forked from ashawkey/stable-dreamfusion
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmain.py
159 lines (124 loc) · 8.94 KB
/
main.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
import torch
import argparse
from nerf.provider import NeRFDataset
from nerf.utils import *
from optimizer import Shampoo
from nerf.gui import NeRFGUI
# torch.autograd.set_detect_anomaly(True)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--text', default=None, help="text prompt")
parser.add_argument('--negative', default='', type=str, help="negative text prompt")
parser.add_argument('-O', action='store_true', help="equals --fp16 --cuda_ray --dir_text")
parser.add_argument('-O2', action='store_true', help="equals --backbone vanilla --dir_text")
parser.add_argument('--test', action='store_true', help="test mode")
parser.add_argument('--save_mesh', action='store_true', help="export an obj mesh with texture")
parser.add_argument('--eval_interval', type=int, default=10, help="evaluate on the valid set every interval epochs")
parser.add_argument('--workspace', type=str, default='workspace')
parser.add_argument('--guidance', type=str, default='stable-diffusion', help='choose from [stable-diffusion, clip]')
parser.add_argument('--seed', type=int, default=0)
### training options
parser.add_argument('--iters', type=int, default=10000, help="training iters")
parser.add_argument('--lr', type=float, default=1e-3, help="initial learning rate")
parser.add_argument('--ckpt', type=str, default='latest')
parser.add_argument('--cuda_ray', action='store_true', help="use CUDA raymarching instead of pytorch")
parser.add_argument('--max_steps', type=int, default=512, help="max num steps sampled per ray (only valid when using --cuda_ray)")
parser.add_argument('--num_steps', type=int, default=64, help="num steps sampled per ray (only valid when not using --cuda_ray)")
parser.add_argument('--upsample_steps', type=int, default=32, help="num steps up-sampled per ray (only valid when not using --cuda_ray)")
parser.add_argument('--update_extra_interval', type=int, default=16, help="iter interval to update extra status (only valid when using --cuda_ray)")
parser.add_argument('--max_ray_batch', type=int, default=4096, help="batch size of rays at inference to avoid OOM (only valid when not using --cuda_ray)")
parser.add_argument('--albedo', action='store_true', help="only use albedo shading to train, overrides --albedo_iters")
parser.add_argument('--albedo_iters', type=int, default=1000, help="training iters that only use albedo shading")
parser.add_argument('--uniform_sphere_rate', type=float, default=0.5, help="likelihood of sampling camera location uniformly on the sphere surface area")
# model options
parser.add_argument('--bg_radius', type=float, default=1.4, help="if positive, use a background model at sphere(bg_radius)")
parser.add_argument('--density_thresh', type=float, default=10, help="threshold for density grid to be occupied")
# network backbone
parser.add_argument('--fp16', action='store_true', help="use amp mixed precision training")
parser.add_argument('--backbone', type=str, default='grid', choices=['grid', 'vanilla'], help="nerf backbone")
parser.add_argument('--sd_version', type=str, default='2.0', choices=['1.5', '2.0'], help="stable diffusion version")
# rendering resolution in training, decrease this if CUDA OOM.
parser.add_argument('--w', type=int, default=64, help="render width for NeRF in training")
parser.add_argument('--h', type=int, default=64, help="render height for NeRF in training")
parser.add_argument('--jitter_pose', action='store_true', help="add jitters to the randomly sampled camera poses")
### dataset options
parser.add_argument('--bound', type=float, default=1, help="assume the scene is bounded in box(-bound, bound)")
parser.add_argument('--dt_gamma', type=float, default=0, help="dt_gamma (>=0) for adaptive ray marching. set to 0 to disable, >0 to accelerate rendering (but usually with worse quality)")
parser.add_argument('--min_near', type=float, default=0.1, help="minimum near distance for camera")
parser.add_argument('--radius_range', type=float, nargs='*', default=[1.0, 1.5], help="training camera radius range")
parser.add_argument('--fovy_range', type=float, nargs='*', default=[40, 70], help="training camera fovy range")
parser.add_argument('--dir_text', action='store_true', help="direction-encode the text prompt, by appending front/side/back/overhead view")
parser.add_argument('--suppress_face', action='store_true', help="also use negative dir text prompt.")
