119 lines
3.2 KiB
Python
119 lines
3.2 KiB
Python
from collections import OrderedDict
|
|
import numpy as np
|
|
import torch
|
|
from lietorch import SO3, SE3, Sim3
|
|
from .graph_utils import graph_to_edge_list
|
|
from .projective_ops import projective_transform
|
|
|
|
|
|
def pose_metrics(dE):
|
|
""" Translation/Rotation/Scaling metrics from Sim3 """
|
|
t, q, s = dE.data.split([3, 4, 1], -1)
|
|
ang = SO3(q).log().norm(dim=-1)
|
|
|
|
# convert radians to degrees
|
|
r_err = (180 / np.pi) * ang
|
|
t_err = t.norm(dim=-1)
|
|
s_err = (s - 1.0).abs()
|
|
return r_err, t_err, s_err
|
|
|
|
|
|
def fit_scale(Ps, Gs):
|
|
b = Ps.shape[0]
|
|
t1 = Ps.data[...,:3].detach().reshape(b, -1)
|
|
t2 = Gs.data[...,:3].detach().reshape(b, -1)
|
|
|
|
s = (t1*t2).sum(-1) / ((t2*t2).sum(-1) + 1e-8)
|
|
return s
|
|
|
|
|
|
def geodesic_loss(Ps, Gs, graph, gamma=0.9, do_scale=True):
|
|
""" Loss function for training network """
|
|
|
|
# relative pose
|
|
ii, jj, kk = graph_to_edge_list(graph)
|
|
dP = Ps[:,jj] * Ps[:,ii].inv()
|
|
|
|
n = len(Gs)
|
|
geodesic_loss = 0.0
|
|
|
|
for i in range(n):
|
|
w = gamma ** (n - i - 1)
|
|
dG = Gs[i][:,jj] * Gs[i][:,ii].inv()
|
|
|
|
if do_scale:
|
|
s = fit_scale(dP, dG)
|
|
dG = dG.scale(s[:,None])
|
|
|
|
# pose error
|
|
d = (dG * dP.inv()).log()
|
|
|
|
if isinstance(dG, SE3):
|
|
tau, phi = d.split([3,3], dim=-1)
|
|
geodesic_loss += w * (
|
|
tau.norm(dim=-1).mean() +
|
|
phi.norm(dim=-1).mean())
|
|
|
|
elif isinstance(dG, Sim3):
|
|
tau, phi, sig = d.split([3,3,1], dim=-1)
|
|
geodesic_loss += w * (
|
|
tau.norm(dim=-1).mean() +
|
|
phi.norm(dim=-1).mean() +
|
|
0.05 * sig.norm(dim=-1).mean())
|
|
|
|
dE = Sim3(dG * dP.inv()).detach()
|
|
r_err, t_err, s_err = pose_metrics(dE)
|
|
|
|
metrics = {
|
|
'rot_error': r_err.mean().item(),
|
|
'tr_error': t_err.mean().item(),
|
|
'bad_rot': (r_err < .1).float().mean().item(),
|
|
'bad_tr': (t_err < .01).float().mean().item(),
|
|
}
|
|
|
|
return geodesic_loss, metrics
|
|
|
|
|
|
def residual_loss(residuals, gamma=0.9):
|
|
""" loss on system residuals """
|
|
residual_loss = 0.0
|
|
n = len(residuals)
|
|
|
|
for i in range(n):
|
|
w = gamma ** (n - i - 1)
|
|
residual_loss += w * residuals[i].abs().mean()
|
|
|
|
return residual_loss, {'residual': residual_loss.item()}
|
|
|
|
|
|
def flow_loss(Ps, disps, poses_est, disps_est, intrinsics, graph, gamma=0.9):
|
|
""" optical flow loss """
|
|
|
|
N = Ps.shape[1]
|
|
graph = OrderedDict()
|
|
for i in range(N):
|
|
graph[i] = [j for j in range(N) if abs(i-j)==1]
|
|
|
|
ii, jj, kk = graph_to_edge_list(graph)
|
|
coords0, val0 = projective_transform(Ps, disps, intrinsics, ii, jj)
|
|
val0 = val0 * (disps[:,ii] > 0).float().unsqueeze(dim=-1)
|
|
|
|
n = len(poses_est)
|
|
flow_loss = 0.0
|
|
|
|
for i in range(n):
|
|
w = gamma ** (n - i - 1)
|
|
coords1, val1 = projective_transform(poses_est[i], disps_est[i], intrinsics, ii, jj)
|
|
|
|
v = (val0 * val1).squeeze(dim=-1)
|
|
epe = v * (coords1 - coords0).norm(dim=-1)
|
|
flow_loss += w * epe.mean()
|
|
|
|
epe = epe.reshape(-1)[v.reshape(-1) > 0.5]
|
|
metrics = {
|
|
'f_error': epe.mean().item(),
|
|
'1px': (epe<1.0).float().mean().item(),
|
|
}
|
|
|
|
return flow_loss, metrics
|