train_utils.py

def aggregate_loss_dict(agg_loss_dict):
	mean_vals = {}
	for output in agg_loss_dict:
		for key in output:
			mean_vals[key] = mean_vals.setdefault(key, []) + [output[key]]
	for key in mean_vals:
		if len(mean_vals[key]) > 0:
			mean_vals[key] = sum(mean_vals[key]) / len(mean_vals[key])
		else:
			print('{} has no value'.format(key))
			mean_vals[key] = 0
	return mean_vals