Spaces:
Running
Running
File size: 14,965 Bytes
b7eedf7 |
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 |
import torch
import lietorch
import numpy as np
import matplotlib.pyplot as plt
from lietorch import SE3
from modules.corr import CorrBlock, AltCorrBlock
import geom.projective_ops as pops
from glob import glob
class FactorGraph:
def __init__(self, video, update_op, device="cuda:0", corr_impl="volume", max_factors=-1, upsample=False):
self.video = video
self.update_op = update_op
self.device = device
self.max_factors = max_factors
self.corr_impl = corr_impl
self.upsample = upsample
# operator at 1/8 resolution
self.ht = ht = video.ht // 8
self.wd = wd = video.wd // 8
self.coords0 = pops.coords_grid(ht, wd, device=device)
self.ii = torch.as_tensor([], dtype=torch.long, device=device)
self.jj = torch.as_tensor([], dtype=torch.long, device=device)
self.age = torch.as_tensor([], dtype=torch.long, device=device)
self.corr, self.net, self.inp = None, None, None
self.damping = 1e-6 * torch.ones_like(self.video.disps)
self.target = torch.zeros([1, 0, ht, wd, 2], device=device, dtype=torch.float)
self.weight = torch.zeros([1, 0, ht, wd, 2], device=device, dtype=torch.float)
# inactive factors
self.ii_inac = torch.as_tensor([], dtype=torch.long, device=device)
self.jj_inac = torch.as_tensor([], dtype=torch.long, device=device)
self.ii_bad = torch.as_tensor([], dtype=torch.long, device=device)
self.jj_bad = torch.as_tensor([], dtype=torch.long, device=device)
self.target_inac = torch.zeros([1, 0, ht, wd, 2], device=device, dtype=torch.float)
self.weight_inac = torch.zeros([1, 0, ht, wd, 2], device=device, dtype=torch.float)
def __filter_repeated_edges(self, ii, jj):
""" remove duplicate edges """
keep = torch.zeros(ii.shape[0], dtype=torch.bool, device=ii.device)
eset = set(
[(i.item(), j.item()) for i, j in zip(self.ii, self.jj)] +
[(i.item(), j.item()) for i, j in zip(self.ii_inac, self.jj_inac)])
for k, (i, j) in enumerate(zip(ii, jj)):
keep[k] = (i.item(), j.item()) not in eset
return ii[keep], jj[keep]
def print_edges(self):
ii = self.ii.cpu().numpy()
jj = self.jj.cpu().numpy()
ix = np.argsort(ii)
ii = ii[ix]
jj = jj[ix]
w = torch.mean(self.weight, dim=[0,2,3,4]).cpu().numpy()
w = w[ix]
for e in zip(ii, jj, w):
print(e)
print()
def filter_edges(self):
""" remove bad edges """
conf = torch.mean(self.weight, dim=[0,2,3,4])
mask = (torch.abs(self.ii-self.jj) > 2) & (conf < 0.001)
self.ii_bad = torch.cat([self.ii_bad, self.ii[mask]])
self.jj_bad = torch.cat([self.jj_bad, self.jj[mask]])
self.rm_factors(mask, store=False)
def clear_edges(self):
self.rm_factors(self.ii >= 0)
self.net = None
self.inp = None
@torch.cuda.amp.autocast(enabled=True)
def add_factors(self, ii, jj, remove=False):
""" add edges to factor graph """
if not isinstance(ii, torch.Tensor):
ii = torch.as_tensor(ii, dtype=torch.long, device=self.device)
if not isinstance(jj, torch.Tensor):
jj = torch.as_tensor(jj, dtype=torch.long, device=self.device)
# remove duplicate edges
ii, jj = self.__filter_repeated_edges(ii, jj)
if ii.shape[0] == 0:
return
# place limit on number of factors
if self.max_factors > 0 and self.ii.shape[0] + ii.shape[0] > self.max_factors \
and self.corr is not None and remove:
ix = torch.arange(len(self.age))[torch.argsort(self.age).cpu()]
self.rm_factors(ix >= self.max_factors - ii.shape[0], store=True)
net = self.video.nets[ii].to(self.device).unsqueeze(0)
# correlation volume for new edges
if self.corr_impl == "volume":
c = (ii == jj).long()
fmap1 = self.video.fmaps[ii,0].to(self.device).unsqueeze(0)
fmap2 = self.video.fmaps[jj,c].to(self.device).unsqueeze(0)
corr = CorrBlock(fmap1, fmap2)
self.corr = corr if self.corr is None else self.corr.cat(corr)
inp = self.video.inps[ii].to(self.device).unsqueeze(0)
self.inp = inp if self.inp is None else torch.cat([self.inp, inp], 1)
with torch.cuda.amp.autocast(enabled=False):
target, _ = self.video.reproject(ii, jj)
