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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
import safetensors
import safetensors.torch
from src.audio2pose_models.audio2pose import Audio2Pose
from src.audio2exp_models.networks import SimpleWrapperV2
from src.audio2exp_models.audio2exp import Audio2Exp
from src.utils.safetensor_helper import load_x_from_safetensor
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, sadtalker_path, device):
#load config
fcfg_pose = open(sadtalker_path['audio2pose_yaml_path'])
cfg_pose = CN.load_cfg(fcfg_pose)
cfg_pose.freeze()
fcfg_exp = open(sadtalker_path['audio2exp_yaml_path'])
cfg_exp = CN.load_cfg(fcfg_exp)
cfg_exp.freeze()
# load audio2pose_model
self.audio2pose_model = Audio2Pose(cfg_pose, None, 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:
if sadtalker_path['use_safetensor']:
checkpoints = safetensors.torch.load_file(sadtalker_path['checkpoint'])
self.audio2pose_model.load_state_dict(load_x_from_safetensor(checkpoints, 'audio2pose'))
else:
load_cpk(sadtalker_path['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:
if sadtalker_path['use_safetensor']:
checkpoints = safetensors.torch.load_file(sadtalker_path['checkpoint'])
netG.load_state_dict(load_x_from_safetensor(checkpoints, 'audio2exp'))
else:
load_cpk(sadtalker_path['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<num_frames:
div = num_frames//refpose_num_frames
re = num_frames%refpose_num_frames
refpose_coeff_list = [refpose_coeff for i in range(div)]
refpose_coeff_list.append(refpose_coeff[:re, :])
refpose_coeff = np.concatenate(refpose_coeff_list, axis=0)
#### relative head pose
coeffs_pred_numpy[:, 64:70] = coeffs_pred_numpy[:, 64:70] + ( refpose_coeff[:num_frames, :] - refpose_coeff[0:1, :] )
return coeffs_pred_numpy
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