-
Notifications
You must be signed in to change notification settings - Fork 94
/
run_nerf.py
767 lines (628 loc) · 34.9 KB
/
run_nerf.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
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
import os, sys
import numpy as np
import json
import random
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
import cv2
from kornia import create_meshgrid
from render_utils import *
from run_nerf_helpers import *
from load_llff import *
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
np.random.seed(1)
DEBUG = False
def config_parser():
import configargparse
parser = configargparse.ArgumentParser()
parser.add_argument('--config', is_config_file=True,
help='config file path')
parser.add_argument("--expname", type=str,
help='experiment name')
parser.add_argument("--basedir", type=str, default='./logs/',
help='where to store ckpts and logs')
parser.add_argument("--datadir", type=str, default='./data/llff/fern',
help='input data directory')
parser.add_argument("--render_lockcam_slowmo", action='store_true',
help='render fixed view + slowmo')
parser.add_argument("--render_slowmo_bt", action='store_true',
help='render space-time interpolation')
parser.add_argument("--final_height", type=int, default=288,
help='training image height, default is 512x288')
# training options
parser.add_argument("--netdepth", type=int, default=8,
help='layers in network')
parser.add_argument("--netwidth", type=int, default=256,
help='channels per layer')
parser.add_argument("--netdepth_fine", type=int, default=8,
help='layers in fine network')
parser.add_argument("--netwidth_fine", type=int, default=256,
help='channels per layer in fine network')
parser.add_argument("--N_rand", type=int, default=32*32*4,
help='batch size (number of random rays per gradient step)')
parser.add_argument("--lrate", type=float, default=5e-4,
help='learning rate')
parser.add_argument("--lrate_decay", type=int, default=300,
help='exponential learning rate decay (in 1000 steps)')
parser.add_argument("--chunk", type=int, default=1024*128,
help='number of rays processed in parallel, decrease if running out of memory')
parser.add_argument("--netchunk", type=int, default=1024*128,
help='number of pts sent through network in parallel, decrease if running out of memory')
parser.add_argument("--no_batching", action='store_true',
help='only take random rays from 1 image at a time')
parser.add_argument("--no_reload", action='store_true',
help='do not reload weights from saved ckpt')
parser.add_argument("--ft_path", type=str, default=None,
help='specific weights npy file to reload for coarse network')
# rendering options
parser.add_argument("--N_samples", type=int, default=64,
help='number of coarse samples per ray')
parser.add_argument("--N_importance", type=int, default=0,
help='number of additional fine samples per ray')
parser.add_argument("--perturb", type=float, default=1.,
help='set to 0. for no jitter, 1. for jitter')
parser.add_argument("--use_viewdirs", action='store_true',
help='use full 5D input instead of 3D')
parser.add_argument("--i_embed", type=int, default=0,
help='set 0 for default positional encoding, -1 for none')
parser.add_argument("--multires", type=int, default=10,
help='log2 of max freq for positional encoding (3D location)')
parser.add_argument("--multires_views", type=int, default=4,
help='log2 of max freq for positional encoding (2D direction)')
parser.add_argument("--raw_noise_std", type=float, default=0.,
help='std dev of noise added to regularize sigma_a output, 1e0 recommended')
parser.add_argument("--render_bt", action='store_true',
help='render bullet time')
parser.add_argument("--render_test", action='store_true',
help='do not optimize, reload weights and render out render_poses path')
parser.add_argument("--render_factor", type=int, default=0,
