File size: 3,019 Bytes
2de1f98
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import os
import os.path as osp

# will be update in exp
num_gpus = -1
exp_name = 'output/exp1/pre_analysis'

# quick access
save_epoch = 1
lr = 1e-5
end_epoch = 10
train_batch_size = 32

syncbn = True
bbox_ratio = 1.2

# continue
continue_train = False
start_over = True

# dataset setting
agora_fix_betas = True
agora_fix_global_orient_transl = True
agora_valid_root_pose = True

# all
dataset_list = ['Human36M', 'MSCOCO', 'MPII', 'AGORA', 'EHF', 'SynBody', 'GTA_Human2', \
    'EgoBody_Egocentric', 'EgoBody_Kinect', 'UBody', 'PW3D', 'MuCo', 'PROX']
trainset_3d = ['MSCOCO','AGORA', 'UBody']
trainset_2d = ['PW3D', 'MPII', 'Human36M']
trainset_humandata = ['BEDLAM', 'SPEC', 'GTA_Human2','SynBody', 'PoseTrack',
                    'EgoBody_Egocentric', 'PROX', 'CrowdPose',
                    'EgoBody_Kinect', 'MPI_INF_3DHP', 'RICH', 'MuCo', 'InstaVariety',
                    'Behave', 'UP3D', 'ARCTIC',
                    'OCHuman', 'CHI3D', 'RenBody_HiRes', 'MTP', 'HumanSC3D', 'RenBody',
                    'FIT3D', 'Talkshow' , 'SSP3D', 'LSPET']
testset = 'EHF'

use_cache = True
# downsample
BEDLAM_train_sample_interval = 5
EgoBody_Kinect_train_sample_interval = 10
train_sample_interval = 10 # UBody
MPI_INF_3DHP_train_sample_interval = 5
InstaVariety_train_sample_interval = 10
RenBody_HiRes_train_sample_interval = 5
ARCTIC_train_sample_interval = 10
# RenBody_train_sample_interval = 10
FIT3D_train_sample_interval = 10
Talkshow_train_sample_interval = 10

# strategy 
data_strategy = 'balance' # 'balance' need to define total_data_len
total_data_len = 4500000

# model
smplx_loss_weight = 1.0 #2 for agora_model for smplx shape
smplx_pose_weight = 10.0

smplx_kps_3d_weight = 100.0
smplx_kps_2d_weight = 1.0
net_kps_2d_weight = 1.0

agora_benchmark = 'agora_model' # 'agora_model', 'test_only'

model_type = 'smpler_x_l'
encoder_config_file = 'main/transformer_utils/configs/smpler_x/encoder/body_encoder_large.py'
encoder_pretrained_model_path = 'pretrained_models/vitpose_large.pth'
feat_dim = 1024


## =====FIXED ARGS============================================================
## model setting
upscale = 4
hand_pos_joint_num = 20
face_pos_joint_num = 72
num_task_token = 24
num_noise_sample = 0

## UBody setting
train_sample_interval = 10
test_sample_interval = 100
make_same_len = False

## input, output size
input_img_shape = (512, 384)
input_body_shape = (256, 192)
output_hm_shape = (16, 16, 12)
input_hand_shape = (256, 256)
output_hand_hm_shape = (16, 16, 16)
output_face_hm_shape = (8, 8, 8)
input_face_shape = (192, 192)
focal = (5000, 5000)  # virtual focal lengths
princpt = (input_body_shape[1] / 2, input_body_shape[0] / 2)  # virtual principal point position
body_3d_size = 2
hand_3d_size = 0.3
face_3d_size = 0.3
camera_3d_size = 2.5

## training config
print_iters = 100
lr_mult = 1

## testing config
test_batch_size = 32

## others
num_thread = 2
vis = False

## directory
output_dir, model_dir, vis_dir, log_dir, result_dir, code_dir = None, None, None, None, None, None