remove dropout in predict, fix #77, update postprocess

paddlepalm/backbone/bert.py

@@ -42,8 +42,8 @@ class BERT(Backbone):
 
        
         self._hidden_act = hidden_act
-        self._prepostprocess_dropout = hidden_dropout_prob
-        self._attention_dropout = attention_probs_dropout_prob
+        self._prepostprocess_dropout = 0. if phase == 'predict' else hidden_dropout_prob
+        self._attention_dropout = 0. if phase == 'predict' else attention_probs_dropout_prob
 
         self._word_emb_name = "word_embedding"
         self._pos_emb_name = "pos_embedding"

paddlepalm/backbone/ernie.py

@@ -45,8 +45,8 @@ class ERNIE(Backbone):
         self._task_types = task_type_vocab_size
 
         self._hidden_act = hidden_act
-        self._prepostprocess_dropout = hidden_dropout_prob
-        self._attention_dropout = attention_probs_dropout_prob
+        self._prepostprocess_dropout = 0. if phase == 'predict' else hidden_dropout_prob
+        self._attention_dropout = 0. if phase == 'predict' else attention_probs_dropout_prob
 
         self._word_emb_name = "word_embedding"
         self._pos_emb_name = "pos_embedding"

paddlepalm/head/cls.py

@@ -94,14 +94,17 @@ class Classify(Head):
     def epoch_postprocess(self, post_inputs, output_dir=None):
         # there is no post_inputs needed and not declared in epoch_inputs_attrs, hence no elements exist in post_inputs
         if not self._is_training:
-            if output_dir is None:
-                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 = 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')
-            print('Predictions saved at '+os.path.join(output_dir, 'predictions.json'))
+            results = []
+            for i in range(len(self._preds)):
+                label = int(np.argmax(np.array(self._preds[i])))
+                result = {'index': i, 'label': label, 'logits': self._preds[i], 'probs': self._probs[i]}
+                results.append(result)
+            if output_dir is not None:
+                with open(os.path.join(output_dir, 'predictions.json'), 'w') as writer:
+                    for result in results:
+                        result = json.dumps(result)
+                        writer.write(result+'\n')
+                print('Predictions saved at '+os.path.join(output_dir, 'predictions.json'))
+            return results
 
                 

paddlepalm/head/match.py

@@ -174,15 +174,18 @@ class Match(Head):
     def epoch_postprocess(self, post_inputs, output_dir=None):
         # there is no post_inputs needed and not declared in epoch_inputs_attrs, hence no elements exist in post_inputs
         if not self._is_training:
-            if output_dir is None:
-                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)):
-                    if self._learning_strategy == 'pointwise':
-                        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]}
-                    result = json.dumps(result, ensure_ascii=False)
-                    writer.write(result+'\n')
-            print('Predictions saved at '+os.path.join(output_dir, 'predictions.json'))
+            results = []
+            for i in range(len(self._preds)):
+                if self._learning_strategy == 'pointwise':
+                    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]}
+                results.append(result)
+            if output_dir is not None:
+                with open(os.path.join(output_dir, 'predictions.json'), 'w') as writer:
+                    for result in results:
+                        result = json.dumps(result, ensure_ascii=False)
+                        writer.write(result+'\n')
+                print('Predictions saved at '+os.path.join(output_dir, 'predictions.json'))
+            return results

paddlepalm/head/mlm.py

@@ -128,13 +128,15 @@ class MaskLM(Head):
     def epoch_postprocess(self, post_inputs, output_dir=None):
         # there is no post_inputs needed and not declared in epoch_inputs_attrs, hence no elements exist in post_inputs
         if not self._is_training:
-            if output_dir is None:
-                for p in self._preds:
-                    print(p)
-            else:
+            results = []
+            for i in range(len(self._preds)):
+                result = {'index': i, 'word_id': self._preds[i]}
+                results.append(result)
+            if output_dir is not None:
                 with open(os.path.join(output_dir, 'predictions.json'), 'w') as writer:
-                    for p in self._preds:
-                        writer.write(str(p)+'\n')
+                    for result in results:
+                        result = json.dumps(result)
+                        writer.write(result+'\n')
                 print('Predictions saved at '+os.path.join(output_dir, 'predictions.json'))
-
+            return results
 

paddlepalm/head/mrc.py

@@ -154,21 +154,21 @@ class MRC(Head):
         """(optional interface) this func will be called after evaluation/predicting process and each epoch during training process."""
 
         if not self._is_training:
-            if output_dir is None:
-                raise ValueError('argument output_dir not found in config. Please add it into config dict/file.')
-            examples = post_inputs['reader']['examples']
-            features = post_inputs['reader']['features']
-            if not os.path.exists(output_dir):
-                os.makedirs(output_dir)
-            output_prediction_file = os.path.join(output_dir, "predictions.json")
-            output_nbest_file = os.path.join(output_dir, "nbest_predictions.json")
-            output_null_log_odds_file = os.path.join(output_dir, "null_odds.json")
-            _write_predictions(examples, features, self._pred_results,
-                              self._n_best_size, self._max_answer_length,
-                              self._do_lower_case, output_prediction_file,
-                              output_nbest_file, output_null_log_odds_file,
-                              self._with_negative,
-                              self._null_score_diff_threshold, self._verbose)
+            if output_dir is not None:
+                examples = post_inputs['reader']['examples']
+                features = post_inputs['reader']['features']
+                if not os.path.exists(output_dir):
+                    os.makedirs(output_dir)
+                output_prediction_file = os.path.join(output_dir, "predictions.json")
+                output_nbest_file = os.path.join(output_dir, "nbest_predictions.json")
+                output_null_log_odds_file = os.path.join(output_dir, "null_odds.json")
+                _write_predictions(examples, features, self._pred_results,
+                                  self._n_best_size, self._max_answer_length,
+                                  self._do_lower_case, output_prediction_file,
+                                  output_nbest_file, output_null_log_odds_file,
+                                  self._with_negative,
+                                  self._null_score_diff_threshold, self._verbose)
+            return self._pred_results
 
 
 def _write_predictions(all_examples, all_features, all_results, n_best_size,

paddlepalm/head/ner.py

@@ -118,9 +118,9 @@ class SequenceLabel(Head):
     def epoch_postprocess(self, post_inputs, output_dir=None):
         # there is no post_inputs needed and not declared in epoch_inputs_attrs, hence no elements exist in post_inputs
         if not self._is_training:
-            if output_dir is None:
-                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 p in self._preds:
-                    writer.write(str(p)+'\n')
-            print('Predictions saved at '+os.path.join(output_dir, 'predictions.json'))
+            if output_dir is not None:
+                with open(os.path.join(output_dir, 'predictions.json'), 'w') as writer:
+                    for p in self._preds:
+                        writer.write(str(p)+'\n')
+                print('Predictions saved at '+os.path.join(output_dir, 'predictions.json'))
+            return self._preds