help='downsampling factor to speed up rendering, set 4 or 8 for fast preview')
# dataset options
parser.add_argument("--dataset_type", type=str, default='llff',
help='options: llff / blender / deepvoxels')
parser.add_argument("--testskip", type=int, default=8,
help='will load 1/N images from test/val sets, useful for large datasets like deepvoxels')
## blender flags
parser.add_argument("--white_bkgd", action='store_true',
help='set to render synthetic data on a white bkgd (always use for dvoxels)')
## llff flags
parser.add_argument("--factor", type=int, default=8,
help='downsample factor for LLFF images')
parser.add_argument("--no_ndc", action='store_true',
help='do not use normalized device coordinates (set for non-forward facing scenes)')
parser.add_argument("--lindisp", action='store_true',
help='sampling linearly in disparity rather than depth')
parser.add_argument("--spherify", action='store_true',
help='set for spherical 360 scenes')
parser.add_argument("--llffhold", type=int, default=8,
help='will take every 1/N images as LLFF test set, paper uses 8')
parser.add_argument("--target_idx", type=int, default=10,
help='target_idx')
parser.add_argument("--num_extra_sample", type=int, default=512,
help='num_extra_sample')
parser.add_argument("--decay_depth_w", action='store_true',
help='decay depth weights')
parser.add_argument("--use_motion_mask", action='store_true',
help='use motion segmentation mask for hard-mining data-driven initialization')
parser.add_argument("--decay_optical_flow_w", action='store_true',
help='decay optical flow weights')
parser.add_argument("--w_depth", type=float, default=0.04,
help='weights of depth loss')
parser.add_argument("--w_optical_flow", type=float, default=0.02,
help='weights of optical flow loss')
parser.add_argument("--w_sm", type=float, default=0.1,
help='weights of scene flow smoothness')
parser.add_argument("--w_sf_reg", type=float, default=0.1,
help='weights of scene flow regularization')
parser.add_argument("--w_cycle", type=float, default=0.1,
help='weights of cycle consistency')
parser.add_argument("--w_prob_reg", type=float, default=0.1,
help='weights of disocculusion weights')
parser.add_argument("--w_entropy", type=float, default=1e-3,
help='w_entropy regularization weight')
parser.add_argument("--decay_iteration", type=int, default=50,
help='data driven priors decay iteration * 1000')
parser.add_argument("--chain_sf", action='store_true',
help='5 frame consistency if true, \
otherwise 3 frame consistency')
parser.add_argument("--start_frame", type=int, default=0)
parser.add_argument("--end_frame", type=int, default=50)
# logging/saving options
parser.add_argument("--i_print", type=int, default=1000,
help='frequency of console printout and metric loggin')
parser.add_argument("--i_img", type=int, default=1000,
help='frequency of tensorboard image logging')
parser.add_argument("--i_weights", type=int, default=10000,
help='frequency of weight ckpt saving')
return parser
def train():
parser = config_parser()
args = parser.parse_args()
# Load data
if args.dataset_type == 'llff':
target_idx = args.target_idx
images, depths, masks, poses, bds, \
render_poses, ref_c2w, motion_coords = load_llff_data(args.datadir,
args.start_frame, args.end_frame,
args.factor,
target_idx=target_idx,
recenter=True, bd_factor=.9,
spherify=args.spherify,
final_height=args.final_height)
hwf = poses[0,:3,-1]
poses = poses[:,:3,:4]
print('Loaded llff', images.shape, render_poses.shape, hwf, args.datadir)
i_test = []
i_val = [] #i_test
i_train = np.array([i for i in np.arange(int(images.shape[0])) if
(i not in i_test and i not in i_val)])
print('DEFINING BOUNDS')
if args.no_ndc:
near = np.percentile(bds[:, 0], 5) * 0.8 #np.ndarray.min(bds) #* .9
far = np.percentile(bds[:, 1], 95) * 1.1 #np.ndarray.max(bds) #* 1.
else:
near = 0.
far = 1.
print('NEAR FAR', near, far)
else:
print('ONLY SUPPORT LLFF!!!!!!!!')
