def evaluate(self):
self.asr_model.eval()
self.write_tr_logs()
dev_info_ls = [
RunningAvgDict(decay_rate=1.) for _ in range(self.num_pretrain)
]
for idx, dev_loader in enumerate(self.data_container.dev_loaders):
tbar = get_bar(
total=len(dev_loader),
desc=f"Eval on {self.accents[idx]} @ step {self.global_step}")
with torch.no_grad():
for cur_b, (x, ilens, ys, olens) in enumerate(dev_loader):
if ilens.max() > self.dev_max_ilen:
tbar.update(1)
continue
batch_size = len(ys)
info = self._eval(idx, x, ilens, ys, olens)
dev_info_ls[idx].add(info, batch_size)
if cur_b % self.log_ival == 0:
logger.log_info(dev_info_ls[idx], prefix='test')
del x, ilens, ys, olens
tbar.update(1)
logger.flush()
tbar.close()
self.dashboard.log_info(f"dev_{self.accents[idx]}",
dev_info_ls[idx])
self.write_dev_logs(f"dev_{self.accents[idx]}",
dev_info_ls[idx])
dev_avg_info = RunningAvgDict(decay_rate=1.0)
for dev_info in dev_info_ls:
dev_avg_info.add({k: float(v) for k, v in dev_info.items()})
self.dashboard.log_info("dev", dev_avg_info)
self.write_dev_logs("dev_avg", dev_avg_info)
cur_cer = float(dev_avg_info['cer'])
cur_wer = float(dev_avg_info['wer'])
if cur_wer < self.best_wer:
self.best_wer = cur_wer
self.save_best_model()
if cur_cer < self.best_cer:
self.best_cer = cur_cer
self.save_best_model('cer', only_stat=True)
self.asr_model.train()
def evaluate(self):
self.asr_model.eval()
dev_info = RunningAvgDict(decay_rate=1.)
tbar = get_bar(total=len(self.dev_set),
desc=f"Eval @step{self.global_step}",
leave=True)
with torch.no_grad():
for cur_b, (x, ilens, ys, olens) in enumerate(self.dev_set):
if ilens.max() > self.dev_max_ilen:
tbar.update(1)
continue
batch_size = len(ys)
info = self._eval(cur_b, x, ilens, ys, olens)
dev_info.add(info, batch_size)
if cur_b % self.log_ival == 0:
logger.log_info(dev_info, prefix='test')
del x, ilens, ys, olens
tbar.update(1)
logger.flush()
tbar.close()
self.dashboard.log_info('dev', dev_info)
self.write_logs(dev_info)
cur_cer = float(dev_info['cer'])
cur_wer = float(dev_info['wer'])
if cur_wer < self.best_wer:
self.best_wer = cur_wer
self.save_best_model()
if cur_cer < self.best_cer:
self.best_cer = cur_cer
self.save_best_model('cer', only_stat=True)
if self.lr_scheduler is not None:
self.lr_scheduler.step(float(dev_info['loss']))
self.asr_model.train()
def check_evaluate(self):
if self.global_step % self.eval_ival == 0:
logger.flush()
self.asr_opt.zero_grad()
self.evaluate()