import os import torch import numpy as np from scipy.io import savemat, loadmat 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, ref_pose_coeff_path=None): 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_len = pose_pred.shape[1] if pose_len<13: pose_len = int((pose_len-1)/2)*2+1 pose_pred = torch.Tensor(savgol_filter(np.array(pose_pred.cpu()), pose_len, 2, axis=1)).to(self.device) else: 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() if ref_pose_coeff_path is not None: coeffs_pred_numpy = self.using_refpose(coeffs_pred_numpy, ref_pose_coeff_path) savemat(os.path.join(coeff_save_dir, '%s##%s.mat'%(batch['pic_name'], batch['audio_name'])), {'coeff_3dmm': coeffs_pred_numpy}) return os.path.join(coeff_save_dir, '%s##%s.mat'%(batch['pic_name'], batch['audio_name'])) def using_refpose(self, coeffs_pred_numpy, ref_pose_coeff_path): num_frames = coeffs_pred_numpy.shape[0] refpose_coeff_dict = loadmat(ref_pose_coeff_path) refpose_coeff = refpose_coeff_dict['coeff_3dmm'][:,64:70] refpose_num_frames = refpose_coeff.shape[0] if refpose_num_frames