-
Notifications
You must be signed in to change notification settings - Fork 2
/
eval_t2m_vq.py
123 lines (103 loc) · 4.73 KB
/
eval_t2m_vq.py
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
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
import sys
import os
from os.path import join as pjoin
import torch
from models.vq.model import RVQVAE
from options.vq_option import arg_parse
from motion_loaders.dataset_motion_loader import get_dataset_motion_loader
import utils.eval_t2m as eval_t2m
from utils.get_opt import get_opt
from models.t2m_eval_wrapper import EvaluatorModelWrapper
import warnings
warnings.filterwarnings('ignore')
import numpy as np
from utils.word_vectorizer import WordVectorizer
def load_vq_model(vq_opt, which_epoch):
# opt_path = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.vq_name, 'opt.txt')
vq_model = RVQVAE(vq_opt,
dim_pose,
vq_opt.nb_code,
vq_opt.code_dim,
vq_opt.code_dim,
vq_opt.down_t,
vq_opt.stride_t,
vq_opt.width,
vq_opt.depth,
vq_opt.dilation_growth_rate,
vq_opt.vq_act,
vq_opt.vq_norm)
ckpt = torch.load(pjoin(vq_opt.checkpoints_dir, vq_opt.dataset_name, vq_opt.name, 'model', which_epoch),
map_location='cpu')
model_key = 'vq_model' if 'vq_model' in ckpt else 'net'
vq_model.load_state_dict(ckpt[model_key])
vq_epoch = ckpt['ep'] if 'ep' in ckpt else -1
print(f'Loading VQ Model {vq_opt.name} Completed!, Epoch {vq_epoch}')
return vq_model, vq_epoch
if __name__ == "__main__":
##### ---- Exp dirs ---- #####
args = arg_parse(False)
args.device = torch.device("cpu" if args.gpu_id == -1 else "cuda:" + str(args.gpu_id))
args.out_dir = pjoin(args.checkpoints_dir, args.dataset_name, args.name, 'eval')
os.makedirs(args.out_dir, exist_ok=True)
f = open(pjoin(args.out_dir, '%s.log'%args.ext), 'w')
dataset_opt_path = 'checkpoints/kit/Comp_v6_KLD005/opt.txt' if args.dataset_name == '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.dataset_name == 'kit' else 22
dim_pose = 251 if args.dataset_name == 'kit' else 263
eval_val_loader, _ = get_dataset_motion_loader(dataset_opt_path, 32, 'test', device=args.device)
print(len(eval_val_loader))
##### ---- Network ---- #####
vq_opt_path = pjoin(args.checkpoints_dir, args.dataset_name, args.name, 'opt.txt')
vq_opt = get_opt(vq_opt_path, device=args.device)
# net = load_vq_model()
model_dir = pjoin(args.checkpoints_dir, args.dataset_name, args.name, 'model')
for file in os.listdir(model_dir):
# if not file.endswith('tar'):
# continue
# if not file.startswith('net_best_fid'):
# continue
if args.which_epoch != "all" and args.which_epoch not in file:
continue
print(file)
net, ep = load_vq_model(vq_opt, file)
net.eval()
net.cuda()
fid = []
div = []
top1 = []
top2 = []
top3 = []
matching = []
mae = []
repeat_time = 20
for i in range(repeat_time):
best_fid, best_div, Rprecision, best_matching, l1_dist = \
eval_t2m.evaluation_vqvae_plus_mpjpe(eval_val_loader, net, i, eval_wrapper=eval_wrapper, num_joint=args.nb_joints)
fid.append(best_fid)
div.append(best_div)
top1.append(Rprecision[0])
top2.append(Rprecision[1])
top3.append(Rprecision[2])
matching.append(best_matching)
mae.append(l1_dist)
fid = np.array(fid)
div = np.array(div)
top1 = np.array(top1)
top2 = np.array(top2)
top3 = np.array(top3)
matching = np.array(matching)
mae = np.array(mae)
print(f'{file} final result, epoch {ep}')
print(f'{file} final result, epoch {ep}', file=f, flush=True)
msg_final = f"\tFID: {np.mean(fid):.3f}, conf. {np.std(fid)*1.96/np.sqrt(repeat_time):.3f}\n" \
f"\tDiversity: {np.mean(div):.3f}, conf. {np.std(div)*1.96/np.sqrt(repeat_time):.3f}\n" \
f"\tTOP1: {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}\n" \
f"\tMatching: {np.mean(matching):.3f}, conf. {np.std(matching)*1.96/np.sqrt(repeat_time):.3f}\n" \
f"\tMAE:{np.mean(mae):.3f}, conf.{np.std(mae)*1.96/np.sqrt(repeat_time):.3f}\n\n"
# logger.info(msg_final)
print(msg_final)
print(msg_final, file=f, flush=True)
f.close()