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| import os | |
| from os.path import join as pjoin | |
| import torch | |
| import torch.nn.functional as F | |
| from models.mask_transformer.transformer import MaskTransformer, ResidualTransformer | |
| from models.vq.model import RVQVAE, LengthEstimator | |
| from options.eval_option import EvalT2MOptions | |
| from utils.get_opt import get_opt | |
| from utils.fixseed import fixseed | |
| from visualization.joints2bvh import Joint2BVHConvertor | |
| from utils.motion_process import recover_from_ric | |
| from utils.plot_script import plot_3d_motion | |
| from utils.paramUtil import t2m_kinematic_chain | |
| import numpy as np | |
| from gen_t2m import load_vq_model, load_res_model, load_trans_model | |
| if __name__ == '__main__': | |
| parser = EvalT2MOptions() | |
| opt = parser.parse() | |
| fixseed(opt.seed) | |
| opt.device = torch.device("cpu" if opt.gpu_id == -1 else "cuda:" + str(opt.gpu_id)) | |
| torch.autograd.set_detect_anomaly(True) | |
| dim_pose = 251 if opt.dataset_name == 'kit' else 263 | |
| root_dir = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.name) | |
| model_dir = pjoin(root_dir, 'model') | |
| result_dir = pjoin('./editing', opt.ext) | |
| joints_dir = pjoin(result_dir, 'joints') | |
| animation_dir = pjoin(result_dir, 'animations') | |
| os.makedirs(joints_dir, exist_ok=True) | |
| os.makedirs(animation_dir,exist_ok=True) | |
| model_opt_path = pjoin(root_dir, 'opt.txt') | |
| model_opt = get_opt(model_opt_path, device=opt.device) | |
| ####################### | |
| ######Loading RVQ###### | |
| ####################### | |
| vq_opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, model_opt.vq_name, 'opt.txt') | |
| vq_opt = get_opt(vq_opt_path, device=opt.device) | |
| vq_opt.dim_pose = dim_pose | |
| vq_model, vq_opt = load_vq_model(vq_opt) | |
| model_opt.num_tokens = vq_opt.nb_code | |
| model_opt.num_quantizers = vq_opt.num_quantizers | |
| model_opt.code_dim = vq_opt.code_dim | |
| ################################# | |
| ######Loading R-Transformer###### | |
| ################################# | |
| res_opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.res_name, 'opt.txt') | |
| res_opt = get_opt(res_opt_path, device=opt.device) | |
| res_model = load_res_model(res_opt, vq_opt, opt) | |
| assert res_opt.vq_name == model_opt.vq_name | |
| ################################# | |
| ######Loading M-Transformer###### | |
| ################################# | |
| t2m_transformer = load_trans_model(model_opt, opt, 'latest.tar') | |
| t2m_transformer.eval() | |
| vq_model.eval() | |
| res_model.eval() | |
| res_model.to(opt.device) | |
| t2m_transformer.to(opt.device) | |
| vq_model.to(opt.device) | |
| ##### ---- Data ---- ##### | |
| max_motion_length = 196 | |
| mean = np.load(pjoin(opt.checkpoints_dir, opt.dataset_name, model_opt.vq_name, 'meta', 'mean.npy')) | |
| std = np.load(pjoin(opt.checkpoints_dir, opt.dataset_name, model_opt.vq_name, 'meta', 'std.npy')) | |
| def inv_transform(data): | |
| return data * std + mean | |
| ### We provided an example source motion (from 'new_joint_vecs') for editing. See './example_data/000612.mp4'### | |
| motion = np.load(opt.source_motion) | |
| m_length = len(motion) | |
| motion = (motion - mean) / std | |
| if max_motion_length > m_length: | |
| motion = np.concatenate([motion, np.zeros((max_motion_length - m_length, motion.shape[1])) ], axis=0) | |
| motion = torch.from_numpy(motion)[None].to(opt.device) | |
| prompt_list = [] | |
| length_list = [] | |
| if opt.motion_length == 0: | |
| opt.motion_length = m_length | |
| print("Using default motion length.") | |
| prompt_list.append(opt.text_prompt) | |
