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""" |
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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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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称 |
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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，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 |
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若不为空，则使用以下路径读取： train_dir + "/" + args.ckpt_load_name + ".ckpt" |
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""" |
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import os |
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import argparse |
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import json |
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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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from upload import UploadOutput |
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### Copy multiple datasets from obs to training image and unzip### |
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def C2netMultiObsToEnv(multi_data_url, data_dir): |
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#--multi_data_url is json data, need to do json parsing for multi_data_url |
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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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zipfile_path = data_dir + "/" + multi_data_json[i]["dataset_name"] |
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try: |
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mox.file.copy(multi_data_json[i]["dataset_url"], zipfile_path) |
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print("Successfully Download {} to {}".format(multi_data_json[i]["dataset_url"],zipfile_path)) |
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#get filename and unzip the dataset |
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filename = os.path.splitext(multi_data_json[i]["dataset_name"])[0] |
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filePath = data_dir + "/" + filename |
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if not os.path.exists(filePath): |
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os.makedirs(filePath) |
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os.system("unzip {} -d {}".format(zipfile_path, filePath)) |
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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"], zipfile_path) + 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 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 ckpt file from obs to training image### |
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### To operate on folders, use mox.file.copy_parallel. If copying a file. |
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### Please use mox.file.copy to operate the file, this operation is to operate the file |
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def ObsUrlToEnv(obs_ckpt_url, ckpt_url): |
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try: |
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mox.file.copy(obs_ckpt_url, ckpt_url) |
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print("Successfully Download {} to {}".format(obs_ckpt_url,ckpt_url)) |
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except Exception as e: |
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print('moxing download {} to {} failed: '.format(obs_ckpt_url, ckpt_url) + str(e)) |
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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 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('--multi_data_url', |
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help='path to multi dataset', |
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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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train_dir = '/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(train_dir): |
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os.makedirs(train_dir) |
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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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### Continue task: re-use the results of previous task |
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### Copy the result of previous task in obs output folder |
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if args.ckpt_load_name != '': |
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ObsToEnv(args.train_url, train_dir) |
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load_param_into_net(network, load_checkpoint(train_dir + "/" + args.ckpt_load_name + ".ckpt")) |
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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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outputDirectory = train_dir + "/" |
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if device_num > 1: |
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outputDirectory = train_dir + "/" + str(get_rank()) + "/" |
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ckpoint_cb = ModelCheckpoint(prefix=args.ckpt_save_name, |
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directory=outputDirectory, |
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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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#Custom callback, upload output after each epoch |
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uploadOutput = UploadOutput(train_dir,args.train_url) |
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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(), uploadOutput]) |
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###Copy the trained output 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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#This step is not required if UploadOutput is called |
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UploadToQizhi(train_dir,args.train_url) |
