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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import torch_scatter
from torch_scatter import scatter_sum
from . import fastba
from . import altcorr
from . import lietorch
from .lietorch import SE3
from .extractor import BasicEncoder, BasicEncoder4
from .blocks import GradientClip, GatedResidual, SoftAgg
from .utils import *
from .ba import BA
from . import projective_ops as pops
autocast = torch.cuda.amp.autocast
import matplotlib.pyplot as plt
DIM = 384
class Update(nn.Module):
def __init__(self, p):
super(Update, self).__init__()
self.c1 = nn.Sequential(
nn.Linear(DIM, DIM),
nn.ReLU(inplace=True),
nn.Linear(DIM, DIM))
self.c2 = nn.Sequential(
nn.Linear(DIM, DIM),
nn.ReLU(inplace=True),
nn.Linear(DIM, DIM))
self.norm = nn.LayerNorm(DIM, eps=1e-3)
self.agg_kk = SoftAgg(DIM)
self.agg_ij = SoftAgg(DIM)
self.gru = nn.Sequential(
nn.LayerNorm(DIM, eps=1e-3),
GatedResidual(DIM),
nn.LayerNorm(DIM, eps=1e-3),
GatedResidual(DIM),
)
self.corr = nn.Sequential(
nn.Linear(2*49*p*p, DIM),
nn.ReLU(inplace=True),
nn.Linear(DIM, DIM),
nn.LayerNorm(DIM, eps=1e-3),
nn.ReLU(inplace=True),
nn.Linear(DIM, DIM),
)
self.d = nn.Sequential(
nn.ReLU(inplace=False),
nn.Linear(DIM, 2),
GradientClip())
self.w = nn.Sequential(
nn.ReLU(inplace=False),
nn.Linear(DIM, 2),
GradientClip(),
nn.Sigmoid())
def forward(self, net, inp, corr, flow, ii, jj, kk):
""" update operator """
net = net + inp + self.corr(corr)
net = self.norm(net)
ix, jx = fastba.neighbors(kk, jj)
mask_ix = (ix >= 0).float().reshape(1, -1, 1)
mask_jx = (jx >= 0).float().reshape(1, -1, 1)
net = net + self.c1(mask_ix * net[:,ix])
net = net + self.c2(mask_jx * net[:,jx])
net = net + self.agg_kk(net, kk)
net = net + self.agg_ij(net, ii*12345 + jj)
net = self.gru(net)
return net, (self.d(net), self.w(net), None)
class Patchifier(nn.Module):
def __init__(self, patch_size=3):
super(Patchifier, self).__init__()
self.patch_size = patch_size
self.fnet = BasicEncoder4(output_dim=128, norm_fn='instance')
self.inet = BasicEncoder4(output_dim=DIM, norm_fn='none')
def __image_gradient(self, images):
gray = ((images + 0.5) * (255.0 / 2)).sum(dim=2)
dx = gray[...,:-1,1:] - gray[...,:-1,:-1]
dy = gray[...,1:,:-1] - gray[...,:-1,:-1]
g = torch.sqrt(dx**2 + dy**2)
g = F.avg_pool2d(g, 4, 4)
return g
def forward(self, images, patches_per_image=80, disps=None, gradient_bias=False, return_color=False):
""" extract patches from input images """
fmap = self.fnet(images) / 4.0
imap = self.inet(images) / 4.0
b, n, c, h, w = fmap.shape
P = self.patch_size
# bias patch selection towards regions with high gradient
if gradient_bias:
g = self.__image_gradient(images)
x = torch.randint(1, w-1, size=[n, 3*patches_per_image], device="cuda")
y = torch.randint(1, h-1, size=[n, 3*patches_per_image], device="cuda")
coords = torch.stack([x, y], dim=-1).float()
g = altcorr.patchify(g[0,:,None], coords, 0).view(n, 3 * patches_per_image)
ix = torch.argsort(g, dim=1)
x = torch.gather(x, 1, ix[:, -patches_per_image:])
y = torch.gather(y, 1, ix[:, -patches_per_image:])
else:
x = torch.randint(1, w-1, size=[n, patches_per_image], device="cuda")
y = torch.randint(1, h-1, size=[n, patches_per_image], device="cuda")
coords = torch.stack([x, y], dim=-1).float()
imap = altcorr.patchify(imap[0], coords, 0).view(b, -1, DIM, 1, 1)
gmap = altcorr.patchify(fmap[0], coords, P//2).view(b, -1, 128, P, P)
if return_color:
clr = altcorr.patchify(images[0], 4*(coords + 0.5), 0).view(b, -1, 3)
