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diversity.py
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import os
import sys
# os.environ["PYOPENGL_PLATFORM"] = "egl"
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
sys.path.append(os.getcwd())
from transformers import Wav2Vec2Processor
from glob import glob
import numpy as np
import json
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
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils import data
from data_utils.rotation_conversion import rotation_6d_to_matrix, matrix_to_axis_angle
from data_utils.lower_body import part2full, pred2poses, poses2pred, poses2poses
from visualise.rendering import RenderTool
import time
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_LS3DCG':
generator = LS3DCG(
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):
if data_root.endswith('.csv'):
raise NotImplementedError
else:
data_class = torch_data
if 'smplx' in config.Model.model_name or 's2g' in config.Model.model_name:
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
)
else:
data_base = torch_data(
data_root=data_root,
speakers=speakers,
split='val',
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,
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
)
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()
infer_set = data_base.all_dataset
infer_loader = data.DataLoader(data_base.all_dataset, batch_size=1, shuffle=False)
return infer_set, infer_loader, norm_stats
def get_vertices(smplx_model, betas, result_list, exp, require_pose=False):
vertices_list = []
poses_list = []
expression = torch.zeros([1, 50])
for i in result_list:
vertices = []
poses = []
for j in range(i.shape[0]):
output = smplx_model(betas=betas,
expression=i[j][165:265].unsqueeze_(dim=0) if exp else expression,
jaw_pose=i[j][0:3].unsqueeze_(dim=0),
leye_pose=i[j][3:6].unsqueeze_(dim=0),
reye_pose=i[j][6:9].unsqueeze_(dim=0),
global_orient=i[j][9:12].unsqueeze_(dim=0),
body_pose=i[j][12:75].unsqueeze_(dim=0),
left_hand_pose=i[j][75:120].unsqueeze_(dim=0),
right_hand_pose=i[j][120:165].unsqueeze_(dim=0),
return_verts=True)
vertices.append(output.vertices.detach().cpu().numpy().squeeze())
# pose = torch.cat([output.body_pose, output.left_hand_pose, output.right_hand_pose], dim=1)
pose = output.body_pose
poses.append(pose.detach().cpu())
vertices = np.asarray(vertices)
vertices_list.append(vertices)
poses = torch.cat(poses, dim=0)
poses_list.append(poses)
if require_pose:
return vertices_list, poses_list
else:
return vertices_list, None
global_orient = torch.tensor([3.0747, -0.0158, -0.0152])
def infer(data_root, g_body, g_face, g_body2, exp_name, infer_loader, infer_set, device, norm_stats, smplx,
smplx_model, rendertool, args=None, config=None):
am = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-phoneme")
am_sr = 16000
num_sample = 1
face = False
if face:
body_static = torch.zeros([1, 162], device='cuda')
body_static[:, 6:9] = torch.tensor([3.0747, -0.0158, -0.0152]).reshape(1, 3).repeat(body_static.shape[0], 1)
stand = False
j = 0
gt_0 = None
for bat in infer_loader:
poses_ = bat['poses'].to(torch.float32).to(device)
if poses_.shape[-1] == 300:
j = j + 1
if j > 1000:
continue
id = bat['speaker'].to('cuda') - 20
if config.Data.pose.expression:
expression = bat['expression'].to(device).to(torch.float32)
poses = torch.cat([poses_, expression], dim=1)
else:
poses = poses_
cur_wav_file = bat['aud_file'][0]
betas = bat['betas'][0].to(torch.float64).to('cuda')
# betas = torch.zeros([1, 300], dtype=torch.float64).to('cuda')
gt = poses.to('cuda').squeeze().transpose(1, 0)
if config.Data.pose.normalization:
gt = denormalize(gt, norm_stats[0], norm_stats[1]).squeeze(dim=0)
if config.Data.pose.convert_to_6d:
if config.Data.pose.expression:
gt_exp = gt[:, -100:]
gt = gt[:, :-100]
gt = gt.reshape(gt.shape[0], -1, 6)
gt = matrix_to_axis_angle(rotation_6d_to_matrix(gt)).reshape(gt.shape[0], -1)
