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 tqdm import tqdm from exit.utils import get_model, generate_src_mask, init_save_folder from models.vqvae_sep import VQVAE_SEP 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'vq') # /{args.exp_name} # os.makedirs(args.out_dir, exist_ok = True) init_save_folder(args) ##### ---- 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') if args.dataname == 'kit' : dataset_opt_path = '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 ---- ##### if args.sep_uplow: net = VQVAE_SEP(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, {'mean': torch.from_numpy(train_loader.dataset.mean).cuda().float(), 'std': torch.from_numpy(train_loader.dataset.std).cuda().float()}, True) else: 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 tqdm(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_joint(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 tqdm(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 if args.sep_uplow: pred_motion, loss_commit, perplexity = net(gt_motion, idx_noise=0) else: pred_motion, loss_commit, perplexity = net(gt_motion) loss_motion = Loss(pred_motion, gt_motion) loss_vel = Loss.forward_joint(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)