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train.py
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train.py
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import os
import time
import logging
import math
import argparse
import numpy as np
import torch
import random
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.utils.data as data
import warnings
from utils.util import setup_logger, print_args
from models import Trainer
def init_dist(backend='nccl', **kwargs):
"""initialization for distributed training"""
if mp.get_start_method(allow_none=True) != 'spawn':
mp.set_start_method('spawn')
rank = int(os.environ['RANK'])
num_gpus = torch.cuda.device_count()
torch.cuda.set_device(rank % num_gpus)
dist.init_process_group(backend=backend, **kwargs)
def set_random_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def main():
warnings.filterwarnings("ignore")
parser = argparse.ArgumentParser(description='Frame Interpolation Training')
parser.add_argument('--random_seed', default=0, type=int)
parser.add_argument('--name', default='train_vfiformer', type=str)
parser.add_argument('--phase', default='train', type=str)
## device setting
parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
## network setting
parser.add_argument('--net_name', default='VFIformer', type=str, help='RIFE')
parser.add_argument('--window_size', default=8, type=int)
## dataloader setting
parser.add_argument('--data_root', default='/home/sharedir/videos/vimeo_triplet/',type=str)
parser.add_argument('--trainset', default='VimeoDataset', type=str, help='VimeoDataset')
parser.add_argument('--testset', default='VimeoDataset', type=str, help='VimeoDataset')
parser.add_argument('--save_test_root', default='generated', type=str)
parser.add_argument('--crop_size', default=192, type=int)
parser.add_argument('--batch_size', default=9, type=int)
parser.add_argument('--num_workers', default=4, type=int)
parser.add_argument('--multi_scale', action='store_true')
parser.add_argument('--data_augmentation', action='store_true')
## optim setting
parser.add_argument('--lr', default=1e-4, type=float)
parser.add_argument('--lr_D', default=1e-4, type=float)
parser.add_argument('--weight_decay', default=1e-4, type=float)
parser.add_argument('--start_iter', default=0, type=int)
parser.add_argument('--max_iter', default=300, type=int)
parser.add_argument('--loss_l1', action='store_true')
parser.add_argument('--loss_ter', action='store_true')
parser.add_argument('--loss_flow', action='store_true')
parser.add_argument('--loss_perceptual', action='store_true')
parser.add_argument('--loss_adv', action='store_true')
parser.add_argument('--gan_type', default='WGAN_GP', type=str)
parser.add_argument('--lambda_l1', default=1, type=float)
parser.add_argument('--lambda_ter', default=1, type=float)
parser.add_argument('--lambda_flow', default=0.01, type=float)
parser.add_argument('--lambda_perceptual', default=1, type=float)
parser.add_argument('--lambda_adv', default=5e-3, type=float)
parser.add_argument('--resume', default='', type=str)
parser.add_argument('--resume_optim', default='', type=str)
parser.add_argument('--resume_scheduler', default='', type=str)
parser.add_argument('--resume_flownet', default='', type=str)
# parser.add_argument('--resume', default='./weights/0126_correct_Swin_Fuse_CrossScaleV2_MaskV5_WoRefine_Small_maxepoch300_gpu8_batch24_crop192_pretrained/snapshot/net_55.pth', type=str)
# parser.add_argument('--resume_optim', default='./weights/0916_RIFE_RAFT_v2A_Multis_Large_Fast_SmallFuse_augnormal_orglr_maxepoch500_gpu4_batch48_init/snapshot/optimizer_G_230.pth', type=str)
# parser.add_argument('--resume_scheduler', default='./weights/0916_RIFE_RAFT_v2A_Multis_Large_Fast_SmallFuse_augnormal_orglr_maxepoch500_gpu4_batch48_init/snapshot/scheduler_100.pth', type=str)
## log setting
parser.add_argument('--log_freq', default=10, type=int)
parser.add_argument('--vis_freq', default=50000, type=int) #50000
parser.add_argument('--save_epoch_freq', default=10, type=int) #100
parser.add_argument('--test_freq', default=100, type=int) #100
parser.add_argument('--save_folder', default='./weights', type=str)
parser.add_argument('--vis_step_freq', default=100, type=int)
parser.add_argument('--use_tb_logger', action='store_true')
parser.add_argument('--save_test_results', action='store_true')
## for evaluate
parser.add_argument('--ref_level', default=1, type=int)
## setup training environment
args = parser.parse_args()
set_random_seed(args.random_seed)
## setup training device
str_ids = args.gpu_ids.split(',')
args.gpu_ids = []
for str_id in str_ids:
id = int(str_id)
if id >= 0:
args.gpu_ids.append(id)
if len(args.gpu_ids) > 0:
torch.cuda.set_device(args.gpu_ids[0])
#### distributed training settings
if args.launcher == 'none': # disabled distributed training
args.dist = False
args.rank = -1
print('Disabled distributed training.')
else:
args.dist = True
init_dist()
args.world_size = torch.distributed.get_world_size()
args.rank = torch.distributed.get_rank()
args.save_folder = os.path.join(args.save_folder, args.name)
args.vis_save_dir = os.path.join(args.save_folder, 'vis')
args.snapshot_save_dir = os.path.join(args.save_folder, 'snapshot')
log_file_path = args.save_folder + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log'
if args.rank <= 0:
if os.path.exists(args.vis_save_dir) == False:
os.makedirs(args.vis_save_dir)
if os.path.exists(args.snapshot_save_dir) == False:
os.mkdir(args.snapshot_save_dir)
setup_logger(log_file_path)
print_args(args)
cudnn.benchmark = True
## train model
trainer = Trainer(args)
trainer.train()
if __name__ == '__main__':
main()