211 lines
8.9 KiB
Python
211 lines
8.9 KiB
Python
#####################################################################################################
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# 继续训练功能：修改训练任务时，若勾选复用上次结果，则可在新训练任务的输出路径中读取到上次结果
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#
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# 示例用法
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# - 增加两个训练参数
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# 'ckpt_save_name' 此次任务的输出文件名，用于保存此次训练的模型文件名称（不带后缀）
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# 'ckpt_load_name' 上一次任务的输出文件名，用于加载上一次输出的模型文件名称（不带后缀），首次训练默认为空，则不读取任何文件
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# - 训练代码中判断 'ckpt_load_name' 是否为空，若不为空，则为继续训练任务
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#####################################################################################################
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import os
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import argparse
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import moxing as mox
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from config import mnist_cfg as cfg
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from dataset import create_dataset
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from dataset_distributed import create_dataset_parallel
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from lenet import LeNet5
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
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from mindspore import load_checkpoint, load_param_into_net
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from mindspore.train import Model
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from mindspore.nn.metrics import Accuracy
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from mindspore.context import ParallelMode
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from mindspore.communication.management import init, get_rank
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import mindspore.ops as ops
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import time
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### Copy single dataset from obs to training image###
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def ObsToEnv(obs_data_url, data_dir):
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try:
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mox.file.copy_parallel(obs_data_url, data_dir)
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print("Successfully Download {} to {}".format(obs_data_url, data_dir))
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except Exception as e:
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print('moxing download {} to {} failed: '.format(obs_data_url, data_dir) + str(e))
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#Set a cache file to determine whether the data has been copied to obs.
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#If this file exists during multi-card training, there is no need to copy the dataset multiple times.
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f = open("/cache/download_input.txt", 'w')
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f.close()
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try:
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if os.path.exists("/cache/download_input.txt"):
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print("download_input succeed")
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except Exception as e:
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print("download_input failed")
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return
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### Copy the output to obs###
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def EnvToObs(train_dir, obs_train_url):
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try:
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mox.file.copy_parallel(train_dir, obs_train_url)
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print("Successfully Upload {} to {}".format(train_dir,obs_train_url))
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except Exception as e:
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print('moxing upload {} to {} failed: '.format(train_dir,obs_train_url) + str(e))
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return
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def EnvUrlToObs(ckpt_url, obs_ckpt_url):
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try:
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mox.file.copy(ckpt_url, obs_ckpt_url)
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print("Successfully Download {} to {}".format(ckpt_url,obs_ckpt_url))
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except Exception as e:
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print('moxing download {} to {} failed: '.format(ckpt_url, obs_ckpt_url) + str(e))
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return
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def DownloadFromQizhi(obs_data_url, data_dir):
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device_num = int(os.getenv('RANK_SIZE'))
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if device_num == 1:
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ObsToEnv(obs_data_url,data_dir)
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context.set_context(mode=context.GRAPH_MODE,device_target=args.device_target)
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if device_num > 1:
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# set device_id and init for multi-card training
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=int(os.getenv('ASCEND_DEVICE_ID')))
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(device_num = device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, parameter_broadcast=True)
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init()
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#Copying obs data does not need to be executed multiple times, just let the 0th card copy the data
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local_rank=int(os.getenv('RANK_ID'))
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if local_rank%8==0:
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ObsToEnv(obs_data_url,data_dir)
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#If the cache file does not exist, it means that the copy data has not been completed,
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#and Wait for 0th card to finish copying data
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while not os.path.exists("/cache/download_input.txt"):
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time.sleep(1)
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return
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def UploadToQizhi(train_dir, obs_train_url):
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device_num = int(os.getenv('RANK_SIZE'))
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local_rank=int(os.getenv('RANK_ID'))
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if device_num == 1:
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EnvToObs(train_dir, obs_train_url)
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if device_num > 1:
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if local_rank%8==0:
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EnvToObs(train_dir, obs_train_url)
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return
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### --data_url,--train_url,--device_target,These 3 parameters must be defined first in a single dataset,
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### otherwise an error will be reported.
