375 lines
13 KiB
Python
375 lines
13 KiB
Python
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from os.path import dirname, join, basename, isfile
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from tqdm import tqdm
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from models import SyncNet_color as SyncNet
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from models import Wav2Lip as Wav2Lip
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import audio
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import torch
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from torch import nn
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from torch import optim
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import torch.backends.cudnn as cudnn
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from torch.utils import data as data_utils
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import numpy as np
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from glob import glob
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import os, random, cv2, argparse
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from hparams import hparams, get_image_list
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parser = argparse.ArgumentParser(description='Code to train the Wav2Lip model without the visual quality discriminator')
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parser.add_argument("--data_root", help="Root folder of the preprocessed LRS2 dataset", required=True, type=str)
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parser.add_argument('--checkpoint_dir', help='Save checkpoints to this directory', required=True, type=str)
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parser.add_argument('--syncnet_checkpoint_path', help='Load the pre-trained Expert discriminator', required=True, type=str)
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parser.add_argument('--checkpoint_path', help='Resume from this checkpoint', default=None, type=str)
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args = parser.parse_args()
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global_step = 0
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global_epoch = 0
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use_cuda = torch.cuda.is_available()
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print('use_cuda: {}'.format(use_cuda))
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syncnet_T = 5
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syncnet_mel_step_size = 16
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class Dataset(object):
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def __init__(self, split):
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self.all_videos = get_image_list(args.data_root, split)
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def get_frame_id(self, frame):
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return int(basename(frame).split('.')[0])
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def get_window(self, start_frame):
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start_id = self.get_frame_id(start_frame)
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vidname = dirname(start_frame)
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window_fnames = []
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for frame_id in range(start_id, start_id + syncnet_T):
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frame = join(vidname, '{}.jpg'.format(frame_id))
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if not isfile(frame):
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return None
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window_fnames.append(frame)
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return window_fnames
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def read_window(self, window_fnames):
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if window_fnames is None: return None
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window = []
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for fname in window_fnames:
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img = cv2.imread(fname)
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if img is None:
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return None
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try:
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img = cv2.resize(img, (hparams.img_size, hparams.img_size))
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except Exception as e:
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return None
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window.append(img)
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return window
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def crop_audio_window(self, spec, start_frame):
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if type(start_frame) == int:
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start_frame_num = start_frame
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else:
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start_frame_num = self.get_frame_id(start_frame) # 0-indexing ---> 1-indexing
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start_idx = int(80. * (start_frame_num / float(hparams.fps)))
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end_idx = start_idx + syncnet_mel_step_size
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return spec[start_idx : end_idx, :]
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def get_segmented_mels(self, spec, start_frame):
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mels = []
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assert syncnet_T == 5
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start_frame_num = self.get_frame_id(start_frame) + 1 # 0-indexing ---> 1-indexing
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if start_frame_num - 2 < 0: return None
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for i in range(start_frame_num, start_frame_num + syncnet_T):
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m = self.crop_audio_window(spec, i - 2)
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if m.shape[0] != syncnet_mel_step_size:
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return None
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mels.append(m.T)
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mels = np.asarray(mels)
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return mels
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def prepare_window(self, window):
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# 3 x T x H x W
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x = np.asarray(window) / 255.
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x = np.transpose(x, (3, 0, 1, 2))
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return x
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def __len__(self):
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return len(self.all_videos)
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def __getitem__(self, idx):
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while 1:
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idx = random.randint(0, len(self.all_videos) - 1)
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vidname = self.all_videos[idx]
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img_names = list(glob(join(vidname, '*.jpg')))
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if len(img_names) <= 3 * syncnet_T:
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continue
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img_name = random.choice(img_names)
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wrong_img_name = random.choice(img_names)
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while wrong_img_name == img_name:
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wrong_img_name = random.choice(img_names)
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window_fnames = self.get_window(img_name)
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wrong_window_fnames = self.get_window(wrong_img_name)
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if window_fnames is None or wrong_window_fnames is None:
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continue
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window = self.read_window(window_fnames)
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if window is None:
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continue
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wrong_window = self.read_window(wrong_window_fnames)
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if wrong_window is None:
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continue
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try:
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wavpath = join(vidname, "audio.wav")
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wav = audio.load_wav(wavpath, hparams.sample_rate)
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orig_mel = audio.melspectrogram(wav).T
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except Exception as e:
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continue
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mel = self.crop_audio_window(orig_mel.copy(), img_name)
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if (mel.shape[0] != syncnet_mel_step_size):
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continue
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indiv_mels = self.get_segmented_mels(orig_mel.copy(), img_name)
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if indiv_mels is None: continue
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window = self.prepare_window(window)
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y = window.copy()
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window[:, :, window.shape[2]//2:] = 0.
