add ernie classification prediction

demo/ernie-classification/cls_predict.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Finetuning on classification task """
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import argparse
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+import paddlehub as hub
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
+parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
+args = parser.parse_args()
+# yapf: enable.
+
+if __name__ == '__main__':
+    # loading Paddlehub ERNIE pretrained model
+    module = hub.Module(name="ernie")
+    input_dict, output_dict, program = module.context(
+        max_seq_len=args.max_seq_len)
+
+    # Sentence classification  dataset reader
+    dataset = hub.dataset.ChnSentiCorp()
+    reader = hub.reader.ClassifyReader(
+        dataset=dataset,
+        vocab_path=module.get_vocab_path(),
+        max_seq_len=args.max_seq_len)
+
+    place = fluid.CUDAPlace(0)
+    exe = fluid.Executor(place)
+    with fluid.program_guard(program):
+        label = fluid.layers.data(name="label", shape=[1], dtype='int64')
+
+        # Use "pooled_output" for classification tasks on an entire sentence.
+        # Use "sequence_outputs" for token-level output.
+        pooled_output = output_dict["pooled_output"]
+
+        # Setup feed list for data feeder
+        # Must feed all the tensor of ERNIE's module need
+
+        # Define a classfication finetune task by PaddleHub's API
+        cls_task = hub.create_text_classification_task(
+            feature=pooled_output, label=label, num_classes=dataset.num_labels)
+
+        # classificatin probability tensor
+        probs = cls_task.variable("probs")
+
+        # load best model checkpoint
+        fluid.io.load_persistables(exe, args.checkpoint_dir)
+
+        feed_list = [
+            input_dict["input_ids"].name, input_dict["position_ids"].name,
+            input_dict["segment_ids"].name, input_dict["input_mask"].name,
+            label.name
+        ]
+
+        data_feeder = fluid.DataFeeder(feed_list=feed_list, place=place)
+        test_reader = reader.data_generator(phase='test', shuffle=False)
+        test_examples = dataset.get_test_examples()
+        for index, batch in enumerate(test_reader()):
+            probs_v = exe.run(
+                feed=data_feeder.feed(batch), fetch_list=[probs.name])
+            print(test_examples[index], probs_v[0][0])

demo/ernie-classification/ernie_tiny_demo.py

@@ -6,10 +6,9 @@ module = hub.Module(name="ernie")
 inputs, outputs, program = module.context(trainable=True, max_seq_len=128)
 
 # Step2
+dataset = hub.dataset.ChnSentiCorp()
 reader = hub.reader.ClassifyReader(
-    dataset=hub.dataset.ChnSentiCorp(),
-    vocab_path=module.get_vocab_path(),
-    max_seq_len=128)
+    dataset=dataset, vocab_path=module.get_vocab_path(), max_seq_len=128)
 
 # Step3
 with fluid.program_guard(program):
@@ -18,7 +17,7 @@ with fluid.program_guard(program):
     pooled_output = outputs["pooled_output"]
 
     cls_task = hub.create_text_classification_task(
-        feature=pooled_output, label=label, num_classes=reader.get_num_labels())
+        feature=pooled_output, label=label, num_classes=dataset.num_labels)
 
 # Step4
 strategy = hub.AdamWeightDecayStrategy(

demo/ernie-classification/question_answering.py

@@ -37,11 +37,11 @@ if __name__ == '__main__':
         trainable=True, max_seq_len=args.max_seq_len)
 
     # Step2: Download dataset and use ClassifyReader to read dataset
+    dataset = hub.dataset.NLPCC_DBQA()
     reader = hub.reader.ClassifyReader(
-        dataset=hub.dataset.NLPCC_DBQA(),
+        dataset=dataset,
         vocab_path=module.get_vocab_path(),
         max_seq_len=args.max_seq_len)
-    num_labels = len(reader.get_labels())
 
     # Step3: construct transfer learning network
     with fluid.program_guard(program):
@@ -59,7 +59,7 @@ if __name__ == '__main__':
         ]
         # Define a classfication finetune task by PaddleHub's API
         cls_task = hub.create_text_classification_task(
-            pooled_output, label, num_classes=num_labels)
+            pooled_output, label, num_classes=dataset.num_labels)
 
         # Step4: Select finetune strategy, setup config and finetune
         strategy = hub.AdamWeightDecayStrategy(

demo/ernie-classification/question_matching.py

@@ -37,11 +37,11 @@ if __name__ == '__main__':
         trainable=True, max_seq_len=args.max_seq_len)
 
     # Step2: Download dataset and use ClassifyReader to read dataset
+    dataset = hub.dataset.LCQMC()
     reader = hub.reader.ClassifyReader(
-        dataset=hub.dataset.LCQMC(),
+        dataset=dataset,
         vocab_path=module.get_vocab_path(),
         max_seq_len=args.max_seq_len)
-    num_labels = len(reader.get_labels())
 