sys.exit()
# Cast intrinsics to right types
H, W, focal = hwf
H, W = int(H), int(W)
hwf = [H, W, focal]
# Create log dir and copy the config file
basedir = args.basedir
args.expname = args.expname + '_F%02d-%02d'%(args.start_frame, args.end_frame)
# args.expname = args.expname + '_sigma_rgb-%.2f'%(args.sigma_rgb) \
# + '_use-rgb-w_' + str(args.use_rgb_w) + '_F%02d-%02d'%(args.start_frame, args.end_frame)
expname = args.expname
os.makedirs(os.path.join(basedir, expname), exist_ok=True)
f = os.path.join(basedir, expname, 'args.txt')
with open(f, 'w') as file:
for arg in sorted(vars(args)):
attr = getattr(args, arg)
file.write('{} = {}\n'.format(arg, attr))
if args.config is not None:
f = os.path.join(basedir, expname, 'config.txt')
with open(f, 'w') as file:
file.write(open(args.config, 'r').read())
# Create nerf model
render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer = create_nerf(args)
global_step = start
bds_dict = {
'near' : near,
'far' : far,
}
render_kwargs_train.update(bds_dict)
render_kwargs_test.update(bds_dict)
if args.render_bt:
print('RENDER VIEW INTERPOLATION')
render_poses = torch.Tensor(render_poses).to(device)
print('target_idx ', target_idx)
num_img = float(poses.shape[0])
img_idx_embed = target_idx/float(num_img) * 2. - 1.0
testsavedir = os.path.join(basedir, expname,
'render-spiral-frame-%03d'%\
target_idx + '_{}_{:06d}'.format('test' if args.render_test else 'path', start))
os.makedirs(testsavedir, exist_ok=True)
with torch.no_grad():
render_bullet_time(render_poses, img_idx_embed, num_img, hwf,
args.chunk, render_kwargs_test,
gt_imgs=images, savedir=testsavedir,
render_factor=args.render_factor)
return
if args.render_lockcam_slowmo:
print('RENDER TIME INTERPOLATION')
num_img = float(poses.shape[0])
ref_c2w = torch.Tensor(ref_c2w).to(device)
print('target_idx ', target_idx)
testsavedir = os.path.join(basedir, expname, 'render-lockcam-slowmo')
os.makedirs(testsavedir, exist_ok=True)
with torch.no_grad():
render_lockcam_slowmo(ref_c2w, num_img, hwf,
args.chunk, render_kwargs_test,
gt_imgs=images, savedir=testsavedir,
render_factor=args.render_factor,
target_idx=target_idx)
return
if args.render_slowmo_bt:
print('RENDER SLOW MOTION')
curr_ts = 0
render_poses = poses #torch.Tensor(poses).to(device)
bt_poses = create_bt_poses(hwf)
bt_poses = bt_poses * 10
with torch.no_grad():
testsavedir = os.path.join(basedir, expname,
'render-slowmo_bt_{}_{:06d}'.format('test' if args.render_test else 'path', start))
os.makedirs(testsavedir, exist_ok=True)
images = torch.Tensor(images)#.to(device)
print('render poses shape', render_poses.shape)
render_slowmo_bt(depths, render_poses, bt_poses,
hwf, args.chunk, render_kwargs_test,
gt_imgs=images, savedir=testsavedir,
render_factor=args.render_factor,
target_idx=10)
# print('Done rendering', i,testsavedir)
return
# Prepare raybatch tensor if batching random rays
N_rand = args.N_rand
# Move training data to GPU
images = torch.Tensor(images)#.to(device)
depths = torch.Tensor(depths)#.to(device)
masks = 1.0 - torch.Tensor(masks).to(device)
poses = torch.Tensor(poses).to(device)
N_iters = 2000 * 1000 #1000000
print('Begin')
print('TRAIN views are', i_train)
print('TEST views are', i_test)
print('VAL views are', i_val)
