128 lines
6.1 KiB
Python
128 lines
6.1 KiB
Python
"""
|
||
######################## Attention! ########################
|
||
使用注意事项：
|
||
1、本示例需要用户定义的参数有--multi_data_url,--pretrain_url,--train_url,这3个参数在任务中必须定义
|
||
具体的含义如下：
|
||
--multi_data_url是启智平台上选择的数据集的obs路径
|
||
--pretrain_url是启智平台上选择的预训练模型文件的obs路径
|
||
--train_url是训练结果回传到启智平台的obs路径
|
||
2、用户需要调用OpenI.C2NETMultiDatasetToEnv等函数，来实现数据集、预训练模型文件的拷贝
|
||
3、智算网络区别于启智：
|
||
(1)智算的数据集拷贝到训练镜像后需要解压，请使用C2NETMultiDatasetToEnv函数
|
||
(2)智算任务结果不需要用户调用函数回传，会在训练结束后自动回传结果
|
||
"""
|
||
|
||
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 import context
|
||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
|
||
from mindspore.train import Model
|
||
from mindspore import load_checkpoint, load_param_into_net
|
||
from mindspore.context import ParallelMode
|
||
from mindspore.communication.management import init, get_rank
|
||
import time
|
||
from openi import c2net_multidataset_to_env as DatasetToEnv
|
||
from openi import pretrain_to_env
|
||
|
||
parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
|
||
|
||
parser.add_argument('--multi_data_url',
|
||
help='使用单数据集训练时，需要定义的参数',
|
||
default= '[{}]')
|
||
parser.add_argument('--pretrain_url',
|
||
help='使用预训练模型单文件或多文件时，需要定义的参数',
|
||
default= '[{}]')
|
||
|
||
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/data'
|
||
train_dir = '/cache/output'
|
||
pretrain_dir = '/cache/pretrain'
|
||
|
||
if not os.path.exists(data_dir):
|
||
os.makedirs(data_dir)
|
||
if not os.path.exists(train_dir):
|
||
os.makedirs(train_dir)
|
||
if not os.path.exists(pretrain_dir):
|
||
os.makedirs(pretrain_dir)
|
||
###拷贝数据集到训练镜像
|
||
DatasetToEnv(args.multi_data_url, data_dir)
|
||
###拷贝多个预训练模型文件到训练环境
|
||
pretrain_to_env(args.pretrain_url, pretrain_dir)
|
||
|
||
device_num = int(os.getenv('RANK_SIZE'))
|
||
#使用单卡时
|
||
if device_num == 1:
|
||
DatasetToEnv(args.multi_data_url,data_dir)
|
||
context.set_context(mode=context.GRAPH_MODE,device_target=args.device_target)
|
||
#使用数据集的方式
|
||
ds_train = create_dataset(os.path.join(data_dir + "/MNISTData", "train"), cfg.batch_size)
|
||
#使用多卡时
|
||
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:
|
||
DatasetToEnv(args.multi_data_url,data_dir)
|
||
#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(data_dir + "/MNISTData", "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())
|
||
###假如选择了模型文件，使用pretrain_dir,注意ckpt_url的方式依然保留，你依然可以使用ckpt_url的方式，但是这种方式将会逐渐废弃
|
||
load_param_into_net(network, load_checkpoint(os.path.join(pretrain_dir, "checkpoint_lenet-1_1875.ckpt")))
|
||
|
||
if args.device_target != "Ascend":
|
||
model = Model(network,net_loss,net_opt,metrics={"accuracy"})
|
||
else:
|
||
model = Model(network, net_loss,net_opt,metrics={"accuracy"},amp_level="O2")
|
||
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
|
||
keep_checkpoint_max=cfg.keep_checkpoint_max)
|
||
print("============== Starting Training ==============")
|
||
#若是数据并行，只需要上传一次输出的模型文件
|
||
outputDirectory = train_dir
|
||
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
|
||
directory=outputDirectory,
|
||
config=config_ck)
|
||
print("============== Starting Training ==============")
|
||
epoch_size = cfg['epoch_size']
|
||
if (args.epoch_size):
|
||
epoch_size = args.epoch_size
|
||
print('epoch_size is: ', epoch_size)
|
||
callback =[time_cb,LossMonitor()]
|
||
model.train(epoch_size,ds_train,callbacks=callback)
|
||
###智算不需要回传训练结果，会在任务结束后自动回传 |