update (#3822)

PaddleNLP/sentiment_classification/README.md

@@ -29,7 +29,7 @@
 
 1. PaddlePaddle 安装
 
-   本项目依赖于 PaddlePaddle Fluid 1.3.2 及以上版本，请参考 [安装指南](http://www.paddlepaddle.org/#quick-start) 进行安装
+   本项目依赖于 PaddlePaddle Fluid 1.6 及以上版本，请参考 [安装指南](http://www.paddlepaddle.org/#quick-start) 进行安装
 
 2. 代码安装
 
@@ -42,7 +42,7 @@
 
 3. 环境依赖
 
-   请参考 PaddlePaddle [安装说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.5/beginners_guide/install/index_cn.html) 部分的内容
+   Python 2 的版本要求 2.7.15+，Python 3 的版本要求 3.5.1+/3.6/3.7，其它环境请参考 PaddlePaddle [安装说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.5/beginners_guide/install/index_cn.html) 部分的内容
 
 ### 代码结构说明
 
@@ -118,10 +118,10 @@ Running type options:
 
 Model config options:
   --model_type {bow_net,cnn_net,lstm_net,bilstm_net,gru_net,textcnn_net}
-                        Model type to run the task. Default: textcnn_net.
+                        Model type to run the task. Default: bilstm_net.
   --init_checkpoint INIT_CHECKPOINT
                         Init checkpoint to resume training from. Default: .
-  --save_checkpoint_dir SAVE_CHECKPOINT_DIR
+  --checkpoints SAVE_CHECKPOINT_DIR
                         Directory path to save checkpoints Default: .
 ...
 """

PaddleNLP/sentiment_classification/download.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.
+
+"""
+Download script, download dataset and pretrain models.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import io
+import os
+import sys
+import time
+import hashlib
+import tarfile
+import requests
+
+def usage():
+    desc = ("\nDownload datasets and pretrained models for Sentiment Classification task.\n"
+        "Usage:\n"
+        "   1. python download.py dataset\n"
+        "   2. python download.py model\n")
+    print(desc)
+
+def extract(fname, dir_path):
+    """
+    Extract tar.gz file
+    """
+    try:
+        tar = tarfile.open(fname, "r:gz")
+        file_names = tar.getnames()
+        for file_name in file_names:
+            tar.extract(file_name, dir_path)
+            print(file_name)
+        tar.close()
+    except Exception as e:
+        raise e
+
+def download(url, filename):
+    """
+    Download file
+    """
+    retry = 0
+    retry_limit = 3
+    chunk_size = 4096
+    while not (os.path.exists(filename):
+        if retry < retry_limit:
+            retry += 1
+        else:
+            raise RuntimeError("Cannot download dataset ({0}) with retry {1} times.".
+                format(url, retry_limit))
+        try:
+            start = time.time()
+            size = 0
+            res = requests.get(url, stream=True)
+            filesize = int(res.headers['content-length'])
+            if res.status_code == 200:
+                print("[Filesize]: %0.2f MB" % (filesize / 1024 / 1024))
+                # save by chunk
+                with io.open(filename, "wb") as fout:
+                    for chunk in res.iter_content(chunk_size=chunk_size):
+                        if chunk:
+                            fout.write(chunk)
+                            size += len(chunk)
+                            pr = '>' * int(size * 50 / filesize)
+                            print('\r[Process ]: %s%.2f%%' % (pr, float(size / filesize*100)), end='')
+            end = time.time()
+            print("\n[CostTime]: %.2f s" % (end - start))
+        except Exception as e:
+            print(e)
+
+def download_dataset(dir_path):
+    BASE_URL = "https://baidu-nlp.bj.bcebos.com/"
+    DATASET_NAME = "sentiment_classification-dataset-1.0.0.tar.gz"
+    file_path = os.path.join(dir_path, DATASET_NAME)
+    url = BASE_URL + DATASET_NAME
+    
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+    # download dataset
+    print("Downloading dataset: %s" % url)
+    download(url, file_path)
+    # extract dataset
+    print("Extracting dataset: %s" % file_path)
+    extract(file_path, dir_path)
+    os.remove(file_path)
+
+def download_model(dir_path):
+    BASE_URL = "https://baidu-nlp.bj.bcebos.com/"
+    MODEL_NAME = "sentiment_classification-1.0.0.tar.gz"
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+    url = BASE_URL + MODEL_NAME
+    model_path = os.path.join(dir_path, model)
+    print("Downloading model: %s" % url)
+    # download model
+    download(url, model_path, MODEL_NAME)
+    # extract model.tar.gz
+    print("Extracting model: %s" % model_path)
+    extract(model_path, dir_path)
+    os.remove(model_path)
+
+if __name__ == "__main__":
+    if len(sys) != 2:
+        usage()
+        sys.exit(1)
+    
+    if sys.argv[1] == "dataset":
+        pwd = os.path.join(os.path.dirname(__file__), "./")
+        download_dataset(pwd)
+    elif sys.argv[1] == "model":
+        pwd = os.path.join(os.path.dirname(__file__), "./models")
+        download_model(pwd)
+    else:
+        usage()

