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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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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 expert lip-sync discriminator') |
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parser.add_argument("--data_root", help="Root folder of the preprocessed LRS2 dataset", required=True) |
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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('--checkpoint_path', help='Resumed 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 crop_audio_window(self, spec, start_frame): |
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start_frame_num = self.get_frame_id(start_frame) |
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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 __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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if random.choice([True, False]): |
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y = torch.ones(1).float() |
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chosen = img_name |
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else: |
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y = torch.zeros(1).float() |
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chosen = wrong_img_name |
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window_fnames = self.get_window(chosen) |
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if window_fnames is None: |
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continue |
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window = [] |
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all_read = True |
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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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all_read = False |
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break |
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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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all_read = False |
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break |
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window.append(img) |
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if not all_read: 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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x = np.concatenate(window, axis=2) / 255. |
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x = x.transpose(2, 0, 1) |
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x = x[:, x.shape[1]//2:] |
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x = torch.FloatTensor(x) |
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mel = torch.FloatTensor(mel.T).unsqueeze(0) |
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return x, mel, y |
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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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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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running_loss = 0. |
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prog_bar = tqdm(enumerate(train_data_loader)) |
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for step, (x, mel, y) in prog_bar: |
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model.train() |
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optimizer.zero_grad() |
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x = x.to(device) |
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mel = mel.to(device) |
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a, v = model(mel, x) |
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y = y.to(device) |
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loss = cosine_loss(a, v, y) |
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loss.backward() |
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optimizer.step() |
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global_step += 1 |
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cur_session_steps = global_step - resumed_step |
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running_loss += loss.item() |
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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 % hparams.syncnet_eval_interval == 0: |
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with torch.no_grad(): |
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eval_model(test_data_loader, global_step, device, model, checkpoint_dir) |
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prog_bar.set_description('Loss: {}'.format(running_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 = 1400 |
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print('Evaluating for {} steps'.format(eval_steps)) |
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losses = [] |
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while 1: |
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for step, (x, mel, y) in enumerate(test_data_loader): |
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model.eval() |
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x = x.to(device) |
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mel = mel.to(device) |
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a, v = model(mel, x) |
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y = y.to(device) |
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loss = cosine_loss(a, v, y) |
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losses.append(loss.item()) |
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if step > eval_steps: break |
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averaged_loss = sum(losses) / len(losses) |
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print(averaged_loss) |
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return |
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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): |
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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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model.load_state_dict(checkpoint["state_dict"]) |
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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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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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checkpoint_path = args.checkpoint_path |
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if not os.path.exists(checkpoint_dir): os.mkdir(checkpoint_dir) |
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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.syncnet_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.syncnet_batch_size, |
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num_workers=8) |
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device = torch.device("cuda" if use_cuda else "cpu") |
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model = SyncNet().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.syncnet_lr) |
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if checkpoint_path is not None: |
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load_checkpoint(checkpoint_path, model, optimizer, reset_optimizer=False) |
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train(device, model, train_data_loader, test_data_loader, optimizer, |
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checkpoint_dir=checkpoint_dir, |
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checkpoint_interval=hparams.syncnet_checkpoint_interval, |
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nepochs=hparams.nepochs) |
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