250 lines
11 KiB
Python
250 lines
11 KiB
Python
"""
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######################## multi-dataset train lenet example ########################
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This example is a multi-dataset training tutorial. If it is a single dataset, please refer to the single dataset
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training tutorial train.py. This example cannot be used for a single dataset!
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"""
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"""
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######################## Instructions for using the training environment ########################
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1、(1)The structure of the dataset uploaded for multi-dataset training in this example
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MNISTData.zip
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├── test
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│ ├── t10k-images-idx3-ubyte
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│ └── t10k-labels-idx1-ubyte
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└── train
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├── train-images-idx3-ubyte
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└── train-labels-idx1-ubyte
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checkpoint_lenet-1_1875.zip
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├── checkpoint_lenet-1_1875.ckpt
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(2)The dataset structure in the training image for multiple datasets in this example
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workroot
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├── MNISTData
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| ├── test
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| └── train
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└── checkpoint_lenet-1_1875
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├── checkpoint_lenet-1_1875.ckpt
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2、Multi-dataset training requires predefined functions
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(1)Defines whether the task is a training environment or a debugging environment.
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def WorkEnvironment(environment):
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if environment == 'train':
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workroot = '/home/work/user-job-dir' #The training task uses this parameter to represent the local path of the training image
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elif environment == 'debug':
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workroot = '/home/ma-user/work' #The debug task uses this parameter to represent the local path of the debug image
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print('current work mode:' + environment + ', workroot:' + workroot)
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return workroot
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(2)Copy multiple datasets from obs to training image
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def MultiObsToEnv(multi_data_url, workroot):
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multi_data_json = json.loads(multi_data_url) #Parse multi_data_url
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for i in range(len(multi_data_json)):
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path = workroot + "/" + multi_data_json[i]["dataset_name"]
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if not os.path.exists(path):
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os.makedirs(path)
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try:
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mox.file.copy_parallel(multi_data_json[i]["dataset_url"], path)
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print("Successfully Download {} to {}".format(multi_data_json[i]["dataset_url"],
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path))
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except Exception as e:
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print('moxing download {} to {} failed: '.format(
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multi_data_json[i]["dataset_url"], path) + str(e))
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return
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***The input and output of the MultiObsToEnv function in this example：
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Input for multi_data_url：
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[
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{
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"dataset_url": "s3://test-opendata/attachment/e/a/eae3a316-42d6-4a43-a484-1fa573eab388e
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ae3a316-42d6-4a43-a484-1fa573eab388/", #obs path of the dataset
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"dataset_name": "MNIST_Data" #the name of the dataset
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},
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{
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"dataset_url": "s3://test-opendata/attachment/2/c/2c59be66-64ec-41ca-b311-f51a486eabf82c
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59be66-64ec-41ca-b311-f51a486eabf8/",
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"dataset_name": "checkpoint_lenet-1_1875"
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}
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]
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Purpose of multi_data_url:
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The purpose of the MultiObsToEnv function is to copy multiple datasets from obs to the training image
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and build the dataset path in the training image.
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For example, the path of the MNIST_Data dataset in this example is /home/work/user-job-dir/MNISTData,
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The path to the checkpoint_lenet-1_1875 dataset is /home/work/user-job-dir/checkpoint_lenet-1_1875
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(3)Copy the output model 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,
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obs_train_url))
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except Exception as e:
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print('moxing upload {} to {} failed: '.format(train_dir,
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obs_train_url) + str(e))
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return
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3、4 parameters need to be defined
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--data_url is the first dataset you selected on the Qizhi platform
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--multi_data_url is the multi-dataset you selected on the Qizhi platform
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--data_url,--multi_data_url,--train_url,--device_target,These 4 parameters must be defined first in a multi-dataset task,
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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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4、How the dataset is used
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Multi-datasets use multi_data_url as input, workroot + dataset name + file or folder name in the dataset as the
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calling path of the dataset in the training image.
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For example, the calling path of the train folder in the MNIST_Data dataset in this example is
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workroot + "/MNIST_Data" +"/train"
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For details, please refer to the following sample code.