gt = torch.cat([gt, gt_exp], -1)
if face:
gt = torch.cat([gt[:, :3], body_static.repeat(gt.shape[0], 1), gt[:, -100:]], dim=-1)
result_list = [gt]
# cur_wav_file = '.\\training_data\\1_song_(Vocals).wav'
pred_face = g_face.infer_on_audio(cur_wav_file,
initial_pose=poses_,
norm_stats=None,
w_pre=False,
# id=id,
frame=None,
am=am,
am_sr=am_sr
)
pred_face = torch.tensor(pred_face).squeeze().to('cuda')
# pred_face = torch.zeros([gt.shape[0], 105])
if config.Data.pose.convert_to_6d:
pred_jaw = pred_face[:, :6].reshape(pred_face.shape[0], -1, 6)
pred_jaw = matrix_to_axis_angle(rotation_6d_to_matrix(pred_jaw)).reshape(pred_face.shape[0], -1)
pred_face = pred_face[:, 6:]
else:
pred_jaw = pred_face[:, :3]
pred_face = pred_face[:, 3:]
# id = torch.tensor([0], device='cuda')
for i in range(num_sample):
pred_res = g_body.infer_on_audio(cur_wav_file,
initial_pose=poses_,
norm_stats=norm_stats,
txgfile=None,
id=id,
# var=var,
fps=30,
w_pre=False
)
pred = torch.tensor(pred_res).squeeze().to('cuda')
if pred.shape[0] < pred_face.shape[0]:
repeat_frame = pred[-1].unsqueeze(dim=0).repeat(pred_face.shape[0] - pred.shape[0], 1)
pred = torch.cat([pred, repeat_frame], dim=0)
else:
pred = pred[:pred_face.shape[0], :]
body_or_face = False
if pred.shape[1] < 275:
body_or_face = True
if config.Data.pose.convert_to_6d:
pred = pred.reshape(pred.shape[0], -1, 6)
pred = matrix_to_axis_angle(rotation_6d_to_matrix(pred))
pred = pred.reshape(pred.shape[0], -1)
pred = torch.cat([pred_jaw, pred, pred_face], dim=-1)
# pred[:, 9:12] = global_orient
pred = part2full(pred, stand)
if face:
pred = torch.cat([pred[:, :3], body_static.repeat(pred.shape[0], 1), pred[:, -100:]], dim=-1)
result_list[0] = poses2pred(result_list[0], stand)
# if gt_0 is None:
# gt_0 = gt
# pred = pred2poses(pred, gt_0)
# result_list[0] = poses2poses(result_list[0], gt_0)
result_list.append(pred)
if g_body2 is not None:
pred_res2 = g_body2.infer_on_audio(cur_wav_file,
initial_pose=poses_,
norm_stats=norm_stats,
txgfile=None,
# var=var,
fps=30,
w_pre=False
)
pred2 = torch.tensor(pred_res2).squeeze().to('cuda')
pred2 = torch.cat([pred2[:, :3], pred2[:, 103:], pred2[:, 3:103]], dim=-1)
# pred2 = part2full(pred2, stand)
# result_list[0] = poses2pred(result_list[0], stand)
# if gt_0 is None:
# gt_0 = gt
# pred2 = pred2poses(pred2, gt_0)
# result_list[0] = poses2poses(result_list[0], gt_0)
result_list[1] = pred2
vertices_list, _ = get_vertices(smplx_model, betas, result_list, config.Data.pose.expression)
result_list = [res.to('cpu') for res in result_list]
dict = np.concatenate(result_list[1:], axis=0)
file_name = 'visualise/video/' + config.Log.name + '/' + \
cur_wav_file.split('\\')[-1].split('.')[-2].split('/')[-1]
np.save(file_name, dict)
rendertool._render_sequences(cur_wav_file, vertices_list[1:], stand=stand, face=face)
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)
face_model_name = args.face_model_name
face_model_path = args.face_model_path
body_model_name = args.body_model_name
body_model_path = args.body_model_path
smplx_path = './visualise/'
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 model...')
generator = init_model(body_model_name, body_model_path, args, config)
generator2 = None
generator_face = init_model(face_model_name, face_model_path, args, config)
print('init dataloader...')
infer_set, infer_loader, norm_stats = init_dataloader(config.Data.data_root, args.speakers, args, config)
print('init smlpx model...')
dtype = torch.float64
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,
# gender='ne',
dtype=dtype, )
smplx_model = smpl.create(**model_params).to('cuda')
print('init rendertool...')
rendertool = RenderTool('visualise/video/' + config.Log.name)
infer(config.Data.data_root, generator, generator_face, generator2, args.exp_name, infer_loader, infer_set, device,
norm_stats, True, smplx_model, rendertool, args, config)
if __name__ == '__main__':
main()