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- """
- ######################## train lenet example ########################
- train lenet and get network model files(.ckpt)
- """
- """
- ######################## 训练环境使用说明 ########################
- 假设已经使用Ascend NPU调试环境调试完代码,欲将调试环境的代码迁移到训练环境进行训练,需要做以下工作:
- 1、调试环境的镜像和训练环境的镜像是两个不同的镜像,所处的运行目录不一致,需要将data_url和train_url的路径进行变换
- 在调试环境中:
- args.data_url = '/home/ma-user/work/data/' //数据集位置
- args.train_url = '/home/ma-user/work/model/' //训练输出的模型位置
- 在训练环境变换为:
- args.data_url = '/home/work/user-job-dir/data/'
- args.train_url = '/home/work/user-job-dir/model/'
- 2、在训练环境中,需要将数据集从obs拷贝到训练镜像中,训练完以后,需要将输出的模型拷贝到obs.
- 将数据集从obs拷贝到训练镜像中:
-
- obs_data_url = args.data_url
- args.data_url = '/home/work/user-job-dir/data/'
- if not os.path.exists(args.data_url):
- os.mkdir(args.data_url)
- try:
- mox.file.copy_parallel(obs_data_url, args.data_url)
- print("Successfully Download {} to {}".format(obs_data_url,
- args.data_url))
- except Exception as e:
- print('moxing download {} to {} failed: '.format(
- obs_data_url, args.data_url) + str(e))
-
- 将输出的模型拷贝到obs:
- obs_train_url = args.train_url
- args.train_url = '/home/work/user-job-dir/model/'
- if not os.path.exists(args.train_url):
- os.mkdir(args.train_url)
- try:
- mox.file.copy_parallel(args.train_url, obs_train_url)
- print("Successfully Upload {} to {}".format(args.train_url,
- obs_train_url))
- except Exception as e:
- print('moxing upload {} to {} failed: '.format(args.train_url,
- obs_train_url) + str(e))
-
- """
-
- import os
- import numpy as np
- import argparse
- import moxing as mox
- from config import mnist_cfg as cfg
- from dataset import create_dataset
- 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.nn.metrics import Accuracy
- from mindspore.common import set_seed
- from mindspore import Tensor, export
-
- #配置默认的工作空间根目录
- # environment = 'debug'
- environment = 'train'
- if environment == 'debug':
- workroot = '/home/ma-user/work' #调试任务使用该参数
- else:
- workroot = '/home/work/user-job-dir' # 训练任务使用该参数
- print('current work mode:' + environment + ', workroot:' + workroot)
-
- parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
-
- # define 2 parameters for running on modelArts
- # data_url,train_url是固定用于在modelarts上训练的参数,表示数据集的路径和输出模型的路径
- parser.add_argument('--data_url',
- help='path to training/inference dataset folder',
- default= workroot + '/data/')
-
- parser.add_argument('--train_url',
- help='model folder to save/load',
- default= workroot + '/model/')
-
- parser.add_argument(
- '--device_target',
- type=str,
- default="Ascend",
- choices=['Ascend', 'CPU'],
- help='device where the code will be implemented (default: CPU),若要在启智平台上使用NPU,需要在启智平台训练界面上加上运行参数device_target=Ascend')
-
- #modelarts已经默认使用data_url和train_url
- parser.add_argument('--epoch_size',
- type=int,
- default=5,
- help='Training epochs.')
-
- set_seed(1)
-
- if __name__ == "__main__":
- args = parser.parse_args()
- print('args:')
- print(args)
-
- data_dir = workroot + '/data' #数据集存放路径
- train_dir = workroot + '/model' #模型存放路径
- #初始化数据存放目录
- if not os.path.exists(data_dir):
- os.mkdir(data_dir)
- #初始化模型存放目录
- obs_train_url = args.train_url
- train_dir = workroot + '/model/'
- if not os.path.exists(train_dir):
- os.mkdir(train_dir)
- ######################## 将数据集从obs拷贝到训练镜像中 (固定写法)########################
- # 在训练环境中定义data_url和train_url,并把数据从obs拷贝到相应的固定路径,以下写法是将数据拷贝到/home/work/user-job-dir/data/目录下,可修改为其他目录
- #创建数据存放的位置
- if environment == 'train':
- obs_data_url = args.data_url
- #将数据拷贝到训练环境
- try:
- mox.file.copy_parallel(obs_data_url, data_dir)
- print("Successfully Download {} to {}".format(obs_data_url,
- data_dir))
- except Exception as e:
- print('moxing download {} to {} failed: '.format(
- obs_data_url, data_dir) + str(e))
- ######################## 将数据集从obs拷贝到训练镜像中 ########################
-
- #注意:这里很重要,指定了训练所用的设备CPU还是Ascend NPU
- context.set_context(mode=context.GRAPH_MODE,
- device_target=args.device_target)
- #创建数据集
- ds_train = create_dataset(os.path.join(data_dir, "train"),
- cfg.batch_size)
- if ds_train.get_dataset_size() == 0:
- raise ValueError(
- "Please check dataset size > 0 and batch_size <= dataset size")
- #创建网络
- 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())
-
- 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)
- #定义模型输出路径
- ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
- directory=train_dir,
- config=config_ck)
- #开始训练
- 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()])
- input = np.random.uniform(0.0, 1.0, size=[1, 1, 32, 32]).astype(np.float32)
- export(network, Tensor(input), file_name=(train_dir +'LeNet5_model'), file_format='MINDIR')
-
- export(network, Tensor(input), file_name=(train_dir +'LeNet5_onnx_model'), file_format='ONNX')
- ######################## 将输出的模型拷贝到obs(固定写法) ########################
- # 把训练后的模型数据从本地的运行环境拷贝回obs,在启智平台相对应的训练任务中会提供下载
- if environment == 'train':
- 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))
- ######################## 将输出的模型拷贝到obs ########################
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