if disps is None:
disps = torch.ones(b, n, h, w, device="cuda")
grid, _ = coords_grid_with_index(disps, device=fmap.device)
patches = altcorr.patchify(grid[0], coords, P//2).view(b, -1, 3, P, P)
index = torch.arange(n, device="cuda").view(n, 1)
index = index.repeat(1, patches_per_image).reshape(-1)
if return_color:
return fmap, gmap, imap, patches, index, clr
return fmap, gmap, imap, patches, index
class CorrBlock:
def __init__(self, fmap, gmap, radius=3, dropout=0.2, levels=[1,4]):
self.dropout = dropout
self.radius = radius
self.levels = levels
self.gmap = gmap
self.pyramid = pyramidify(fmap, lvls=levels)
def __call__(self, ii, jj, coords):
corrs = []
for i in range(len(self.levels)):
corrs += [ altcorr.corr(self.gmap, self.pyramid[i], coords / self.levels[i], ii, jj, self.radius, self.dropout) ]
return torch.stack(corrs, -1).view(1, len(ii), -1)
class VONet(nn.Module):
def __init__(self, use_viewer=False):
super(VONet, self).__init__()
self.P = 3
self.patchify = Patchifier(self.P)
self.update = Update(self.P)
self.DIM = DIM
self.RES = 4
@autocast(enabled=False)
def forward(self, images, poses, disps, intrinsics, M=1024, STEPS=12, P=1, structure_only=False, rescale=False):
""" Estimates SE3 or Sim3 between pair of frames """
images = 2 * (images / 255.0) - 0.5
intrinsics = intrinsics / 4.0
disps = disps[:, :, 1::4, 1::4].float()
fmap, gmap, imap, patches, ix = self.patchify(images, disps=disps)
corr_fn = CorrBlock(fmap, gmap)
b, N, c, h, w = fmap.shape
p = self.P
patches_gt = patches.clone()
Ps = poses
d = patches[..., 2, p//2, p//2]
patches = set_depth(patches, torch.rand_like(d))
kk, jj = flatmeshgrid(torch.where(ix < 8)[0], torch.arange(0,8, device="cuda"))
ii = ix[kk]
imap = imap.view(b, -1, DIM)
net = torch.zeros(b, len(kk), DIM, device="cuda", dtype=torch.float)
Gs = SE3.IdentityLike(poses)
if structure_only:
Gs.data[:] = poses.data[:]
traj = []
bounds = [-64, -64, w + 64, h + 64]
while len(traj) < STEPS:
Gs = Gs.detach()
patches = patches.detach()
n = ii.max() + 1
if len(traj) >= 8 and n < images.shape[1]:
if not structure_only: Gs.data[:,n] = Gs.data[:,n-1]
kk1, jj1 = flatmeshgrid(torch.where(ix < n)[0], torch.arange(n, n+1, device="cuda"))
kk2, jj2 = flatmeshgrid(torch.where(ix == n)[0], torch.arange(0, n+1, device="cuda"))
ii = torch.cat([ix[kk1], ix[kk2], ii])
jj = torch.cat([jj1, jj2, jj])
kk = torch.cat([kk1, kk2, kk])
net1 = torch.zeros(b, len(kk1) + len(kk2), DIM, device="cuda")
net = torch.cat([net1, net], dim=1)
if np.random.rand() < 0.1:
k = (ii != (n - 4)) & (jj != (n - 4))
ii = ii[k]
jj = jj[k]
kk = kk[k]
net = net[:,k]
patches[:,ix==n,2] = torch.median(patches[:,(ix == n-1) | (ix == n-2),2])
n = ii.max() + 1
coords = pops.transform(Gs, patches, intrinsics, ii, jj, kk)
coords1 = coords.permute(0, 1, 4, 2, 3).contiguous()
corr = corr_fn(kk, jj, coords1)
net, (delta, weight, _) = self.update(net, imap[:,kk], corr, None, ii, jj, kk)
lmbda = 1e-4
target = coords[...,p//2,p//2,:] + delta
ep = 10
for itr in range(2):
Gs, patches = BA(Gs, patches, intrinsics, target, weight, lmbda, ii, jj, kk,
bounds, ep=ep, fixedp=1, structure_only=structure_only)
kl = torch.as_tensor(0)
dij = (ii - jj).abs()
k = (dij > 0) & (dij <= 2)
coords = pops.transform(Gs, patches, intrinsics, ii[k], jj[k], kk[k])
coords_gt, valid, _ = pops.transform(Ps, patches_gt, intrinsics, ii[k], jj[k], kk[k], jacobian=True)
traj.append((valid, coords, coords_gt, Gs[:,:n], Ps[:,:n], kl))
return traj
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