增强field中的value_count支持对nested的field的支持

fastNLP/core/field.py

@@ -350,8 +350,15 @@ class FieldArray:
         :return: Counter, key是label，value是出现次数
         """
         count = Counter()
+
+        def cum(cell):
+            if _is_iterable(cell) and not isinstance(cell, str):
+                for cell_ in cell:
+                    cum(cell_)
+            else:
+                count[cell] += 1
         for cell in self.content:
-            count[cell] += 1
+            cum(cell)
         return count
 
     def _after_process(self, new_contents, inplace):

fastNLP/modules/encoder/embedding.py

@@ -326,7 +326,6 @@ class ElmoEmbedding(ContextualEmbedding):
 
         # 根据model_dir_or_name检查是否存在并下载
         PRETRAIN_URL = _get_base_url('elmo')
-        # TODO 把baidu云上的加上去
         PRETRAINED_ELMO_MODEL_DIR = {'en': 'elmo_en-d39843fe.tar.gz',
                                      'cn': 'elmo_cn-5e9b34e2.tar.gz'}
 

reproduction/utils.py

@@ -24,7 +24,7 @@ def check_dataloader_paths(paths:Union[str, Dict[str, str]])->Dict[str, str]:
             if not os.path.isfile(train_fp):
                 raise FileNotFoundError(f"train.txt is not found in folder {paths}.")
             files = {'train': train_fp}
-            for filename in ['test.txt', 'dev.txt']:
+            for filename in ['dev.txt', 'test.txt']:
                 fp = os.path.join(paths, filename)
                 if os.path.isfile(fp):
                     files[filename.split('.')[0]] = fp

test/test_tutorials.py

@@ -80,7 +80,7 @@ class TestTutorial(unittest.TestCase):
         test_data.rename_field('label', 'label_seq')
 
         loss = CrossEntropyLoss(pred="output", target="label_seq")
-        metric = AccuracyMetric(pred="predict", target="label_seq")
+        metric = AccuracyMetric(target="label_seq")
 
         # 实例化Trainer，传入模型和数据，进行训练
         # 先在test_data拟合（确保模型的实现是正确的）
@@ -96,7 +96,7 @@ class TestTutorial(unittest.TestCase):
         # 用train_data训练，在test_data验证
         trainer = Trainer(model=model, train_data=train_data, dev_data=test_data,
                           loss=CrossEntropyLoss(pred="output", target="label_seq"),
-                          metrics=AccuracyMetric(pred="predict", target="label_seq"),
+                          metrics=AccuracyMetric(target="label_seq"),
                           save_path=None,
                           batch_size=32,
                           n_epochs=5)
@@ -106,7 +106,7 @@ class TestTutorial(unittest.TestCase):
         # 调用Tester在test_data上评价效果
         from fastNLP import Tester
 
-        tester = Tester(data=test_data, model=model, metrics=AccuracyMetric(pred="predict", target="label_seq"),
+        tester = Tester(data=test_data, model=model, metrics=AccuracyMetric(target="label_seq"),
                         batch_size=4)
         acc = tester.test()
         print(acc)