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hq_wav2lip_sam_train.py
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hq_wav2lip_sam_train.py
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from os.path import dirname, join, basename, isfile
import math
import sys
from tqdm import tqdm
from random import shuffle
import pandas as pd
import time
import datetime
from lpips import LPIPS
from models import SyncNet_color as SyncNet
from models import Wav2Lip_SAM as Wav2Lip, NLayerDiscriminator
import audio
import torchvision.transforms as T
import torch
import logging
from torch import nn
from torch.nn import functional as F
from torch import optim
import torch.backends.cudnn as cudnn
from torch.utils import data as data_utils
import numpy as np
from random import shuffle
from glob import glob
import os, random, cv2, argparse
from hparams import hparams, get_image_list
import torch.multiprocessing as mp
import torch.distributed as dist
from pytorch_lightning.loggers import CSVLogger
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser(description='Code to train the Wav2Lip model WITH the visual quality discriminator')
parser.add_argument('--checkpoint_dir', help='Save checkpoints to this directory', required=False, default="checkpoints/wav/", type=str)
parser.add_argument('--log_dir', help='Write log files to this directory', required=False, default="logs/wav/", type=str)
parser.add_argument('--exp_num', help='ID number of the experiment', required=False, default="sam", type=str)
parser.add_argument('--syncnet_checkpoint_path', help='Load the pre-trained Expert discriminator', default=None, required=False, type=str)
parser.add_argument('--checkpoint_path', help='Resume generator from this checkpoint', default=None, type=str)
parser.add_argument('--disc_checkpoint_path', help='Resume quality disc from this checkpoint', default=None, type=str)
args = parser.parse_args()
global_step = 0
global_epoch = 0
use_cuda = torch.cuda.is_available()
best_loss = 10000
print('use_cuda: {}'.format(use_cuda))
syncnet_T = 5
syncnet_mel_step_size = 16
disc_iter_start = 30000
sync_iter_start = 250000
hparams.set_hparam('img_size', 384)
def mask_mel(crop_mel):
block_size = 0.1
time_size = math.ceil(block_size * crop_mel.shape[0])
freq_size = math.ceil(block_size * crop_mel.shape[1])
time_lim = crop_mel.shape[0] - time_size
freq_lim = crop_mel.shape[1] - freq_size
time_st = random.randint(0, time_lim)
freq_st = random.randint(0, freq_lim)
mel = crop_mel.copy()
mel[time_st:time_st+time_size] = -4.
mel[:, freq_st:freq_st + freq_size] = -4.
return mel
class Dataset(object):
def __init__(self, split):
self.all_videos = get_image_list(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, f'{frame_id:05}.jpg')
if not isfile(frame):
return None
window_fnames.append(frame)
return window_fnames
def read_wrong_window(self, window_fnames, is_flip):
if window_fnames is None: return None, 0, 0, 0
shuffle(window_fnames)
if random.random() > 0.5:
window_fnames = [random.choice(window_fnames)]*len(window_fnames)
window = []
h, w, c = 0, 0, 0
for fname in window_fnames:
try:
img = cv2.imread(fname)
h, w, c = img.shape
if is_flip:
img = cv2.flip(img, 1)
img = cv2.resize(img, (hparams.img_size, hparams.img_size))
except Exception as e:
return None
window.append(img)
return window, h, w, c
def read_window(self, window_fnames, is_flip):
if window_fnames is None: return None, 0, 0, 0
window = []
h, w, c = 0, 0, 0
for fname in window_fnames:
try:
img = cv2.imread(fname)
h, w, c = img.shape
if is_flip:
img = cv2.flip(img, 1)
img = cv2.resize(img, (hparams.img_size, hparams.img_size))
except Exception as e:
return None
window.append(img)
return window, h, w, c
def crop_audio_window(self, spec, start_frame):
