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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']))
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