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238 lines
9.8 KiB
238 lines
9.8 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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#################train vgg16 example on cifar10########################
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python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
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"""
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import argparse
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import datetime
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import os
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import random
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.communication.management import init, get_rank, get_group_size
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
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from mindspore.train.model import Model
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from mindspore.context import ParallelMode
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from mindspore.train.serialization import load_param_into_net, load_checkpoint
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from src.dataset import vgg_create_dataset
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from src.dataset import classification_dataset
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from src.crossentropy import CrossEntropy
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from src.warmup_step_lr import warmup_step_lr
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from src.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
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from src.warmup_step_lr import lr_steps
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from src.utils.logging import get_logger
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from src.utils.util import get_param_groups
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from src.vgg import vgg16
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random.seed(1)
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np.random.seed(1)
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def parse_args(cloud_args=None):
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"""parameters"""
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parser = argparse.ArgumentParser('mindspore classification training')
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parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
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help='device where the code will be implemented. (Default: Ascend)')
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parser.add_argument('--device_id', type=int, default=1, help='device id of GPU or Ascend. (Default: None)')
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# dataset related
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parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
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parser.add_argument('--data_path', type=str, default='', help='train data dir')
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# network related
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parser.add_argument('--pre_trained', default='', type=str, help='model_path, local pretrained model to load')
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parser.add_argument('--lr_gamma', type=float, default=0.1,
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help='decrease lr by a factor of exponential lr_scheduler')
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parser.add_argument('--eta_min', type=float, default=0., help='eta_min in cosine_annealing scheduler')
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parser.add_argument('--T_max', type=int, default=150, help='T-max in cosine_annealing scheduler')
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# logging and checkpoint related
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parser.add_argument('--log_interval', type=int, default=100, help='logging interval')
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parser.add_argument('--ckpt_path', type=str, default='outputs/', help='checkpoint save location')
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parser.add_argument('--ckpt_interval', type=int, default=5, help='ckpt_interval')
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parser.add_argument('--is_save_on_master', type=int, default=1, help='save ckpt on master or all rank')
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# distributed related
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parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
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parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
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parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
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args_opt = parser.parse_args()
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args_opt = merge_args(args_opt, cloud_args)
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if args_opt.dataset == "cifar10":
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from src.config import cifar_cfg as cfg
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else:
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from src.config import imagenet_cfg as cfg
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args_opt.label_smooth = cfg.label_smooth
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args_opt.label_smooth_factor = cfg.label_smooth_factor
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args_opt.lr_scheduler = cfg.lr_scheduler
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args_opt.loss_scale = cfg.loss_scale
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args_opt.max_epoch = cfg.max_epoch
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args_opt.warmup_epochs = cfg.warmup_epochs
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args_opt.lr = cfg.lr
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args_opt.lr_init = cfg.lr_init
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args_opt.lr_max = cfg.lr_max
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args_opt.momentum = cfg.momentum
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args_opt.weight_decay = cfg.weight_decay
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args_opt.per_batch_size = cfg.batch_size
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args_opt.num_classes = cfg.num_classes
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args_opt.buffer_size = cfg.buffer_size
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args_opt.ckpt_save_max = cfg.keep_checkpoint_max
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args_opt.pad_mode = cfg.pad_mode
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args_opt.padding = cfg.padding
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args_opt.has_bias = cfg.has_bias
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args_opt.batch_norm = cfg.batch_norm
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args_opt.initialize_mode = cfg.initialize_mode
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args_opt.has_dropout = cfg.has_dropout
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args_opt.lr_epochs = list(map(int, cfg.lr_epochs.split(',')))
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args_opt.image_size = list(map(int, cfg.image_size.split(',')))
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return args_opt
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def merge_args(args_opt, cloud_args):
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"""dictionary"""
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args_dict = vars(args_opt)
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if isinstance(cloud_args, dict):
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for key_arg in cloud_args.keys():
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val = cloud_args[key_arg]
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if key_arg in args_dict and val:
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arg_type = type(args_dict[key_arg])
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if arg_type is not None:
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val = arg_type(val)
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args_dict[key_arg] = val
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return args_opt
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if __name__ == '__main__':
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args = parse_args()
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device_num = int(os.environ.get("DEVICE_NUM", 1))
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if args.is_distributed:
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if args.device_target == "Ascend":
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init()
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context.set_context(device_id=args.device_id)
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elif args.device_target == "GPU":
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init()
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args.rank = get_rank()
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args.group_size = get_group_size()
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device_num = args.group_size
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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,
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parameter_broadcast=True, mirror_mean=True)
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else:
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context.set_context(device_id=args.device_id)
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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# select for master rank save ckpt or all rank save, compatible for model parallel
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args.rank_save_ckpt_flag = 0
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if args.is_save_on_master:
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if args.rank == 0:
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args.rank_save_ckpt_flag = 1
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else:
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args.rank_save_ckpt_flag = 1
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# logger
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args.outputs_dir = os.path.join(args.ckpt_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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args.logger = get_logger(args.outputs_dir, args.rank)
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if args.dataset == "cifar10":
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dataset = vgg_create_dataset(args.data_path, args.image_size, args.per_batch_size, args.rank, args.group_size)
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else:
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dataset = classification_dataset(args.data_path, args.image_size, args.per_batch_size,
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args.rank, args.group_size)
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batch_num = dataset.get_dataset_size()
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args.steps_per_epoch = dataset.get_dataset_size()
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args.logger.save_args(args)
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# network
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args.logger.important_info('start create network')
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# get network and init
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network = vgg16(args.num_classes, args)
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# pre_trained
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if args.pre_trained:
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load_param_into_net(network, load_checkpoint(args.pre_trained))
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# lr scheduler
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if args.lr_scheduler == 'exponential':
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lr = warmup_step_lr(args.lr,
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args.lr_epochs,
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args.steps_per_epoch,
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args.warmup_epochs,
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args.max_epoch,
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gamma=args.lr_gamma,
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)
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elif args.lr_scheduler == 'cosine_annealing':
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lr = warmup_cosine_annealing_lr(args.lr,
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args.steps_per_epoch,
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args.warmup_epochs,
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args.max_epoch,
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args.T_max,
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args.eta_min)
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elif args.lr_scheduler == 'step':
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lr = lr_steps(0, lr_init=args.lr_init, lr_max=args.lr_max, warmup_epochs=args.warmup_epochs,
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total_epochs=args.max_epoch, steps_per_epoch=batch_num)
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else:
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raise NotImplementedError(args.lr_scheduler)
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# optimizer
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opt = Momentum(params=get_param_groups(network),
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learning_rate=Tensor(lr),
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momentum=args.momentum,
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weight_decay=args.weight_decay,
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loss_scale=args.loss_scale)
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if args.dataset == "cifar10":
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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model = Model(network, loss_fn=loss, optimizer=opt, metrics={'acc'},
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amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
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else:
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if not args.label_smooth:
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args.label_smooth_factor = 0.0
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loss = CrossEntropy(smooth_factor=args.label_smooth_factor, num_classes=args.num_classes)
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loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
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model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager, amp_level="O2")
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# define callbacks
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time_cb = TimeMonitor(data_size=batch_num)
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loss_cb = LossMonitor(per_print_times=batch_num)
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callbacks = [time_cb, loss_cb]
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if args.rank_save_ckpt_flag:
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ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval * args.steps_per_epoch,
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keep_checkpoint_max=args.ckpt_save_max)
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ckpt_cb = ModelCheckpoint(config=ckpt_config,
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directory=args.outputs_dir,
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prefix='{}'.format(args.rank))
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callbacks.append(ckpt_cb)
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model.train(args.max_epoch, dataset, callbacks=callbacks)
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