gru_fix_bug

model_zoo/official/nlp/gru/README.md

@@ -1,4 +1,4 @@
-![](https://www.mindspore.cn/static/img/logo.a3e472c9.png)
+![](https://www.mindspore.cn/static/img/logo_black.6a5c850d.png)
 
 <!-- TOC -->
 
@@ -52,6 +52,26 @@ In this model, we use the Multi30K dataset as our train and test dataset.As trai
     - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
     - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
+## Requirements
+
+```txt
+nltk
+numpy
+```
+
+To install nltk, you should install nltk as follow:
+
+```bash
+pip install nltk
+```
+
+Then you should download extra packages as follow:
+
+```python
+import nltk
+nltk.download()
+```
+
 # [Quick Start](#content)
 
 After dataset preparation, you can start training and evaluation as follows:

model_zoo/official/nlp/gru/eval.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """Transformer evaluation script."""
-
+import os
 import argparse
 import mindspore.common.dtype as mstype
 from mindspore.common.tensor import Tensor
@@ -41,8 +41,13 @@ def run_gru_eval():
 
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, reserve_class_name_in_scope=False, \
         device_id=args.device_id, save_graphs=False)
+    prefix = "multi30k_test_mindrecord_32"
+    mindrecord_file = os.path.join(args.dataset_path, prefix)
+    if not os.path.exists(mindrecord_file):
+        print("dataset file {} not exists, please check!".format(mindrecord_file))
+        raise ValueError(mindrecord_file)
     dataset = create_gru_dataset(epoch_count=config.num_epochs, batch_size=config.eval_batch_size, \
-        dataset_path=args.dataset_path, rank_size=args.device_num, rank_id=0, do_shuffle=False, is_training=False)
+        dataset_path=mindrecord_file, rank_size=args.device_num, rank_id=0, do_shuffle=False, is_training=False)
     dataset_size = dataset.get_dataset_size()
     print("dataset size is {}".format(dataset_size))
     network = Seq2Seq(config, is_training=False)

model_zoo/official/nlp/gru/scripts/run_distribute_train_ascend.sh

@@ -40,9 +40,9 @@ fi
 DATASET_PATH=$(get_real_path $2)
 echo $DATASET_PATH
 
-if [ ! -f $DATASET_PATH ]
+if [ ! -d $DATASET_PATH ]
 then
-    echo "error: DATASET_PATH=$DATASET_PATH is not a file"
+    echo "error: DATASET_PATH=$DATASET_PATH is not a directory"
 exit 1
 fi
 

model_zoo/official/nlp/gru/scripts/run_eval.sh

@@ -41,9 +41,9 @@ fi
 
 DATASET_PATH=$(get_real_path $2)
 echo $DATASET_PATH
-if [ ! -f $DATASET_PATH ]
+if [ ! -d $DATASET_PATH ]
 then
-    echo "error: DATASET_PATH=$DATASET_PATH is not a file"
+    echo "error: DATASET_PATH=$DATASET_PATH is not a directory"
 exit 1
 fi
 rm -rf ./eval

model_zoo/official/nlp/gru/scripts/run_standalone_train.sh

@@ -33,9 +33,9 @@ get_real_path(){
 
 DATASET_PATH=$(get_real_path $1)
 echo $DATASET_PATH
-if [ ! -f $DATASET_PATH ]
+if [ ! -d $DATASET_PATH ]
 then
-    echo "error: DATASET_PATH=$DATASET_PATH is not a file"
+    echo "error: DATASET_PATH=$DATASET_PATH is not a directory"
 exit 1
 fi
 

model_zoo/official/nlp/gru/train.py

@@ -99,8 +99,13 @@ if __name__ == '__main__':
     else:
         rank = 0
         device_num = 1
+    prefix = "multi30k_train_mindrecord_32_"
+    mindrecord_file = os.path.join(args.dataset_path, prefix+"0")
+    if not os.path.exists(mindrecord_file):
+        print("dataset file {} not exists, please check!".format(mindrecord_file))
+        raise ValueError(mindrecord_file)
     dataset = create_gru_dataset(epoch_count=config.num_epochs, batch_size=config.batch_size,
-                                 dataset_path=args.dataset_path, rank_size=device_num, rank_id=rank)
+                                 dataset_path=mindrecord_file, rank_size=device_num, rank_id=rank)
     dataset_size = dataset.get_dataset_size()
     print("dataset size is {}".format(dataset_size))
     network = Seq2Seq(config)