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| import os | |
| import torch | |
| import numpy as np | |
| from torch.utils.tensorboard import SummaryWriter | |
| import json | |
| import clip | |
| import options.option_transformer as option_trans | |
| import models.vqvae as vqvae | |
| import utils.utils_model as utils_model | |
| import utils.eval_trans as eval_trans | |
| from dataset import dataset_TM_eval | |
| import models.t2m_trans as trans | |
| from options.get_eval_option import get_opt | |
| from models.evaluator_wrapper import EvaluatorModelWrapper | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| ##### ---- Exp dirs ---- ##### | |
| args = option_trans.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') | |
| val_loader = dataset_TM_eval.DATALoader(args.dataname, True, 32, w_vectorizer) | |
| 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) | |
| ##### ---- Network ---- ##### | |
| ## load clip model and datasets | |
| clip_model, clip_preprocess = clip.load("ViT-B/32", device=torch.device('cuda'), jit=False) # Must set jit=False for training | |
| clip.model.convert_weights(clip_model) # Actually this line is unnecessary since clip by default already on float16 | |
| clip_model.eval() | |
| for p in clip_model.parameters(): | |
| p.requires_grad = False | |
| 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) | |
| trans_encoder = trans.Text2Motion_Transformer(num_vq=args.nb_code, | |
| embed_dim=args.embed_dim_gpt, | |
| clip_dim=args.clip_dim, | |
| block_size=args.block_size, | |
| num_layers=args.num_layers, | |
| n_head=args.n_head_gpt, | |
| drop_out_rate=args.drop_out_rate, | |
| fc_rate=args.ff_rate) | |
| print ('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.eval() | |
| net.cuda() | |
| if args.resume_trans is not None: | |
| print ('loading transformer checkpoint from {}'.format(args.resume_trans)) | |
| ckpt = torch.load(args.resume_trans, map_location='cpu') | |
| trans_encoder.load_state_dict(ckpt['trans'], strict=True) | |
| trans_encoder.train() | |
| trans_encoder.cuda() | |
| fid = [] | |
| div = [] | |
| top1 = [] | |
| top2 = [] | |
| top3 = [] | |
| matching = [] | |
| multi = [] | |
| repeat_time = 20 | |
| for i in range(repeat_time): | |
| best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, best_multi, writer, logger = eval_trans.evaluation_transformer_test(args.out_dir, val_loader, net, trans_encoder, logger, writer, 0, best_fid=1000, best_iter=0, best_div=100, best_top1=0, best_top2=0, best_top3=0, best_matching=100, best_multi=0, clip_model=clip_model, eval_wrapper=eval_wrapper, draw=False, savegif=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) | |
| multi.append(best_multi) | |
| 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) | |
| print('multi: ', sum(multi)/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) | |
| multi = np.array(multi) | |
| 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}, Multi. {np.mean(multi):.3f}, conf. {np.std(multi)*1.96/np.sqrt(repeat_time):.3f}" | |
| logger.info(msg_final) |