if type(start_frame) == int:
start_frame_num = start_frame
else:
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 get_segmented_mels(self, spec, start_frame):
mels = []
assert syncnet_T == 5
start_frame_num = self.get_frame_id(start_frame) + 1 # 0-indexing ---> 1-indexing
if start_frame_num - 2 < 0: return None
for i in range(start_frame_num, start_frame_num + syncnet_T):
m = self.crop_audio_window(spec, i - 2)
if m.shape[0] != syncnet_mel_step_size:
return None
mels.append(m.T)
mels = np.asarray(mels)
return mels
def prepare_window(self, window):
# 3 x T x H x W
# b x h x w x c -> tensor c x b x h x w
x = (np.asarray(window))/255.0
x = np.transpose(x, (3, 0, 1, 2))
return x
def __len__(self):
return len(self.all_videos)
def __getitem__(self, idx):
while 1:
idx = random.randint(0, len(self.all_videos) - 1)
is_silence = random.random() > 0.5
is_flip = random.random() > 0.7
vidname = self.all_videos[idx]
img_names = list(glob(join(vidname, '*.jpg')))
if len(img_names) <= 3 * syncnet_T:
# print("Len", vidname)
continue
img_name = random.choice(img_names)
id_img_name = self.get_frame_id(img_name)
wrong_img_name = img_names[(id_img_name + 5) % len(img_names)]
id_wrong_img_name = self.get_frame_id(wrong_img_name)
while wrong_img_name == img_name or abs(id_img_name - id_wrong_img_name) < 5:
wrong_img_name = random.choice(img_names)
id_wrong_img_name = self.get_frame_id(wrong_img_name)
window_fnames = self.get_window(img_name)
wrong_window_fnames = self.get_window(wrong_img_name)
window, h, w, c = self.read_window(window_fnames, is_flip)
if window is None:
continue
wrong_window, h, w, c = self.read_wrong_window(wrong_window_fnames, is_flip)
if wrong_window is None:
continue
try:
mel_out_path = join(vidname, "mel.npy")
if not mel_out_path.endswith(".wav") and os.path.isfile(mel_out_path): # x50 times faster - 0.002 -> 0.01s
with open(mel_out_path, "rb") as f:
orig_mel = np.load(f)
else:
wavpath = os.path.join(vidname, "synced.wav")
if not os.path.isfile(wavpath):
au_names = list(glob(join(vidname, '*.wav')))
au_path = au_names[0]
status = os.system(f"ffmpeg -i {au_path} -ar 16000 {wavpath}")
wav = audio.load_wav(wavpath, hparams.sample_rate)
orig_mel = audio.melspectrogram(wav).T # 0.2 -> 0.9s
with open(mel_out_path, "wb") as f:
np.save(f, orig_mel)
except Exception as e:
continue
mel = self.crop_audio_window(orig_mel.copy(), img_name)
if (mel.shape[0] != syncnet_mel_step_size):
# print("Mel shape", vidname)
continue
indiv_mels = self.get_segmented_mels(orig_mel.copy(), img_name)
if indiv_mels is None:
continue
# ground truth images
window = self.prepare_window(window)
y = window.copy()
window[:, :, window.shape[2]//2 :,:] = 0
# reference images
wrong_window = self.prepare_window(wrong_window)
x = np.concatenate([window, wrong_window], axis=0)
x = torch.FloatTensor(x)
mel = torch.FloatTensor(mel.T).unsqueeze(0)
indiv_mels = torch.FloatTensor(indiv_mels).unsqueeze(1)
y = torch.FloatTensor(y)
return x, indiv_mels, mel, y, vidname
def save_sample_images(x, g, gt, vidname, global_step, checkpoint_dir):
x = (x.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.0).astype(np.uint8)
g = (g.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.0).astype(np.uint8)
gt = (gt.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.0).astype(np.uint8)
refs, inps = x[..., 3:], x[..., :3]
folder = join(checkpoint_dir, "samples_step{:09d}".format(global_step))
if not os.path.exists(folder): os.mkdir(folder)
collage = np.concatenate((refs, inps, g, gt), axis=-2)
for batch_idx, c in enumerate(collage):
image_id = vidname[batch_idx].split('/')[-2]
print(image_id)
for t in range(len(c)):
cv2.imwrite('{}/{}_{}.jpg'.format(folder, image_id, t), c[t], [cv2.IMWRITE_JPEG_QUALITY, 100])
