Spaces:
Sleeping
Sleeping
File size: 6,027 Bytes
bc3753a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
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) |