undocumented

fastNLP/io/loader/coreference.py

@@ -26,8 +26,8 @@ class CRLoader(JsonLoader):
         super().__init__(fields, dropna)
         # self.fields = {"doc_key":Const.INPUTS(0),"speakers":Const.INPUTS(1),"clusters":Const.TARGET,"sentences":Const.INPUTS(2)}
         # TODO check 1
-        self.fields = {"doc_key": "raw_key", "speakers": "raw_speakers", "clusters": "raw_clusters",
-                       "sentences": "raw_words"}
+        self.fields = {"doc_key": Const.RAW_WORDS(0), "speakers": Const.RAW_WORDS(1), "clusters": Const.RAW_WORDS(2),
+                       "sentences": Const.RAW_WORDS(3)}
 
     def _load(self, path):
         """

fastNLP/io/pipe/coreference.py

@@ -46,10 +46,10 @@ class CoreferencePipe(Pipe):
         :return:
         """
         genres = {g: i for i, g in enumerate(["bc", "bn", "mz", "nw", "pt", "tc", "wb"])}
-        vocab = Vocabulary().from_dataset(*data_bundle.datasets.values(), field_name="raw_words")
+        vocab = Vocabulary().from_dataset(*data_bundle.datasets.values(), field_name= Const.RAW_WORDS(3))
         vocab.build_vocab()
         word2id = vocab.word2idx
-        data_bundle.set_vocab(vocab,"vocab")
+        data_bundle.set_vocab(vocab,Const.INPUT)
         if self.config.char_path:
             char_dict = get_char_dict(self.config.char_path)
         else:
@@ -65,14 +65,14 @@ class CoreferencePipe(Pipe):
 
         for name, ds in data_bundle.datasets.items():
             # genre
-            ds.apply(lambda x: genres[x["raw_key"][:2]], new_field_name=Const.INPUTS(0))
+            ds.apply(lambda x: genres[x[Const.RAW_WORDS(0)][:2]], new_field_name=Const.INPUTS(0))
 
             # speaker_ids_np
-            ds.apply(lambda x: speaker2numpy(x["raw_speakers"], self.config.max_sentences, is_train=name == 'train'),
+            ds.apply(lambda x: speaker2numpy(x[Const.RAW_WORDS(1)], self.config.max_sentences, is_train=name == 'train'),
                      new_field_name=Const.INPUTS(1))
 
             # sentences
-            ds.rename_field("raw_words",Const.INPUTS(2))
+            ds.rename_field(Const.RAW_WORDS(3),Const.INPUTS(2))
 
             # doc_np
             ds.apply(lambda x: doc2numpy(x[Const.INPUTS(2)], word2id, char_dict, max(self.config.filter),
@@ -88,7 +88,7 @@ class CoreferencePipe(Pipe):
                      new_field_name=Const.INPUT_LEN)
 
             # clusters
-            ds.rename_field("raw_clusters", Const.TARGET)
+            ds.rename_field(Const.RAW_WORDS(2), Const.TARGET)
 
 
             ds.set_ignore_type(Const.TARGET)

reproduction/coreference_resolution/train.py

@@ -8,6 +8,7 @@ from fastNLP.core.callback import Callback, GradientClipCallback
 from fastNLP.core.trainer import Trainer
 
 from fastNLP.io.pipe.coreference import CoreferencePipe
+from fastNLP.core.const import Const
 
 from reproduction.coreference_resolution.model.config import Config
 from reproduction.coreference_resolution.model.model_re import Model
@@ -45,7 +46,7 @@ if __name__ == "__main__":
     print("数据集划分：\ntrain:", str(len(data_bundle.get_dataset("train"))),
           "\ndev:" + str(len(data_bundle.get_dataset("dev"))) + "\ntest:" + str(len(data_bundle.get_dataset('test'))))
     # print(data_info)
-    model = Model(data_bundle.get_vocab("vocab"), config)
+    model = Model(data_bundle.get_vocab(Const.INPUT), config)
     print(model)
 
     loss = SoftmaxLoss()

reproduction/coreference_resolution/valid.py

@@ -17,7 +17,7 @@ if __name__=='__main__':
         {'train': config.train_path, 'dev': config.dev_path, 'test': config.test_path})
     metirc = CRMetric()
     model = torch.load(args.path)
-    tester = Tester(bundle.datasets['test'],model,metirc,batch_size=1,device="cuda:0")
+    tester = Tester(bundle.get_dataset("test"),model,metirc,batch_size=1,device="cuda:0")
     tester.test()
     print('test over')