-
Notifications
You must be signed in to change notification settings - Fork 2
/
run.py
166 lines (138 loc) · 5.45 KB
/
run.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
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
from lib.config import cfg, args
import numpy as np
import sys
sys.path.append('./pytorch3d')
def run_evaluate():
# metric evaluation
from lib.datasets import make_data_loader
from lib.evaluators import make_evaluator
import tqdm
import torch
from lib.networks import make_network
from lib.utils import net_utils
from lib.networks.renderer import make_renderer
import re
if cfg.test.full_eval:
cfg.test.exp_folder_name += 'full_eval'
cfg.flag_train = False
cfg.perturb = 0
network = make_network(cfg).cuda()
net_utils.load_network(network,
cfg.trained_model_dir,
resume=cfg.resume,
epoch=cfg.test.epoch)
network.train()
data_loader = make_data_loader(cfg, is_train=False)
renderer = make_renderer(cfg, network)
evaluator = make_evaluator(cfg)
with torch.no_grad():
for batch in tqdm.tqdm(data_loader):
for k in batch:
if k != 'meta':
if k == 'tar_img_path':
continue
if k == 'input_img_paths':
continue
if k == 'human_name':
continue
if isinstance(batch[k], tuple) or isinstance(batch[k],
list):
batch[k] = [b.cuda() for b in batch[k]]
else:
batch[k] = batch[k].cuda()
# Fast rendering
output = renderer.render_fast(batch, is_train=False)
evaluator.evaluate(output, batch)
evaluator.summarize()
def run_visualize():
# video visualization
from lib.networks import make_network
from lib.datasets import make_data_loader
from lib.utils.net_utils import load_network
from lib.utils import net_utils
import tqdm
import torch
from lib.visualizers import make_visualizer
from lib.networks.renderer import make_renderer
cfg.perturb = 0
network = make_network(cfg).cuda()
load_network(network,
cfg.trained_model_dir,
resume=cfg.resume,
epoch=cfg.test.epoch)
network.train()
data_loader = make_data_loader(cfg, is_train=False)
renderer = make_renderer(cfg, network)
visualizer = make_visualizer(cfg)
for batch in tqdm.tqdm(data_loader):
# batch to cuda
for k in batch:
if k == 'img_path':
continue
if k == 'input_img_paths':
continue
if k == 'tar_img_path':
continue
if k == 'human_name':
continue
if k != 'meta':
if isinstance(batch[k], tuple) or isinstance(batch[k], list):
batch[k] = [b.cuda() for b in batch[k]]
else:
batch[k] = batch[k].cuda()
### Visualize input images.
# import torchvision
# import os
# # import torch.distributed as dist
# cached_pth = cfg.trained_model_dir.replace('trained_model', 'cached_imgs')
# os.makedirs(cached_pth, exist_ok=True)
# # torchvision.utils.save_image(batch['input_imgs'][0][0], cached_pth + '/E_{}_it_{}_rank_{}.jpg'.format(epoch, iteration, dist.get_rank()))
# torchvision.utils.save_image(batch['input_imgs'][0][0], cached_pth + '/batch_vos.jpg')
with torch.no_grad():
output = renderer.render_fast(batch)
visualizer.visualize(output, batch)
def run_reconstruction():
# mesh reconstuction
from lib.networks import make_network
from lib.datasets import make_data_loader
from lib.utils.net_utils import load_network
from lib.utils import net_utils
import tqdm
import torch
from lib.visualizers import make_visualizer
from lib.networks.renderer import make_renderer
cfg.perturb = 0
network = make_network(cfg).cuda()
load_network(network,
cfg.trained_model_dir,
resume=cfg.resume,
epoch=cfg.test.epoch)
network.train()
data_loader = make_data_loader(cfg, is_train=False)
renderer = make_renderer(cfg, network)
visualizer = make_visualizer(cfg)
for batch in tqdm.tqdm(data_loader):
# batch to cuda
for k in batch:
if k == 'img_path':
continue
if k == 'input_img_paths':
continue
if k == 'tar_img_path':
continue
if k == 'human_name':
continue
if k != 'meta':
if isinstance(batch[k], tuple) or isinstance(batch[k], list):
batch[k] = [b.cuda() for b in batch[k]]
else:
batch[k] = batch[k].cuda()
with torch.no_grad():
# mesh reconstruction does not support fast rendering ATM.
output = renderer.render(batch)
visualizer.visualize(output, batch)
def run_light_stage():
from lib.utils.light_stage import ply_to_occupancy
ply_to_occupancy.ply_to_occupancy()
if __name__ == '__main__':
globals()['run_' + args.type]()