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from os.path import dirname, join, basename, isfile
from tqdm import tqdm
from models import SyncNet_color as SyncNet
import audio
import torch
from torch import nn
from torch import optim
import torch.backends.cudnn as cudnn
from torch.utils import data as data_utils
import numpy as np
from glob import glob
import os, random, cv2, argparse
from hparams import hparams, get_image_list
parser = argparse.ArgumentParser(description='Code to train the expert lip-sync discriminator')
parser.add_argument("--data_root", help="Root folder of the preprocessed LRS2 dataset", required=True)
parser.add_argument('--checkpoint_dir', help='Save checkpoints to this directory', required=True, type=str)
parser.add_argument('--checkpoint_path', help='Resumed from this checkpoint', default=None, type=str)
args = parser.parse_args()
global_step = 0
global_epoch = 0
use_cuda = torch.cuda.is_available()
print('use_cuda: {}'.format(use_cuda))
syncnet_T = 5
syncnet_mel_step_size = 16
class Dataset(object):
def __init__(self, split):
self.all_videos = get_image_list(args.data_root, split)
def get_frame_id(self, frame):
return int(basename(frame).split('.')[0])
def get_window(self, start_frame):
start_id = self.get_frame_id(start_frame)
vidname = dirname(start_frame)
window_fnames = []
for frame_id in range(start_id, start_id + syncnet_T):
frame = join(vidname, '{}.jpg'.format(frame_id))
if not isfile(frame):
return None
window_fnames.append(frame)
return window_fnames
def crop_audio_window(self, spec, start_frame):
# num_frames = (T x hop_size * fps) / sample_rate
start_frame_num = self.get_frame_id(start_frame)
start_idx = int(80. * (start_frame_num / float(hparams.fps)))
end_idx = start_idx + syncnet_mel_step_size
return spec[start_idx : end_idx, :]
def __len__(self):
return len(self.all_videos)
def __getitem__(self, idx):
while 1:
idx = random.randint(0, len(self.all_videos) - 1)
vidname = self.all_videos[idx]
img_names = list(glob(join(vidname, '*.jpg')))
if len(img_names) <= 3 * syncnet_T:
continue
img_name = random.choice(img_names)
wrong_img_name = random.choice(img_names)
while wrong_img_name == img_name:
wrong_img_name = random.choice(img_names)
if random.choice([True, False]):
y = torch.ones(1).float()
chosen = img_name
else:
y = torch.zeros(1).float()
chosen = wrong_img_name
window_fnames = self.get_window(chosen)
if window_fnames is None:
continue
window = []
all_read = True
for fname in window_fnames:
img = cv2.imread(fname)
if img is None:
all_read = False
break
try:
img = cv2.resize(img, (hparams.img_size, hparams.img_size))
except Exception as e:
all_read = False
break
window.append(img)
if not all_read: continue
try:
wavpath = join(vidname, "audio.wav")
wav = audio.load_wav(wavpath, hparams.sample_rate)
orig_mel = audio.melspectrogram(wav).T
except Exception as e:
continue
mel = self.crop_audio_window(orig_mel.copy(), img_name)
if (mel.shape[0] != syncnet_mel_step_size):
continue
# H x W x 3 * T
x = np.concatenate(window, axis=2) / 255.
x = x.transpose(2, 0, 1)
x = x[:, x.shape[1]//2:]
x = torch.FloatTensor(x)
mel = torch.FloatTensor(mel.T).unsqueeze(0)
return x, mel, y
logloss = nn.BCELoss()
def cosine_loss(a, v, y):
d = nn.functional.cosine_similarity(a, v)
loss = logloss(d.unsqueeze(1), y)
return loss
def train(device, model, train_data_loader, test_data_loader, optimizer,
checkpoint_dir=None, checkpoint_interval=None, nepochs=None):
global global_step, global_epoch
resumed_step = global_step
while global_epoch < nepochs:
running_loss = 0.
prog_bar = tqdm(enumerate(train_data_loader))
for step, (x, mel, y) in prog_bar:
model.train()
optimizer.zero_grad()
# Transform data to CUDA device
x = x.to(device)
mel = mel.to(device)
a, v = model(mel, x)
y = y.to(device)
loss = cosine_loss(a, v, y)
loss.backward()
optimizer.step()
global_step += 1
cur_session_steps = global_step - resumed_step
running_loss += loss.item()
if global_step == 1 or global_step % checkpoint_interval == 0:
save_checkpoint(
model, optimizer, global_step, checkpoint_dir, global_epoch)
if global_step % hparams.syncnet_eval_interval == 0:
with torch.no_grad():
eval_model(test_data_loader, global_step, device, model, checkpoint_dir)
prog_bar.set_description('Loss: {}'.format(running_loss / (step + 1)))
global_epoch += 1
def eval_model(test_data_loader, global_step, device, model, checkpoint_dir):
eval_steps = 1400
print('Evaluating for {} steps'.format(eval_steps))
losses = []
while 1:
for step, (x, mel, y) in enumerate(test_data_loader):
model.eval()
# Transform data to CUDA device
x = x.to(device)
mel = mel.to(device)
a, v = model(mel, x)
y = y.to(device)
loss = cosine_loss(a, v, y)
losses.append(loss.item())
if step > eval_steps: break
averaged_loss = sum(losses) / len(losses)
print(averaged_loss)
return
def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch):
checkpoint_path = join(
checkpoint_dir, "checkpoint_step{:09d}.pth".format(global_step))
optimizer_state = optimizer.state_dict() if hparams.save_optimizer_state else None
torch.save({
"state_dict": model.state_dict(),
"optimizer": optimizer_state,
"global_step": step,
"global_epoch": epoch,
}, checkpoint_path)
print("Saved checkpoint:", checkpoint_path)
def _load(checkpoint_path):
if use_cuda:
checkpoint = torch.load(checkpoint_path)
else:
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
return checkpoint
def load_checkpoint(path, model, optimizer, reset_optimizer=False):
global global_step
global global_epoch
print("Load checkpoint from: {}".format(path))
checkpoint = _load(path)
model.load_state_dict(checkpoint["state_dict"])
if not reset_optimizer:
optimizer_state = checkpoint["optimizer"]
if optimizer_state is not None:
print("Load optimizer state from {}".format(path))
optimizer.load_state_dict(checkpoint["optimizer"])
global_step = checkpoint["global_step"]
global_epoch = checkpoint["global_epoch"]
return model
if __name__ == "__main__":
checkpoint_dir = args.checkpoint_dir
checkpoint_path = args.checkpoint_path
if not os.path.exists(checkpoint_dir): os.mkdir(checkpoint_dir)
# Dataset and Dataloader setup
train_dataset = Dataset('train')
test_dataset = Dataset('val')
train_data_loader = data_utils.DataLoader(
train_dataset, batch_size=hparams.syncnet_batch_size, shuffle=True,
num_workers=hparams.num_workers)
test_data_loader = data_utils.DataLoader(
test_dataset, batch_size=hparams.syncnet_batch_size,
num_workers=8)
device = torch.device("cuda" if use_cuda else "cpu")
# Model
model = SyncNet().to(device)
print('total trainable params {}'.format(sum(p.numel() for p in model.parameters() if p.requires_grad)))
optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad],
lr=hparams.syncnet_lr)
if checkpoint_path is not None:
load_checkpoint(checkpoint_path, model, optimizer, reset_optimizer=False)
train(device, model, train_data_loader, test_data_loader, optimizer,
checkpoint_dir=checkpoint_dir,
checkpoint_interval=hparams.syncnet_checkpoint_interval,
nepochs=hparams.nepochs)
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