You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
295 lines
11 KiB
295 lines
11 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
"""train ImageNet."""
|
|
import os
|
|
import time
|
|
import argparse
|
|
import datetime
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor, context
|
|
from mindspore import ParallelMode
|
|
from mindspore.nn.optim import Momentum
|
|
from mindspore.communication.management import init, get_rank, get_group_size
|
|
from mindspore.train.callback import ModelCheckpoint
|
|
from mindspore.train.callback import CheckpointConfig, Callback
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
|
from mindspore.train.model import Model
|
|
from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager
|
|
|
|
from src.dataset import classification_dataset
|
|
from src.crossentropy import CrossEntropy
|
|
from src.warmup_step_lr import warmup_step_lr
|
|
from src.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
|
|
from src.utils.logging import get_logger
|
|
from src.utils.optimizers__init__ import get_param_groups
|
|
from src.image_classification import get_network
|
|
from src.utils.auto_mixed_precision import auto_mixed_precision
|
|
from src.config import config
|
|
|
|
|
|
class BuildTrainNetwork(nn.Cell):
|
|
"""build training network"""
|
|
def __init__(self, network, criterion):
|
|
super(BuildTrainNetwork, self).__init__()
|
|
self.network = network
|
|
self.criterion = criterion
|
|
|
|
def construct(self, input_data, label):
|
|
output = self.network(input_data)
|
|
loss = self.criterion(output, label)
|
|
return loss
|
|
|
|
class ProgressMonitor(Callback):
|
|
"""monitor loss and time"""
|
|
def __init__(self, args):
|
|
super(ProgressMonitor, self).__init__()
|
|
self.me_epoch_start_time = 0
|
|
self.me_epoch_start_step_num = 0
|
|
self.args = args
|
|
self.ckpt_history = []
|
|
|
|
def begin(self, run_context):
|
|
self.args.logger.info('start network train...')
|
|
|
|
def epoch_begin(self, run_context):
|
|
pass
|
|
|
|
def epoch_end(self, run_context, *me_args):
|
|
cb_params = run_context.original_args()
|
|
me_step = cb_params.cur_step_num - 1
|
|
|
|
real_epoch = me_step // self.args.steps_per_epoch
|
|
time_used = time.time() - self.me_epoch_start_time
|
|
fps_mean = self.args.per_batch_size * (me_step-self.me_epoch_start_step_num) * self.args.group_size / time_used
|
|
self.args.logger.info('epoch[{}], iter[{}], loss:{}, mean_fps:{:.2f}'
|
|
'imgs/sec'.format(real_epoch, me_step, cb_params.net_outputs, fps_mean))
|
|
|
|
if self.args.rank_save_ckpt_flag:
|
|
import glob
|
|
ckpts = glob.glob(os.path.join(self.args.outputs_dir, '*.ckpt'))
|
|
for ckpt in ckpts:
|
|
ckpt_fn = os.path.basename(ckpt)
|
|
if not ckpt_fn.startswith('{}-'.format(self.args.rank)):
|
|
continue
|
|
if ckpt in self.ckpt_history:
|
|
continue
|
|
self.ckpt_history.append(ckpt)
|
|
self.args.logger.info('epoch[{}], iter[{}], loss:{}, ckpt:{},'
|
|
'ckpt_fn:{}'.format(real_epoch, me_step, cb_params.net_outputs, ckpt, ckpt_fn))
|
|
|
|
|
|
self.me_epoch_start_step_num = me_step
|
|
self.me_epoch_start_time = time.time()
|
|
|
|
def step_begin(self, run_context):
|
|
pass
|
|
|
|
def step_end(self, run_context, *me_args):
|
|
pass
|
|
|
|
def end(self, run_context):
|
|
self.args.logger.info('end network train...')
