import os import sys 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()