tw/npu_new/train_for_multidataset.py

"""
######################## 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)

(2)Download the input from Qizhi And Init 
function DownloadFromQizhi(multi_data_url, data_dir)

(2)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 = parser.parse_args()
    data_dir = '/cache/data'  
    train_dir = '/cache/output'
    if not os.path.exists(data_dir):
        os.makedirs(data_dir)
    if not os.path.exists(train_dir):
        os.makedirs(train_dir)
    ###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)