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import os
import sys
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
sys.path.append(os.getcwd())
from tqdm import tqdm
from transformers import Wav2Vec2Processor
from evaluation.metrics import LVD
import numpy as np
import smplx as smpl
from nets import *
from trainer.options import parse_args
from data_utils import torch_data
from trainer.config import load_JsonConfig
from data_utils.get_j import get_joints
import torch
from torch.utils import data
def init_model(model_name, model_path, args, config):
if model_name == 's2g_face':
generator = s2g_face(
args,
config,
)
elif model_name == 's2g_body_vq':
generator = s2g_body_vq(
args,
config,
)
elif model_name == 's2g_body_pixel':
generator = s2g_body_pixel(
args,
config,
)
else:
raise NotImplementedError
model_ckpt = torch.load(model_path, map_location=torch.device('cpu'))
if model_name == 'smplx_S2G':
generator.generator.load_state_dict(model_ckpt['generator']['generator'])
elif 'generator' in list(model_ckpt.keys()):
generator.load_state_dict(model_ckpt['generator'])
else:
model_ckpt = {'generator': model_ckpt}
generator.load_state_dict(model_ckpt)
return generator
def init_dataloader(data_root, speakers, args, config):
data_base = torch_data(
data_root=data_root,
speakers=speakers,
split='test',
limbscaling=False,
normalization=config.Data.pose.normalization,
norm_method=config.Data.pose.norm_method,
split_trans_zero=False,
num_pre_frames=config.Data.pose.pre_pose_length,
num_generate_length=config.Data.pose.generate_length,
num_frames=30,
aud_feat_win_size=config.Data.aud.aud_feat_win_size,
aud_feat_dim=config.Data.aud.aud_feat_dim,
feat_method=config.Data.aud.feat_method,
smplx=True,
audio_sr=22000,
convert_to_6d=config.Data.pose.convert_to_6d,
expression=config.Data.pose.expression,
config=config
)
if config.Data.pose.normalization:
norm_stats_fn = os.path.join(os.path.dirname(args.model_path), "norm_stats.npy")
norm_stats = np.load(norm_stats_fn, allow_pickle=True)
data_base.data_mean = norm_stats[0]
data_base.data_std = norm_stats[1]
else:
norm_stats = None
data_base.get_dataset()
test_set = data_base.all_dataset
test_loader = data.DataLoader(test_set, batch_size=1, shuffle=False)
return test_set, test_loader, norm_stats
def face_loss(gt, gt_param, pr, pr_param):
loss_dict = {}
jaw_xyz = gt[:, 22:25, :] - pr[:, 22:25, :]
jaw_dist = jaw_xyz.norm(p=2, dim=-1)
jaw_dist = jaw_dist.sum(dim=-1).mean()
loss_dict['jaw_l1'] = jaw_dist
landmark_xyz = gt[:, 74:] - pr[:, 74:]
landmark_dist = landmark_xyz.norm(p=2, dim=-1)
landmark_dist = landmark_dist.sum(dim=-1).mean()
loss_dict['landmark_l1'] = landmark_dist
face_gt = torch.cat([gt[:, 22:25], gt[:, 74:]], dim=1)
face_pr = torch.cat([pr[:, 22:25], pr[:, 74:]], dim=1)
loss_dict['LVD'] = LVD(face_gt, face_pr, symmetrical=False, weight=False)
return loss_dict
def test(test_loader, generator, smplx_model, args, config):
print('start testing')
am = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-phoneme")
am_sr = 16000
loss_dict = {}
with torch.no_grad():
i = 0
for bat in tqdm(test_loader, desc="Testing......"):
i = i + 1
aud, poses, exp = bat['aud_feat'].to('cuda').to(torch.float32), bat['poses'].to('cuda').to(torch.float32), \
bat['expression'].to('cuda').to(torch.float32)
id = bat['speaker'].to('cuda') - 20
betas = bat['betas'][0].to('cuda').to(torch.float64)
poses = torch.cat([poses, exp], dim=-2).transpose(-1, -2).squeeze()
# poses = to3d(poses, config)
cur_wav_file = bat['aud_file'][0]
pred_face = generator.infer_on_audio(cur_wav_file,
id=id,
frame=poses.shape[0],
am=am,
am_sr=am_sr
)
pred_face = torch.tensor(pred_face).to('cuda').squeeze()
if pred_face.shape[1] > 103:
pred_face = pred_face[:, :103]
zero_poses = torch.zeros([pred_face.shape[0], 162], device='cuda')
full_param = torch.cat([pred_face[:, :3], zero_poses, pred_face[:, 3:]], dim=-1)
poses[:, 3:165] = full_param[:, 3:165]
gt_joints = get_joints(smplx_model, betas, poses)
pred_joints = get_joints(smplx_model, betas, full_param)
bat_loss_dict = face_loss(gt_joints, poses, pred_joints, full_param)
if loss_dict: # 非空
for key in list(bat_loss_dict.keys()):
loss_dict[key] += bat_loss_dict[key]
else:
for key in list(bat_loss_dict.keys()):
loss_dict[key] = bat_loss_dict[key]
for key in loss_dict.keys():
loss_dict[key] = loss_dict[key] / i
print(key + '=' + str(loss_dict[key].item()))
def main():
parser = parse_args()
args = parser.parse_args()
device = torch.device(args.gpu)
torch.cuda.set_device(device)
config = load_JsonConfig(args.config_file)
os.environ['smplx_npz_path'] = config.smplx_npz_path
os.environ['extra_joint_path'] = config.extra_joint_path
os.environ['j14_regressor_path'] = config.j14_regressor_path
print('init dataloader...')
test_set, test_loader, norm_stats = init_dataloader(config.Data.data_root, args.speakers, args, config)
print('init model...')
face_model_name = args.face_model_name
face_model_path = args.face_model_path
generator_face = init_model(face_model_name, face_model_path, args, config)
print('init smlpx model...')
dtype = torch.float64
smplx_path = './visualise/'
model_params = dict(model_path=smplx_path,
model_type='smplx',
create_global_orient=True,
create_body_pose=True,
create_betas=True,
num_betas=300,
create_left_hand_pose=True,
create_right_hand_pose=True,
use_pca=False,
flat_hand_mean=False,
create_expression=True,
num_expression_coeffs=100,
num_pca_comps=12,
create_jaw_pose=True,
create_leye_pose=True,
create_reye_pose=True,
create_transl=False,
dtype=dtype, )
smplx_model = smpl.create(**model_params).to('cuda')
test(test_loader, generator_face, smplx_model, args, config)
if __name__ == '__main__':
main()
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