|
|
|
|
|
|
def parse_args(cloud_args=None):
|
|
"""parameters"""
|
|
parser = argparse.ArgumentParser('mindspore classification training')
|
|
parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='run platform')
|
|
|
|
# dataset related
|
|
parser.add_argument('--data_dir', type=str, default='', help='train data dir')
|
|
parser.add_argument('--per_batch_size', default=128, type=int, help='batch size for per gpu')
|
|
# network related
|
|
parser.add_argument('--pretrained', default='', type=str, help='model_path, local pretrained model to load')
|
|
|
|
# distributed related
|
|
parser.add_argument('--is_distributed', type=int, default=1, help='if multi device')
|
|
# roma obs
|
|
parser.add_argument('--train_url', type=str, default="", help='train url')
|
|
|
|
args, _ = parser.parse_known_args()
|
|
args = merge_args(args, cloud_args)
|
|
args.image_size = config.image_size
|
|
args.num_classes = config.num_classes
|
|
args.lr = config.lr
|
|
args.lr_scheduler = config.lr_scheduler
|
|
args.lr_epochs = config.lr_epochs
|
|
args.lr_gamma = config.lr_gamma
|
|
args.eta_min = config.eta_min
|
|
args.T_max = config.T_max
|
|
args.max_epoch = config.max_epoch
|
|
args.backbone = config.backbone
|
|
args.warmup_epochs = config.warmup_epochs
|
|
args.weight_decay = config.weight_decay
|
|
args.momentum = config.momentum
|
|
args.is_dynamic_loss_scale = config.is_dynamic_loss_scale
|
|
args.loss_scale = config.loss_scale
|
|
args.label_smooth = config.label_smooth
|
|
args.label_smooth_factor = config.label_smooth_factor
|
|
args.ckpt_interval = config.ckpt_interval
|
|
args.ckpt_save_max = config.ckpt_save_max
|
|
args.ckpt_path = config.ckpt_path
|
|
args.is_save_on_master = config.is_save_on_master
|
|
args.rank = config.rank
|
|
args.group_size = config.group_size
|
|
args.lr_epochs = list(map(int, args.lr_epochs.split(',')))
|
|
args.image_size = list(map(int, args.image_size.split(',')))
|
|
|
|
return args
|
|
|
|
def merge_args(args, cloud_args):
|
|
"""dictionary"""
|
|
args_dict = vars(args)
|
|
if isinstance(cloud_args, dict):
|
|
for key in cloud_args.keys():
|
|
val = cloud_args[key]
|
|
if key in args_dict and val:
|
|
arg_type = type(args_dict[key])
|
|
if arg_type is not type(None):
|
|
val = arg_type(val)
|
|
args_dict[key] = val
|
|
return args
|
|
|
|
def train(cloud_args=None):
|
|
"""training process"""
|
|
args = parse_args(cloud_args)
|
|
context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
|
|
device_target=args.platform, save_graphs=False)
|
|
if os.getenv('DEVICE_ID', "not_set").isdigit():
|
|
context.set_context(device_id=int(os.getenv('DEVICE_ID')))
|
|
|
|
# init distributed
|
|
if args.is_distributed:
|
|
if args.platform == "Ascend":
|
|
init()
|
|
else:
|
|
init("nccl")
|
|
args.rank = get_rank()
|
|
args.group_size = get_group_size()
|
|
parallel_mode = ParallelMode.DATA_PARALLEL
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=args.group_size,
|
|
parameter_broadcast=True, mirror_mean=True)
|
|
else:
|
|
args.rank = 0
|
|
args.group_size = 1
|
|
|
|
if args.is_dynamic_loss_scale == 1:
|
|
args.loss_scale = 1 # for dynamic loss scale can not set loss scale in momentum opt
|
|
|
|
# select for master rank save ckpt or all rank save, compatiable for model parallel
|
|
args.rank_save_ckpt_flag = 0
