Merge pull request #72 from wangxiao1021/api

fix #68 #71 and other bugs

README_zh.md

@@ -4,7 +4,7 @@
 
 PaddlePALM (PArallel Learning from Multi-tasks) 是一个灵活，通用且易于使用的NLP大规模预训练和多任务学习框架。 PALM是一个旨在**快速开发高性能NLP模型**的上层框架。
 
-使用PaddlePALM，可以非常轻松灵活的探索具有多种任务辅助训练的“高鲁棒性”阅读理解模型，基于PALM训练的模型[D-Net](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/MRQA2019-D-NET)在[EMNLP2019国际阅读理解评测](mrqa .github.io)中夺得冠军。
+使用PaddlePALM，可以非常轻松灵活的探索具有多种任务辅助训练的“高鲁棒性”阅读理解模型，基于PALM训练的模型[D-Net](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/MRQA2019-D-NET)在[EMNLP2019国际阅读理解评测](https://mrqa.github.io/)中夺得冠军。
 
 <p align="center">
 	<img src="https://tva1.sinaimg.cn/large/006tNbRwly1gbjkuuwrmlj30hs0hzdh2.jpg" alt="Sample"  width="300" height="333">
@@ -196,10 +196,10 @@ Available pretrain items:
 
 更多实现细节请见示例: 
 
-- [Sentiment Classification](https://github.com/PaddlePaddle/PALM/tree/master/examples/classification)
-- [Quora Question Pairs matching](https://github.com/PaddlePaddle/PALM/tree/master/examples/matching)
-- [Tagging](https://github.com/PaddlePaddle/PALM/tree/master/examples/tagging)
-- [SQuAD machine Reading Comprehension](https://github.com/PaddlePaddle/PALM/tree/master/examples/mrc).
+- [情感分析](https://github.com/PaddlePaddle/PALM/tree/master/examples/classification)
+- [Quora问题相似度匹配](https://github.com/PaddlePaddle/PALM/tree/master/examples/matching)
+- [命名实体识别](https://github.com/PaddlePaddle/PALM/tree/master/examples/tagging)
+- [类SQuAD机器阅读理解](https://github.com/PaddlePaddle/PALM/tree/master/examples/mrc)
 
 
 #### 多任务学习
@@ -218,7 +218,7 @@ multi_head_trainer的保存/加载和预测操作与trainer相同。
 
 更多实现`multi_head_trainer`的细节，请见
 
-- [ATIS: joint training of dialogue intent recognition and slot filling](https://github.com/PaddlePaddle/PALM/tree/master/examples/multi-task)
+- [ATIS: 对话意图识别和插槽填充的联合训练](https://github.com/PaddlePaddle/PALM/tree/master/examples/multi-task)
 
 #### 设置saver
 

examples/classification/run.py

 # coding=utf-8
 import paddlepalm as palm
 import json
-from paddlepalm.distribute import gpu_dev_count
 
 
 if __name__ == '__main__':

examples/matching/run.py

 # coding=utf-8
 import paddlepalm as palm
 import json
-from paddlepalm.distribute import gpu_dev_count
 
 if __name__ == '__main__':
 

examples/mrc/run.py

 # coding=utf-8
 import paddlepalm as palm
 import json
-from paddlepalm.distribute import gpu_dev_count
 
 
 if __name__ == '__main__':

examples/multi-task/run.py

 # coding=utf-8
 import paddlepalm as palm
 import json
-from paddlepalm.distribute import gpu_dev_count
 
 
 if __name__ == '__main__':
@@ -80,4 +79,4 @@ if __name__ == '__main__':
     # save_steps = 10
     trainer.set_saver(save_path=save_path, save_steps=save_steps, save_type=save_type)
     # step 8-3: start training
-    trainer.train(print_steps=print_steps)
\ No newline at end of file
+    trainer.train(print_steps=print_steps)

examples/predict/run.py

 # coding=utf-8
 import paddlepalm as palm
 import json
-from paddlepalm.distribute import gpu_dev_count
 
 
 if __name__ == '__main__':

examples/tagging/run.py

 # coding=utf-8
 import paddlepalm as palm
 import json
-from paddlepalm.distribute import gpu_dev_count
 
 if __name__ == '__main__':
  
@@ -64,9 +63,9 @@ if __name__ == '__main__':
     # step 7: fit prepared reader and data
     trainer.fit_reader(seq_label_reader)
 
-    # # step 8-1*: load pretrained parameters
+    # step 8-1*: load pretrained parameters
     trainer.load_pretrain(pre_params)
-    # # step 8-2*: set saver to save model
+    # step 8-2*: set saver to save model
     save_steps = 1951
     # print('save_steps: {}'.format(save_steps))
     trainer.set_saver(save_path=save_path, save_steps=save_steps, save_type=save_type)

paddlepalm/head/cls.py

@@ -98,7 +98,7 @@ class Classify(Head):
                 raise ValueError('argument output_dir not found in config. Please add it into config dict/file.')
             with open(os.path.join(output_dir, 'predictions.json'), 'w') as writer:
                 for i in range(len(self._preds)):
-                    label = np.argmax(np.array(self._preds[i]))
+                    label = int(np.argmax(np.array(self._preds[i])))
                     result = {'index': i, 'label': label, 'logits': self._preds[i], 'probs': self._probs[i]}
                     result = json.dumps(result)
                     writer.write(result+'\n')

paddlepalm/head/match.py

@@ -179,7 +179,7 @@ class Match(Head):
             with open(os.path.join(output_dir, 'predictions.json'), 'w') as writer:
                 for i in range(len(self._preds)):
                     if self._learning_strategy == 'pointwise':
-                        label = np.argmax(np.array(self._preds[i]))
+                        label = int(np.argmax(np.array(self._preds[i])))
                         result = {'index': i, 'label': label, 'logits': self._preds_logits[i], 'probs': self._preds[i]}
                     elif self._learning_strategy == 'pairwise':
                         result = {'index': i, 'probs': self._preds[i][0]}

paddlepalm/reader/utils/reader4ernie.py

@@ -37,7 +37,7 @@ from paddlepalm.reader.utils.mlm_batching import prepare_batch_data
 
 log = logging.getLogger(__name__)
 
-if six.PY3:
+if six.PY3 and hasattr(sys.stdout, 'buffer'):
     import io
     sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
     sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')