train max_step_6500

@@ -104,20 +104,20 @@ def dict_to_namedtuple(d):
 
 def parse_args(kwargs):
     # combine with default args
-    kwargs['dataset'] =  kwargs['dataset'] if 'dataset' in kwargs else 'cifar10'
-    kwargs['network'] =  kwargs['network'] if 'network' in kwargs else 'resnet_cifar10'
+    kwargs['dataset'] =  kwargs['dataset'] if 'dataset' in kwargs else 'BraTS'
+    kwargs['network'] =  kwargs['network'] if 'network' in kwargs else 'resnet50'
     kwargs['optimizer'] = kwargs['optimizer'] if 'optimizer' in kwargs else 'adam'
-    kwargs['learning_rate'] = kwargs['learning_rate'] if 'learning_rate' in kwargs else 0.1
+    kwargs['learning_rate'] = kwargs['learning_rate'] if 'learning_rate' in kwargs else 0.0001
     kwargs['seed'] =  kwargs['seed'] if 'seed' in kwargs else None
     kwargs['use_cuda'] =  kwargs['use_cuda'] if 'use_cuda' in kwargs else True
     kwargs['use_cuda'] =  kwargs['use_cuda'] and torch.cuda.is_available()
     kwargs['num_workers'] = kwargs['num_workers'] if 'num_workers' in kwargs else 4
-    kwargs['print_step'] = kwargs['print_step'] if 'print_step' in kwargs else 2000
-    kwargs['val_step'] = kwargs['val_step'] if 'val_step' in kwargs else 2000
+    kwargs['print_step'] = kwargs['print_step'] if 'print_step' in kwargs else 500
+    kwargs['val_step'] = kwargs['val_step'] if 'val_step' in kwargs else 500
     kwargs['scheduler'] = kwargs['scheduler'] if 'scheduler' in kwargs else 'exp'
     kwargs['batch_size'] = kwargs['batch_size'] if 'batch_size' in kwargs else 128
     kwargs['start_step'] = kwargs['start_step'] if 'start_step' in kwargs else 0
-    kwargs['max_step'] = kwargs['max_step'] if 'max_step' in kwargs else 64000
+    kwargs['max_step'] = kwargs['max_step'] if 'max_step' in kwargs else 6500
     kwargs['fast_auto_augment'] = kwargs['fast_auto_augment'] if 'fast_auto_augment' in kwargs else False
     kwargs['augment_path'] = kwargs['augment_path'] if 'augment_path' in kwargs else None