weight = torch.zeros_like(target)
self.ii = torch.cat([self.ii, ii], 0)
self.jj = torch.cat([self.jj, jj], 0)
self.age = torch.cat([self.age, torch.zeros_like(ii)], 0)
# reprojection factors
self.net = net if self.net is None else torch.cat([self.net, net], 1)
self.target = torch.cat([self.target, target], 1)
self.weight = torch.cat([self.weight, weight], 1)
@torch.cuda.amp.autocast(enabled=True)
def rm_factors(self, mask, store=False):
""" drop edges from factor graph """
# store estimated factors
if store:
self.ii_inac = torch.cat([self.ii_inac, self.ii[mask]], 0)
self.jj_inac = torch.cat([self.jj_inac, self.jj[mask]], 0)
self.target_inac = torch.cat([self.target_inac, self.target[:,mask]], 1)
self.weight_inac = torch.cat([self.weight_inac, self.weight[:,mask]], 1)
self.ii = self.ii[~mask]
self.jj = self.jj[~mask]
self.age = self.age[~mask]
if self.corr_impl == "volume":
self.corr = self.corr[~mask]
if self.net is not None:
self.net = self.net[:,~mask]
if self.inp is not None:
self.inp = self.inp[:,~mask]
self.target = self.target[:,~mask]
self.weight = self.weight[:,~mask]
@torch.cuda.amp.autocast(enabled=True)
def rm_keyframe(self, ix):
""" drop edges from factor graph """
with self.video.get_lock():
self.video.images[ix] = self.video.images[ix+1]
self.video.poses[ix] = self.video.poses[ix+1]
self.video.disps[ix] = self.video.disps[ix+1]
self.video.disps_sens[ix] = self.video.disps_sens[ix+1]
self.video.intrinsics[ix] = self.video.intrinsics[ix+1]
self.video.nets[ix] = self.video.nets[ix+1]
self.video.inps[ix] = self.video.inps[ix+1]
self.video.fmaps[ix] = self.video.fmaps[ix+1]
self.video.tstamp[ix] = self.video.tstamp[ix+1]
self.video.masks[ix] = self.video.masks[ix+1]
m = (self.ii_inac == ix) | (self.jj_inac == ix)
self.ii_inac[self.ii_inac >= ix] -= 1
self.jj_inac[self.jj_inac >= ix] -= 1
if torch.any(m):
self.ii_inac = self.ii_inac[~m]
self.jj_inac = self.jj_inac[~m]
self.target_inac = self.target_inac[:,~m]
self.weight_inac = self.weight_inac[:,~m]
m = (self.ii == ix) | (self.jj == ix)
self.ii[self.ii >= ix] -= 1
self.jj[self.jj >= ix] -= 1
self.rm_factors(m, store=False)
@torch.cuda.amp.autocast(enabled=True)
def update(self, t0=None, t1=None, itrs=3, use_inactive=False, EP=1e-7, motion_only=False):
""" run update operator on factor graph """
# motion features
with torch.cuda.amp.autocast(enabled=False):
coords1, mask = self.video.reproject(self.ii, self.jj)
motn = torch.cat([coords1 - self.coords0, self.target - coords1], dim=-1)
motn = motn.permute(0,1,4,2,3).clamp(-64.0, 64.0)
# correlation features
corr = self.corr(coords1)
self.net, delta, weight, damping, upmask = \
self.update_op(self.net, self.inp, corr, motn, self.ii, self.jj)
##### save confidecnce weight for vis #####
# for k in range(len(self.ii)):
# w = weight[:, k].detach().cpu().numpy()
# idx_i = self.ii[k]
# idx_j = self.jj[k]
# np.save(f'pred_conf/{idx_i:04d}_{idx_j:04d}.npy', w)
#############################################
# Shapes:
# weight: [1, k, h//8, w//8, 2]
# self.ii: [k]; self.jj: [k]
msk = self.video.masks[self.ii] > 0
weight[:,msk] = 0.0
if t0 is None:
t0 = max(1, self.ii.min().item()+1)
with torch.cuda.amp.autocast(enabled=False):
self.target = coords1 + delta.to(dtype=torch.float)
self.weight = weight.to(dtype=torch.float)
ht, wd = self.coords0.shape[0:2]
self.damping[torch.unique(self.ii)] = damping
if use_inactive:
m = (self.ii_inac >= t0 - 3) & (self.jj_inac >= t0 - 3)
ii = torch.cat([self.ii_inac[m], self.ii], 0)
jj = torch.cat([self.jj_inac[m], self.jj], 0)
target = torch.cat([self.target_inac[:,m], self.target], 1)
weight = torch.cat([self.weight_inac[:,m], self.weight], 1)
else:
ii, jj, target, weight = self.ii, self.jj, self.target, self.weight
damping = .2 * self.damping[torch.unique(ii)].contiguous() + EP
target = target.view(-1, ht, wd, 2).permute(0,3,1,2).contiguous()
weight = weight.view(-1, ht, wd, 2).permute(0,3,1,2).contiguous()
# dense bundle adjustment
self.video.ba(target, weight, damping, ii, jj, t0, t1,