uv_grid = create_meshgrid(H, W, normalized_coordinates=False)[0].cuda() # (H, W, 2)
# Summary writers
writer = SummaryWriter(os.path.join(basedir, 'summaries', expname))
num_img = float(images.shape[0])
decay_iteration = max(args.decay_iteration,
args.end_frame - args.start_frame)
decay_iteration = min(decay_iteration, 250)
chain_bwd = 0
for i in range(start, N_iters):
chain_bwd = 1 - chain_bwd
time0 = time.time()
print('expname ', expname, ' chain_bwd ', chain_bwd,
' lindisp ', args.lindisp, ' decay_iteration ', decay_iteration)
print('Random FROM SINGLE IMAGE')
# Random from one image
img_i = np.random.choice(i_train)
if i % (decay_iteration * 1000) == 0:
torch.cuda.empty_cache()
target = images[img_i].cuda()
pose = poses[img_i, :3,:4]
depth_gt = depths[img_i].cuda()
hard_coords = torch.Tensor(motion_coords[img_i]).cuda()
mask_gt = masks[img_i].cuda()
if img_i == 0:
flow_fwd, fwd_mask = read_optical_flow(args.datadir, img_i,
args.start_frame, fwd=True)
flow_bwd, bwd_mask = np.zeros_like(flow_fwd), np.zeros_like(fwd_mask)
elif img_i == num_img - 1:
flow_bwd, bwd_mask = read_optical_flow(args.datadir, img_i,
args.start_frame, fwd=False)
flow_fwd, fwd_mask = np.zeros_like(flow_bwd), np.zeros_like(bwd_mask)
else:
flow_fwd, fwd_mask = read_optical_flow(args.datadir,
img_i, args.start_frame,
fwd=True)
flow_bwd, bwd_mask = read_optical_flow(args.datadir,
img_i, args.start_frame,
fwd=False)
# # ======================== TEST
TEST = False
if TEST:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
print('CHECK DEPTH and FLOW and exiting')
print(images[img_i].shape)
print(flow_fwd.shape, img_i)
warped_im2 = warp_flow(images[img_i + 1].cpu().numpy(), flow_fwd)
warped_im0 = warp_flow(images[img_i - 1].cpu().numpy(), flow_bwd)
mask_gt = masks[img_i].cpu().numpy()
plt.figure(figsize=(12, 6))
plt.subplot(2, 3, 1)
plt.imshow(target.cpu().numpy())
plt.subplot(2, 3, 4)
plt.imshow(depth_gt.cpu().numpy(), cmap='jet')
plt.subplot(2, 3, 2)
plt.imshow(flow_to_image(flow_fwd)/255. * fwd_mask[..., np.newaxis])
plt.subplot(2, 3, 3)
plt.imshow(flow_to_image(flow_bwd)/255. * bwd_mask[..., np.newaxis])
plt.subplot(2, 3, 5)
plt.imshow(mask_gt, cmap='gray')
cv2.imwrite('im_%d.jpg'%(img_i),
np.uint8(np.clip(target.cpu().numpy()[:, :, ::-1], 0, 1) * 255))
cv2.imwrite('im_%d_warp.jpg'%(img_i + 1),
np.uint8(np.clip(warped_im2[:, :, ::-1], 0, 1) * 255))
cv2.imwrite('im_%d_warp.jpg'%(img_i - 1),
np.uint8(np.clip(warped_im0[:, :, ::-1], 0, 1) * 255))
plt.savefig('depth_flow_%d.jpg'%img_i)
sys.exit()
# END OF TEST
flow_fwd = torch.Tensor(flow_fwd).cuda()
fwd_mask = torch.Tensor(fwd_mask).cuda()
flow_bwd = torch.Tensor(flow_bwd).cuda()
bwd_mask = torch.Tensor(bwd_mask).cuda()
# more correct way for flow loss
flow_fwd = flow_fwd + uv_grid
flow_bwd = flow_bwd + uv_grid
if N_rand is not None:
rays_o, rays_d = get_rays(H, W, focal, torch.Tensor(pose)) # (H, W, 3), (H, W, 3)
coords = torch.stack(torch.meshgrid(torch.linspace(0, H-1, H), torch.linspace(0, W-1, W)), -1) # (H, W, 2)
coords = torch.reshape(coords, [-1,2]) # (H * W, 2)
if args.use_motion_mask and i < decay_iteration * 1000:
print('HARD MINING STAGE !')
num_extra_sample = args.num_extra_sample
print('num_extra_sample ', num_extra_sample)
