fix bugs

paddlehub/finetune/strategy.py

@@ -511,10 +511,6 @@ class CombinedStrategy(DefaultStrategy):
                     unfreeze_depths=self.
                     sorted_depth[:self.max_depth * self.epoch //
                                  self.scheduler["gradual_unfreeze"]["blocks"]])
-            else:
-                logger.warning(
-                    "The max op-depth in the network is %s. That results in that can't use the gradual unfreeze finetune strategy."
-                    % (self.max_depth))
         elif self.scheduler["gradual_unfreeze"]["params_layer"]:
             max_layer = max(
                 self.scheduler["gradual_unfreeze"]["params_layer"].values())
@@ -631,8 +627,9 @@ class ULMFiTStrategy(CombinedStrategy):
                  ratio=32,
                  dis_blocks=3,
                  factor=2.6,
+                 dis_params_layer=None,
                  frz_blocks=3,
-                 params_layer=None):
+                 frz_params_layer=None):
 
         scheduler = {
             "slanted_triangle": {
@@ -641,12 +638,12 @@ class ULMFiTStrategy(CombinedStrategy):
             },
             "gradual_unfreeze": {
                 "blocks": frz_blocks,
-                "params_layer": params_layer
+                "params_layer": frz_params_layer
             },
             "discriminative": {
                 "blocks": dis_blocks,
                 "factor": factor,
-                "params_layer": params_layer
+                "params_layer": dis_params_layer
             }
         }
         regularization = {}

paddlehub/finetune/task/base_task.py

@@ -951,12 +951,6 @@ class BaseTask(object):
         Returns:
             RunState: the running result of predict phase
         """
-
-        if isinstance(self._base_data_reader, hub.reader.LACClassifyReader):
-            raise Exception(
-                "LACClassifyReader does not support predictor, please close accelerate_mode"
-            )
-
         global_run_states = []
         period_run_states = []
 
@@ -993,16 +987,22 @@ class BaseTask(object):
             data (list): the data will be predicted.
             load_best_model (bool): load the best model or not
             return_result (bool): return a readable result or just the raw run result
-            accelerate_mode (bool): use high-performance predictor or not
+            accelerate_mode (bool): use high-performance predictor or not.
 
         Returns:
             RunState: the running result of predict phase
         """
-        if not version_compare(paddle.__version__, "1.6.2") and accelerate_mode:
-            logger.warning(
-                "Fail to open predict accelerate mode as it does not support paddle < 1.6.2. Please update PaddlePaddle."
-            )
-            accelerate_mode = False
+        if accelerate_mode:
+            if not version_compare(paddle.__version__, "1.6.1"):
+                logger.warning(
+                    "Fail to open predict accelerate mode as it does not support paddle < 1.6.2. Please update PaddlePaddle."
+                )
+                accelerate_mode = False
+            if isinstance(self._base_data_reader, hub.reader.LACClassifyReader):
+                logger.warning(
+                    "LACClassifyReader does not support predictor, the accelerate_mode is closed now."
+                )
+                accelerate_mode = False
         self.accelerate_mode = accelerate_mode
 
         with self.phase_guard(phase="predict"):

paddlehub/finetune/task/reading_comprehension_task.py

@@ -205,7 +205,7 @@ def get_predictions(all_examples, all_features, all_results, n_best_size,
         for (feature_index, feature) in enumerate(features):
             if feature.unique_id not in unique_id_to_result:
                 logger.info(
-                    "As using pyreader, the last one batch is so small that the feature %s in the last batch is discarded "
+                    "As using multidevice, the last one batch is so small that the feature %s in the last batch is discarded "
                     % feature.unique_id)
                 continue
             result = unique_id_to_result[feature.unique_id]

paddlehub/module/nlp_module.py

@@ -397,7 +397,8 @@ class TransformerModule(NLPBaseModule):
 
         return inputs, outputs, module_program
 
-    def get_embedding(self, texts, use_gpu=False, batch_size=1):
+    def get_embedding(self, texts, max_seq_len=512, use_gpu=False,
+                      batch_size=1):
         """
         get pooled_output and sequence_output for input texts.
         Warnings: this method depends on Paddle Inference Library, it may not work properly in PaddlePaddle <= 1.6.2.
@@ -405,6 +406,7 @@ class TransformerModule(NLPBaseModule):
         Args:
             texts (list): each element is a text sample, each sample include text_a and text_b where text_b can be omitted.
                           for example: [[sample0_text_a, sample0_text_b], [sample1_text_a, sample1_text_b], ...]
+            max_seq_len (int): the max sequence length.
             use_gpu (bool): use gpu or not, default False.
             batch_size (int): the data batch size, default 1.
 
@@ -417,12 +419,12 @@ class TransformerModule(NLPBaseModule):
         ) or self.emb_job["batch_size"] != batch_size or self.emb_job[
                 "use_gpu"] != use_gpu:
             inputs, outputs, program = self.context(
-                trainable=True, max_seq_len=self.MAX_SEQ_LEN)
+                trainable=True, max_seq_len=max_seq_len)
 
             reader = hub.reader.ClassifyReader(
                 dataset=None,
                 vocab_path=self.get_vocab_path(),
-                max_seq_len=self.MAX_SEQ_LEN,
+                max_seq_len=max_seq_len,
                 sp_model_path=self.get_spm_path() if hasattr(
                     self, "get_spm_path") else None,
                 word_dict_path=self.get_word_dict_path() if hasattr(

paddlehub/reader/nlp_reader.py

@@ -1213,7 +1213,7 @@ class LACClassifyReader(BaseReader):
 
             return processed
 
-        if not self.has_processed[phase]:
+        if not self.has_processed[phase] or phase == "predict":
             logger.info(
                 "processing %s data now... this may take a few minutes" % phase)
             for i in range(len(data)):