PaddleNLP/sentiment_classification/inference_model_ernie.py

@@ -14,7 +14,7 @@ import utils
 import reader
 from run_ernie_classifier import ernie_pyreader
 from models.representation.ernie import ErnieConfig
-from models.representation.ernie import ernie_encoder
+from models.representation.ernie import ernie_encoder, ernie_encoder_with_paddle_hub
 from preprocess.ernie import task_reader
 
 def do_save_inference_model(args):
@@ -39,8 +39,11 @@ def do_save_inference_model(args):
             infer_pyreader, ernie_inputs, labels = ernie_pyreader(
                 args,
                 pyreader_name="infer_reader")
-
-            embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
+            
+            if args.use_paddle_hub:
+                embeddings = ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len)
+            else:
+                embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
 
             probs = create_model(args,
                     embeddings,

PaddleNLP/sentiment_classification/reader.py

@@ -72,12 +72,12 @@ class SentaProcessor(object):
         Generate data for train, dev or infer
         """
         if phase == "train":
-            return paddle.batch(self.get_train_examples(self.data_dir, epoch, self.max_seq_len), batch_size)
+            return fluid.io.batch(self.get_train_examples(self.data_dir, epoch, self.max_seq_len), batch_size)
             #return self.get_train_examples(self.data_dir, epoch, self.max_seq_len)
         elif phase == "dev":
-            return paddle.batch(self.get_dev_examples(self.data_dir, epoch, self.max_seq_len), batch_size)
+            return fluid.io.batch(self.get_dev_examples(self.data_dir, epoch, self.max_seq_len), batch_size)
         elif phase == "infer":
-            return paddle.batch(self.get_test_examples(self.data_dir, epoch, self.max_seq_len), batch_size)
+            return fluid.io.batch(self.get_test_examples(self.data_dir, epoch, self.max_seq_len), batch_size)
         else:
             raise ValueError(
                 "Unknown phase, which should be in ['train', 'dev', 'infer'].")

PaddleNLP/sentiment_classification/run_classifier.py

@@ -21,6 +21,7 @@ from nets import cnn_net
 from nets import bilstm_net
 from nets import gru_net
 from models.model_check import check_cuda
+from models.model_check import check_version
 from config import PDConfig
 
 import paddle
@@ -39,11 +40,11 @@ def create_model(args,
     """
     
     data = fluid.layers.data(
-            name="src_ids", shape=[-1, args.max_seq_len, 1], dtype='int64')
+            name="src_ids", shape=[-1, args.max_seq_len], dtype='int64')
     label = fluid.layers.data(
             name="label", shape=[-1, 1], dtype="int64")
     seq_len = fluid.layers.data(
-            name="seq_len", shape=[-1, 1], dtype="int64")
+            name="seq_len", shape=[-1], dtype="int64")
     
     data_reader = fluid.io.PyReader(feed_list=[data, label, seq_len], 
         capacity=4, iterable=False)

PaddleNLP/sentiment_classification/run_ernie_classifier.py

@@ -48,16 +48,21 @@ def ernie_pyreader(args, pyreader_name):
     labels = fluid.layers.data(
         name="labels", shape=[-1, 1], dtype="int64")
     seq_lens = fluid.layers.data(
-        name="seq_lens", shape=[-1, 1], dtype="int64")
-
-    pyreader = fluid.io.PyReader(feed_list=[src_ids, sent_ids, pos_ids, input_mask, labels, seq_lens],
-        capacity=4, iterable=False)
+        name="seq_lens", shape=[-1], dtype="int64")
+    
+    pyreader = fluid.io.DataLoader.from_generator(
+        feed_list=[src_ids, sent_ids, pos_ids, input_mask, labels, seq_lens],
+        capacity=50,
+        iterable=False,
+        use_double_buffer=True)
+    
     ernie_inputs = {
         "src_ids": src_ids,
         "sent_ids": sent_ids,
         "pos_ids": pos_ids,
         "input_mask": input_mask,
         "seq_lens": seq_lens}
+    
     return pyreader, ernie_inputs, labels
 
 def create_model(args,
@@ -299,15 +304,15 @@ def main(args):
 
     if args.do_train:
         train_exe = exe
-        train_pyreader.decorate_batch_generator(train_data_generator)
+        train_pyreader.set_batch_generator(train_data_generator)
     else:
         train_exe = None
     if args.do_val:
         test_exe = exe
-        test_pyreader.decorate_batch_generator(test_data_generator)
+        test_pyreader.set_batch_generator(test_data_generator)
     if args.do_infer:
         test_exe = exe
-        infer_pyreader.decorate_batch_generator(infer_data_generator)
+        infer_pyreader.set_batch_generator(infer_data_generator)
 
     if args.do_train:
         train_pyreader.start()