Spaces:
Sleeping
Sleeping
File size: 8,437 Bytes
99afdfe |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 |
import os
import sys
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
sys.path.append(os.getcwd())
from tqdm import tqdm
from transformers import Wav2Vec2Processor
from evaluation.FGD import EmbeddingSpaceEvaluator
from evaluation.metrics import LVD
import numpy as np
import smplx as smpl
from data_utils.lower_body import part2full, poses2pred
from data_utils.utils import get_mfcc_ta
from nets import *
from nets.utils import get_path, get_dpath
from trainer.options import parse_args
from data_utils import torch_data
from trainer.config import load_JsonConfig
import torch
from torch.utils import data
from data_utils.get_j import to3d, get_joints
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,
)
elif model_name == 's2g_body_ae':
generator = s2g_body_ae(
args,
config,
)
else:
raise NotImplementedError
model_ckpt = torch.load(model_path, map_location=torch.device('cpu'))
generator.load_state_dict(model_ckpt['generator'])
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 body_loss(gt, prs):
loss_dict = {}
# LVD
v_diff = LVD(gt[:, :22, :], prs[:, :, :22, :], symmetrical=False, weight=False)
loss_dict['LVD'] = v_diff
# Accuracy
error = (gt - prs).norm(p=2, dim=-1).sum(dim=-1).mean()
loss_dict['error'] = error
# Diversity
var = prs.var(dim=0).norm(p=2, dim=-1).sum(dim=-1).mean()
loss_dict['diverse'] = var
return loss_dict
def test(test_loader, generator, FGD_handler, smplx_model, config):
print('start testing')
am = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-phoneme")
am_sr = 16000
loss_dict = {}
B = 2
with torch.no_grad():
count = 0
for bat in tqdm(test_loader, desc="Testing......"):
count = count + 1
# if count == 10:
# break
_, 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)
cur_wav_file = bat['aud_file'][0]
zero_face = torch.zeros([B, poses.shape[1], 103], device='cuda')
joints_list = []
pred = generator.infer_on_audio(cur_wav_file,
id=id,
fps=30,
B=B,
am=am,
am_sr=am_sr,
frame=poses.shape[0]
)
pred = torch.tensor(pred, device='cuda')
FGD_handler.push_samples(pred, poses)
poses = poses.squeeze()
poses = to3d(poses, config)
if pred.shape[2] > 129:
pred = pred[:, :, 103:]
pred = torch.cat([zero_face[:, :pred.shape[1], :3], pred, zero_face[:, :pred.shape[1], 3:]], dim=-1)
full_pred = []
for j in range(B):
f_pred = part2full(pred[j])
full_pred.append(f_pred)
for i in range(full_pred.__len__()):
full_pred[i] = full_pred[i].unsqueeze(dim=0)
full_pred = torch.cat(full_pred, dim=0)
pred_joints = get_joints(smplx_model, betas, full_pred)
poses = poses2pred(poses)
poses = torch.cat([zero_face[0, :, :3], poses[:, 3:165], zero_face[0, :, 3:]], dim=-1)
gt_joints = get_joints(smplx_model, betas, poses[:pred_joints.shape[1]])
FGD_handler.push_joints(pred_joints, gt_joints)
aud = get_mfcc_ta(cur_wav_file, fps=30, sr=16000, am='not None', encoder_choice='onset')
FGD_handler.push_aud(torch.from_numpy(aud))
bat_loss_dict = body_loss(gt_joints, pred_joints)
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] / count
print(key + '=' + str(loss_dict[key].item()))
# MAAC = FGD_handler.get_MAAC()
# print(MAAC)
fgd_dist, feat_dist = FGD_handler.get_scores()
print('fgd_dist=', fgd_dist.item())
print('feat_dist=', feat_dist.item())
BCscore = FGD_handler.get_BCscore()
print('Beat consistency score=', BCscore)
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...')
model_name = args.body_model_name
# model_path = get_path(model_name, model_type)
model_path = args.body_model_path
generator = init_model(model_name, model_path, args, config)
ae = init_model('s2g_body_ae', './experiments/feature_extractor.pth', args,
config)
FGD_handler = EmbeddingSpaceEvaluator(ae, None, 'cuda')
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, FGD_handler, smplx_model, config)
if __name__ == '__main__':
main()
|