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train.py
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train.py
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from torch.utils.data import DataLoader
import os
import builtins
import argparse
import torch
import torch.distributed as dist
import time
import FSPNet_model
import dataset
import loss
def parse_args():
parser = argparse.ArgumentParser("FSPNet-Transformer")
parser.add_argument('--base_lr', default=(1e-4), type=float, help='learning rate')
parser.add_argument('--batch_size_per_gpu', default=2, type=int, help='batch size per GPU')
parser.add_argument("--resume", default=None)
parser.add_argument('--gpu', default=None, type=int)
parser.add_argument('--path', type=str, help='path to train dataset')
parser.add_argument('--pretrain', type=str, help='path to pretrain model')
parser.add_argument('--ft_for_MoCA', default=None, type=str, help='path to pretrain model')
# DDP configs:
parser.add_argument('--world-size', default=-1, type=int,
help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
help='node rank for distributed training')
parser.add_argument('--dist-url', default='env:https://', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
help='distributed backend')
parser.add_argument('--local_rank', default=-1, type=int,
help='local rank for distributed training')
args = parser.parse_args()
return args
def main(args):
# DDP setting
if "WORLD_SIZE" in os.environ:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1
ngpus_per_node = torch.cuda.device_count()
if args.distributed:
if args.local_rank != -1: # for torch.distributed.launch
args.rank = args.local_rank
args.gpu = args.local_rank
elif 'SLURM_PROCID' in os.environ: # for slurm scheduler
args.rank = int(os.environ['SLURM_PROCID'])
args.gpu = args.rank % torch.cuda.device_count()
print("args.rank = {}; args.gpu = {}".format(args.rank, args.gpu))
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
# suppress printing if not on master gpu
if args.rank!=0:
def print_pass(*args):
pass
builtins.print = print_pass
### model ###
net = FSPNet_model.Model(args.pretrain, img_size=384)
net = torch.nn.SyncBatchNorm.convert_sync_batchnorm(net)
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
os.system("nvidia-smi")
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
net.cuda(args.gpu)
net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[args.gpu])
model_without_ddp = net.module
else:
net.cuda()
net = torch.nn.parallel.DistributedDataParallel(net)
model_without_ddp = net.module
else:
raise NotImplementedError("Only DistributedDataParallel is supported.")
### optimizer ###
# optimizer = torch.optim.Adam(model.parameters(), lr=args.base_lr)
encoder_param=[]
decoer_param=[]
for name, param in net.named_parameters():
if "encoder" in name:
encoder_param.append(param)
else:
decoer_param.append(param)
# optimizer = torch.optim.SGD([{"params": encoder_param, "lr":args.base_lr*0.1},{"params":decoer_param, "lr":args.base_lr}], momentum=0.9, weight_decay=1e-5)
optimizer = torch.optim.Adam([{"params": encoder_param, "lr":args.base_lr*0.1},{"params":decoer_param, "lr":args.base_lr}])
### resume training if necessary ###
if args.resume is not None:
ckpt = torch.load(args.resume, map_location='cpu')
net.load_state_dict(ckpt['model'])
optimizer.load_state_dict(ckpt['optimizer'])
### Fine tuning for MoCA ###
if args.ft_for_MoCA is not None:
ckpt = torch.load(args.ft_for_MoCA, map_location='cpu')
net.load_state_dict(ckpt)
print("Fine tuning for MoCA, ckpt from: {}".format(args.ft_for_MoCA))
### data ###
Dir = [args.path]
Dataset = dataset.TrainDataset(Dir)
Datasampler = torch.utils.data.distributed.DistributedSampler(Dataset, shuffle=True)
Dataloader = DataLoader(Dataset, batch_size=args.batch_size_per_gpu, num_workers=args.batch_size_per_gpu, collate_fn=dataset.my_collate_fn, sampler=Datasampler, drop_last=True)
# torch.backends.cudnn.benchmark = True
### main loop ###
star_time=time.time()
for curr_epoch in range(0, 201):
if curr_epoch==100 or curr_epoch==150:
for param_group in optimizer.param_groups:
param_group['lr']= param_group['lr']*0.1
print("Learning rate:", param_group['lr'])
Datasampler.set_epoch(curr_epoch)
net.train()
running_loss_all, running_loss_m = 0., 0.
count = 0
for data in Dataloader:
count += 1
img, label = data['img'].cuda(args.rank), data['label'].cuda(args.rank)
out = net(img)
all_loss, m_loss = loss.multi_bce(out, label)
optimizer.zero_grad()
all_loss.backward()
optimizer.step()
running_loss_all += all_loss.item()
running_loss_m += m_loss.item()
if count % 20 == 0 and args.rank == 0:
print("Epoch:{}, Iter:{}, all_loss:{:.5f}, main_loss:{:.5f}".format(curr_epoch, count, running_loss_all / count, running_loss_m / count))
if args.rank == 0 and curr_epoch % 2 == 0:
ckpt_save_root = "/path_to_ckpt_save_root/ckpt_save"
if not os.path.exists(ckpt_save_root):
os.mkdir(ckpt_save_root)
torch.save(net.state_dict(),
ckpt_save_root+"/model_{}_loss_{:.5f}.pth".format(curr_epoch, running_loss_m / count)
)
if __name__ == '__main__':
args = parse_args()
main(args)