Wrong checkpoint metric used in load_best_model

[geoffreyangus]

Describe the bug
The bug is that the wrong checkpoint_metric is used in load_best_model at the end of EmmentalLearner.learn. I believe that it has to do with the fact that utils.merge doesn't delete entries, it just replaces them. This leaves us with multiple entries in logging_config.checkpointer_config.checkpointer_metric.

To Reproduce
Steps to reproduce the behavior:

1. Initialize an Emmental experiment
2. Run the following code snippet:

Meta.update_config(config={
    'learner_config': {
        'n_epochs': 2,
        'valid_split': 'valid',
        'optimizer_config': {'optimizer': 'adam', 'lr': 0.01, 'l2': 0.000},
        'lr_scheduler_config': {}
    },
    'logging_config': {
        'evaluation_freq': 1,
        'checkpointing': True,
        'checkpointer_config': {
            'checkpoint_metric': {
                'model/all/valid/loss': 'min'
            }
        }
    }
})
print(Meta.config['logging_config'])

3. At this point, it should be clear that there are multiple values in logging_config.checkpointer_config.checkpoint_metric. However, in order to see how this affects downstream tasks, run EmmentalLearner.learn

...
model = EmmentalModel(name='model', tasks=tasks)
learner = EmmentalLearner()
learner.learn()

4. Finally, print list(learner.logging_manager.checkpointer.checkpoint_metric.keys())[0], which shows the value used by Checkpointer.load_best_model function in order to determine if a best model was found (checkpointer.py, line ~253). The value from the default config should appear at this point instead of the value from the updated config.

Expected behavior
I expect the checkpoint metric I defined in the updated config to be used in Checkpointer.load_best_model.

Environment

• OS: Ubuntu 16.04
• Emmental Version: 0.0.4
• Python 3.6

[senwu]

This issue should be fixed in #47. Please check! Feel free to reopen if the issue still exists.