|
|
if args.is_save_on_master:
|
|
if args.rank == 0:
|
|
args.rank_save_ckpt_flag = 1
|
|
else:
|
|
args.rank_save_ckpt_flag = 1
|
|
|
|
# logger
|
|
args.outputs_dir = os.path.join(args.ckpt_path,
|
|
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
|
|
args.logger = get_logger(args.outputs_dir, args.rank)
|
|
|
|
# dataloader
|
|
de_dataset = classification_dataset(args.data_dir, args.image_size,
|
|
args.per_batch_size, 1,
|
|
args.rank, args.group_size, num_parallel_workers=8)
|
|
de_dataset.map_model = 4 # !!!important
|
|
args.steps_per_epoch = de_dataset.get_dataset_size()
|
|
|
|
args.logger.save_args(args)
|
|
|
|
# network
|
|
args.logger.important_info('start create network')
|
|
# get network and init
|
|
network = get_network(args.backbone, args.num_classes, platform=args.platform)
|
|
if network is None:
|
|
raise NotImplementedError('not implement {}'.format(args.backbone))
|
|
|
|
# load pretrain model
|
|
if os.path.isfile(args.pretrained):
|
|
param_dict = load_checkpoint(args.pretrained)
|
|
param_dict_new = {}
|
|
for key, values in param_dict.items():
|
|
if key.startswith('moments.'):
|
|
continue
|
|
elif key.startswith('network.'):
|
|
param_dict_new[key[8:]] = values
|
|
else:
|
|
param_dict_new[key] = values
|
|
load_param_into_net(network, param_dict_new)
|
|
args.logger.info('load model {} success'.format(args.pretrained))
|
|
|
|
# lr scheduler
|
|
if args.lr_scheduler == 'exponential':
|
|
lr = warmup_step_lr(args.lr,
|
|
args.lr_epochs,
|
|
args.steps_per_epoch,
|
|
args.warmup_epochs,
|
|
args.max_epoch,
|
|
gamma=args.lr_gamma,
|
|
)
|
|
elif args.lr_scheduler == 'cosine_annealing':
|
|
lr = warmup_cosine_annealing_lr(args.lr,
|
|
args.steps_per_epoch,
|
|
args.warmup_epochs,
|
|
args.max_epoch,
|
|
args.T_max,
|
|
args.eta_min)
|
|
else:
|
|
raise NotImplementedError(args.lr_scheduler)
|
|
|
|
# optimizer
|
|
opt = Momentum(params=get_param_groups(network),
|
|
learning_rate=Tensor(lr),
|
|
momentum=args.momentum,
|
|
weight_decay=args.weight_decay,
|
|
loss_scale=args.loss_scale)
|
|
|
|
|
|
# loss
|
|
if not args.label_smooth:
|
|
args.label_smooth_factor = 0.0
|
|
loss = CrossEntropy(smooth_factor=args.label_smooth_factor, num_classes=args.num_classes)
|
|
|
|
if args.is_dynamic_loss_scale == 1:
|
|
loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536, scale_factor=2, scale_window=2000)
|
|
else:
|
|
loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
|
|
|
|
if args.platform == "Ascend":
|
|
model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager,
|
|
metrics={'acc'}, amp_level="O3")
|
|
else:
|
|
auto_mixed_precision(network)
|
|
model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager, metrics={'acc'})
|
|
|
|
# checkpoint save
|
|
progress_cb = ProgressMonitor(args)
|
|
callbacks = [progress_cb,]
|
|
if args.rank_save_ckpt_flag:
|
|
ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval * args.steps_per_epoch,
|
|
keep_checkpoint_max=args.ckpt_save_max)
|
|
ckpt_cb = ModelCheckpoint(config=ckpt_config,
|
|
directory=args.outputs_dir,
|
|
prefix='{}'.format(args.rank))
|
|
callbacks.append(ckpt_cb)
|
|
|
|
model.train(args.max_epoch, de_dataset, callbacks=callbacks, dataset_sink_mode=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
train()
|