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train_gopro.py
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train_gopro.py
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import os
import math
import time
import random
import argparse
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from datasets import GoPro_Train_Dataset, GoPro_Test_Dataset
from metric import calculate_psnr, calculate_ssim
from utils import AverageMeter
import logging
def get_lr(args, iters):
ratio = 0.5 * (1.0 + np.cos(iters / (args.epochs * args.iters_per_epoch) * math.pi))
lr = (args.lr_start - args.lr_end) * ratio + args.lr_end
return lr
def set_lr(optimizer, lr):
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def train(args, ddp_model):
local_rank = args.local_rank
print('Distributed Data Parallel Training IFRNet on Rank {}'.format(local_rank))
if local_rank == 0:
os.makedirs(args.log_path, exist_ok=True)
log_path = os.path.join(args.log_path, time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()))
os.makedirs(log_path, exist_ok=True)
logger = logging.getLogger()
logger.setLevel('INFO')
BASIC_FORMAT = '%(asctime)s:%(levelname)s:%(message)s'
DATE_FORMAT = '%Y-%m-%d %H:%M:%S'
formatter = logging.Formatter(BASIC_FORMAT, DATE_FORMAT)
chlr = logging.StreamHandler()
chlr.setFormatter(formatter)
chlr.setLevel('INFO')
fhlr = logging.FileHandler(os.path.join(log_path, 'train.log'))
fhlr.setFormatter(formatter)
logger.addHandler(chlr)
logger.addHandler(fhlr)
logger.info(args)
dataset_train = GoPro_Train_Dataset(dataset_dir='/home/ltkong/Datasets/GOPRO', augment=True)
sampler = DistributedSampler(dataset_train)
dataloader_train = DataLoader(dataset_train, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=True, drop_last=True, sampler=sampler)
args.iters_per_epoch = dataloader_train.__len__()
iters = args.resume_epoch * args.iters_per_epoch
dataset_val = GoPro_Test_Dataset(dataset_dir='/home/ltkong/Datasets/GOPRO')
dataloader_val = DataLoader(dataset_val, batch_size=2, num_workers=4, pin_memory=True, shuffle=False, drop_last=True)
optimizer = optim.AdamW(ddp_model.parameters(), lr=args.lr_start, weight_decay=0)
time_stamp = time.time()
avg_rec = AverageMeter()
avg_geo = AverageMeter()
avg_dis = AverageMeter()
best_psnr = 0.0
for epoch in range(args.resume_epoch, args.epochs):
sampler.set_epoch(epoch)
for i, data in enumerate(dataloader_train):
for l in range(len(data)):
data[l] = data[l].to(args.device)
img0, img1, img2, img3, img4, img5, img6, img7, img8, emb1, emb2, emb3, emb4, emb5, emb6, emb7 = data
img0 = torch.cat([img0, img0, img0, img0, img0, img0, img0], 0)
img8 = torch.cat([img8, img8, img8, img8, img8, img8, img8], 0)
imgt = torch.cat([img1, img2, img3, img4, img5, img6, img7], 0)
embt = torch.cat([emb1, emb2, emb3, emb4, emb5, emb6, emb7], 0)
data_time_interval = time.time() - time_stamp
time_stamp = time.time()
lr = get_lr(args, iters)
set_lr(optimizer, lr)
optimizer.zero_grad()
imgt_pred, loss_rec, loss_geo, loss_dis = ddp_model(img0, img8, embt, imgt, None)
loss = loss_rec + loss_geo + loss_dis
loss.backward()
optimizer.step()
avg_rec.update(loss_rec.cpu().data)
avg_geo.update(loss_geo.cpu().data)
avg_dis.update(loss_dis.cpu().data)
train_time_interval = time.time() - time_stamp
if (iters+1) % 50 == 0 and local_rank == 0:
logger.info('epoch:{}/{} iter:{}/{} time:{:.2f}+{:.2f} lr:{:.5e} loss_rec:{:.4e} loss_geo:{:.4e} loss_dis:{:.4e}'.format(epoch+1, args.epochs, iters+1, args.epochs * args.iters_per_epoch, data_time_interval, train_time_interval, lr, avg_rec.avg, avg_geo.avg, avg_dis.avg))
