update

npu_mnist_example/train_multi_card.py

@@ -19,6 +19,7 @@
 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
@@ -29,7 +30,6 @@ from mindspore.train import Model
 from mindspore.context import ParallelMode
 from mindspore.communication.management import init, get_rank
 import time
-#导入openi包
 from c2net.context import prepare, upload_output
 
 
@@ -50,10 +50,7 @@ parser.add_argument('--epoch_size',
 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()
-    MnistDataset_mindspore_path = ''
-    Mindspore_MNIST_Example_Model_path = ''
-    output_path = ''
-
+  
     device_num = int(os.getenv('RANK_SIZE'))
     #使用多卡时        
     # set device_id and init for multi-card training
@@ -63,32 +60,19 @@ if __name__ == "__main__":
     init()
     #Copying obs data does not need to be executed multiple times, just let the 0th card copy the data
     local_rank=int(os.getenv('RANK_ID'))
-    if local_rank%8==0:
-        #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"
-        #获取预训练模型路径
-        Mindspore_MNIST_Example_Model_path = c2net_context.pretrain_model_path+"/"+"Mindspore_MNIST_Example_Model"
-        output_path = c2net_context.output_path
-        #Set a cache file to determine whether the data has been copied to obs. 
-        #If this file exists during multi-card training, there is no need to copy the dataset multiple times.
-        f = open("/cache/download_input.txt", 'w')    
-        f.close()
-        try:
-            if os.path.exists("/cache/download_input.txt"):
-                print("download_input succeed")
-        except Exception as e:
-            print("download_input failed")
-    while not os.path.exists("/cache/download_input.txt"):
-        time.sleep(1)               
-    ds_train = create_dataset_parallel(os.path.join(MnistDataset_mindspore_path, "train"),  cfg.batch_size)
-
+    #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"
+    #获取预训练模型路径
+    Mindspore_MNIST_Example_Model_path = c2net_context.pretrain_model_path+"/"+"Mindspore_MNIST_Example_Model"
+    output_path = c2net_context.output_path
+    ds_train = create_dataset_parallel(os.path.join(MnistDataset_mindspore_path, "train"),  cfg.batch_size)       
     network = LeNet5(cfg.num_classes)
     net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
     time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
-    load_param_into_net(network, load_checkpoint(os.path.join(Mindspore_MNIST_Example_Model_path, "checkpoint_lenet-1_1875.ckpt")))
+    #load_param_into_net(network, load_checkpoint(os.path.join(Mindspore_MNIST_Example_Model_path, "checkpoint_lenet-1_1875.ckpt")))
     if args.device_target != "Ascend":
         model = Model(network,
                       net_loss,