diff --git a/tools/program.py b/tools/program.py
index cbca715a8e94f7e1b290ad1402a5ceab0fc4c262..fb9e3802a0818b2ee92117d10bda6b70261abace 100755
--- a/tools/program.py
+++ b/tools/program.py
@@ -212,7 +212,7 @@ def train(config,
             stats['lr'] = lr
             train_stats.update(stats)
 
-            if cal_metric_during_train:  # onlt rec and cls need
+            if cal_metric_during_train:  # only rec and cls need
                 batch = [item.numpy() for item in batch]
                 post_result = post_process_class(preds, batch[1])
                 eval_class(post_result, batch)
@@ -238,21 +238,21 @@ def train(config,
             # eval
             if global_step > start_eval_step and \
                     (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
-                cur_metirc = eval(model, valid_dataloader, post_process_class,
+                cur_metric = eval(model, valid_dataloader, post_process_class,
                                   eval_class)
-                cur_metirc_str = 'cur metirc, {}'.format(', '.join(
-                    ['{}: {}'.format(k, v) for k, v in cur_metirc.items()]))
-                logger.info(cur_metirc_str)
+                cur_metric_str = 'cur metric, {}'.format(', '.join(
+                    ['{}: {}'.format(k, v) for k, v in cur_metric.items()]))
+                logger.info(cur_metric_str)
 
                 # logger metric
                 if vdl_writer is not None:
-                    for k, v in cur_metirc.items():
+                    for k, v in cur_metric.items():
                         if isinstance(v, (float, int)):
                             vdl_writer.add_scalar('EVAL/{}'.format(k),
-                                                  cur_metirc[k], global_step)
-                if cur_metirc[main_indicator] >= best_model_dict[
+                                                  cur_metric[k], global_step)
+                if cur_metric[main_indicator] >= best_model_dict[
                         main_indicator]:
-                    best_model_dict.update(cur_metirc)
+                    best_model_dict.update(cur_metric)
                     best_model_dict['best_epoch'] = epoch
                     save_model(
                         model,
@@ -263,7 +263,7 @@ def train(config,
                         prefix='best_accuracy',
                         best_model_dict=best_model_dict,
                         epoch=epoch)
-                best_str = 'best metirc, {}'.format(', '.join([
+                best_str = 'best metric, {}'.format(', '.join([
                     '{}: {}'.format(k, v) for k, v in best_model_dict.items()
                 ]))
                 logger.info(best_str)
@@ -294,7 +294,7 @@ def train(config,
                 prefix='iter_epoch_{}'.format(epoch),
                 best_model_dict=best_model_dict,
                 epoch=epoch)
-    best_str = 'best metirc, {}'.format(', '.join(
+    best_str = 'best metric, {}'.format(', '.join(
         ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
     logger.info(best_str)
     if dist.get_rank() == 0 and vdl_writer is not None:
@@ -323,13 +323,13 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
             eval_class(post_result, batch)
             pbar.update(1)
             total_frame += len(images)
-        # Get final metirc，eg. acc or hmean
-        metirc = eval_class.get_metric()
+        # Get final metric，eg. acc or hmean
+        metric = eval_class.get_metric()
 
     pbar.close()
     model.train()
-    metirc['fps'] = total_frame / total_time
-    return metirc
+    metric['fps'] = total_frame / total_time
+    return metric
 
 
 def preprocess(is_train=False):