Spaces:
Sleeping
Sleeping
File size: 3,661 Bytes
4275cae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
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) |