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