update lstm support infomation

docs/source_en/network_list.md

@@ -15,4 +15,4 @@
 | Computer Version (CV)  | Targets Detection  | [YoloV3](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/yolov3.py)         |  Supported |  Doing | Doing
 | Computer Version (CV) | Semantic Segmentation  | [Deeplabv3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/deeplabv3/src/deeplabv3.py)                                          |  Supported |  Doing | Doing
 | Natural Language Processing (NLP) | Natural Language Understanding  | [BERT](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/bert/src/bert_model.py)                                          |  Supported |  Doing | Doing
-| Natural Language Processing (NLP) | Natural Language Understanding  | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/lstm.py)                                          |  Doing |  Supported | Doing
+| Natural Language Processing (NLP) | Natural Language Understanding  | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lstm/src/lstm.py)                                          |  Doing |  Supported | Supported

docs/source_zh_cn/network_list.md

@@ -15,4 +15,4 @@
 | 计算机视觉（CV）  | 目标检测（Targets Detection）  | [YoloV3](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/yolov3.py)         |  Supported |  Doing | Doing
 | 计算机视觉（CV） | 语义分割（Semantic Segmentation）  | [Deeplabv3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/deeplabv3/src/deeplabv3.py)                                       |  Supported |  Doing | Doing
 | 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）  | [BERT](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/bert/src/bert_model.py)                                          |  Supported |  Doing | Doing
-| 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）  | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/lstm.py)                                          |  Doing |  Supported | Doing
+| 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）  | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lstm/src/lstm.py)                                          |  Doing |  Supported | Supported

tutorials/tutorial_code/lstm/main.py

@@ -36,7 +36,7 @@ from mindspore.mindrecord import FileWriter
 from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 # Install gensim with 'pip install gensim'
 import gensim
 
@@ -281,26 +281,25 @@ def create_dataset(base_path, batch_size, num_epochs, is_train):
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='MindSpore LSTM Example')
     parser.add_argument('--preprocess', type=str, default='false', choices=['true', 'false'],
-                        help='Whether to perform data preprocessing')
+                        help='whether to preprocess data.')
     parser.add_argument('--mode', type=str, default="train", choices=['train', 'test'],
                         help='implement phase, set to train or test')
-    # Download dataset from 'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz' and extract to 'aclimdb_path'
     parser.add_argument('--aclimdb_path', type=str, default="./aclImdb",
-                        help='path where the dataset is store')
-    # Download glove from 'http://nlp.stanford.edu/data/glove.6B.zip' and extract to 'glove_path'
-    # Add a new line '400000 300' at the beginning of 'glove.6B.300d.txt' with '40000' for total words and '300' for vector length
+                        help='path where the dataset is stored.')
     parser.add_argument('--glove_path', type=str, default="./glove",
-                        help='path where the glove is store')
+                        help='path where the GloVe is stored.')
     parser.add_argument('--preprocess_path', type=str, default="./preprocess",
-                        help='path where the pre-process data is store')
-    parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if mode is test, must provide\
-                        path where the trained ckpt file')
+                        help='path where the pre-process data is stored.')
+    parser.add_argument('--ckpt_path', type=str, default="./",
+                        help='if mode is test, must provide path where the trained ckpt file.')
+    parser.add_argument('--device_target', type=str, default="GPU", choices=['GPU', 'CPU'],
+                        help='the target device to run, support "GPU", "CPU". Default: "GPU".')
     args = parser.parse_args()
 
     context.set_context(
         mode=context.GRAPH_MODE,
         save_graphs=False,
-        device_target="GPU")
+        device_target=args.device_target)
 
     if args.preprocess == 'true':
         print("============== Starting Data Pre-processing ==============")
@@ -329,13 +328,20 @@ if __name__ == '__main__':
         config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
                                      keep_checkpoint_max=cfg.keep_checkpoint_max)
         ckpoint_cb = ModelCheckpoint(prefix="lstm", directory=args.ckpt_path, config=config_ck)
-        model.train(cfg.num_epochs, ds_train, callbacks=[ckpoint_cb, loss_cb])
+        time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
+        if args.device_target == "CPU":
+            model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb], dataset_sink_mode=False)
+        else:
+            model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb])
     elif args.mode == 'test':
         print("============== Starting Testing ==============")
         ds_eval = create_dataset(args.preprocess_path, cfg.batch_size, 1, False)
         param_dict = load_checkpoint(args.ckpt_path)
         load_param_into_net(network, param_dict)
-        acc = model.eval(ds_eval)
+        if args.device_target == "CPU":
+            acc = model.eval(ds_eval, dataset_sink_mode=False)
+        else:
+            acc = model.eval(ds_eval)
         print("============== Accuracy:{} ==============".format(acc))
     else:
         raise RuntimeError('mode should be train or test, rather than {}'.format(args.mode))