select_inds_hard = np.random.choice(hard_coords.shape[0],
size=[min(hard_coords.shape[0],
num_extra_sample)],
replace=False) # (N_rand,)
select_inds_all = np.random.choice(coords.shape[0],
size=[N_rand],
replace=False) # (N_rand,)
select_coords_hard = hard_coords[select_inds_hard].long()
select_coords_all = coords[select_inds_all].long()
select_coords = torch.cat([select_coords_all, select_coords_hard], 0)
else:
select_inds = np.random.choice(coords.shape[0],
size=[N_rand],
replace=False) # (N_rand,)
select_coords = coords[select_inds].long() # (N_rand, 2)
rays_o = rays_o[select_coords[:, 0],
select_coords[:, 1]] # (N_rand, 3)
rays_d = rays_d[select_coords[:, 0],
select_coords[:, 1]] # (N_rand, 3)
batch_rays = torch.stack([rays_o, rays_d], 0)
target_rgb = target[select_coords[:, 0],
select_coords[:, 1]] # (N_rand, 3)
target_depth = depth_gt[select_coords[:, 0],
select_coords[:, 1]]
target_mask = mask_gt[select_coords[:, 0],
select_coords[:, 1]].unsqueeze(-1)
target_of_fwd = flow_fwd[select_coords[:, 0],
select_coords[:, 1]]
target_fwd_mask = fwd_mask[select_coords[:, 0],
select_coords[:, 1]].unsqueeze(-1)#.repeat(1, 2)
target_of_bwd = flow_bwd[select_coords[:, 0],
select_coords[:, 1]]
target_bwd_mask = bwd_mask[select_coords[:, 0],
select_coords[:, 1]].unsqueeze(-1)#.repeat(1, 2)
img_idx_embed = img_i/num_img * 2. - 1.0
##### Core optimization loop #####
if args.chain_sf and i > decay_iteration * 1000 * 2:
chain_5frames = True
else:
chain_5frames = False
print('chain_5frames ', chain_5frames, ' chain_bwd ', chain_bwd)
ret = render(img_idx_embed,
chain_bwd,
chain_5frames,
num_img, H, W, focal,
chunk=args.chunk,
rays=batch_rays,
verbose=i < 10, retraw=True,
**render_kwargs_train)
pose_post = poses[min(img_i + 1, int(num_img) - 1), :3,:4]
pose_prev = poses[max(img_i - 1, 0), :3,:4]
render_of_fwd, render_of_bwd = compute_optical_flow(pose_post,
pose, pose_prev,
H, W, focal,
ret)
optimizer.zero_grad()
weight_map_post = ret['prob_map_post']
weight_map_prev = ret['prob_map_prev']
weight_post = 1. - ret['raw_prob_ref2post']
weight_prev = 1. - ret['raw_prob_ref2prev']
prob_reg_loss = args.w_prob_reg * (torch.mean(torch.abs(ret['raw_prob_ref2prev'])) \
+ torch.mean(torch.abs(ret['raw_prob_ref2post'])))
# dynamic rendering loss
if i <= decay_iteration * 1000:
# dynamic rendering loss
render_loss = img2mse(ret['rgb_map_ref_dy'], target_rgb)
render_loss += compute_mse(ret['rgb_map_post_dy'],
target_rgb,
weight_map_post.unsqueeze(-1))
render_loss += compute_mse(ret['rgb_map_prev_dy'],
target_rgb,
weight_map_prev.unsqueeze(-1))
else:
print('only compute dynamic render loss in masked region')
weights_map_dd = ret['weights_map_dd'].unsqueeze(-1).detach()
# dynamic rendering loss
render_loss = compute_mse(ret['rgb_map_ref_dy'],
target_rgb,
weights_map_dd)
render_loss += compute_mse(ret['rgb_map_post_dy'],
target_rgb,
weight_map_post.unsqueeze(-1) * weights_map_dd)
render_loss += compute_mse(ret['rgb_map_prev_dy'],
target_rgb,
weight_map_prev.unsqueeze(-1) * weights_map_dd)
# union rendering loss
render_loss += img2mse(ret['rgb_map_ref'][:N_rand, ...],
target_rgb[:N_rand, ...])
sf_cycle_loss = args.w_cycle * compute_mae(ret['raw_sf_ref2post'],
-ret['raw_sf_post2ref'],
weight_post.unsqueeze(-1), dim=3)
