input_size = 512
 model = dict(
@@ -24,6 +23,7 @@ model = dict(
         anchor_ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]),
         target_means=(.0, .0, .0, .0),
         target_stds=(0.1, 0.1, 0.2, 0.2)))
+cudnn_benchmark = True
 train_cfg = dict(
     assigner=dict(
         type='MaxIoUAssigner',

tools/test.py

@@ -66,6 +66,9 @@ def main():
         raise ValueError('The output file must be a pkl file.')
 
     cfg = mmcv.Config.fromfile(args.config)
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
+        torch.backends.cudnn.benchmark = True
     cfg.model.pretrained = None
     cfg.data.test.test_mode = True
 

tools/train.py

@@ -43,8 +43,8 @@ def main():
     args = parse_args()
 
     cfg = Config.fromfile(args.config)
-    # set benchmark
-    if cfg.get('benchmark', False):
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
         torch.backends.cudnn.benchmark = True
     # update configs according to CLI args
     if args.work_dir is not None: