添加 'c2net_listdata.py'

c2net_listdata.py

@@ -0,0 +1,114 @@
+"""
+######################## train lenet example ########################
+train lenet and get network model files(.ckpt) 
+"""
+#!/usr/bin/python
+#coding=utf-8
+
+
+import os
+import argparse
+
+import moxing as mox
+from config import mnist_cfg as cfg
+from dataset import create_dataset
+from dataset_distributed import create_dataset_parallel
+from lenet import LeNet5
+import json
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+from mindspore import load_checkpoint, load_param_into_net
+from mindspore.context import ParallelMode
+from mindspore.communication.management import init, get_rank
+import time
+
+### Copy multiple datasets from obs to training image ###  
+def MultiObsToEnv(multi_data_url, data_dir):
+    #--multi_data_url is json data, need to do json parsing for multi_data_url
+    multi_data_json = json.loads(multi_data_url)  
+    for i in range(len(multi_data_json)):
+        path = data_dir + "/" + multi_data_json[i]["dataset_name"]
+        file_path = data_dir + "/" + os.path.splitext(multi_data_json[i]["dataset_name"])[0]
+        if not os.path.exists(file_path):
+            os.makedirs(file_path)
+        try:
+            mox.file.copy_parallel(multi_data_json[i]["dataset_url"], path) 
+            print("Successfully Download {} to {}".format(multi_data_json[i]["dataset_url"],path))
+            #unzip dataset
+            os.system("unzip -d %s %s" % (file_path, path))
+        except Exception as e:
+            print('moxing download {} to {} failed: '.format(
+                multi_data_json[i]["dataset_url"], path) + str(e))
+    #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")
+    return 
+                                                      
+def DownloadFromQizhi(multi_data_url, data_dir):
+    device_num = int(os.getenv('RANK_SIZE'))
+    if device_num == 1:
+        MultiObsToEnv(multi_data_url,data_dir)
+        context.set_context(mode=context.GRAPH_MODE,device_target=args.device_target)
+    if device_num > 1:
+        # set device_id and init for multi-card training
+        context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=int(os.getenv('ASCEND_DEVICE_ID')))
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(device_num = device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, parameter_broadcast=True)
+        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:
+            MultiObsToEnv(multi_data_url,data_dir)
+        #If the cache file does not exist, it means that the copy data has not been completed,
+        #and Wait for 0th card to finish copying data
+        while not os.path.exists("/cache/download_input.txt"):
+            time.sleep(1)  
+    return
+
+parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
+### --multi_data_url,--ckpt_url,--device_target,These 4 parameters must be defined first in a multi-dataset,
+### otherwise an error will be reported. 
+### There is no need to add these parameters to the running parameters of the Qizhi platform, 
+### because they are predefined in the background, you only need to define them in your code.
+
+parser.add_argument('--multi_data_url',
+                    help='dataset path in obs')
+
+parser.add_argument('--ckpt_url',
+                    help='pre_train_model path in obs')
+
+parser.add_argument(
+    '--device_target',
+    type=str,
+    default="Ascend",
+    choices=['Ascend', 'CPU'],
+    help='device where the code will be implemented (default: Ascend),if to use the CPU on the Qizhi platform:device_target=CPU')
+
+parser.add_argument('--epoch_size',
+                    type=int,
+                    default=5,
+                    help='Training epochs.')
+
+if __name__ == "__main__":
+    args, unknown = parser.parse_known_args()
+    data_dir = '/cache/dataset'  
+    train_dir = '/cache/output'
+    if not os.path.exists(data_dir):
+        os.makedirs(data_dir)
+    if not os.path.exists(train_dir):
+        os.makedirs(train_dir)
+    ###Initialize and copy data to training image
+    DownloadFromQizhi(args.multi_data_url, data_dir)
+    print("--------start ls:")
+    os.system("cd /cache/dataset; ls -al")
+    print("--------end ls-----------")
+