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train.py
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import argparse
import logging
import os
import time
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch.nn.parallel.distributed import DistributedDataParallel
import torch.utils.data.distributed
from torch.nn.utils import clip_grad_norm_
from torch.nn import CrossEntropyLoss
from utils import losses
from config.config import config as cfg
from utils.dataset import MXFaceDataset, DataLoaderX
from utils.utils_callbacks import CallBackVerification, CallBackLogging, CallBackModelCheckpoint
from utils.utils_logging import AverageMeter, init_logging
from backbones.iresnet import iresnet100, iresnet50
torch.backends.cudnn.benchmark = True
def main(args):
dist.init_process_group(backend='nccl', init_method='env://')
local_rank = args.local_rank
torch.cuda.set_device(local_rank)
rank = dist.get_rank()
world_size = dist.get_world_size()
if not os.path.exists(cfg.output) and rank == 0:
os.makedirs(cfg.output)
else:
time.sleep(2)
log_root = logging.getLogger()
init_logging(log_root, rank, cfg.output)
trainset = MXFaceDataset(root_dir=cfg.rec, local_rank=local_rank)
train_sampler = torch.utils.data.distributed.DistributedSampler(
trainset, shuffle=True)
train_loader = DataLoaderX(
local_rank=local_rank, dataset=trainset, batch_size=cfg.batch_size,
sampler=train_sampler, num_workers=0, pin_memory=True, drop_last=True)
# load model
if cfg.network == "iresnet100":
backbone = iresnet100(num_features=cfg.embedding_size, use_se=cfg.SE).to(local_rank)
elif cfg.network == "iresnet50":
backbone = iresnet50(dropout=0.4,num_features=cfg.embedding_size, use_se=cfg.SE).to(local_rank)
else:
backbone = None
logging.info("load backbone failed!")
exit()
if args.resume:
try:
backbone_pth = os.path.join(cfg.output, str(cfg.global_step) + "backbone.pth")
backbone.load_state_dict(torch.load(backbone_pth, map_location=torch.device(local_rank)))
if rank == 0:
logging.info("backbone resume loaded successfully!")
except (FileNotFoundError, KeyError, IndexError, RuntimeError):
logging.info("load backbone resume init, failed!")
for ps in backbone.parameters():
dist.broadcast(ps, 0)
backbone = DistributedDataParallel(
module=backbone, broadcast_buffers=False, device_ids=[local_rank])
backbone.train()
# get header
if cfg.loss == "ElasticArcFace":
header = losses.ElasticArcFace(in_features=cfg.embedding_size, out_features=cfg.num_classes, s=cfg.s, m=cfg.m,std=cfg.std).to(local_rank)
elif cfg.loss == "ElasticArcFacePlus":
header = losses.ElasticArcFace(in_features=cfg.embedding_size, out_features=cfg.num_classes, s=cfg.s, m=cfg.m,
std=cfg.std, plus=True).to(local_rank)
elif cfg.loss == "ElasticCosFace":
header = losses.ElasticCosFace(in_features=cfg.embedding_size, out_features=cfg.num_classes, s=cfg.s, m=cfg.m,std=cfg.std).to(local_rank)
elif cfg.loss == "ElasticCosFacePlus":
header = losses.ElasticCosFace(in_features=cfg.embedding_size, out_features=cfg.num_classes, s=cfg.s, m=cfg.m,
std=cfg.std, plus=True).to(local_rank)
elif cfg.loss == "ArcFace":
header = losses.ArcFace(in_features=cfg.embedding_size, out_features=cfg.num_classes, s=cfg.s, m=cfg.m).to(local_rank)
elif cfg.loss == "CosFace":
header = losses.CosFace(in_features=cfg.embedding_size, out_features=cfg.num_classes, s=cfg.s, m=cfg.m).to(
local_rank)
else:
print("Header not implemented")
if args.resume:
try:
header_pth = os.path.join(cfg.output, str(cfg.global_step) + "header.pth")
header.load_state_dict(torch.load(header_pth, map_location=torch.device(local_rank)))
if rank == 0:
logging.info("header resume loaded successfully!")
except (FileNotFoundError, KeyError, IndexError, RuntimeError):
logging.info("header resume init, failed!")
header = DistributedDataParallel(
module=header, broadcast_buffers=False, device_ids=[local_rank])
header.train()
opt_backbone = torch.optim.SGD(
params=[{'params': backbone.parameters()}],
lr=cfg.lr / 512 * cfg.batch_size * world_size,
momentum=0.9, weight_decay=cfg.weight_decay)
opt_header = torch.optim.SGD(
params=[{'params': header.parameters()}],
lr=cfg.lr / 512 * cfg.batch_size * world_size,
momentum=0.9, weight_decay=cfg.weight_decay)
scheduler_backbone = torch.optim.lr_scheduler.LambdaLR(
optimizer=opt_backbone, lr_lambda=cfg.lr_func)
scheduler_header = torch.optim.lr_scheduler.LambdaLR(
optimizer=opt_header, lr_lambda=cfg.lr_func)
criterion = CrossEntropyLoss()
start_epoch = 0
total_step = int(len(trainset) / cfg.batch_size / world_size * cfg.num_epoch)
if rank == 0: logging.info("Total Step is: %d" % total_step)
if args.resume:
rem_steps = (total_step - cfg.global_step)
cur_epoch = cfg.num_epoch - int(cfg.num_epoch / total_step * rem_steps)
logging.info("resume from estimated epoch {}".format(cur_epoch))
logging.info("remaining steps {}".format(rem_steps))
start_epoch = cur_epoch
scheduler_backbone.last_epoch = cur_epoch
scheduler_header.last_epoch = cur_epoch
# --------- this could be solved more elegant ----------------
opt_backbone.param_groups[0]['lr'] = scheduler_backbone.get_lr()[0]
opt_header.param_groups[0]['lr'] = scheduler_header.get_lr()[0]
print("last learning rate: {}".format(scheduler_header.get_lr()))
# ------------------------------------------------------------
callback_verification = CallBackVerification(cfg.eval_step, rank, cfg.val_targets, cfg.rec)
callback_logging = CallBackLogging(50, rank, total_step, cfg.batch_size, world_size, writer=None)
callback_checkpoint = CallBackModelCheckpoint(rank, cfg.output)
loss = AverageMeter()
global_step = cfg.global_step
for epoch in range(start_epoch, cfg.num_epoch):
train_sampler.set_epoch(epoch)
for _, (img, label) in enumerate(train_loader):
global_step += 1
img = img.cuda(local_rank, non_blocking=True)
label = label.cuda(local_rank, non_blocking=True)
features = F.normalize(backbone(img))
thetas = header(features, label)
loss_v = criterion(thetas, label)
loss_v.backward()
clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2)
opt_backbone.step()
opt_header.step()
opt_backbone.zero_grad()
opt_header.zero_grad()
loss.update(loss_v.item(), 1)
callback_logging(global_step, loss, epoch)
callback_verification(global_step, backbone)
scheduler_backbone.step()
scheduler_header.step()
callback_checkpoint(global_step, backbone, header)
dist.destroy_process_group()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='PyTorch margin penalty loss training')
parser.add_argument('--local_rank', type=int, default=0, help='local_rank')
parser.add_argument('--resume', type=int, default=0, help="resume training")
args_ = parser.parse_args()
main(args_)