logloss = nn.BCELoss()
# logloss = nn.MSELoss()
def cosine_loss(a, v, y):
d = nn.functional.cosine_similarity(a, v)
loss = logloss(d.unsqueeze(1), y)
return loss
device = torch.device("cuda" if use_cuda else "cpu")
recon_loss = nn.L1Loss()
def get_sync_loss(mel, g, syncnet):
# print("Syncing", g.shape, gt.shape)
if syncnet is None:
return torch.Tensor([10])
g = g[:, :, :, g.size(3)//2:]
g = torch.cat([g[:, :, i] for i in range(syncnet_T)], dim=1)
a, v = syncnet(mel, g)
y = torch.ones(g.size(0), 1).float().to(device)
return cosine_loss(a, v, y)
def hinge_d_loss(logits_real, logits_fake):
loss_real = torch.mean(F.relu(1. - logits_real))
loss_fake = torch.mean(F.relu(1. + logits_fake))
return loss_real, loss_fake
def train(device, model, disc, train_data_loader, test_data_loader, optimizer, disc_optimizer, checkpoint_dir=None, checkpoint_interval=None, nepochs=None, log_interval=None,syncnet=None):
global global_step, global_epoch
resumed_step = global_step
if not os.path.isdir(args.log_dir): os.makedirs(args.log_dir)
logger = CSVLogger(args.log_dir, name=f"train{args.exp_num}")
valLogger = CSVLogger(args.log_dir, name=f"val{args.exp_num}")
bce_loss = nn.BCELoss()
syncnet_wt = hparams.syncnet_wt
arr_disc_fake_loss = []
arr_disc_real_loss = []
arr_perceptual_loss = []
loss_fn_vgg = nn.DataParallel(LPIPS(net='vgg').to(device).eval()).to(device)
while global_epoch < nepochs:
try:
stop_training = False
# print('Starting Epoch: {}'.format(global_epoch))
running_sync_loss, running_l1_loss, running_perceptual_loss = 0., 0., 0., 0.
running_disc_real_loss, running_disc_fake_loss = 0., 0.
running_vgg_loss= 0.
st = time.time()
offset = 0
for step, (x, indiv_mels, mel, gt, vidname) in enumerate(train_data_loader):
load_time = time.time() - st
st = time.time()
disc.train()
model.train()
x = x.to(device)
mel = mel.to(device)
indiv_mels = indiv_mels.to(device)
gt = gt.to(device)
optimizer.zero_grad()
disc_optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=False):
g = model(indiv_mels, x)
if global_step > disc_iter_start:
fake_output = disc(g)
perceptual_loss = -torch.mean(fake_output)
else:
perceptual_loss = torch.tensor(0.)
l1loss = recon_loss(g, gt)
vgg_loss = loss_fn_vgg(torch.cat([g[:, :, i] for i in range(g.size(2))], dim=0) * 2 - 1,
torch.cat([gt[:, :, i] for i in range(gt.size(2))], dim=0) * 2 - 1)
vgg_loss = vgg_loss.mean()
nll_loss = l1loss + vgg_loss
if global_step > sync_iter_start and syncnet_wt > 0. and syncnet is not None:
sync_loss = get_sync_loss(mel, g, syncnet)
else:
sync_loss = torch.tensor(0.)
if global_step > disc_iter_start:
d_weight = 0.025
else:
d_weight = 0.
loss = syncnet_wt * sync_loss + d_weight * perceptual_loss + nll_loss
loss.backward()
optimizer.step()
### Remove all gradients before Training disc
disc_optimizer.zero_grad()
if global_step > disc_iter_start:
real_output = disc(gt)
fake_output = disc(g.detach())
disc_real_loss, disc_fake_loss = hinge_d_loss(real_output, fake_output)
d_loss = 0.5 * (disc_fake_loss + disc_real_loss)
d_loss.backward()
disc_optimizer.step()
else:
disc_real_loss = torch.tensor(0.)
disc_fake_loss = torch.tensor(0.)
running_disc_real_loss += disc_real_loss.item()
arr_disc_real_loss.append(running_disc_real_loss/(step+1-offset))
running_disc_fake_loss += disc_fake_loss.item()
arr_disc_fake_loss.append(running_disc_fake_loss/(step+1-offset))
# Logs
global_step += 1
cur_session_steps = global_step - resumed_step
running_l1_loss += l1loss.item()
if global_step > sync_iter_start and syncnet_wt > 0. and syncnet is not None:
running_sync_loss += sync_loss.item()
else:
running_sync_loss += torch.tensor(0.)
if hparams.disc_wt > 0.:
running_perceptual_loss += perceptual_loss.item()
else:
running_perceptual_loss += torch.tensor(0.)
