更新npu继续训练示例

npu_mnist_example/train_continue.py

@@ -13,7 +13,6 @@ import os
 import argparse
 from config import mnist_cfg as cfg
 from dataset import create_dataset
-from dataset_distributed import create_dataset_parallel
 from lenet import LeNet5
 import mindspore.nn as nn
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
@@ -22,14 +21,11 @@ from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
 from mindspore.communication.management import get_rank
 
-from openi import obs_copy_file
-from openi import obs_copy_folder
-from openi import openi_multidataset_to_env
+#导入c2net包
+from c2net.context import prepare
+from c2net.context.moxing_helper import obs_copy_file, obs_copy_folder
 
 parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
-parser.add_argument('--multi_data_url',
-                    help='path to training/inference dataset folder',
-                    default= '[{}]')
 
 parser.add_argument('--train_url',
                     help='output folder to save/load',
@@ -58,7 +54,13 @@ parser.add_argument('--ckpt_save_name',
 
 
 if __name__ == "__main__":
+    ###请在代码中加入args, unknown = parser.parse_known_args()，可忽略掉--ckpt_url参数报错等参数问题
     args, unknown = parser.parse_known_args()
+    #初始化导入数据集和预训练模型到容器内
+    c2net_context = prepare()
+    #获取数据集路径
+    MnistDataset_mindspore_path = c2net_context.dataset_path+"/"+"MnistDataset_mindspore"
+
     data_dir = '/cache/data'  
     base_path = '/cache/output'
 
@@ -70,13 +72,10 @@ if __name__ == "__main__":
     except Exception as e:
         print("path already exists")
 
-    openi_multidataset_to_env(args.multi_data_url, data_dir)
 
     device_num = int(os.getenv('RANK_SIZE'))
     if device_num == 1:
-        ds_train = create_dataset(os.path.join(data_dir + "/MNISTData", "train"),  cfg.batch_size)
-    if device_num > 1:
-        ds_train = create_dataset_parallel(os.path.join(data_dir + "/MNISTData", "train"),  cfg.batch_size)
+        ds_train = create_dataset(os.path.join(MnistDataset_mindspore_path, "train"),  cfg.batch_size)
     if ds_train.get_dataset_size() == 0:
         raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")