add switch for data shuffle

model_zoo/official/nlp/bert/run_classifier.py

@@ -133,14 +133,17 @@ def run_classifier():
     """run classifier task"""
     parser = argparse.ArgumentParser(description="run classifier")
     parser.add_argument("--device_target", type=str, default="Ascend", help="Device type, default is Ascend")
-    parser.add_argument("--assessment_method", type=str, default="accuracy", help="assessment_method include: "
-                                                                                  "[MCC, Spearman_correlation, "
-                                                                                  "Accuracy], default is accuracy")
-    parser.add_argument("--do_train", type=str, default="false", help="Eable train, default is false")
-    parser.add_argument("--do_eval", type=str, default="false", help="Eable eval, default is false")
+    parser.add_argument("--assessment_method", type=str, default="accuracy",
+                        help="assessment_method including [MCC, Spearman_correlation, Accuracy], default is accuracy")
+    parser.add_argument("--do_train", type=str, default="false", help="Enable train, default is false")
+    parser.add_argument("--do_eval", type=str, default="false", help="Enable eval, default is false")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--epoch_num", type=int, default="1", help="Epoch number, default is 1.")
     parser.add_argument("--num_class", type=int, default="2", help="The number of class, default is 2.")
+    parser.add_argument("--train_data_shuffle", type=str, default="true",
+                        help="Enable train data shuffle, default is true")
+    parser.add_argument("--eval_data_shuffle", type=str, default="false",
+                        help="Enable eval data shuffle, default is false")
     parser.add_argument("--save_finetune_checkpoint_path", type=str, default="", help="Save checkpoint path")
     parser.add_argument("--load_pretrain_checkpoint_path", type=str, default="", help="Load checkpoint file path")
     parser.add_argument("--load_finetune_checkpoint_path", type=str, default="", help="Load checkpoint file path")
@@ -182,7 +185,8 @@ def run_classifier():
         ds = create_classification_dataset(batch_size=bert_net_cfg.batch_size, repeat_count=1,
                                            assessment_method=assessment_method,
                                            data_file_path=args_opt.train_data_file_path,
-                                           schema_file_path=args_opt.schema_file_path)
+                                           schema_file_path=args_opt.schema_file_path,
+                                           do_shuffle=(args_opt.train_data_shuffle.lower() == "true"))
         do_train(ds, netwithloss, load_pretrain_checkpoint_path, save_finetune_checkpoint_path, epoch_num)
 
         if args_opt.do_eval.lower() == "true":
@@ -197,7 +201,8 @@ def run_classifier():
         ds = create_classification_dataset(batch_size=bert_net_cfg.batch_size, repeat_count=1,
                                            assessment_method=assessment_method,
                                            data_file_path=args_opt.eval_data_file_path,
-                                           schema_file_path=args_opt.schema_file_path)
+                                           schema_file_path=args_opt.schema_file_path,
+                                           do_shuffle=(args_opt.eval_data_shuffle.lower() == "true"))
         do_eval(ds, BertCLS, args_opt.num_class, assessment_method, load_finetune_checkpoint_path)
 
 if __name__ == "__main__":

model_zoo/official/nlp/bert/run_ner.py

@@ -150,6 +150,10 @@ def run_ner():
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--epoch_num", type=int, default="1", help="Epoch number, default is 1.")
     parser.add_argument("--num_class", type=int, default="2", help="The number of class, default is 2.")
+    parser.add_argument("--train_data_shuffle", type=str, default="true",
+                        help="Enable train data shuffle, default is true")
+    parser.add_argument("--eval_data_shuffle", type=str, default="false",
+                        help="Enable eval data shuffle, default is false")
     parser.add_argument("--vocab_file_path", type=str, default="", help="Vocab file path, used in clue benchmark")
     parser.add_argument("--label2id_file_path", type=str, default="", help="label2id file path, used in clue benchmark")
     parser.add_argument("--save_finetune_checkpoint_path", type=str, default="", help="Save checkpoint path")
@@ -208,7 +212,8 @@ def run_ner():
     if args_opt.do_train.lower() == "true":
         ds = create_ner_dataset(batch_size=bert_net_cfg.batch_size, repeat_count=1,
                                 assessment_method=assessment_method, data_file_path=args_opt.train_data_file_path,
-                                schema_file_path=args_opt.schema_file_path)
+                                schema_file_path=args_opt.schema_file_path,
+                                do_shuffle=(args_opt.train_data_shuffle.lower() == "true"))
         do_train(ds, netwithloss, load_pretrain_checkpoint_path, save_finetune_checkpoint_path, epoch_num)
 