running_vgg_loss += vgg_loss.item()
arr_perceptual_loss.append(running_perceptual_loss/(step+1-offset))
# logs
if global_step == 1 or global_step % log_interval == 0:
logger.log_metrics({
"Generator/l1_loss/train": running_l1_loss/(step+1-offset),
"syncnet_wt": syncnet_wt,
"Generator/sync_loss/train": running_sync_loss/(step+1-offset),
"Generator/perceptual_loss/train": running_perceptual_loss/(step+1-offset),
"Discriminator/fake_loss/train": running_disc_fake_loss/(step+1-offset),
"Discriminator/real_loss/train": running_disc_real_loss/(step+1-offset)
}, step=global_step)
logger.save()
if global_step % checkpoint_interval == 0:
save_checkpoint(
model, optimizer, global_step, checkpoint_dir, global_epoch, prefix="gen_")
save_checkpoint(disc, disc_optimizer, global_step, checkpoint_dir, global_epoch, prefix='disc_')
g = torch.clamp_(g, -1, 1)
save_sample_images(x, g, gt, vidname, global_step, checkpoint_dir)
del x, g, gt, indiv_mels, mel
train_time = time.time() - st
print('Step {} | L1: {:.4} | Vgg: {:.4} | SW: {:.4} | Sync: {:.4} | DW: {:.4} | Percep: {:.4} | Fake: {:.4}, Real: {:.4} | Load: {:.4}, Train: {:.4}'
.format(global_step,
running_l1_loss / (step + 1-offset),
running_vgg_loss / (step + 1-offset),
syncnet_wt,
running_sync_loss / (step + 1-offset),
d_weight,
running_perceptual_loss / (step + 1-offset),
running_disc_fake_loss / (step + 1-offset),
running_disc_real_loss / (step + 1-offset),
load_time, train_time))
st = time.time()
if syncnet_wt > 0. and global_step > sync_iter_start and global_step % hparams.eval_interval == 0:
with torch.no_grad():
average_loss = eval_model(test_data_loader, device, model, disc, syncnet)
logging.warning("Average loss: {}".format(average_loss))
save_checkpoint(
model, optimizer, global_step, checkpoint_dir, global_epoch, prefix="gen_", loss_val=average_loss)
save_checkpoint(disc, disc_optimizer, global_step, checkpoint_dir, global_epoch, prefix='disc_',loss_val=average_loss)
if average_loss <= 0.3: # stop training
print("Average loss is less than 0.3. Stopping training.")
stop_training = True
break
if stop_training:
break
global_epoch += 1
except Exception as e:
print(e)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print(exc_type, fname, exc_tb.tb_lineno)
# print("KeyboardInterrupt")
break
print("Saving models and logs...")
save_checkpoint(model, optimizer, global_step, checkpoint_dir, global_epoch, prefix="gen_")
save_checkpoint(disc, disc_optimizer, global_step, checkpoint_dir, global_epoch, prefix='disc_')
logger.save()
valLogger.save()
# fidLogger.save()
def eval_model(test_data_loader, device, model, disc, syncnet):
eval_steps = 20
logging.warning('Evaluating for {} steps'.format(eval_steps))
running_sync_loss = 0.