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wrong_window = self.prepare_window(wrong_window)
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x = np.concatenate([window, wrong_window], axis=0)
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x = torch.FloatTensor(x)
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mel = torch.FloatTensor(mel.T).unsqueeze(0)
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indiv_mels = torch.FloatTensor(indiv_mels).unsqueeze(1)
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y = torch.FloatTensor(y)
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return x, indiv_mels, mel, y
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def save_sample_images(x, g, gt, global_step, checkpoint_dir):
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x = (x.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8)
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g = (g.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8)
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gt = (gt.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8)
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refs, inps = x[..., 3:], x[..., :3]
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folder = join(checkpoint_dir, "samples_step{:09d}".format(global_step))
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if not os.path.exists(folder): os.mkdir(folder)
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collage = np.concatenate((refs, inps, g, gt), axis=-2)
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for batch_idx, c in enumerate(collage):
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for t in range(len(c)):
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cv2.imwrite('{}/{}_{}.jpg'.format(folder, batch_idx, t), c[t])
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logloss = nn.BCELoss()
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def cosine_loss(a, v, y):
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d = nn.functional.cosine_similarity(a, v)
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loss = logloss(d.unsqueeze(1), y)
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return loss
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device = torch.device("cuda" if use_cuda else "cpu")
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syncnet = SyncNet().to(device)
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for p in syncnet.parameters():
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p.requires_grad = False
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recon_loss = nn.L1Loss()
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def get_sync_loss(mel, g):
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g = g[:, :, :, g.size(3)//2:]
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g = torch.cat([g[:, :, i] for i in range(syncnet_T)], dim=1)
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# B, 3 * T, H//2, W
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a, v = syncnet(mel, g)
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y = torch.ones(g.size(0), 1).float().to(device)
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return cosine_loss(a, v, y)
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def train(device, model, train_data_loader, test_data_loader, optimizer,
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checkpoint_dir=None, checkpoint_interval=None, nepochs=None):
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global global_step, global_epoch
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resumed_step = global_step
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while global_epoch < nepochs:
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print('Starting Epoch: {}'.format(global_epoch))
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running_sync_loss, running_l1_loss = 0., 0.
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prog_bar = tqdm(enumerate(train_data_loader))
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for step, (x, indiv_mels, mel, gt) in prog_bar:
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model.train()
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optimizer.zero_grad()
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# Move data to CUDA device
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x = x.to(device)
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mel = mel.to(device)
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indiv_mels = indiv_mels.to(device)
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gt = gt.to(device)
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g = model(indiv_mels, x)
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if hparams.syncnet_wt > 0.:
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sync_loss = get_sync_loss(mel, g)
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else:
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sync_loss = 0.
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l1loss = recon_loss(g, gt)
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loss = hparams.syncnet_wt * sync_loss + (1 - hparams.syncnet_wt) * l1loss
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loss.backward()
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optimizer.step()
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if global_step % checkpoint_interval == 0:
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save_sample_images(x, g, gt, global_step, checkpoint_dir)
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global_step += 1
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cur_session_steps = global_step - resumed_step
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running_l1_loss += l1loss.item()
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if hparams.syncnet_wt > 0.:
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running_sync_loss += sync_loss.item()
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else:
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running_sync_loss += 0.
