[bugfix]修复 coreference resolution复现代码中参数名字不对应的bug (fastnlp#323)

* pipeline

* 修复找不到对应参数的bug

* 增加requirement文件

reproduction/coreference_resolution/model/metric.py

@@ -17,7 +17,8 @@ def __init__(self):
         self.evaluators = [Evaluator(m) for m in (muc, b_cubed, ceafe)]
 
     # TODO 改名为evaluate，输入也
-    def evaluate(self, predicted, mention_to_predicted,clusters):
+    def evaluate(self, predicted, mention_to_predicted,target):
+        clusters = target
         for e in self.evaluators:
             e.update(predicted,mention_to_predicted, clusters)
 

reproduction/coreference_resolution/model/model_re.py

@@ -17,7 +17,6 @@
 np.random.seed(0) # numpy
 random.seed(0)
 
-
 class ffnn(nn.Module):
     def __init__(self, input_size, hidden_size, output_size):
         super(ffnn, self).__init__()
@@ -565,19 +564,37 @@ def forward(self, words1 , words2, words3, words4, chars, seq_len):
 
         return ans
 
-    def predict(self, sentences, doc_np, speaker_ids_np, genre, char_index, seq_len):
+    def predict(self, words1 , words2, words3, words4, chars, seq_len):
+        """
+        实际输入都是tensor
+        :param sentences: 句子，被fastNLP转化成了numpy，
+        :param doc_np: 被fastNLP转化成了Tensor
+        :param speaker_ids_np: 被fastNLP转化成了Tensor
+        :param genre: 被fastNLP转化成了Tensor
+        :param char_index: 被fastNLP转化成了Tensor
+        :param seq_len: 被fastNLP转化成了Tensor
+        :return:
+        """
+
+        sentences = words1
+        doc_np = words2
+        speaker_ids_np = words3
+        genre = words4
+        char_index = chars
+
+    # def predict(self, sentences, doc_np, speaker_ids_np, genre, char_index, seq_len):
         ans = self(sentences,
                    doc_np,
                    speaker_ids_np,
                    genre,
                    char_index,
                    seq_len)
-
-        predicted_antecedents = self.get_predicted_antecedents(ans["antecedents"], ans["antecedent_scores"])
-        predicted_clusters, mention_to_predicted = self.get_predicted_clusters(ans["mention_start_tensor"],
-                                                                               ans["mention_end_tensor"],
+        predicted_antecedents = self.get_predicted_antecedents(ans["antecedents"], ans["antecedent_scores"].cpu())
+        predicted_clusters, mention_to_predicted = self.get_predicted_clusters(ans["mention_start_tensor"].cpu(),
+                                                                               ans["mention_end_tensor"].cpu(),
                                                                                predicted_antecedents)
 
+
         return {'predicted':predicted_clusters,"mention_to_predicted":mention_to_predicted}
 
 

reproduction/coreference_resolution/requirements.txt

@@ -0,0 +1,5 @@
+prettytable==0.7.2
+allennlp==0.8.2
+scikit-learn==0.22.2
+pyhocon==0.3.50
+torch==1.1

reproduction/coreference_resolution/train.py

@@ -1,3 +1,5 @@
+import sys
+sys.path.append('../..')
 
 import torch
 from torch.optim import Adam