         if args_opt.do_eval.lower() == "true":
@@ -222,7 +227,8 @@ def run_ner():
     if args_opt.do_eval.lower() == "true":
         ds = create_ner_dataset(batch_size=bert_net_cfg.batch_size, repeat_count=1,
                                 assessment_method=assessment_method, data_file_path=args_opt.eval_data_file_path,
-                                schema_file_path=args_opt.schema_file_path)
+                                schema_file_path=args_opt.schema_file_path,
+                                do_shuffle=(args_opt.eval_data_shuffle.lower() == "true"))
         do_eval(ds, BertNER, args_opt.use_crf, number_labels, assessment_method, args_opt.eval_data_file_path,
                 load_finetune_checkpoint_path, args_opt.vocab_file_path, args_opt.label2id_file_path, tag_to_index)
 

model_zoo/official/nlp/bert/run_squad.py

@@ -140,6 +140,10 @@ def run_squad():
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--epoch_num", type=int, default="1", help="Epoch number, default is 1.")
     parser.add_argument("--num_class", type=int, default="2", help="The number of class, default is 2.")
+    parser.add_argument("--train_data_shuffle", type=str, default="true",
+                        help="Enable train data shuffle, default is true")
+    parser.add_argument("--eval_data_shuffle", type=str, default="false",
+                        help="Enable eval data shuffle, default is false")
     parser.add_argument("--vocab_file_path", type=str, default="", help="Vocab file path")
     parser.add_argument("--eval_json_path", type=str, default="", help="Evaluation json file path, can be eval.json")
     parser.add_argument("--save_finetune_checkpoint_path", type=str, default="", help="Save checkpoint path")
@@ -186,7 +190,8 @@ def run_squad():
     if args_opt.do_train.lower() == "true":
         ds = create_squad_dataset(batch_size=bert_net_cfg.batch_size, repeat_count=1,
                                   data_file_path=args_opt.train_data_file_path,
-                                  schema_file_path=args_opt.schema_file_path)
+                                  schema_file_path=args_opt.schema_file_path,
+                                  do_shuffle=(args_opt.train_data_shuffle.lower() == "true"))
         do_train(ds, netwithloss, load_pretrain_checkpoint_path, save_finetune_checkpoint_path, epoch_num)
         if args_opt.do_eval.lower() == "true":
             if save_finetune_checkpoint_path == "":
@@ -199,7 +204,8 @@ def run_squad():
     if args_opt.do_eval.lower() == "true":
         ds = create_squad_dataset(batch_size=bert_net_cfg.batch_size, repeat_count=1,
                                   data_file_path=args_opt.eval_data_file_path,
-                                  schema_file_path=args_opt.schema_file_path, is_training=False)
+                                  schema_file_path=args_opt.schema_file_path, is_training=False,
+                                  do_shuffle=(args_opt.eval_data_shuffle.lower() == "true"))
         do_eval(ds, args_opt.vocab_file_path, args_opt.eval_json_path,
                 load_finetune_checkpoint_path, bert_net_cfg.seq_length)
 

model_zoo/official/nlp/bert/scripts/run_classifier.sh

@@ -34,6 +34,8 @@ python ${PROJECT_DIR}/../run_classifier.py  \
     --device_id=0 \
     --epoch_num=1 \
     --num_class=2 \
+    --train_data_shuffle="true" \
+    --eval_data_shuffle="false" \
     --save_finetune_checkpoint_path="" \
     --load_pretrain_checkpoint_path="" \
     --load_finetune_checkpoint_path="" \

model_zoo/official/nlp/bert/scripts/run_ner.sh

@@ -35,6 +35,8 @@ python ${PROJECT_DIR}/../run_ner.py  \
     --device_id=0 \
     --epoch_num=1 \
     --num_class=2 \
+    --train_data_shuffle="true" \
+    --eval_data_shuffle="false" \
     --vocab_file_path="" \
     --label2id_file_path="" \
     --save_finetune_checkpoint_path="" \

