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train_fine_tune.py
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train_fine_tune.py
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import argparse
import os
import torch
from torch.utils import data
from fine_tune.dataset import Finetune256
import torchvision
import torchvision.transforms as transforms
from fine_tune.trainer_fine_tune import Trainer
from torch.utils.tensorboard import SummaryWriter
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn.functional as F
from torchvision import utils
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
def data_sampler(dataset, shuffle):
if shuffle:
return data.RandomSampler(dataset)
else:
return data.SequentialSampler(dataset)
def sample_data(loader):
while True:
for batch in loader:
yield batch
def display_img(idx, img, name, writer):
img = img.clamp(-1, 1)
img = ((img - img.min()) / (img.max() - img.min())).data
writer.add_images(tag='%s' % (name), global_step=idx, img_tensor=img)
def write_loss(i, vgg_loss, l1_loss, g_loss, d_loss, writer):
writer.add_scalar('vgg_loss', vgg_loss.item(), i)
writer.add_scalar('l1_loss', l1_loss.item(), i)
writer.add_scalar('gen_loss', g_loss.item(), i)
writer.add_scalar('dis_loss', d_loss.item(), i)
writer.flush()
def ddp_setup(args, rank, world_size):
os.environ['MASTER_ADDR'] = args.addr
os.environ['MASTER_PORT'] = args.port
dist.init_process_group("nccl", rank=rank, world_size=world_size)
def main(rank, world_size, args):
# init distributed computing
ddp_setup(args, rank, world_size)
torch.cuda.set_device(rank)
device = torch.device("cuda")
# make logging folder
log_path = os.path.join(args.exp_path, args.exp_name + '/log')
checkpoint_path = os.path.join(args.exp_path, args.exp_name + '/checkpoint')
os.makedirs(log_path, exist_ok=True)
os.makedirs(checkpoint_path, exist_ok=True)
writer = SummaryWriter(log_path)
print('==> preparing dataset')
transform = torchvision.transforms.Compose([
transforms.Resize((args.size, args.size)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))]
)
dataset = Finetune256('/data/ts/datasets/person_specific_dataset/AD-NeRF/video_crop_frame/Obama', True, transform = transform)
dataset_test = Finetune256('/data/ts/datasets/person_specific_dataset/AD-NeRF/video_crop_frame/Obama', False, transform = transform)
loader = data.DataLoader(
dataset,
num_workers=8,
batch_size=args.batch_size // world_size,
sampler=data.distributed.DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=True),
pin_memory=True,
drop_last=False,
)
loader_test = data.DataLoader(
dataset_test,
num_workers=8,
batch_size=4,
sampler=data.distributed.DistributedSampler(dataset_test, num_replicas=world_size, rank=rank, shuffle=False),
pin_memory=True,
drop_last=False,
)
loader = sample_data(loader)
loader_test = sample_data(loader_test)
print('==> initializing trainer')
# Trainer
trainer = Trainer(args, device, rank)
# resume
if args.resume_ckpt is not None:
args.start_iter = trainer.resume(args.resume_ckpt)
print('==> resume from iteration %d' % (args.start_iter))
print('==> training')
pbar = range(args.iter)
for idx in pbar:
i = idx + args.start_iter
# laoding data
img_source, img_target = next(loader)
img_source = img_source.to(rank, non_blocking=True)
img_target = img_target.to(rank, non_blocking=True)
# update generator
vgg_loss, l1_loss, gan_g_loss, img_recon = trainer.gen_update(img_source, img_target)
# update discriminator
gan_d_loss = trainer.dis_update(img_target, img_recon)
if rank == 0:
# write to log
write_loss(idx, vgg_loss, l1_loss, gan_g_loss, gan_d_loss, writer)
# display
if i % args.display_freq == 0 and rank == 0:
print("[Iter %d/%d] [vgg loss: %f] [l1 loss: %f] [g loss: %f] [d loss: %f]"
% (i, args.iter, vgg_loss.item(), l1_loss.item(), gan_g_loss.item(), gan_d_loss.item()))
if rank == 0:
img_test_source, img_test_target = next(loader_test)
img_test_source = img_test_source.to(rank, non_blocking=True)
img_test_target = img_test_target.to(rank, non_blocking=True)
img_recon = trainer.sample(img_test_source, img_test_target)
sample = F.interpolate(torch.cat((img_test_source.detach(),img_test_target.detach(), img_recon.detach()), dim=0), 256)
# sample = torch.flip(sample, [1])
utils.save_image(
sample,
os.path.join(checkpoint_path, "step_%05d.jpg"%(i)),
nrow=4,
normalize=True,
range=(-1, 1),
)
# display_img(i, img_test_source, 'source', writer)
# display_img(i, img_test_target, 'target', writer)
# display_img(i, img_recon, 'recon', writer)
# display_img(i, img_source_ref, 'source_ref', writer)
# writer.flush()
# save model
if i % args.save_freq == 0 and rank == 0:
trainer.save(i, checkpoint_path)
return
if __name__ == "__main__":
# training params
parser = argparse.ArgumentParser()
parser.add_argument("--iter", type=int, default=800000)
parser.add_argument("--size", type=int, default=256)
parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--only_fine_tune_dec", action='store_true', help='Only fine tune dec in Generator')
parser.add_argument("--d_reg_every", type=int, default=16)
parser.add_argument("--g_reg_every", type=int, default=4)
parser.add_argument("--resume_ckpt", type=str, default='ckpts/EDTalk_lip_pose.pt')
parser.add_argument("--datapath", type=str, default='/data/ts/datasets/person_specific_dataset/AD-NeRF/video_crop_frame/Obama')
parser.add_argument("--lr", type=float, default=0.002)
parser.add_argument("--start_iter", type=int, default=0)
parser.add_argument("--display_freq", type=int, default=20)
parser.add_argument("--save_freq", type=int, default=10000)
parser.add_argument("--exp_path", type=str, default='/data/ts/checkpoints/EDTalk/fine_tune/')
parser.add_argument("--exp_name", type=str, default='Obama')
parser.add_argument("--addr", type=str, default='localhost')
parser.add_argument("--port", type=str, default='12345')
opts = parser.parse_args()
n_gpus = torch.cuda.device_count()
assert n_gpus >= 2
world_size = n_gpus
print('==> training on %d gpus' % n_gpus)
mp.spawn(main, args=(world_size, opts,), nprocs=world_size, join=True)