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if global_step == 1 or global_step % checkpoint_interval == 0:
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save_checkpoint(
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model, optimizer, global_step, checkpoint_dir, global_epoch)
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if global_step == 1 or global_step % hparams.eval_interval == 0:
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with torch.no_grad():
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average_sync_loss = eval_model(test_data_loader, global_step, device, model, checkpoint_dir)
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if average_sync_loss < .75:
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hparams.set_hparam('syncnet_wt', 0.01) # without image GAN a lesser weight is sufficient
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prog_bar.set_description('L1: {}, Sync Loss: {}'.format(running_l1_loss / (step + 1),
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running_sync_loss / (step + 1)))
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global_epoch += 1
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def eval_model(test_data_loader, global_step, device, model, checkpoint_dir):
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eval_steps = 700
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print('Evaluating for {} steps'.format(eval_steps))
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sync_losses, recon_losses = [], []
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step = 0
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while 1:
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for x, indiv_mels, mel, gt in test_data_loader:
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step += 1
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model.eval()
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# Move data to CUDA device
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x = x.to(device)
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gt = gt.to(device)
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indiv_mels = indiv_mels.to(device)
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mel = mel.to(device)
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g = model(indiv_mels, x)
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sync_loss = get_sync_loss(mel, g)
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l1loss = recon_loss(g, gt)
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sync_losses.append(sync_loss.item())
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recon_losses.append(l1loss.item())
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if step > eval_steps:
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averaged_sync_loss = sum(sync_losses) / len(sync_losses)
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averaged_recon_loss = sum(recon_losses) / len(recon_losses)
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print('L1: {}, Sync loss: {}'.format(averaged_recon_loss, averaged_sync_loss))
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return averaged_sync_loss
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def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch):
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checkpoint_path = join(
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checkpoint_dir, "checkpoint_step{:09d}.pth".format(global_step))
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optimizer_state = optimizer.state_dict() if hparams.save_optimizer_state else None
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torch.save({
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"state_dict": model.state_dict(),
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"optimizer": optimizer_state,
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"global_step": step,
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"global_epoch": epoch,
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}, checkpoint_path)
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print("Saved checkpoint:", checkpoint_path)
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def _load(checkpoint_path):
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if use_cuda:
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checkpoint = torch.load(checkpoint_path)
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else:
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checkpoint = torch.load(checkpoint_path,
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map_location=lambda storage, loc: storage)
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return checkpoint
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def load_checkpoint(path, model, optimizer, reset_optimizer=False, overwrite_global_states=True):
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global global_step
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global global_epoch
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print("Load checkpoint from: {}".format(path))
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checkpoint = _load(path)
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s = checkpoint["state_dict"]
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new_s = {}
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for k, v in s.items():
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new_s[k.replace('module.', '')] = v
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model.load_state_dict(new_s)
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if not reset_optimizer:
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optimizer_state = checkpoint["optimizer"]
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if optimizer_state is not None:
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print("Load optimizer state from {}".format(path))
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optimizer.load_state_dict(checkpoint["optimizer"])
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if overwrite_global_states:
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global_step = checkpoint["global_step"]
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global_epoch = checkpoint["global_epoch"]
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return model
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if __name__ == "__main__":
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checkpoint_dir = args.checkpoint_dir
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# Dataset and Dataloader setup
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train_dataset = Dataset('train')
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test_dataset = Dataset('val')
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train_data_loader = data_utils.DataLoader(
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train_dataset, batch_size=hparams.batch_size, shuffle=True,
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num_workers=hparams.num_workers)
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test_data_loader = data_utils.DataLoader(
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test_dataset, batch_size=hparams.batch_size,
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num_workers=4)
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device = torch.device("cuda" if use_cuda else "cpu")
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# Model
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model = Wav2Lip().to(device)
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print('total trainable params {}'.format(sum(p.numel() for p in model.parameters() if p.requires_grad)))
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optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad],
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||
|
|
lr=hparams.initial_learning_rate)
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||
|
|
|
||
|
|
if args.checkpoint_path is not None:
|
||
|
|
load_checkpoint(args.checkpoint_path, model, optimizer, reset_optimizer=False)
|
||
|
|
|
||
|
|
load_checkpoint(args.syncnet_checkpoint_path, syncnet, None, reset_optimizer=True, overwrite_global_states=False)
|
||
|
|
|
||
|
|
if not os.path.exists(checkpoint_dir):
|
||
|
|
os.mkdir(checkpoint_dir)
|
||
|
|
|
||
|
|
# Train!
|
||
|
|
train(device, model, train_data_loader, test_data_loader, optimizer,
|
||
|
|
checkpoint_dir=checkpoint_dir,
|
||
|
|
checkpoint_interval=hparams.checkpoint_interval,
|
||
|
|
nepochs=hparams.nepochs)
|