model_zoo/official/nlp/bert/scripts/run_squad.sh

@@ -33,6 +33,8 @@ python ${PROJECT_DIR}/../run_squad.py  \
     --device_id=0 \
     --epoch_num=1 \
     --num_class=2 \
+    --train_data_shuffle="true" \
+    --eval_data_shuffle="false" \
     --vocab_file_path="" \
     --eval_json_path="" \
     --save_finetune_checkpoint_path="" \

model_zoo/official/nlp/bert/src/assessment_method.py

@@ -34,7 +34,6 @@ class Accuracy():
         logit_id = np.argmax(logits, axis=-1)
         self.acc_num += np.sum(labels == logit_id)
         self.total_num += len(labels)
-        print("=========================accuracy is ", self.acc_num / self.total_num)
 
 class F1():
     '''

model_zoo/official/nlp/bert/src/dataset.py

@@ -53,11 +53,11 @@ def create_bert_dataset(device_num=1, rank=0, do_shuffle="true", data_dir=None,
 
 
 def create_ner_dataset(batch_size=1, repeat_count=1, assessment_method="accuracy",
-                       data_file_path=None, schema_file_path=None):
+                       data_file_path=None, schema_file_path=None, do_shuffle=True):
     """create finetune or evaluation dataset"""
     type_cast_op = C.TypeCast(mstype.int32)
     ds = de.TFRecordDataset([data_file_path], schema_file_path if schema_file_path != "" else None,
-                            columns_list=["input_ids", "input_mask", "segment_ids", "label_ids"])
+                            columns_list=["input_ids", "input_mask", "segment_ids", "label_ids"], shuffle=do_shuffle)
     if assessment_method == "Spearman_correlation":
         type_cast_op_float = C.TypeCast(mstype.float32)
         ds = ds.map(input_columns="label_ids", operations=type_cast_op_float)
@@ -76,11 +76,11 @@ def create_ner_dataset(batch_size=1, repeat_count=1, assessment_method="accuracy
 
 
 def create_classification_dataset(batch_size=1, repeat_count=1, assessment_method="accuracy",
-                                  data_file_path=None, schema_file_path=None):
+                                  data_file_path=None, schema_file_path=None, do_shuffle=True):
     """create finetune or evaluation dataset"""
     type_cast_op = C.TypeCast(mstype.int32)
     ds = de.TFRecordDataset([data_file_path], schema_file_path if schema_file_path != "" else None,
-                            columns_list=["input_ids", "input_mask", "segment_ids", "label_ids"])
+                            columns_list=["input_ids", "input_mask", "segment_ids", "label_ids"], shuffle=do_shuffle)
     if assessment_method == "Spearman_correlation":
         type_cast_op_float = C.TypeCast(mstype.float32)
         ds = ds.map(input_columns="label_ids", operations=type_cast_op_float)
@@ -98,14 +98,15 @@ def create_classification_dataset(batch_size=1, repeat_count=1, assessment_metho
     return ds
 
 
-def create_squad_dataset(batch_size=1, repeat_count=1, data_file_path=None, schema_file_path=None, is_training=True):
+def create_squad_dataset(batch_size=1, repeat_count=1, data_file_path=None, schema_file_path=None,
+                         is_training=True, do_shuffle=True):
     """create finetune or evaluation dataset"""
     type_cast_op = C.TypeCast(mstype.int32)
     if is_training:
         ds = de.TFRecordDataset([data_file_path], schema_file_path if schema_file_path != "" else None,
-                                columns_list=["input_ids", "input_mask", "segment_ids",
-                                              "start_positions", "end_positions",
-                                              "unique_ids", "is_impossible"])
+                                columns_list=["input_ids", "input_mask", "segment_ids", "start_positions",
+                                              "end_positions", "unique_ids", "is_impossible"],
+                                shuffle=do_shuffle)
         ds = ds.map(input_columns="start_positions", operations=type_cast_op)
         ds = ds.map(input_columns="end_positions", operations=type_cast_op)
     else: