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| import os | |
| import json | |
| import torch | |
| from torch.utils.tensorboard import SummaryWriter | |
| import numpy as np | |
| import models.vqvae as vqvae | |
| import options.option_vq as option_vq | |
| import utils.utils_model as utils_model | |
| from dataset import 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') | |
| import numpy as np | |
| ##### ---- 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)) | |
| from utils.word_vectorizer import WordVectorizer | |
| w_vectorizer = WordVectorizer('./glove', 'our_vab') | |
| dataset_opt_path = 'checkpoints/kit/Comp_v6_KLD005/opt.txt' if args.dataname == 'kit' else 'checkpoints/t2m/Comp_v6_KLD005/opt.txt' | |
| wrapper_opt = get_opt(dataset_opt_path, torch.device('cuda')) | |
| eval_wrapper = EvaluatorModelWrapper(wrapper_opt) | |
| ##### ---- Dataloader ---- ##### | |
| args.nb_joints = 21 if args.dataname == 'kit' else 22 | |
| val_loader = dataset_TM_eval.DATALoader(args.dataname, True, 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() | |
| fid = [] | |
| div = [] | |
| top1 = [] | |
| top2 = [] | |
| top3 = [] | |
| matching = [] | |
| repeat_time = 20 | |
| for i in range(repeat_time): | |
| 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, draw=False, save=False, savenpy=(i==0)) | |
| fid.append(best_fid) | |
| div.append(best_div) | |
| top1.append(best_top1) | |
| top2.append(best_top2) | |
| top3.append(best_top3) | |
| matching.append(best_matching) | |
| print('final result:') | |
| print('fid: ', sum(fid)/repeat_time) | |
| print('div: ', sum(div)/repeat_time) | |
| print('top1: ', sum(top1)/repeat_time) | |
| print('top2: ', sum(top2)/repeat_time) | |
| print('top3: ', sum(top3)/repeat_time) | |
| print('matching: ', sum(matching)/repeat_time) | |
| fid = np.array(fid) | |
| div = np.array(div) | |
| top1 = np.array(top1) | |
| top2 = np.array(top2) | |
| top3 = np.array(top3) | |
| matching = np.array(matching) | |
| msg_final = f"FID. {np.mean(fid):.3f}, conf. {np.std(fid)*1.96/np.sqrt(repeat_time):.3f}, Diversity. {np.mean(div):.3f}, conf. {np.std(div)*1.96/np.sqrt(repeat_time):.3f}, TOP1. {np.mean(top1):.3f}, conf. {np.std(top1)*1.96/np.sqrt(repeat_time):.3f}, TOP2. {np.mean(top2):.3f}, conf. {np.std(top2)*1.96/np.sqrt(repeat_time):.3f}, TOP3. {np.mean(top3):.3f}, conf. {np.std(top3)*1.96/np.sqrt(repeat_time):.3f}, Matching. {np.mean(matching):.3f}, conf. {np.std(matching)*1.96/np.sqrt(repeat_time):.3f}" | |
| logger.info(msg_final) |