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| import os | |
| import torch | |
| import numpy as np | |
| from scipy.io import savemat | |
| from yacs.config import CfgNode as CN | |
| from scipy.signal import savgol_filter | |
| from src.audio2pose_models.audio2pose import Audio2Pose | |
| from src.audio2exp_models.networks import SimpleWrapperV2 | |
| from src.audio2exp_models.audio2exp import Audio2Exp | |
| def load_cpk(checkpoint_path, model=None, optimizer=None, device="cpu"): | |
| checkpoint = torch.load(checkpoint_path, map_location=torch.device(device)) | |
| if model is not None: | |
| model.load_state_dict(checkpoint['model']) | |
| if optimizer is not None: | |
| optimizer.load_state_dict(checkpoint['optimizer']) | |
| return checkpoint['epoch'] | |
| class Audio2Coeff(): | |
| def __init__(self, audio2pose_checkpoint, audio2pose_yaml_path, | |
| audio2exp_checkpoint, audio2exp_yaml_path, | |
| wav2lip_checkpoint, device): | |
| #load config | |
| fcfg_pose = open(audio2pose_yaml_path) | |
| cfg_pose = CN.load_cfg(fcfg_pose) | |
| cfg_pose.freeze() | |
| fcfg_exp = open(audio2exp_yaml_path) | |
| cfg_exp = CN.load_cfg(fcfg_exp) | |
| cfg_exp.freeze() | |
| # load audio2pose_model | |
| self.audio2pose_model = Audio2Pose(cfg_pose, wav2lip_checkpoint, device=device) | |
| self.audio2pose_model = self.audio2pose_model.to(device) | |
| self.audio2pose_model.eval() | |
| for param in self.audio2pose_model.parameters(): | |
| param.requires_grad = False | |
| try: | |
| load_cpk(audio2pose_checkpoint, model=self.audio2pose_model, device=device) | |
| except: | |
| raise Exception("Failed in loading audio2pose_checkpoint") | |
| # load audio2exp_model | |
| netG = SimpleWrapperV2() | |
| netG = netG.to(device) | |
| for param in netG.parameters(): | |
| netG.requires_grad = False | |
| netG.eval() | |
| try: | |
| load_cpk(audio2exp_checkpoint, model=netG, device=device) | |
| except: | |
| raise Exception("Failed in loading audio2exp_checkpoint") | |
| self.audio2exp_model = Audio2Exp(netG, cfg_exp, device=device, prepare_training_loss=False) | |
| self.audio2exp_model = self.audio2exp_model.to(device) | |
| for param in self.audio2exp_model.parameters(): | |
| param.requires_grad = False | |
| self.audio2exp_model.eval() | |
| self.device = device | |
| def generate(self, batch, coeff_save_dir, pose_style): | |
| with torch.no_grad(): | |
| #test | |
| results_dict_exp= self.audio2exp_model.test(batch) | |
| exp_pred = results_dict_exp['exp_coeff_pred'] #bs T 64 | |
| #for class_id in range(1): | |
| #class_id = 0#(i+10)%45 | |
| #class_id = random.randint(0,46) #46 styles can be selected | |
| batch['class'] = torch.LongTensor([pose_style]).to(self.device) | |
| results_dict_pose = self.audio2pose_model.test(batch) | |
| pose_pred = results_dict_pose['pose_pred'] #bs T 6 | |
| pose_pred = torch.Tensor(savgol_filter(np.array(pose_pred.cpu()), 13, 2, axis=1)).to(self.device) | |
| coeffs_pred = torch.cat((exp_pred, pose_pred), dim=-1) #bs T 70 | |
| coeffs_pred_numpy = coeffs_pred[0].clone().detach().cpu().numpy() | |
| savemat(os.path.join(coeff_save_dir, '%s##%s.mat'%(batch['pic_name'], batch['audio_name'])), | |
| {'coeff_3dmm': coeffs_pred_numpy}) | |
| torch.cuda.empty_cache() | |
| return os.path.join(coeff_save_dir, '%s##%s.mat'%(batch['pic_name'], batch['audio_name'])) | |