Fix the tokenize encoding bug and Update cv dataset (add train dev test examples)

paddlehub/dataset/base_cv_dataset.py

@@ -35,6 +35,10 @@ class ImageClassificationDataset(object):
         self.num_labels = 0
         self.label_list = []
 
+        self.train_examples = []
+        self.dev_examples = []
+        self.test_examples = []
+
     def _download_dataset(self, dataset_path, url):
         if not os.path.exists(dataset_path):
             result, tips, dataset_path = default_downloader.download_file_and_uncompress(
@@ -47,7 +51,7 @@ class ImageClassificationDataset(object):
                 exit()
         return dataset_path
 
-    def _parse_data(self, data_path, shuffle=False):
+    def _parse_data(self, data_path, shuffle=False, phase=None):
         def _base_reader():
             data = []
             with open(data_path, "r") as file:
@@ -68,6 +72,13 @@ class ImageClassificationDataset(object):
                     label = items[-1]
                     data.append((image_path, items[-1]))
 
+            if phase == 'train':
+                self.train_examples = data
+            elif phase == 'dev':
+                self.dev_examples = data
+            elif phase == 'test':
+                self.test_examples = data
+
             if shuffle:
                 np.random.shuffle(data)
 
@@ -85,13 +96,22 @@ class ImageClassificationDataset(object):
 
     def train_data(self, shuffle=True):
         train_data_path = os.path.join(self.base_path, self.train_list_file)
-        return self._parse_data(train_data_path, shuffle)
+        return self._parse_data(train_data_path, shuffle, phase='train')
 
     def test_data(self, shuffle=False):
         test_data_path = os.path.join(self.base_path, self.test_list_file)
-        return self._parse_data(test_data_path, shuffle)
+        return self._parse_data(test_data_path, shuffle, phase='dev')
 
     def validate_data(self, shuffle=False):
         validate_data_path = os.path.join(self.base_path,
                                           self.validate_list_file)
-        return self._parse_data(validate_data_path, shuffle)
+        return self._parse_data(validate_data_path, shuffle, phase='test')
+
+    def get_train_examples(self):
+        return self.train_examples
+
+    def get_dev_examples(self):
+        return self.dev_examples
+
+    def get_test_examples(self):
+        return self.test_examples

paddlehub/finetune/task.py

@@ -520,8 +520,10 @@ class BasicTask(object):
                 self.save_checkpoint()
 
                 # Final evaluation
-                self.eval(phase="dev")
-                self.eval(phase="test")
+                if self._base_data_reader.get_dev_examples() != []:
+                    self.eval(phase="dev")
+                if self._base_data_reader.get_test_examples() != []:
+                    self.eval(phase="test")
 
             self._finetune_end_event(run_states)
             return run_states

paddlehub/reader/cv_reader.py

@@ -122,3 +122,12 @@ class ImageClassificationReader(object):
                     yield (image, label)
 
         return paddle.batch(_data_reader, batch_size=batch_size)
+
+    def get_train_examples(self):
+        return self.dataset.train_examples
+
+    def get_dev_examples(self):
+        return self.dataset.dev_examples
+
+    def get_test_examples(self):
+        return self.dataset.test_examples

paddlehub/reader/nlp_reader.py

@@ -42,7 +42,8 @@ class BaseReader(object):
                  label_map_config=None,
                  max_seq_len=512,
                  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)
@@ -52,6 +53,10 @@ class BaseReader(object):
         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)
 
@@ -232,7 +237,6 @@ class BaseReader(object):
                        phase='train',
                        shuffle=True,
                        data=None):
-
         if phase == 'train':
             shuffle = True
             examples = self.get_train_examples()
@@ -313,12 +317,25 @@ class ClassifyReader(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:
+                return_list = [
+                    padded_token_ids, padded_position_ids, padded_text_type_ids,
+                    input_mask, padded_task_ids
+                ]
         return return_list
 
 
@@ -355,11 +372,30 @@ class SequenceLabelReader(BaseReader):
                 padded_token_ids, padded_position_ids, padded_text_type_ids,
                 input_mask, padded_label_ids, batch_seq_lens
             ]
+
+            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, padded_label_ids,
+                    batch_seq_lens
+                ]
+
         else:
             return_list = [
                 padded_token_ids, padded_position_ids, padded_text_type_ids,
                 input_mask, batch_seq_lens
             ]
+
+            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_seq_lens
+                ]
+
         return return_list
 
     def _reseg_token_label(self, tokens, tokenizer, phase, labels=None):
@@ -585,11 +621,27 @@ class MultiLabelClassifyReader(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 _convert_example_to_record(self,

paddlehub/reader/tokenization.py

@@ -18,8 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import codecs
 import collections
+import io
 import unicodedata
 
 import six
@@ -71,7 +71,7 @@ def printable_text(text):
 def load_vocab(vocab_file):
     """Loads a vocabulary file into a dictionary."""
     vocab = collections.OrderedDict()
-    fin = codecs.open(vocab_file, "r", "UTF-8")
+    fin = io.open(vocab_file, "r", "UTF-8")
     for num, line in enumerate(fin):
         items = convert_to_unicode(line.strip()).split("\t")
         if len(items) > 2: