regression support ERNIE2 (#156)

* regression support ERNIE2

* modify vdl_log_dir to tb_log_dir

* support ernie2

demo/regression/regression.py

@@ -34,6 +34,7 @@ parser.add_argument("--max_seq_len", type=int, default=512, help="Number of word
 parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.")
 parser.add_argument("--use_pyreader", type=ast.literal_eval, default=False, help="Whether use pyreader to feed data.")
 parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
+parser.add_argument("--use_taskid", type=ast.literal_eval, default=False, help="Whether to use taskid ,if yes to use ernie v2.")
 args = parser.parse_args()
 # yapf: enable.
 
@@ -42,7 +43,10 @@ if __name__ == '__main__':
     # Download dataset and use ClassifyReader to read dataset
     if args.dataset.lower() == "sts-b":
         dataset = hub.dataset.GLUE("STS-B")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        if args.use_taskid:
+            module = hub.Module(name="ernie_v2_eng_base")
+        else:
+            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
     else:
         raise ValueError("%s dataset is not defined" % args.dataset)
 
@@ -51,7 +55,8 @@ if __name__ == '__main__':
     reader = hub.reader.RegressionReader(
         dataset=dataset,
         vocab_path=module.get_vocab_path(),
-        max_seq_len=args.max_seq_len)
+        max_seq_len=args.max_seq_len,
+        use_task_id=args.use_taskid)
 
     # Construct transfer learning network
     # Use "pooled_output" for classification tasks on an entire sentence.
@@ -67,6 +72,9 @@ if __name__ == '__main__':
         inputs["input_mask"].name,
     ]
 
+    if args.use_taskid:
+        feed_list.append(inputs["task_ids"].name)
+
     # Select finetune strategy, setup config and finetune
     strategy = hub.AdamWeightDecayStrategy(
         weight_decay=args.weight_decay,

demo/regression/run_regssion.sh

@@ -17,3 +17,4 @@ python -u regression.py \
                    --num_epoch=3 \
                    --use_pyreader=True \
                    --use_data_parallel=True \
+                   --use_taskid=False \

demo/text-classification/text_classifier.py

@@ -145,7 +145,7 @@ if __name__ == '__main__':
     ]
 
     if args.use_taskid:
-        feed_list = feed_list.append(inputs["task_ids"].name)
+        feed_list.append(inputs["task_ids"].name)
 
     # Select finetune strategy, setup config and finetune
     strategy = hub.AdamWeightDecayStrategy(

paddlehub/finetune/config.py

@@ -55,7 +55,6 @@ class RunConfig(object):
             self._strategy = strategy
         self._enable_memory_optim = enable_memory_optim
         if checkpoint_dir is None:
-
             now = int(time.time())
             time_str = time.strftime("%Y%m%d%H%M%S", time.localtime(now))
             self._checkpoint_dir = "ckpt_" + time_str

paddlehub/finetune/task/basic_task.py

@@ -140,8 +140,8 @@ class BasicTask(object):
         # log item
         if not os.path.exists(self.config.checkpoint_dir):
             mkdir(self.config.checkpoint_dir)
-        vdl_log_dir = os.path.join(self.config.checkpoint_dir, "visualization")
-        self.tb_writer = SummaryWriter(vdl_log_dir)
+        tb_log_dir = os.path.join(self.config.checkpoint_dir, "visualization")
+        self.tb_writer = SummaryWriter(tb_log_dir)
 
         # run environment
         self._phases = []

paddlehub/reader/nlp_reader.py

@@ -761,7 +761,8 @@ class RegressionReader(BaseReader):
                  label_map_config=None,
                  max_seq_len=128,
                  do_lower_case=True,
-                 random_seed=None):
+                 random_seed=None,
+                 use_task_id=False):
         self.max_seq_len = max_seq_len
         self.tokenizer = tokenization.FullTokenizer(
             vocab_file=vocab_path, do_lower_case=do_lower_case)
@@ -771,6 +772,10 @@ class RegressionReader(BaseReader):
         self.cls_id = self.vocab["[CLS]"]
         self.sep_id = self.vocab["[SEP]"]
         self.in_tokens = False
+        self.use_task_id = use_task_id
+
+        if self.use_task_id:
+            self.task_id = 0
 
         np.random.seed(random_seed)
 
@@ -811,12 +816,28 @@ class RegressionReader(BaseReader):
                 padded_token_ids, padded_position_ids, padded_text_type_ids,
                 input_mask, batch_labels
             ]
+
+            if self.use_task_id:
+                padded_task_ids = np.ones_like(
+                    padded_token_ids, dtype="int64") * self.task_id
+                return_list = [
+                    padded_token_ids, padded_position_ids, padded_text_type_ids,
+                    input_mask, padded_task_ids, batch_labels
+                ]
         else:
             return_list = [
                 padded_token_ids, padded_position_ids, padded_text_type_ids,
                 input_mask
             ]
 
+            if self.use_task_id:
+                padded_task_ids = np.ones_like(
+                    padded_token_ids, dtype="int64") * self.task_id
+                return_list = [
+                    padded_token_ids, padded_position_ids, padded_text_type_ids,
+                    input_mask, padded_task_ids
+                ]
+
         return return_list
 
     def data_generator(self,