count = 0
for step, (x, indiv_mels, mel, gt, vidname) in enumerate(test_data_loader):
model.eval()
disc.eval()
x = x.to(device)
mel = mel.to(device)
indiv_mels = indiv_mels.to(device)
gt = gt.to(device)
with torch.cuda.amp.autocast(enabled=False):
g = model(indiv_mels, x)
sync_loss = get_sync_loss(mel, g, syncnet)
running_sync_loss += sync_loss.item()
count = step + 1
if step >= eval_steps:
break
logging.warning('Step {} | Sync: {:.6}'
.format(step , (running_sync_loss) / (step + 1)))
return (running_sync_loss) / (count)
def save_ckpt(model, optimizer, step, checkpoint_dir, epoch, model_name):
checkpoint_path = join(
checkpoint_dir, model_name)
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,
"best_loss": best_loss,
}, checkpoint_path)
print("Saved checkpoint:", checkpoint_path)
def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch, prefix='', loss_val=1000):
# save best.pth
global best_loss
if not os.path.isdir(checkpoint_dir):
os.makedirs(checkpoint_dir)
date = str(datetime.datetime.now()).split(" ")[0]
post_fix = f'checkpoint_{hparams.img_size}_{hparams.batch_size}_{global_step:09d}_{date}.pth'
if loss_val <= best_loss:
best_loss = loss_val
best_name = f"{prefix}best_wav128_1e4.pth"
save_ckpt(model, optimizer, step, checkpoint_dir, epoch, best_name)
last_name = f"{prefix}last_wav128_1e4.pth"
save_ckpt(model, optimizer, step, checkpoint_dir, epoch, last_name)
save_ckpt(model, optimizer, step, checkpoint_dir, epoch, f"{prefix}{post_fix}")
ckpt_list = os.listdir(checkpoint_dir)
ckpt_list = [file for file in ckpt_list if prefix in file and "checkpoint_" in file]
num_ckpts = hparams.num_checkpoints
if len(ckpt_list) <= num_ckpts*2:
return
ckpt_list.sort(key=lambda x: int(x.replace(".pth", "").split("_")[-2]))
num_elim = len(ckpt_list) - num_ckpts
elim_ckpt = ckpt_list[:num_elim]
for ckpt in elim_ckpt:
ckpt_path = os.path.join(checkpoint_dir, ckpt)
os.remove(ckpt_path)
print("Deleted", ckpt_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, overwrite_global_states=True):
global global_step
global global_epoch
print("Load checkpoint from: {}".format(path))
checkpoint = _load(path)
s = checkpoint["state_dict"]
new_s = {}
for k, v in s.items():
new_s[k.replace('module.', '')] = v
model.load_state_dict(new_s)
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"])
if overwrite_global_states:
global_step = checkpoint["global_step"]
global_epoch = checkpoint["global_epoch"]
return model
def run():
checkpoint_dir = os.path.join(args.checkpoint_dir, args.exp_num)
if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir)
train_dataset = Dataset('filelists/train.txt')
test_dataset = Dataset('filelists/test.txt')
hparams.set_hparam('batch_size', 64)
hparams.set_hparam('syncnet_wt', 0.03)
train_data_loader = data_utils.DataLoader(
train_dataset, batch_size=hparams.batch_size, shuffle=True,
num_workers=hparams.num_workers, drop_last=True)
# TODO: uncomment this
test_data_loader = data_utils.DataLoader(
test_dataset, batch_size=hparams.batch_size,
num_workers=1, drop_last=True)
device = torch.device("cuda" if use_cuda else "cpu")
# TODO: uncomment this
syncnet = SyncNet().to(device)
model = Wav2Lip().to(device)
disc = NLayerDiscriminator().to(device)
if args.syncnet_checkpoint_path is not None:
print("Loading syncnet from checkpoint: {}".format(args.syncnet_checkpoint_path))
load_checkpoint(args.syncnet_checkpoint_path, syncnet, None, reset_optimizer=True,
overwrite_global_states=False)
syncnet = nn.DataParallel(syncnet).to(device)
syncnet.eval()
optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad],
lr=hparams.initial_learning_rate, betas=(0.5, 0.999))
disc_optimizer = optim.Adam([p for p in disc.parameters() if p.requires_grad], lr=hparams.disc_initial_learning_rate, betas=(0.5, 0.999))
if args.checkpoint_path is not None:
load_checkpoint(args.checkpoint_path, model, optimizer, reset_optimizer=False)
if args.disc_checkpoint_path is not None:
load_checkpoint(args.disc_checkpoint_path, disc, disc_optimizer,
reset_optimizer=False, overwrite_global_states=False)
model = nn.DataParallel(model).to(device)
disc = nn.DataParallel(disc).to(device)
if not os.path.exists(checkpoint_dir):
os.mkdir(checkpoint_dir)
train(device, model, disc, train_data_loader, test_data_loader, optimizer, disc_optimizer,
checkpoint_dir=checkpoint_dir,
checkpoint_interval=hparams.checkpoint_interval,
nepochs=hparams.nepochs,
log_interval=hparams.log_interval,syncnet=syncnet)
def main():
"""Assume Single Node Multi GPUs Training Only"""
# assert torch.cuda.is_available(), "CPU training is not allowed."
n_gpus = torch.cuda.device_count()
run()
if __name__ == "__main__":
main()