avg_rec.reset()
avg_geo.reset()
avg_dis.reset()
iters += 1
time_stamp = time.time()
if (epoch+1) % args.eval_interval == 0 and local_rank == 0:
psnr = evaluate(args, ddp_model, dataloader_val, epoch, logger)
if psnr > best_psnr:
best_psnr = psnr
torch.save(ddp_model.state_dict(), '{}/{}_{}.pth'.format(log_path, args.model_name, 'best'))
torch.save(ddp_model.state_dict(), '{}/{}_{}.pth'.format(log_path, args.model_name, 'latest'))
dist.barrier()
def evaluate(args, ddp_model, dataloader_val, epoch, logger):
loss_rec_list = []
loss_geo_list = []
loss_dis_list = []
psnr_list = []
time_stamp = time.time()
for i, data in enumerate(dataloader_val):
for l in range(len(data)):
data[l] = data[l].to(args.device)
img0, img1, img2, img3, img4, img5, img6, img7, img8, emb1, emb2, emb3, emb4, emb5, emb6, emb7 = data
img0 = torch.cat([img0, img0, img0, img0, img0, img0, img0], 0)
img8 = torch.cat([img8, img8, img8, img8, img8, img8, img8], 0)
imgt = torch.cat([img1, img2, img3, img4, img5, img6, img7], 0)
embt = torch.cat([emb1, emb2, emb3, emb4, emb5, emb6, emb7], 0)
with torch.no_grad():
imgt_pred, loss_rec, loss_geo, loss_dis = ddp_model(img0, img8, embt, imgt, None)
loss_rec_list.append(loss_rec.cpu().numpy())
loss_geo_list.append(loss_geo.cpu().numpy())
loss_dis_list.append(loss_dis.cpu().numpy())
for j in range(img0.shape[0]):
psnr = calculate_psnr(imgt_pred[j].unsqueeze(0), imgt[j].unsqueeze(0)).cpu().data
psnr_list.append(psnr)
eval_time_interval = time.time() - time_stamp
logger.info('eval epoch:{}/{} time:{:.2f} loss_rec:{:.4e} loss_geo:{:.4e} loss_dis:{:.4e} psnr:{:.3f}'.format(epoch+1, args.epochs, eval_time_interval, np.array(loss_rec_list).mean(), np.array(loss_geo_list).mean(), np.array(loss_dis_list).mean(), np.array(psnr_list).mean()))
return np.array(psnr_list).mean()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='IFRNet')
parser.add_argument('--model_name', default='IFRNet', type=str, help='IFRNet, IFRNet_L, IFRNet_S')
parser.add_argument('--local_rank', default=-1, type=int)
parser.add_argument('--world_size', default=4, type=int)
parser.add_argument('--epochs', default=600, type=int)
parser.add_argument('--eval_interval', default=8, type=int)
parser.add_argument('--batch_size', default=2, type=int)
parser.add_argument('--lr_start', default=1e-4, type=float)
parser.add_argument('--lr_end', default=1e-5, type=float)
parser.add_argument('--log_path', default='checkpoint', type=str)
parser.add_argument('--resume_epoch', default=0, type=int)
parser.add_argument('--resume_path', default=None, type=str)
args = parser.parse_args()
dist.init_process_group(backend='nccl', world_size=args.world_size)
torch.cuda.set_device(args.local_rank)
args.device = torch.device('cuda', args.local_rank)
seed = 1234
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = True
if args.model_name == 'IFRNet':
from models.IFRNet import Model
elif args.model_name == 'IFRNet_L':
from models.IFRNet_L import Model
elif args.model_name == 'IFRNet_S':
from models.IFRNet_S import Model
args.log_path = args.log_path + '/' + args.model_name
args.num_workers = args.batch_size * 4
model = Model().to(args.device)
ddp_model = DDP(model, device_ids=[args.local_rank], output_device=args.local_rank)
if args.resume_epoch != 0:
ddp_model.load_state_dict(torch.load(args.resume_path, map_location='cpu'))
train(args, ddp_model)
dist.destroy_process_group()