| length_list.append(opt.motion_length) | |
| if opt.text_prompt == "": | |
| raise "Using an empty text prompt." | |
| token_lens = torch.LongTensor(length_list) // 4 | |
| token_lens = token_lens.to(opt.device).long() | |
| m_length = token_lens * 4 | |
| captions = prompt_list | |
| print_captions = captions[0] | |
| _edit_slice = opt.mask_edit_section | |
| edit_slice = [] | |
| for eds in _edit_slice: | |
| _start, _end = eds.split(',') | |
| _start = eval(_start) | |
| _end = eval(_end) | |
| edit_slice.append([_start, _end]) | |
| sample = 0 | |
| kinematic_chain = t2m_kinematic_chain | |
| converter = Joint2BVHConvertor() | |
| with torch.no_grad(): | |
| tokens, features = vq_model.encode(motion) | |
| ### build editing mask, TOEDIT marked as 1 ### | |
| edit_mask = torch.zeros_like(tokens[..., 0]) | |
| seq_len = tokens.shape[1] | |
| for _start, _end in edit_slice: | |
| if isinstance(_start, float): | |
| _start = int(_start*seq_len) | |
| _end = int(_end*seq_len) | |
| else: | |
| _start //= 4 | |
| _end //= 4 | |
| edit_mask[:, _start: _end] = 1 | |
| print_captions = f'{print_captions} [{_start*4/20.}s - {_end*4/20.}s]' | |
| edit_mask = edit_mask.bool() | |
| for r in range(opt.repeat_times): | |
| print("-->Repeat %d"%r) | |
| with torch.no_grad(): | |
| mids = t2m_transformer.edit( | |
| captions, tokens[..., 0].clone(), m_length//4, | |
| timesteps=opt.time_steps, | |
| cond_scale=opt.cond_scale, | |
| temperature=opt.temperature, | |
| topk_filter_thres=opt.topkr, | |
| gsample=opt.gumbel_sample, | |
| force_mask=opt.force_mask, | |
| edit_mask=edit_mask.clone(), | |
| ) | |
| if opt.use_res_model: | |
| mids = res_model.generate(mids, captions, m_length//4, temperature=1, cond_scale=2) | |
| else: | |
| mids.unsqueeze_(-1) | |
| pred_motions = vq_model.forward_decoder(mids) | |
| pred_motions = pred_motions.detach().cpu().numpy() | |
| source_motions = motion.detach().cpu().numpy() | |
| data = inv_transform(pred_motions) | |
| source_data = inv_transform(source_motions) | |
| for k, (caption, joint_data, source_data) in enumerate(zip(captions, data, source_data)): | |
| print("---->Sample %d: %s %d"%(k, caption, m_length[k])) | |
| animation_path = pjoin(animation_dir, str(k)) | |
| joint_path = pjoin(joints_dir, str(k)) | |
| os.makedirs(animation_path, exist_ok=True) | |
| os.makedirs(joint_path, exist_ok=True) | |
| joint_data = joint_data[:m_length[k]] | |
| joint = recover_from_ric(torch.from_numpy(joint_data).float(), 22).numpy() | |
| source_data = source_data[:m_length[k]] | |
| soucre_joint = recover_from_ric(torch.from_numpy(source_data).float(), 22).numpy() | |
| bvh_path = pjoin(animation_path, "sample%d_repeat%d_len%d_ik.bvh"%(k, r, m_length[k])) | |
| _, ik_joint = converter.convert(joint, filename=bvh_path, iterations=100) | |
| bvh_path = pjoin(animation_path, "sample%d_repeat%d_len%d.bvh" % (k, r, m_length[k])) | |
| _, joint = converter.convert(joint, filename=bvh_path, iterations=100, foot_ik=False) | |
| save_path = pjoin(animation_path, "sample%d_repeat%d_len%d.mp4"%(k, r, m_length[k])) | |
| ik_save_path = pjoin(animation_path, "sample%d_repeat%d_len%d_ik.mp4"%(k, r, m_length[k])) | |
| source_save_path = pjoin(animation_path, "sample%d_source_len%d.mp4"%(k, m_length[k])) | |
| plot_3d_motion(ik_save_path, kinematic_chain, ik_joint, title=print_captions, fps=20) | |
| plot_3d_motion(save_path, kinematic_chain, joint, title=print_captions, fps=20) | |
| plot_3d_motion(source_save_path, kinematic_chain, soucre_joint, title='None', fps=20) | |
| np.save(pjoin(joint_path, "sample%d_repeat%d_len%d.npy"%(k, r, m_length[k])), joint) | |
| np.save(pjoin(joint_path, "sample%d_repeat%d_len%d_ik.npy"%(k, r, m_length[k])), ik_joint) |