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###There is no need to add these parameters to the running parameters of the Qizhi platform,
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###because they are predefined in the background, you only need to define them in your code.
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parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
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parser.add_argument('--data_url',
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help='path to training/inference dataset folder',
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default= '/cache/data/')
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parser.add_argument('--train_url',
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help='output folder to save/load',
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default= '/cache/output/')
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parser.add_argument(
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'--device_target',
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type=str,
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default="Ascend",
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choices=['Ascend', 'CPU'],
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help='device where the code will be implemented (default: Ascend),if to use the CPU on the Qizhi platform:device_target=CPU')
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parser.add_argument('--epoch_size',
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type=int,
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default=5,
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help='Training epochs.')
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### continue task parameters
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parser.add_argument('--ckpt_load_name',
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help='model name to save/load',
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default= '')
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parser.add_argument('--ckpt_save_name',
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help='model name to save/load',
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default= 'checkpoint')
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if __name__ == "__main__":
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args, unknown = parser.parse_known_args()
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data_dir = '/cache/data'
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base_path = '/cache/output'
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try:
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if not os.path.exists(data_dir):
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os.makedirs(data_dir)
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if not os.path.exists(base_path):
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os.makedirs(base_path)
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except Exception as e:
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print("path already exists")
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###Initialize and copy data to training image
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###Copy data from obs to training image
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DownloadFromQizhi(args.data_url, data_dir)
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###The dataset path is used here:data_dir +"/train"
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device_num = int(os.getenv('RANK_SIZE'))
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if device_num == 1:
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ds_train = create_dataset(os.path.join(data_dir, "train"), cfg.batch_size)
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if device_num > 1:
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ds_train = create_dataset_parallel(os.path.join(data_dir, "train"), cfg.batch_size)
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if ds_train.get_dataset_size() == 0:
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raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
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network = LeNet5(cfg.num_classes)
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net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
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time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
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### 继续训练模型加载
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if args.ckpt_load_name:
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ObsToEnv(args.train_url, base_path)
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load_path = "{}/{}.ckpt".format(base_path,args.ckpt_load_name)
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param_dict = load_checkpoint(load_path)
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load_param_into_net(network, param_dict)
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print("Successfully load ckpt file:{}, saved_net_work:{}".format(load_path,param_dict))
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### 保存已有模型名避免重复回传结果
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outputFiles = os.listdir(base_path)
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if args.device_target != "Ascend":
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model = Model(network,
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net_loss,
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net_opt,
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metrics={"accuracy": Accuracy()})
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else:
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model = Model(network,
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net_loss,
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net_opt,
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metrics={"accuracy": Accuracy()},
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amp_level="O2")
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config_ck = CheckpointConfig(
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save_checkpoint_steps=cfg.save_checkpoint_steps,
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keep_checkpoint_max=cfg.keep_checkpoint_max)
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#Note that this method saves the model file on each card. You need to specify the save path on each card.
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# In this example, get_rank() is added to distinguish different paths.
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if device_num == 1:
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save_path = base_path + "/"
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if device_num > 1:
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save_path = base_path + "/" + str(get_rank()) + "/"
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ckpoint_cb = ModelCheckpoint(prefix=args.ckpt_save_name,
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directory=save_path,
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config=config_ck)
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print("============== Starting Training ==============")
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epoch_size = cfg['epoch_size']
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if (args.epoch_size):
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epoch_size = args.epoch_size
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print('epoch_size is: ', epoch_size)
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model.train(epoch_size,
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ds_train,
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callbacks=[time_cb, ckpoint_cb,
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LossMonitor()])
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### 将训练容器中的新输出模型 回传到启智社区
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outputFilesNew = os.listdir(base_path)
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new_models = [i for i in outputFilesNew if i not in outputFiles]
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for n in new_models:
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ckpt_url = base_path + "/" + n
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obs_ckpt_url = args.train_url + "/" + n
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EnvUrlToObs(ckpt_url, obs_ckpt_url) |