parser.add_argument('--angle_overhead', type=float, default=30, help="[0, angle_overhead] is the overhead region")
parser.add_argument('--angle_front', type=float, default=60, help="[0, angle_front] is the front region, [180, 180+angle_front] the back region, otherwise the side region.")
parser.add_argument('--lambda_entropy', type=float, default=1e-4, help="loss scale for alpha entropy")
parser.add_argument('--lambda_opacity', type=float, default=0, help="loss scale for alpha value")
parser.add_argument('--lambda_orient', type=float, default=1e-2, help="loss scale for orientation")
parser.add_argument('--lambda_smooth', type=float, default=0, help="loss scale for surface smoothness")
### GUI options
parser.add_argument('--gui', action='store_true', help="start a GUI")
parser.add_argument('--W', type=int, default=800, help="GUI width")
parser.add_argument('--H', type=int, default=800, help="GUI height")
parser.add_argument('--radius', type=float, default=3, help="default GUI camera radius from center")
parser.add_argument('--fovy', type=float, default=60, help="default GUI camera fovy")
parser.add_argument('--light_theta', type=float, default=60, help="default GUI light direction in [0, 180], corresponding to elevation [90, -90]")
parser.add_argument('--light_phi', type=float, default=0, help="default GUI light direction in [0, 360), azimuth")
parser.add_argument('--max_spp', type=int, default=1, help="GUI rendering max sample per pixel")
opt = parser.parse_args()
if opt.O:
opt.fp16 = True
opt.dir_text = True
opt.cuda_ray = True
elif opt.O2:
# only use fp16 if not evaluating normals (else lead to NaNs in training...)
if opt.albedo:
opt.fp16 = True
opt.dir_text = True
opt.backbone = 'vanilla'
if opt.albedo:
opt.albedo_iters = opt.iters
if opt.backbone == 'vanilla':
from nerf.network import NeRFNetwork
elif opt.backbone == 'grid':
from nerf.network_grid import NeRFNetwork
else:
raise NotImplementedError(f'--backbone {opt.backbone} is not implemented!')
print(opt)
seed_everything(opt.seed)
model = NeRFNetwork(opt)
print(model)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if opt.test:
guidance = None # no need to load guidance model at test
trainer = Trainer('df', opt, model, guidance, device=device, workspace=opt.workspace, fp16=opt.fp16, use_checkpoint=opt.ckpt)
if opt.gui:
gui = NeRFGUI(opt, trainer)
gui.render()
else:
test_loader = NeRFDataset(opt, device=device, type='test', H=opt.H, W=opt.W, size=100).dataloader()
trainer.test(test_loader)
if opt.save_mesh:
trainer.save_mesh(resolution=256)
else:
train_loader = NeRFDataset(opt, device=device, type='train', H=opt.h, W=opt.w, size=100).dataloader()
optimizer = lambda model: torch.optim.Adam(model.get_params(opt.lr), betas=(0.9, 0.99), eps=1e-15)
# optimizer = lambda model: Shampoo(model.get_params(opt.lr))
scheduler = lambda optimizer: optim.lr_scheduler.LambdaLR(optimizer, lambda iter: 0.1 ** min(iter / opt.iters, 1))
# scheduler = lambda optimizer: optim.lr_scheduler.OneCycleLR(optimizer, max_lr=opt.lr, total_steps=opt.iters, pct_start=0.1)
if opt.guidance == 'stable-diffusion':
from nerf.sd import StableDiffusion
guidance = StableDiffusion(device, opt.sd_version)
elif opt.guidance == 'clip':
from nerf.clip import CLIP
guidance = CLIP(device)
else:
raise NotImplementedError(f'--guidance {opt.guidance} is not implemented.')
trainer = Trainer('df', opt, model, guidance, device=device, workspace=opt.workspace, optimizer=optimizer, ema_decay=None, fp16=opt.fp16, lr_scheduler=scheduler, use_checkpoint=opt.ckpt, eval_interval=opt.eval_interval, scheduler_update_every_step=True)
if opt.gui:
trainer.train_loader = train_loader # attach dataloader to trainer
gui = NeRFGUI(opt, trainer)
gui.render()
else:
valid_loader = NeRFDataset(opt, device=device, type='val', H=opt.H, W=opt.W, size=5).dataloader()
max_epoch = np.ceil(opt.iters / len(train_loader)).astype(np.int32)
trainer.train(train_loader, valid_loader, max_epoch)