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import torch
import yaml
import os
import safetensors
from safetensors.torch import save_file
from yacs.config import CfgNode as CN
import sys
sys.path.append('/apdcephfs/private_shadowcun/SadTalker')
from src.face3d.models import networks
from src.facerender.modules.keypoint_detector import HEEstimator, KPDetector
from src.facerender.modules.mapping import MappingNet
from src.facerender.modules.generator import OcclusionAwareGenerator, OcclusionAwareSPADEGenerator
from src.audio2pose_models.audio2pose import Audio2Pose
from src.audio2exp_models.networks import SimpleWrapperV2
from src.test_audio2coeff import load_cpk
size = 256
############ face vid2vid
config_path = os.path.join('src', 'config', 'facerender.yaml')
current_root_path = '.'
path_of_net_recon_model = os.path.join(current_root_path, 'checkpoints', 'epoch_20.pth')
net_recon = networks.define_net_recon(net_recon='resnet50', use_last_fc=False, init_path='')
checkpoint = torch.load(path_of_net_recon_model, map_location='cpu')
net_recon.load_state_dict(checkpoint['net_recon'])
with open(config_path) as f:
config = yaml.safe_load(f)
generator = OcclusionAwareSPADEGenerator(**config['model_params']['generator_params'],
**config['model_params']['common_params'])
kp_extractor = KPDetector(**config['model_params']['kp_detector_params'],
**config['model_params']['common_params'])
he_estimator = HEEstimator(**config['model_params']['he_estimator_params'],
**config['model_params']['common_params'])
mapping = MappingNet(**config['model_params']['mapping_params'])
def load_cpk_facevid2vid(checkpoint_path, generator=None, discriminator=None,
kp_detector=None, he_estimator=None, optimizer_generator=None,
optimizer_discriminator=None, optimizer_kp_detector=None,
optimizer_he_estimator=None, device="cpu"):
checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
if generator is not None:
generator.load_state_dict(checkpoint['generator'])
if kp_detector is not None:
kp_detector.load_state_dict(checkpoint['kp_detector'])
if he_estimator is not None:
he_estimator.load_state_dict(checkpoint['he_estimator'])
if discriminator is not None:
try:
discriminator.load_state_dict(checkpoint['discriminator'])
except:
print ('No discriminator in the state-dict. Dicriminator will be randomly initialized')
if optimizer_generator is not None:
optimizer_generator.load_state_dict(checkpoint['optimizer_generator'])
if optimizer_discriminator is not None:
try:
optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
except RuntimeError as e:
print ('No discriminator optimizer in the state-dict. Optimizer will be not initialized')
if optimizer_kp_detector is not None:
optimizer_kp_detector.load_state_dict(checkpoint['optimizer_kp_detector'])
if optimizer_he_estimator is not None:
optimizer_he_estimator.load_state_dict(checkpoint['optimizer_he_estimator'])
return checkpoint['epoch']
def load_cpk_facevid2vid_safetensor(checkpoint_path, generator=None,
kp_detector=None, he_estimator=None,
device="cpu"):
checkpoint = safetensors.torch.load_file(checkpoint_path)
if generator is not None:
x_generator = {}
for k,v in checkpoint.items():
if 'generator' in k:
x_generator[k.replace('generator.', '')] = v
generator.load_state_dict(x_generator)
if kp_detector is not None:
x_generator = {}
for k,v in checkpoint.items():
if 'kp_extractor' in k:
x_generator[k.replace('kp_extractor.', '')] = v
kp_detector.load_state_dict(x_generator)
if he_estimator is not None:
x_generator = {}
for k,v in checkpoint.items():
if 'he_estimator' in k:
x_generator[k.replace('he_estimator.', '')] = v
he_estimator.load_state_dict(x_generator)
return None
free_view_checkpoint = '/apdcephfs/private_shadowcun/SadTalker/checkpoints/facevid2vid_'+str(size)+'-model.pth.tar'
load_cpk_facevid2vid(free_view_checkpoint, kp_detector=kp_extractor, generator=generator, he_estimator=he_estimator)
wav2lip_checkpoint = os.path.join(current_root_path, 'checkpoints', 'wav2lip.pth')
audio2pose_checkpoint = os.path.join(current_root_path, 'checkpoints', 'auido2pose_00140-model.pth')
audio2pose_yaml_path = os.path.join(current_root_path, 'src', 'config', 'auido2pose.yaml')
audio2exp_checkpoint = os.path.join(current_root_path, 'checkpoints', 'auido2exp_00300-model.pth')
audio2exp_yaml_path = os.path.join(current_root_path, 'src', 'config', 'auido2exp.yaml')
fcfg_pose = open(audio2pose_yaml_path)
cfg_pose = CN.load_cfg(fcfg_pose)
cfg_pose.freeze()
audio2pose_model = Audio2Pose(cfg_pose, wav2lip_checkpoint)
audio2pose_model.eval()
load_cpk(audio2pose_checkpoint, model=audio2pose_model, device='cpu')
# load audio2exp_model
netG = SimpleWrapperV2()
netG.eval()
load_cpk(audio2exp_checkpoint, model=netG, device='cpu')
class SadTalker(torch.nn.Module):
def __init__(self, kp_extractor, generator, netG, audio2pose, face_3drecon):
super(SadTalker, self).__init__()
self.kp_extractor = kp_extractor
self.generator = generator
self.audio2exp = netG
self.audio2pose = audio2pose
self.face_3drecon = face_3drecon
model = SadTalker(kp_extractor, generator, netG, audio2pose_model, net_recon)
# here, we want to convert it to safetensor
save_file(model.state_dict(), "checkpoints/SadTalker_V0.0.2_"+str(size)+".safetensors")
### test
load_cpk_facevid2vid_safetensor('checkpoints/SadTalker_V0.0.2_'+str(size)+'.safetensors', kp_detector=kp_extractor, generator=generator, he_estimator=None) |