the trainer looks bug free

Author: wddabc

bmstparser/src/mstlstm.py

@@ -211,12 +211,6 @@ def __evaluateLabel(self, sentence, i, j):
 
         return output.data.numpy()[0], output[0]
 
-    def Save(self, filename):
-        self.model.save(filename)
-
-    def Load(self, filename):
-        self.model.load(filename)
-
     def predict(self, sentence):
         for entry in sentence:
             wordvec = self.wlookup(scalar(int(self.vocab.get(entry.norm, 0)))) if self.wdims > 0 else None
@@ -269,7 +263,7 @@ def predict(self, sentence):
                 scores, exprs = self.__evaluateLabel(sentence, head, modifier + 1)
                 sentence[modifier + 1].pred_relation = self.irels[max(enumerate(scores), key=itemgetter(1))[0]]
 
-    def get_loss(self, sentence, errs, lerrs):
+    def forward(self, sentence, errs, lerrs):
 
         for entry in sentence:
             c = float(self.wordsCount.get(entry.norm, 0))
@@ -358,6 +352,9 @@ def save(self, fn):
         torch.save(self.model.state_dict(), tmp)
         shutil.move(tmp, fn)
 
+    def load(self, fn):
+        self.model.load_state_dict(torch.load(fn))
+
     def train(self, conll_path):
         print torch.__version__
         batch = 1
@@ -384,7 +381,7 @@ def train(self, conll_path):
                     etotal = 0
 
                 conll_sentence = [entry for entry in sentence if isinstance(entry, utils.ConllEntry)]
-                e = self.model.get_loss(conll_sentence, errs, lerrs)
+                e = self.model.forward(conll_sentence, errs, lerrs)
                 eerrors += e
                 eloss += e
                 mloss += e

bmstparser/src/parser.py

@@ -45,22 +45,21 @@
 
         print 'Initializing lstm mstparser:'
         parser = mstlstm.MSTParserLSTM(words, pos, rels, w2i, stored_opt)
-
-        parser.Load(options.model)
+        parser.load(options.model)
         conllu = (os.path.splitext(options.conll_test.lower())[1] == '.conllu')
-        tespath = os.path.join(options.output, 'test_pred.conll' if not conllu else 'test_pred.conllu')
+        testpath = os.path.join(options.output, 'test_pred.conll' if not conllu else 'test_pred.conllu')
 
         ts = time.time()
         test_res = list(parser.predict(options.conll_test))
         te = time.time()
         print 'Finished predicting test.', te - ts, 'seconds.'
-        utils.write_conll(tespath, test_res)
+        utils.write_conll(testpath, test_res)
 
         if not conllu:
-            os.system('perl src/utils/eval.pl -g ' + options.conll_test + ' -s ' + tespath + ' > ' + tespath + '.txt')
+            os.system('perl src/utils/eval.pl -g ' + options.conll_test + ' -s ' + testpath + ' > ' + testpath + '.txt')
         else:
             os.system(
-                'python src/utils/evaluation_script/conll17_ud_eval.py -v -w src/utils/evaluation_script/weights.clas ' + options.conll_test + ' ' + tespath + ' > ' + testpath + '.txt')
+                'python src/utils/evaluation_script/conll17_ud_eval.py -v -w src/utils/evaluation_script/weights.clas ' + options.conll_test + ' ' + testpath + ' > ' + testpath + '.txt')
     else:
         print 'Preparing vocab'
         words, w2i, pos, rels = utils.vocab(options.conll_train)