TypeError: '<' not supported between instances of 'NoneType' and 'int'
Hi, I am trying to do classification fine tuning using bert-base-uncased.  I am using examples from master and pytorch_pretrained_bert==0.6.2.  Here are my repro steps:

1.  I create a train.tsv and dev.tsv file with my own domain data.  The files contain sentences and labels separated by a tab.  I put these files in /tmp/bertdata

2.  I do fine tuning using: python run_classifier.py --data_dir /tmp/bertdata --bert_model bert-base-uncased --task_name sst-2 --do_lower_case --do_train --output_dir tmp.  This works fine and a model, config json, and vocab.txt are placed in tmp

3. I try to use the fine tuned model on the dev.tsv set:  python run_classifier.py --data_dir /tmp/bertdata --bert_model tmp --task_name sst-2 --do_lower_case --do_eval --output_dir tmp_result.  When I do that, I get this error:

Traceback (most recent call last):
  File "run_classifier.py", line 1024, in <module>
    main()
  File "run_classifier.py", line 794, in main
    t_total=num_train_optimization_steps)
  File "/home/ec2-user/anaconda3/lib/python3.6/site-packages/pytorch_pretrained_bert/optimization.py", line 215, in __init__
    schedule = schedule_type(warmup=warmup, t_total=t_total)
  File "/home/ec2-user/anaconda3/lib/python3.6/site-packages/pytorch_pretrained_bert/optimization.py", line 45, in __init__
    if t_total < 0:
TypeError: '<' not supported between instances of 'NoneType' and 'int'

Anything obvious I am doing wrong?  Thanks!
I also get this problem when predicting，Did you  solved the problem？<|||||>Here is the problem during initialization of the optimizer:
` t_total=num_train_optimization_steps)`

This var is initialized with `None` for the first time `num_train_optimization_steps = None`
and it's initialized correctly only when `--do_train` flag is passed to the script
```
    if args.do_train:
        train_examples = processor.get_train_examples(args.data_dir)
        num_train_optimization_steps = int(
            len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
        if args.local_rank != -1:
            num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
```
but in case of `--do_eval` this var is `None` and you got an error from description.

It's a bug, I think, and should be fixed. For you local needs, just initialize the optimizer "somehow" - it's not used while evaluating.<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
