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| import os | |
| from os.path import join as pjoin | |
| import torch | |
| from torch.utils.data import DataLoader | |
| from models.vq.model import RVQVAE | |
| from models.vq.vq_trainer import RVQTokenizerTrainer | |
| from options.vq_option import arg_parse | |
| from data.t2m_dataset import MotionDataset | |
| from utils import paramUtil | |
| import numpy as np | |
| from models.t2m_eval_wrapper import EvaluatorModelWrapper | |
| from utils.get_opt import get_opt | |
| from motion_loaders.dataset_motion_loader import get_dataset_motion_loader | |
| from utils.motion_process import recover_from_ric | |
| from utils.plot_script import plot_3d_motion | |
| os.environ["OMP_NUM_THREADS"] = "1" | |
| def plot_t2m(data, save_dir): | |
| data = train_dataset.inv_transform(data) | |
| for i in range(len(data)): | |
| joint_data = data[i] | |
| joint = recover_from_ric(torch.from_numpy(joint_data).float(), opt.joints_num).numpy() | |
| save_path = pjoin(save_dir, '%02d.mp4' % (i)) | |
| plot_3d_motion(save_path, kinematic_chain, joint, title="None", fps=fps, radius=radius) | |
| if __name__ == "__main__": | |
| # torch.autograd.set_detect_anomaly(True) | |
| opt = arg_parse(True) | |
| opt.device = torch.device("cpu" if opt.gpu_id == -1 else "cuda:" + str(opt.gpu_id)) | |
| print(f"Using Device: {opt.device}") | |
| opt.save_root = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.name) | |
| opt.model_dir = pjoin(opt.save_root, 'model') | |
| opt.meta_dir = pjoin(opt.save_root, 'meta') | |
| opt.eval_dir = pjoin(opt.save_root, 'animation') | |
| opt.log_dir = pjoin('./log/vq/', opt.dataset_name, opt.name) | |
| os.makedirs(opt.model_dir, exist_ok=True) | |
| os.makedirs(opt.meta_dir, exist_ok=True) | |
| os.makedirs(opt.eval_dir, exist_ok=True) | |
| os.makedirs(opt.log_dir, exist_ok=True) | |
| if opt.dataset_name == "t2m": | |
| opt.data_root = './dataset/HumanML3D/' | |
| opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs') | |
| opt.text_dir = pjoin(opt.data_root, 'texts') | |
| opt.joints_num = 22 | |
| dim_pose = 263 | |
| fps = 20 | |
| radius = 4 | |
| kinematic_chain = paramUtil.t2m_kinematic_chain | |
| dataset_opt_path = './checkpoints/t2m/Comp_v6_KLD005/opt.txt' | |
| elif opt.dataset_name == "kit": | |
| opt.data_root = './dataset/KIT-ML/' | |
| opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs') | |
| opt.text_dir = pjoin(opt.data_root, 'texts') | |
| opt.joints_num = 21 | |
| radius = 240 * 8 | |
| fps = 12.5 | |
| dim_pose = 251 | |
| opt.max_motion_length = 196 | |
| kinematic_chain = paramUtil.kit_kinematic_chain | |
| dataset_opt_path = './checkpoints/kit/Comp_v6_KLD005/opt.txt' | |
| else: | |
| raise KeyError('Dataset Does not Exists') | |
| wrapper_opt = get_opt(dataset_opt_path, torch.device('cuda')) | |
| eval_wrapper = EvaluatorModelWrapper(wrapper_opt) | |
| mean = np.load(pjoin(opt.data_root, 'Mean.npy')) | |
| std = np.load(pjoin(opt.data_root, 'Std.npy')) | |
| train_split_file = pjoin(opt.data_root, 'train.txt') | |
| val_split_file = pjoin(opt.data_root, 'val.txt') | |
| net = RVQVAE(opt, | |
| dim_pose, | |
| opt.nb_code, | |
| opt.code_dim, | |
| opt.code_dim, | |
| opt.down_t, | |
| opt.stride_t, | |
| opt.width, | |
| opt.depth, | |
| opt.dilation_growth_rate, | |
| opt.vq_act, | |
| opt.vq_norm) | |
| pc_vq = sum(param.numel() for param in net.parameters()) | |
| print(net) | |
| # print("Total parameters of discriminator net: {}".format(pc_vq)) | |
| # all_params += pc_vq_dis | |
| print('Total parameters of all models: {}M'.format(pc_vq/1000_000)) | |
| trainer = RVQTokenizerTrainer(opt, vq_model=net) | |
| train_dataset = MotionDataset(opt, mean, std, train_split_file) | |
| val_dataset = MotionDataset(opt, mean, std, val_split_file) | |
| train_loader = DataLoader(train_dataset, batch_size=opt.batch_size, drop_last=True, num_workers=4, | |
| shuffle=True, pin_memory=True) | |
| val_loader = DataLoader(val_dataset, batch_size=opt.batch_size, drop_last=True, num_workers=4, | |
| shuffle=True, pin_memory=True) | |
| eval_val_loader, _ = get_dataset_motion_loader(dataset_opt_path, 32, 'test', device=opt.device) | |
| trainer.train(train_loader, val_loader, eval_val_loader, eval_wrapper, plot_t2m) | |
| ## train_vq.py --dataset_name kit --batch_size 512 --name VQVAE_dp2 --gpu_id 3 | |
| ## train_vq.py --dataset_name kit --batch_size 256 --name VQVAE_dp2_b256 --gpu_id 2 | |
| ## train_vq.py --dataset_name kit --batch_size 1024 --name VQVAE_dp2_b1024 --gpu_id 1 | |
| ## python train_vq.py --dataset_name kit --batch_size 256 --name VQVAE_dp1_b256 --gpu_id 2 |