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train.py
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train.py
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import os
import sys
import torch
import argparse
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import cv2
import numpy as np
import time
import logging
from tqdm import tqdm
from utils import accuracy, AvgrageMeter, LabelSmoothCrossEntropyLoss, save_checkpoint, sgd_optimizer, get_train_dataloader, get_val_dataloader
from networks.resnet import resnet50
from stat_util import StatisticsUtil
def get_args():
parser = argparse.ArgumentParser("ResNet-50 with MABN on Imagenet")
parser.add_argument('--batch_size', type=int, default=256, help='batch size')
parser.add_argument('--total_epoch', type=int, default=120, help='total epochs')
parser.add_argument('--learning_rate', type=float, default=0.1, help='init learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--weight_decay', type=float, default=1e-4, help='weight decay')
parser.add_argument('--save', type=str, default='./MABN', help='path for saving trained models')
parser.add_argument('--save_interval', type=int, default=2, help='save interval')
parser.add_argument('--train_dir', type=str, default='data/train', help='path to training dataset')
parser.add_argument('--val_dir', type=str, default='data/val', help='path to validation dataset')
parser.add_argument('--checkpoint_dir', type=str, default=None, help='path to checkpoint')
parser.add_argument('--test_only', action='store_true', help='if only test the trained model')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--gpu_num', type=int, default=8, help='number of gpus')
parser.add_argument('--record_statistics', action='store_true')
args = parser.parse_args()
return args
def main():
args = get_args()
use_gpu = False
if torch.cuda.is_available():
use_gpu = True
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env:https://')
if args.local_rank == 0:
log_format = '[%(asctime)s] %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%d %I:%M:%S')
t = time.time()
local_time = time.localtime(t)
if not os.path.exists('{}'.format(args.save)):
os.makedirs('{}'.format(args.save))
fh = logging.FileHandler(os.path.join('{}/log.train-{}-{}-{}-{}-{}-{}'.format(args.save, \
local_time.tm_year, local_time.tm_mon, local_time.tm_mday, \
local_time.tm_hour, local_time.tm_min, local_time.tm_sec)))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
logging.info(args)
if not args.test_only:
assert os.path.exists(args.train_dir)
args.train_dataloader = get_train_dataloader(args.train_dir, \
args.batch_size//args.gpu_num, args.total_epoch,args.local_rank)
assert os.path.exists(args.val_dir)
if args.local_rank == 0:
args.val_dataloader = get_val_dataloader(args.val_dir)
print('rank {:d}: load data successfully'.format(args.local_rank))
model = resnet50()
optimizer = sgd_optimizer(model, args.learning_rate, args.momentum, args.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
[30, 60, 90, 120], 0.1)
if args.checkpoint_dir is not None:
state_dict = torch.load(args.checkpoint_dir, map_location='cpu')
model.load_state_dict(state_dict['model'])
if 'optimizer' in state_dict.keys():
optimizer.load_state_dict(state_dict['optimizer'])
if 'scheduler' in state_dict.keys():
scheduler.load_state_dict(state_dict['scheduler'])
if 'iteration' in state_dict.keys():
start_epoch = state_dict['iteration']
else:
start_epoch = 0
args.loss_function = LabelSmoothCrossEntropyLoss().cuda()
device = torch.device("cuda")
model.to(device)
for name, param in model.named_parameters():
if 'momentum_buffer' in optimizer.state[param]:
optimizer.state[param]['momentum_buffer'] = optimizer.state[param]['momentum_buffer'].cuda()
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], \
output_device=args.local_rank, broadcast_buffers=False)
args.optimizer = optimizer
args.scheduler = scheduler
if not args.test_only:
train(model, device, args, start_epoch=start_epoch+1)
if args.local_rank == 0:
validate(model, device, args)
def train(model, device, args, start_epoch):
optimizer = args.optimizer
scheduler = args.scheduler
loss_function = args.loss_function
train_iterator = iter(args.train_dataloader)
model.train()
if args.record_statistics:
statistic_util = StatisticsUtil()
statistic_util.bind_bn_statistics(model, batch_size=args.batch_size)
for i in range(start_epoch, args.total_epoch+1):
Top1_err, Top5_err, Loss = 0.0, 0.0, 0.0
if args.local_rank == 0:
pbar = tqdm(range(5000))
for iteration in range(5000):
if args.record_statistics:
statistic_util.set_step(i * 5000 + iteration)
data, label = next(train_iterator)
data = data.cuda()
target = label.type(torch.long).cuda()
output = model(data)
loss = loss_function(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
prec1, prec5 = accuracy(output, target, topk=(1, 5))
Loss += loss.item()
Top1_err += 100 - prec1.item()
Top5_err += 100 - prec5.item()
if args.local_rank == 0:
pbar.update()
scheduler.step()
if args.local_rank == 0:
printInfo = 'TRAIN Epoch {}: lr = {:.6f},\tloss = {:.6f},\t'.format(i, scheduler.get_lr()[0], Loss/5000) + \
'Top-1 err = {:.6f},\t'.format(Top1_err/5000) + \
'Top-5 err = {:.6f},\t'.format(Top5_err/5000)
logging.info(printInfo)
if i % args.save_interval == 0 and args.local_rank == 0:
save_checkpoint(model, i, args.save, optimizer, scheduler)
def validate(model, device, args):
objs = AvgrageMeter()
top1 = AvgrageMeter()
top5 = AvgrageMeter()
loss_function = args.loss_function
val_dataloader = args.val_dataloader
L = len(val_dataloader)
model.eval()
with torch.no_grad():
data_iterator = enumerate(val_dataloader)
for _ in tqdm(range(250)):
_, data = next(data_iterator)
target = data[1].type(torch.LongTensor)
data, target = data[0].to(device), target.to(device)
output = model(data)
loss = loss_function(output, target)
prec1, prec5 = accuracy(output, target, topk=(1, 5))
n = data.size(0)
objs.update(loss.item())
top1.update(prec1.item())
top5.update(prec5.item())
if args.local_rank == 0:
logInfo = 'TEST: loss = {:.6f},\t'.format(objs.avg) + \
'Top-1 err = {:.6f},\t'.format(100 - top1.avg) + \
'Top-5 err = {:.6f},\t'.format(100 - top5.avg)
logging.info(logInfo)
if __name__ == "__main__":
main()