itrs=itrs, lm=1e-4, ep=0.1, motion_only=motion_only)
if self.upsample:
self.video.upsample(torch.unique(self.ii), upmask)
self.age += 1
@torch.cuda.amp.autocast(enabled=False)
def update_lowmem(self, t0=None, t1=None, itrs=2, use_inactive=False, EP=1e-7, steps=8):
""" run update operator on factor graph - reduced memory implementation """
# alternate corr implementation
t = self.video.counter.value
num, rig, ch, ht, wd = self.video.fmaps.shape
corr_op = AltCorrBlock(self.video.fmaps.view(1, num*rig, ch, ht, wd))
print("Global BA Iteration with {} steps".format(steps))
for step in range(steps):
# print("Global BA Iteration #{}".format(step+1))
with torch.cuda.amp.autocast(enabled=False):
coords1, mask = self.video.reproject(self.ii, self.jj)
motn = torch.cat([coords1 - self.coords0, self.target - coords1], dim=-1)
motn = motn.permute(0,1,4,2,3).clamp(-64.0, 64.0)
s = 8
for i in range(0, self.jj.max()+1, s):
v = (self.ii >= i) & (self.ii < i + s)
iis = self.ii[v]
jjs = self.jj[v]
ht, wd = self.coords0.shape[0:2]
corr1 = corr_op(coords1[:,v], rig * iis, rig * jjs + (iis == jjs).long())
with torch.cuda.amp.autocast(enabled=True):
net, delta, weight, damping, upmask = \
self.update_op(self.net[:,v], self.video.inps[None,iis], corr1, motn[:,v], iis, jjs)
if self.upsample:
self.video.upsample(torch.unique(iis), upmask)
# Shapes:
# weight: [1, k, h//8, w//8, 2]
# self.ii: [k]; self.jj: [k]
msk = self.video.masks[iis] > 0
weight[:,msk] = 0.0
self.net[:,v] = net
self.target[:,v] = coords1[:,v] + delta.float()
self.weight[:,v] = weight.float()
self.damping[torch.unique(iis)] = damping
damping = .2 * self.damping[torch.unique(self.ii)].contiguous() + EP
target = self.target.view(-1, ht, wd, 2).permute(0,3,1,2).contiguous()
weight = self.weight.view(-1, ht, wd, 2).permute(0,3,1,2).contiguous()
# dense bundle adjustment
self.video.ba(target, weight, damping, self.ii, self.jj, 1, t,
itrs=itrs, lm=1e-5, ep=1e-2, motion_only=False)
self.video.dirty[:t] = True
def add_neighborhood_factors(self, t0, t1, r=3):
""" add edges between neighboring frames within radius r """
ii, jj = torch.meshgrid(torch.arange(t0,t1), torch.arange(t0,t1), indexing='ij')
ii = ii.reshape(-1).to(dtype=torch.long, device=self.device)
jj = jj.reshape(-1).to(dtype=torch.long, device=self.device)
c = 1 if self.video.stereo else 0
keep = ((ii - jj).abs() > c) & ((ii - jj).abs() <= r)
self.add_factors(ii[keep], jj[keep])
def add_proximity_factors(self, t0=0, t1=0, rad=2, nms=2, beta=0.25, thresh=16.0, remove=False):
""" add edges to the factor graph based on distance """
t = self.video.counter.value
ix = torch.arange(t0, t)
jx = torch.arange(t1, t)
ii, jj = torch.meshgrid(ix, jx, indexing='ij')
ii = ii.reshape(-1)
jj = jj.reshape(-1)
d = self.video.distance(ii, jj, beta=beta)
d[ii - rad < jj] = np.inf
d[d > 100] = np.inf
ii1 = torch.cat([self.ii, self.ii_bad, self.ii_inac], 0)
jj1 = torch.cat([self.jj, self.jj_bad, self.jj_inac], 0)
for i, j in zip(ii1.cpu().numpy(), jj1.cpu().numpy()):
for di in range(-nms, nms+1):
for dj in range(-nms, nms+1):
if abs(di) + abs(dj) <= max(min(abs(i-j)-2, nms), 0):
i1 = i + di
j1 = j + dj
if (t0 <= i1 < t) and (t1 <= j1 < t):
d[(i1-t0)*(t-t1) + (j1-t1)] = np.inf
es = []
for i in range(t0, t):
if self.video.stereo:
es.append((i, i))
d[(i-t0)*(t-t1) + (i-t1)] = np.inf
for j in range(max(i-rad-1,0), i):
es.append((i,j))
es.append((j,i))
d[(i-t0)*(t-t1) + (j-t1)] = np.inf
ix = torch.argsort(d)
for k in ix:
if d[k].item() > thresh:
continue
if len(es) > self.max_factors:
break
i = ii[k]
j = jj[k]
# bidirectional
es.append((i, j))
es.append((j, i))
for di in range(-nms, nms+1):
for dj in range(-nms, nms+1):
if abs(di) + abs(dj) <= max(min(abs(i-j)-2, nms), 0):
i1 = i + di
j1 = j + dj
if (t0 <= i1 < t) and (t1 <= j1 < t):
d[(i1-t0)*(t-t1) + (j1-t1)] = np.inf
ii, jj = torch.as_tensor(es, device=self.device).unbind(dim=-1)
self.add_factors(ii, jj, remove)
|