sf_cycle_loss += args.w_cycle * compute_mae(ret['raw_sf_ref2prev'],
-ret['raw_sf_prev2ref'],
weight_prev.unsqueeze(-1), dim=3)
# regularization loss
render_sf_ref2prev = torch.sum(ret['weights_ref_dy'].unsqueeze(-1) * ret['raw_sf_ref2prev'], -1)
render_sf_ref2post = torch.sum(ret['weights_ref_dy'].unsqueeze(-1) * ret['raw_sf_ref2post'], -1)
sf_reg_loss = args.w_sf_reg * (torch.mean(torch.abs(render_sf_ref2prev)) \
+ torch.mean(torch.abs(render_sf_ref2post)))
divsor = i // (decay_iteration * 1000)
decay_rate = 10
if args.decay_depth_w:
w_depth = args.w_depth/(decay_rate ** divsor)
else:
w_depth = args.w_depth
if args.decay_optical_flow_w:
w_of = args.w_optical_flow/(decay_rate ** divsor)
else:
w_of = args.w_optical_flow
depth_loss = w_depth * compute_depth_loss(ret['depth_map_ref_dy'], -target_depth)
print('w_depth ', w_depth, 'w_of ', w_of)
if img_i == 0:
print('only fwd flow')
flow_loss = w_of * compute_mae(render_of_fwd,
target_of_fwd,
target_fwd_mask)#torch.sum(torch.abs(render_of_fwd - target_of_fwd) * target_fwd_mask)/(torch.sum(target_fwd_mask) + 1e-8)
elif img_i == num_img - 1:
print('only bwd flow')
flow_loss = w_of * compute_mae(render_of_bwd,
target_of_bwd,
target_bwd_mask)#torch.sum(torch.abs(render_of_bwd - target_of_bwd) * target_bwd_mask)/(torch.sum(target_bwd_mask) + 1e-8)
else:
flow_loss = w_of * compute_mae(render_of_fwd,
target_of_fwd,
target_fwd_mask)#torch.sum(torch.abs(render_of_fwd - target_of_fwd) * target_fwd_mask)/(torch.sum(target_fwd_mask) + 1e-8)
flow_loss += w_of * compute_mae(render_of_bwd,
target_of_bwd,
target_bwd_mask)#torch.sum(torch.abs(render_of_bwd - target_of_bwd) * target_bwd_mask)/(torch.sum(target_bwd_mask) + 1e-8)
# scene flow smoothness loss
sf_sm_loss = args.w_sm * (compute_sf_sm_loss(ret['raw_pts_ref'],
ret['raw_pts_post'],
H, W, focal) \
+ compute_sf_sm_loss(ret['raw_pts_ref'],
ret['raw_pts_prev'],
H, W, focal))
# scene flow least kinectic loss
sf_sm_loss += args.w_sm * compute_sf_lke_loss(ret['raw_pts_ref'],
ret['raw_pts_post'],
ret['raw_pts_prev'],
H, W, focal)
sf_sm_loss += args.w_sm * compute_sf_lke_loss(ret['raw_pts_ref'],
ret['raw_pts_post'],
ret['raw_pts_prev'],
H, W, focal)
entropy_loss = args.w_entropy * torch.mean(-ret['raw_blend_w'] * torch.log(ret['raw_blend_w'] + 1e-8))
# # ====================================== two-frames chain loss ===============================
if chain_bwd:
sf_sm_loss += args.w_sm * compute_sf_lke_loss(ret['raw_pts_prev'],
ret['raw_pts_ref'],
ret['raw_pts_pp'],
H, W, focal)
else:
sf_sm_loss += args.w_sm * compute_sf_lke_loss(ret['raw_pts_post'],
ret['raw_pts_pp'],
ret['raw_pts_ref'],
H, W, focal)
if chain_5frames:
print('5 FRAME RENDER LOSS ADDED')
render_loss += compute_mse(ret['rgb_map_pp_dy'],
target_rgb,
weights_map_dd)
loss = sf_reg_loss + sf_cycle_loss + \
render_loss + flow_loss + \
sf_sm_loss + prob_reg_loss + \
depth_loss + entropy_loss
print('render_loss ', render_loss.item(),
' bidirection_loss ', sf_cycle_loss.item(),
' sf_reg_loss ', sf_reg_loss.item())
print('depth_loss ', depth_loss.item(),
' flow_loss ', flow_loss.item(),
' sf_sm_loss ', sf_sm_loss.item())
print('prob_reg_loss ', prob_reg_loss.item(),
' entropy_loss ', entropy_loss.item())
loss.backward()
optimizer.step()
