224 lines
9.9 KiB
Python
224 lines
9.9 KiB
Python
"""
|
|
######################## multi-dataset train lenet example ########################
|
|
This example is a multi-dataset training tutorial. If it is a single dataset, please refer to the single dataset
|
|
training tutorial train.py. This example cannot be used for a single dataset!
|
|
"""
|
|
"""
|
|
######################## Instructions for using the training environment ########################
|
|
1、(1)The structure of the dataset uploaded for multi-dataset training in this example
|
|
MNISTData.zip
|
|
├── test
|
|
└── train
|
|
|
|
checkpoint_lenet-1_1875.zip
|
|
├── checkpoint_lenet-1_1875.ckpt
|
|
|
|
(2)The dataset structure in the training image for multiple datasets in this example
|
|
workroot
|
|
├── MNISTData
|
|
| ├── test
|
|
| └── train
|
|
└── checkpoint_lenet-1_1875
|
|
├── checkpoint_lenet-1_1875.ckpt
|
|
|
|
2、Multi-dataset training requires predefined functions
|
|
(1)Copy multi-dataset from obs to training image
|
|
function MultiObsToEnv(multi_data_url, data_dir)
|
|
|
|
(2)Copy the output to obs
|
|
function EnvToObs(train_dir, obs_train_url)
|
|
|
|
(3)Download the input from Qizhi And Init
|
|
function DownloadFromQizhi(multi_data_url, data_dir)
|
|
|
|
(4)Upload the output to Qizhi
|
|
function UploadToQizhi(train_dir, obs_train_url)
|
|
|
|
3、4 parameters need to be defined
|
|
--data_url is the first dataset you selected on the Qizhi platform
|
|
--multi_data_url is the multi-dataset you selected on the Qizhi platform
|
|
|
|
--data_url,--multi_data_url,--train_url,--device_target,These 4 parameters must be defined first in a multi-dataset task,
|
|
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
|
|
|
|
4、How the dataset is used
|
|
Multi-datasets use multi_data_url as input, data_dir + dataset name + file or folder name in the dataset as the
|
|
calling path of the dataset in the training image.
|
|
For example, the calling path of the train folder in the MNIST_Data dataset in this example is
|
|
data_dir + "/MNIST_Data" +"/train"
|
|
|
|
For details, please refer to the following sample code.
|
|
"""
|
|
|
|
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"]
|
|
if not os.path.exists(path):
|
|
os.makedirs(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))
|
|
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
|
|
### Copy the output model to obs ###
|
|
def EnvToObs(train_dir, obs_train_url):
|
|
try:
|
|
mox.file.copy_parallel(train_dir, obs_train_url)
|
|
print("Successfully Upload {} to {}".format(train_dir,
|
|
obs_train_url))
|
|
except Exception as e:
|
|
print('moxing upload {} to {} failed: '.format(train_dir,
|
|
obs_train_url) + str(e))
|
|
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
|
|
def UploadToQizhi(train_dir, obs_train_url):
|
|
device_num = int(os.getenv('RANK_SIZE'))
|
|
local_rank=int(os.getenv('RANK_ID'))
|
|
if device_num == 1:
|
|
EnvToObs(train_dir, obs_train_url)
|
|
if device_num > 1:
|
|
if local_rank%8==0:
|
|
EnvToObs(train_dir, obs_train_url)
|
|
return
|
|
|
|
parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
|
|
### --data_url,--multi_data_url,--train_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('--data_url',
|
|
help='path to training/inference dataset folder',
|
|
default= '/cache/data1/')
|
|
|
|
parser.add_argument('--multi_data_url',
|
|
help='path to multi dataset',
|
|
default= '/cache/data/')
|
|
|
|
parser.add_argument('--train_url',
|
|
help='model folder to save/load',
|
|
default= '/cache/output/')
|
|
|
|
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'
|
|
try:
|
|
if not os.path.exists(data_dir):
|
|
os.makedirs(data_dir)
|
|
if not os.path.exists(train_dir):
|
|
os.makedirs(train_dir)
|
|
except Exception as e:
|
|
print("path already exists")
|
|
###Initialize and copy data to training image
|
|
DownloadFromQizhi(args.multi_data_url, data_dir)
|
|
###The dataset path is used here:data_dir + "/MNIST_Data" +"/train"
|
|
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)
|
|
if ds_train.get_dataset_size() == 0:
|
|
raise ValueError(
|
|
"Please check dataset size > 0 and batch_size <= dataset 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())
|
|
###The dataset path is used here:data_dir + "/checkpoint_lenet-1_1875"+"/checkpoint_lenet-1_1875.ckpt"
|
|
load_param_into_net(network, load_checkpoint(os.path.join(data_dir + "/checkpoint_lenet-1_1875",
|
|
"checkpoint_lenet-1_1875.ckpt")))
|
|
if args.device_target != "Ascend":
|
|
model = Model(network,net_loss,net_opt,metrics={"accuracy": Accuracy()})
|
|
else:
|
|
model = Model(network, net_loss,net_opt,metrics={"accuracy": Accuracy()},amp_level="O2")
|
|
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
|
|
keep_checkpoint_max=cfg.keep_checkpoint_max)
|
|
#Note that this method saves the model file on each card. You need to specify the save path on each card.
|
|
# In this example, get_rank() is added to distinguish different paths.
|
|
if device_num == 1:
|
|
outputDirectory = train_dir + "/"
|
|
if device_num > 1:
|
|
outputDirectory = train_dir + "/" + str(get_rank()) + "/"
|
|
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)
|
|
model.train(epoch_size,
|
|
ds_train,
|
|
callbacks=[time_cb, ckpoint_cb,
|
|
LossMonitor()])
|
|
###Copy the trained output data from the local running environment back to obs,
|
|
###and download it in the training task corresponding to the Qizhi platform
|
|
UploadToQizhi(train_dir,args.train_url)
|
|
|