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| import os | |
| import json | |
| import torch | |
| import torch.optim as optim | |
| from torch.utils.tensorboard import SummaryWriter | |
| import models.vqvae as vqvae | |
| import utils.losses as losses | |
| import options.option_vq as option_vq | |
| import utils.utils_model as utils_model | |
| from dataset import dataset_VQ, dataset_TM_eval | |
| import utils.eval_trans as eval_trans | |
| from options.get_eval_option import get_opt | |
| from models.evaluator_wrapper import EvaluatorModelWrapper | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| from utils.word_vectorizer import WordVectorizer | |
| from multiprocessing import freeze_support | |
| def update_lr_warm_up(optimizer, nb_iter, warm_up_iter, lr): | |
| current_lr = lr * (nb_iter + 1) / (warm_up_iter + 1) | |
| for param_group in optimizer.param_groups: | |
| param_group["lr"] = current_lr | |
| return optimizer, current_lr | |
| ##### ---- Exp dirs ---- ##### | |
| args = option_vq.get_args_parser() | |
| torch.manual_seed(args.seed) | |
| args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}') | |
| os.makedirs(args.out_dir, exist_ok = True) | |
| ##### ---- Logger ---- ##### | |
| logger = utils_model.get_logger(args.out_dir) | |
| writer = SummaryWriter(args.out_dir) | |
| logger.info(json.dumps(vars(args), indent=4, sort_keys=True)) | |
| w_vectorizer = WordVectorizer('./glove', 'our_vab') | |
| # w_vectorizer = WordVectorizer('D:\project\\faithfulpose\T2M-GPT-main\glove', 'our_vab') | |
| if args.dataname == 'kit' : | |
| dataset_opt_path = 'checkpoints/kit/Comp_v6_KLD005/opt.txt' | |
| # dataset_opt_path = 'D:\project\\faithfulpose\T2M-GPT-main\checkpoints\kit\Comp_v6_KLD005\opt.txt' | |
| args.nb_joints = 21 | |
| else : | |
| dataset_opt_path = 'checkpoints/t2m/Comp_v6_KLD005/opt.txt' | |
| args.nb_joints = 22 | |
| logger.info(f'Training on {args.dataname}, motions are with {args.nb_joints} joints') | |
| wrapper_opt = get_opt(dataset_opt_path, torch.device('cuda')) | |
| eval_wrapper = EvaluatorModelWrapper(wrapper_opt) | |
| ##### ---- Dataloader ---- ##### | |
| train_loader = dataset_VQ.DATALoader(args.dataname, | |
| args.batch_size, | |
| window_size=args.window_size, | |
| unit_length=2**args.down_t) | |
| train_loader_iter = dataset_VQ.cycle(train_loader) | |
| val_loader = dataset_TM_eval.DATALoader(args.dataname, False, | |
| 32, | |
| w_vectorizer, | |
| unit_length=2**args.down_t) | |
| ##### ---- Network ---- ##### | |
| net = vqvae.HumanVQVAE(args, ## use args to define different parameters in different quantizers | |
| args.nb_code, | |
| args.code_dim, | |
| args.output_emb_width, | |
| args.down_t, | |
| args.stride_t, | |
| args.width, | |
| args.depth, | |
| args.dilation_growth_rate, | |
| args.vq_act, | |
| args.vq_norm) | |
| if args.resume_pth : | |
| logger.info('loading checkpoint from {}'.format(args.resume_pth)) | |
| ckpt = torch.load(args.resume_pth, map_location='cpu') | |
| net.load_state_dict(ckpt['net'], strict=True) | |
| net.train() | |
| net.cuda() | |
| ##### ---- Optimizer & Scheduler ---- ##### | |
| optimizer = optim.AdamW(net.parameters(), lr=args.lr, betas=(0.9, 0.99), weight_decay=args.weight_decay) | |
| scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_scheduler, gamma=args.gamma) | |
| Loss = losses.ReConsLoss(args.recons_loss, args.nb_joints) | |
| ##### ------ warm-up ------- ##### | |
| avg_recons, avg_perplexity, avg_commit = 0., 0., 0. | |
| for nb_iter in range(1, args.warm_up_iter): | |
| optimizer, current_lr = update_lr_warm_up(optimizer, nb_iter, args.warm_up_iter, args.lr) | |
| gt_motion = next(train_loader_iter) | |
| gt_motion = gt_motion.cuda().float() # (bs, 64, dim) | |
| pred_motion, loss_commit, perplexity = net(gt_motion) | |
| loss_motion = Loss(pred_motion, gt_motion) | |
| loss_vel = Loss.forward_vel(pred_motion, gt_motion) | |
| loss = loss_motion + args.commit * loss_commit + args.loss_vel * loss_vel | |
| optimizer.zero_grad() | |
| loss.backward() | |
| optimizer.step() | |
| avg_recons += loss_motion.item() | |
| avg_perplexity += perplexity.item() | |
| avg_commit += loss_commit.item() | |
| if nb_iter % args.print_iter == 0 : | |
| avg_recons /= args.print_iter | |
| avg_perplexity /= args.print_iter | |
| avg_commit /= args.print_iter | |
| logger.info(f"Warmup. Iter {nb_iter} : lr {current_lr:.5f} \t Commit. {avg_commit:.5f} \t PPL. {avg_perplexity:.2f} \t Recons. {avg_recons:.5f}") | |
| avg_recons, avg_perplexity, avg_commit = 0., 0., 0. | |
| # ##### ---- Training ---- ##### | |
| avg_recons, avg_perplexity, avg_commit = 0., 0., 0. | |
| best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, writer, logger = eval_trans.evaluation_vqvae(args.out_dir, val_loader, net, logger, writer, 0, best_fid=1000, best_iter=0, best_div=100, best_top1=0, best_top2=0, best_top3=0, best_matching=100, eval_wrapper=eval_wrapper) | |
| for nb_iter in range(1, args.total_iter + 1): | |
| gt_motion = next(train_loader_iter) | |
| gt_motion = gt_motion.cuda().float() # bs, nb_joints, joints_dim, seq_len | |
| pred_motion, loss_commit, perplexity = net(gt_motion) | |
| loss_motion = Loss(pred_motion, gt_motion) | |
| loss_vel = Loss.forward_vel(pred_motion, gt_motion) | |
| loss = loss_motion + args.commit * loss_commit + args.loss_vel * loss_vel | |
| optimizer.zero_grad() | |
| loss.backward() | |
| optimizer.step() | |
| scheduler.step() | |
| avg_recons += loss_motion.item() | |
| avg_perplexity += perplexity.item() | |
| avg_commit += loss_commit.item() | |
| if nb_iter % args.print_iter == 0 : | |
| avg_recons /= args.print_iter | |
| avg_perplexity /= args.print_iter | |
| avg_commit /= args.print_iter | |
| writer.add_scalar('./Train/L1', avg_recons, nb_iter) | |
| writer.add_scalar('./Train/PPL', avg_perplexity, nb_iter) | |
| writer.add_scalar('./Train/Commit', avg_commit, nb_iter) | |
| logger.info(f"Train. Iter {nb_iter} : \t Commit. {avg_commit:.5f} \t PPL. {avg_perplexity:.2f} \t Recons. {avg_recons:.5f}") | |
| avg_recons, avg_perplexity, avg_commit = 0., 0., 0., | |
| if nb_iter % args.eval_iter==0 : | |
| best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, writer, logger = eval_trans.evaluation_vqvae(args.out_dir, val_loader, net, logger, writer, nb_iter, best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, eval_wrapper=eval_wrapper) | |