Pass drop connect arg through to EfficientNet models

timm/models/factory.py

@@ -25,12 +25,13 @@ def create_model(
     """
     margs = dict(pretrained=pretrained, num_classes=num_classes, in_chans=in_chans)
 
-    # Not all models have support for batchnorm params passed as args, only gen_efficientnet variants
+    # Only gen_efficientnet models have support for batchnorm params or drop_connect_rate passed as args
-    supports_bn_params = is_model_in_modules(model_name, ['gen_efficientnet'])
+    is_efficientnet = is_model_in_modules(model_name, ['gen_efficientnet'])
-    if not supports_bn_params and any([x in kwargs for x in ['bn_tf', 'bn_momentum', 'bn_eps']]):
+    if not is_efficientnet:
         kwargs.pop('bn_tf', None)
         kwargs.pop('bn_momentum', None)
         kwargs.pop('bn_eps', None)
+        kwargs.pop('drop_connect_rate', None)
 
     if is_model(model_name):
         create_fn = model_entrypoint(model_name)

train.py

@@ -65,6 +65,8 @@ parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N',
                     help='input batch size for training (default: 32)')
 parser.add_argument('--drop', type=float, default=0.0, metavar='DROP',
                     help='Dropout rate (default: 0.)')
+parser.add_argument('--drop-connect', type=float, default=0.0, metavar='DROP',
+                    help='Drop connect rate (default: 0.)')
 # Optimizer parameters
 parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
                     help='Optimizer (default: "sgd"')
@@ -208,6 +210,7 @@ def main():
         pretrained=args.pretrained,
         num_classes=args.num_classes,
         drop_rate=args.drop,
+        drop_connect_rate=args.drop_connect,
         global_pool=args.gp,
         bn_tf=args.bn_tf,
         bn_momentum=args.bn_momentum,
@@ -253,7 +256,7 @@ def main():
             if args.local_rank == 0:
                 logging.info('Restoring NVIDIA AMP state from checkpoint')
             amp.load_state_dict(resume_state['amp'])
-    resume_state = None
+    resume_state = None  # clear it
 
     model_ema = None
     if args.model_ema: