-
Notifications
You must be signed in to change notification settings - Fork 17
/
train_scannet.py
135 lines (108 loc) · 5.36 KB
/
train_scannet.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
import os
import torch
import numpy as np
from networks.render import dm_nerf
from networks.tester import render_test
from config import initial, create_nerf
from networks.penalizer import ins_penalizer
from datasets.loader_scannet import load_data
from networks.helpers import get_select_crop, z_val_sample
from networks.evaluator import ins_criterion, img2mse, mse2psnr
np.random.seed(0)
torch.cuda.manual_seed(4)
def train():
model_fine.train()
model_coarse.train()
N_iters = 500000 + 1
z_val_coarse = z_val_sample(args.N_train, args.near, args.far, args.N_samples)
for i in range(0, N_iters):
img_i = np.random.choice(i_train)
gt_rgb = images[img_i].to(args.device)
pose = poses[img_i, :3, :4].to(args.device)
gt_label = gt_labels[img_i].to(args.device)
ins_index = ins_indices[img_i]
target_c, target_i, batch_rays, args.N_ins = get_select_crop(gt_rgb, pose, K, gt_label, ins_index, crop_mask,
args.N_train)
all_info = dm_nerf(batch_rays, position_embedder, view_embedder, model_coarse, model_fine, z_val_coarse, args)
# coarse losses
rgb_loss_coarse = img2mse(all_info['rgb_coarse'], target_c)
psnr_coarse = mse2psnr(rgb_loss_coarse)
ins_loss_coarse, valid_ce_coarse, invalid_ce_coarse, valid_siou_coarse = \
ins_criterion(all_info['ins_coarse'], target_i, args.ins_num)
# fine losses
rgb_loss_fine = img2mse(all_info['rgb_fine'], target_c)
psnr_fine = mse2psnr(rgb_loss_fine)
ins_loss_fine, valid_ce_fine, invalid_ce_fine, valid_siou_fine = \
ins_criterion(all_info['ins_fine'], target_i, args.ins_num)
# without penalize loss
ins_loss = ins_loss_fine + ins_loss_coarse
rgb_loss = rgb_loss_fine + rgb_loss_coarse
total_loss = ins_loss + rgb_loss
# use penalize
if args.penalize:
emptiness_coarse = ins_penalizer(all_info['raw_coarse'], all_info['z_vals_coarse'],
all_info['depth_coarse'], batch_rays[1], args)
emptiness_fine = ins_penalizer(all_info['raw_fine'], all_info['z_vals_fine'],
all_info['depth_fine'], batch_rays[1], args)
emptiness_loss = emptiness_fine + emptiness_coarse
total_loss = total_loss + emptiness_loss
# optimizing
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
# losses decay
### update learning rate ###
decay_rate = 0.1
decay_steps = args.lrate_decay * 1000
new_lrate = args.lrate * (decay_rate ** ((i) / decay_steps))
for param_group in optimizer.param_groups:
param_group['lr'] = new_lrate
###################################
if i % args.i_print == 0:
print(f"[TRAIN] Iter: {i} PSNR: {psnr_fine.item()} Total_Loss: {total_loss.item()} RGB_Loss: {rgb_loss.item()} Ins_Loss: {ins_loss.item()}")
if i % args.i_save == 0:
path = os.path.join(args.basedir, args.expname, args.log_time, '{:06d}.tar'.format(i))
save_model = {
'iteration': i,
'network_coarse_state_dict': model_coarse.state_dict(),
'network_fine_state_dict': model_fine.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}
torch.save(save_model, path)
if i % args.i_test == 0:
model_coarse.eval()
model_fine.eval()
args.is_train = False
selected_indices = np.random.choice(len(i_test), size=[10], replace=False)
selected_i_test = i_test[selected_indices]
testsavedir = os.path.join(args.basedir, args.expname, args.log_time, 'testset_{:06d}'.format(i))
matched_file = os.path.join(testsavedir, 'matching_log.txt')
os.makedirs(testsavedir, exist_ok=True)
with torch.no_grad():
test_poses = torch.Tensor(poses[selected_i_test].to(args.device))
test_imgs = images[selected_i_test]
test_gt_labels = gt_labels[selected_i_test].to(args.device)
render_test(position_embedder, view_embedder, model_coarse, model_fine, test_poses, hwk, args,
gt_imgs=test_imgs, gt_labels=test_gt_labels, ins_rgbs=ins_rgbs, savedir=testsavedir,
matched_file=matched_file, crop_mask=crop_mask)
print('Training model saved!')
args.is_train = True
model_coarse.train()
model_fine.train()
if __name__ == '__main__':
args = initial()
# load data
images, poses, hwk, i_split, gt_labels, ins_rgbs, args.ins_num, ins_indices, crop_mask = load_data(args)
print('Load data from', args.datadir)
i_train, i_test = i_split
H, W, K = hwk
# Create nerf model
position_embedder, view_embedder, model_coarse, model_fine, args = create_nerf(args)
# Create optimizer
grad_vars = list(model_coarse.parameters()) + list(model_fine.parameters())
optimizer = torch.optim.Adam(params=grad_vars, lr=args.lrate, betas=(0.9, 0.999))
# move data to gpu
images = torch.Tensor(images).cpu()
gt_labels = torch.Tensor(gt_labels).type(torch.int16).cpu()
poses = torch.Tensor(poses).cpu()
train()