     # Step3: construct transfer learning network
     with fluid.program_guard(program):
@@ -59,7 +59,7 @@ if __name__ == '__main__':
         ]
         # Define a classfication finetune task by PaddleHub's API
         cls_task = hub.create_text_classification_task(
-            pooled_output, label, num_classes=num_labels)
+            pooled_output, label, num_classes=dataset.num_labels)
 
         # Step4: Select finetune strategy, setup config and finetune
         strategy = hub.AdamWeightDecayStrategy(

demo/ernie-classification/run_predict.sh

+export CUDA_VISIBLE_DEVICES=1
+
+CKPT_DIR="./ckpt_sentiment_cls/best_model"
+python -u cls_predict.py --checkpoint_dir $CKPT_DIR --max_seq_len 128

demo/ernie-classification/sentiment_cls.py

@@ -37,8 +37,9 @@ if __name__ == '__main__':
         trainable=True, max_seq_len=args.max_seq_len)
 
     # Step2: Download dataset and use ClassifyReader to read dataset
+    dataset = hub.dataset.ChnSentiCorp()
     reader = hub.reader.ClassifyReader(
-        dataset=hub.dataset.ChnSentiCorp(),
+        dataset=dataset,
         vocab_path=module.get_vocab_path(),
         max_seq_len=args.max_seq_len)
 
@@ -58,7 +59,9 @@ if __name__ == '__main__':
         ]
         # Define a classfication finetune task by PaddleHub's API
         cls_task = hub.create_text_classification_task(
-            pooled_output, label, num_classes=reader.get_num_labels())
+            feature=pooled_output,
+            label=label,
+            num_classes=dataset.num_labels())
 
         # Step4: Select finetune strategy, setup config and finetune
         strategy = hub.AdamWeightDecayStrategy(

paddlehub/dataset/chnsenticorp.py

@@ -70,6 +70,13 @@ class ChnSentiCorp(HubDataset):
     def get_labels(self):
         return ["0", "1"]
 
+    @property
+    def num_labels(self):
+        """
+        Return the number of labels in the dataset.
+        """
+        return len(self.get_labels())
+
     def _read_tsv(self, input_file, quotechar=None):
         """Reads a tab separated value file."""
         with open(input_file, "r") as f:

paddlehub/dataset/dataset.py

@@ -40,6 +40,13 @@ class InputExample(object):
         self.text_b = text_b
         self.label = label
 
+    def __str__(self):
+        if self.text_b is None:
+            return "text={}\tlabel={}".format(self.text_a, self.label)
+        else:
+            return "text_a={}\ttext_b{},label={}".format(
+                self.text_a, self.text_b, label)
+
 
 class HubDataset(object):
     def get_train_examples(self):
@@ -56,3 +63,6 @@ class HubDataset(object):
 
     def get_labels(self):
         raise NotImplementedError()
+
+    def num_labels(self):
+        raise NotImplementedError()

paddlehub/dataset/lcqmc.py

@@ -66,6 +66,13 @@ class LCQMC(HubDataset):
         """See base class."""
         return ["0", "1"]
 
+    @property
+    def num_labels(self):
+        """
+        Return the number of labels in the dataset.
+        """
+        return len(self.get_labels())
+
     def _read_tsv(self, input_file, quotechar=None):
         """Reads a tab separated value file."""
         with open(input_file, "r") as f:

paddlehub/dataset/msra_ner.py

@@ -79,6 +79,13 @@ class MSRA_NER(HubDataset):
     def get_labels(self):
         return ["B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "O"]
 
+    @property
+    def num_labels(self):
+        """
+        Return the number of labels in the dataset.
+        """
+        return len(self.get_labels())
+
     def get_label_map(self):
         return self.label_map
 

paddlehub/dataset/nlpcc_dbqa.py

@@ -72,6 +72,13 @@ class NLPCC_DBQA(HubDataset):
         """See base class."""
         return ["0", "1"]
 
+    @property
+    def num_labels(self):
+        """
+        Return the number of labels in the dataset.
+        """
+        return len(self.get_labels())
+
     def _read_tsv(self, input_file, quotechar=None):
         """Reads a tab separated value file."""
         with open(input_file, "r") as f:

paddlehub/reader/nlp_reader.py

@@ -80,9 +80,6 @@ class BaseReader(object):
         """Gets the list of labels for this data set."""
         return self.dataset.get_labels()
 
-    def get_num_labels(self):
-        return len(self.dataset.get_labels())
-
     def get_train_progress(self):
         """Gets progress for training phase."""
         return self.current_example, self.current_epoch
@@ -211,7 +208,7 @@ class BaseReader(object):
             )
         return self.num_examples[phase]
 
-    def data_generator(self, batch_size, phase='train', shuffle=True):
+    def data_generator(self, batch_size=1, phase='train', shuffle=True):
 
         if phase == 'train':
             examples = self.get_train_examples()