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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_distributed import create_dataset_parallel
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from dataset import create_dataset
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from lenet import LeNet5
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import json
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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.train import Model
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from mindspore.nn.metrics import Accuracy
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from mindspore.common import set_seed
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from mindspore import load_checkpoint, load_param_into_net
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from mindspore.context import ParallelMode
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from mindspore.communication.management import init, get_rank, get_group_size
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import mindspore.ops as ops
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# set device_id and init
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device_id = int(os.getenv('ASCEND_DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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context.set_context(device_id=device_id)
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init()
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### Defines whether the task is a training environment or a debugging environment ###
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def WorkEnvironment(environment):
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if environment == 'train':
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workroot = '/home/work/user-job-dir'
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elif environment == 'debug':
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workroot = '/home/ma-user/work'
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print('current work mode:' + environment + ', workroot:' + workroot)
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return workroot
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### Copy multiple datasets from obs to training image ###
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def MultiObsToEnv(multi_data_url, workroot):
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multi_data_json = json.loads(multi_data_url)
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for i in range(len(multi_data_json)):
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path = workroot + "/" + multi_data_json[i]["dataset_name"]
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if not os.path.exists(path):
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os.makedirs(path)
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try:
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mox.file.copy_parallel(multi_data_json[i]["dataset_url"], path)
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print("Successfully Download {} to {}".format(multi_data_json[i]["dataset_url"],
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path))
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except Exception as e:
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print('moxing download {} to {} failed: '.format(
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multi_data_json[i]["dataset_url"], path) + str(e))
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return
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### Copy the output model 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,
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obs_train_url))
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except Exception as e:
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print('moxing upload {} to {} failed: '.format(train_dir,
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obs_train_url) + str(e))
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return
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parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
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### --data_url,--multi_data_url,--train_url,--device_target,These 4 parameters must be defined first in a multi-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.add_argument('--data_url',
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help='path to training/inference dataset folder',
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default= WorkEnvironment('train') + '/data/')
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parser.add_argument('--multi_data_url',
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help='path to multi dataset',
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default= WorkEnvironment('train'))
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parser.add_argument('--train_url',
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help='model folder to save/load',
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default= WorkEnvironment('train') + '/model/')
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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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set_seed(114514)
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if __name__ == "__main__":
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args = parser.parse_args()
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# After defining the training environment, first execute the WorkEnv function and the GetMultiDataPath function to
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# copy multiple datasets from obs to the training image
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environment = 'train'
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workroot = WorkEnvironment(environment)
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MultiObsToEnv(args.multi_data_url, workroot)
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### Define the output path in the training image
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train_dir = workroot + '/model'
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if not os.path.exists(train_dir):
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os.makedirs(train_dir)
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### Copy the dataset from obs to the training image ###
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)
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ds_train = create_dataset_parallel(os.path.join(workroot + "/MNISTData", "train"),
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cfg.batch_size)
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if ds_train.get_dataset_size() == 0:
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raise ValueError(
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"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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### Load the trained model:workroot + "/checkpoint_lenet-1_1875"+"/checkpoint_lenet-1_1875.ckpt"
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load_param_into_net(network, load_checkpoint(os.path.join(workroot + "/checkpoint_lenet-1_1875",
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"checkpoint_lenet-1_1875.ckpt")))
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if args.device_target != "Ascend":
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model = Model(network,net_loss,net_opt,metrics={"accuracy": Accuracy()})
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else:
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model = Model(network, net_loss,net_opt,metrics={"accuracy": Accuracy()},amp_level="O2")
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config_ck = CheckpointConfig(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 the example, get_rank() is added to distinguish different paths.
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ckpoint_cb = ModelCheckpoint(prefix="data_parallel",
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directory=train_dir + "/" + str(get_rank()) + "/",
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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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###Copy the trained model data from the local running environment back to obs,
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###and download it in the training task corresponding to the Qizhi platform
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EnvToObs(train_dir, args.train_url)
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