How to get inference_config parameters while finetune on my own dataset?

[wsxwd]

No description provided.

[GongyeLiu]

Hi, @wsxwd

The inference_config is primarily used to normalize the reward values. This configuration is optional, the inference code should works fine without it.

VideoAlign/inference.py

Lines 67 to 74 in f9a9654

	def _norm(self, reward):
	if self.inference_config is None:
	return reward
	else:
	reward['VQ'] = (reward['VQ'] - self.inference_config['VQ_mean']) / self.inference_config['VQ_std']
	reward['MQ'] = (reward['MQ'] - self.inference_config['MQ_mean']) / self.inference_config['MQ_std']
	reward['TA'] = (reward['TA'] - self.inference_config['TA_mean']) / self.inference_config['TA_std']
	return reward

In practice, we perform inference on all the samples in the validation set to compute the mean and standard deviation in inference_config. You might consider applying a similar strategy for your dataset.

I hope this helps!