def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
LabelSmoothedCrossEntropyCriterion.reduce_metrics(logging_outputs)
mask_loss_sum = sum(log.get('mask_loss', 0) for log in logging_outputs)
# mask_loss_final_sum = sum(log.get('mask_loss_final', 0) for log in logging_outputs)
p_sum = sum(log.get('p2', 0) for log in logging_outputs)
sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)
mask_sum = sum(log.get('mask_ave', 0) for log in logging_outputs)
metrics.log_scalar('mask_loss',
mask_loss_sum / sample_size / math.log(2),
sample_size,
round=6)
# metrics.log_scalar('mask_loss_final', mask_loss_final_sum / sample_size / math.log(2), sample_size, round=3)
metrics.log_scalar('p_2', p_sum / sample_size, sample_size, round=5)
metrics.log_scalar('mask_ave',
mask_sum / sample_size,
sample_size,
round=3)
metrics.log_scalar('new_weight',
logging_outputs[0].get("new_weight", 0) / 4,
len(logging_outputs),
round=3)
def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
LabelSmoothedCrossEntropyCriterion.reduce_metrics(logging_outputs)
word_error = sum(log.get('word_error', 0) for log in logging_outputs)
word_count = sum(log.get('word_count', 0) for log in logging_outputs)
char_error = sum(log.get('char_error', 0) for log in logging_outputs)
char_count = sum(log.get('char_count', 0) for log in logging_outputs)
if word_count > 0: # model.training == False
metrics.log_scalar('wer', float(word_error) / word_count * 100, word_count, round=4)
if char_count > 0: # model.training == False
metrics.log_scalar('cer', float(char_error) / char_count * 100, char_count, round=4)