# NOTE: IMPORTANT!
### update learning rate ###
decay_rate = 0.1
decay_steps = args.lrate_decay * 1000
new_lrate = args.lrate * (decay_rate ** (global_step / decay_steps))
for param_group in optimizer.param_groups:
param_group['lr'] = new_lrate
################################
dt = time.time()-time0
print(f"Step: {global_step}, Loss: {loss}, Time: {dt}")
##### end #####
# Rest is logging
if i%args.i_weights==0:
path = os.path.join(basedir, expname, '{:06d}.tar'.format(i))
if args.N_importance > 0:
torch.save({
'global_step': global_step,
'network_fn_state_dict': render_kwargs_train['network_fn'].state_dict(),
'network_rigid': render_kwargs_train['network_rigid'].state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, path)
else:
torch.save({
'global_step': global_step,
'network_fn_state_dict': render_kwargs_train['network_fn'].state_dict(),
'network_rigid': render_kwargs_train['network_rigid'].state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, path)
print('Saved checkpoints at', path)
if i % args.i_print == 0 and i > 0:
writer.add_scalar("train/loss", loss.item(), i)
writer.add_scalar("train/render_loss", render_loss.item(), i)
writer.add_scalar("train/depth_loss", depth_loss.item(), i)
writer.add_scalar("train/flow_loss", flow_loss.item(), i)
writer.add_scalar("train/prob_reg_loss", prob_reg_loss.item(), i)
writer.add_scalar("train/sf_reg_loss", sf_reg_loss.item(), i)
writer.add_scalar("train/sf_cycle_loss", sf_cycle_loss.item(), i)
writer.add_scalar("train/sf_sm_loss", sf_sm_loss.item(), i)
if i%args.i_img == 0:
# img_i = np.random.choice(i_val)
target = images[img_i]
pose = poses[img_i, :3,:4]
target_depth = depths[img_i] - torch.min(depths[img_i])
# img_idx_embed = img_i/num_img * 2. - 1.0
# if img_i == 0:
# flow_fwd, fwd_mask = read_optical_flow(args.datadir, img_i,
# args.start_frame, fwd=True)
# flow_bwd, bwd_mask = np.zeros_like(flow_fwd), np.zeros_like(fwd_mask)
# elif img_i == num_img - 1:
# flow_bwd, bwd_mask = read_optical_flow(args.datadir, img_i,
# args.start_frame, fwd=False)
# flow_fwd, fwd_mask = np.zeros_like(flow_bwd), np.zeros_like(bwd_mask)
# else:
# flow_fwd, fwd_mask = read_optical_flow(args.datadir,
# img_i, args.start_frame,
# fwd=True)
# flow_bwd, bwd_mask = read_optical_flow(args.datadir,
# img_i, args.start_frame,
# fwd=False)
# flow_fwd_rgb = torch.Tensor(flow_to_image(flow_fwd)/255.)#.cuda()
# writer.add_image("val/gt_flow_fwd",
# flow_fwd_rgb, global_step=i, dataformats='HWC')
# flow_bwd_rgb = torch.Tensor(flow_to_image(flow_bwd)/255.)#.cuda()
# writer.add_image("val/gt_flow_bwd",
# flow_bwd_rgb, global_step=i, dataformats='HWC')
with torch.no_grad():
ret = render(img_idx_embed,
chain_bwd, False,
num_img, H, W, focal,
chunk=1024*16,
c2w=pose,
**render_kwargs_test)
# pose_post = poses[min(img_i + 1, int(num_img) - 1), :3,:4]
# pose_prev = poses[max(img_i - 1, 0), :3,:4]
# render_of_fwd, render_of_bwd = compute_optical_flow(pose_post, pose, pose_prev,
# H, W, focal, ret, n_dim=2)
# render_flow_fwd_rgb = torch.Tensor(flow_to_image(render_of_fwd.cpu().numpy())/255.)#.cuda()
# render_flow_bwd_rgb = torch.Tensor(flow_to_image(render_of_bwd.cpu().numpy())/255.)#.cuda()
writer.add_image("val/rgb_map_ref", torch.clamp(ret['rgb_map_ref'], 0., 1.),
global_step=i, dataformats='HWC')
writer.add_image("val/depth_map_ref", normalize_depth(ret['depth_map_ref']),
global_step=i, dataformats='HW')
writer.add_image("val/rgb_map_rig", torch.clamp(ret['rgb_map_rig'], 0., 1.),
global_step=i, dataformats='HWC')
writer.add_image("val/depth_map_rig", normalize_depth(ret['depth_map_rig']),
global_step=i, dataformats='HW')
writer.add_image("val/rgb_map_ref_dy", torch.clamp(ret['rgb_map_ref_dy'], 0., 1.),
global_step=i, dataformats='HWC')
writer.add_image("val/depth_map_ref_dy", normalize_depth(ret['depth_map_ref_dy']),
global_step=i, dataformats='HW')
# writer.add_image("val/rgb_map_pp_dy", torch.clamp(ret['rgb_map_pp_dy'], 0., 1.),
# global_step=i, dataformats='HWC')
writer.add_image("val/gt_rgb", target,
global_step=i, dataformats='HWC')
writer.add_image("val/monocular_disp",
torch.clamp(target_depth /percentile(target_depth, 97), 0., 1.),
global_step=i, dataformats='HW')
writer.add_image("val/weights_map_dd",
ret['weights_map_dd'],
global_step=i,
dataformats='HW')
# torch.cuda.empty_cache()
global_step += 1
if __name__=='__main__':
torch.set_default_tensor_type('torch.cuda.FloatTensor')
train()