def eval_1__write(self, fout, ig_gt, g_hat):
x_hat = se3.log(g_hat) # --> [-1, 6]
mx_gt = se3.log(ig_gt) # --> [-1, 6]
for i in range(x_hat.size(0)):
x_hat1 = x_hat[i] # [6]
mx_gt1 = mx_gt[i] # [6]
vals = torch.cat((x_hat1, -mx_gt1)) # [12]
valn = vals.cpu().numpy().tolist()
print(','.join(map(str, valn)), file=fout)
fout.flush()
def evaluate(self, solver, testloader, device):
solver.eval()
with open(self.filename, 'w') as fout:
self.eval_1__header(fout)
with torch.no_grad():
for i, data in enumerate(testloader):
p0, p1, igt = data # igt: p0->p1
# # compute trans from p1->p0
# g = se3.log(igt) # --> [-1, 6]
# igt = se3.exp(-g) # [-1, 4, 4]
p0, p1 = self.ablation_study(p0, p1)
p0 = p0.to(device) # template (1, N, 3)
p1 = p1.to(device) # source (1, M, 3)
solver.estimate_t(p0, p1, self.max_iter)
est_g = solver.g # (1, 4, 4)
ig_gt = igt.cpu().contiguous().view(-1, 4,
4) # --> [1, 4, 4]
g_hat = est_g.cpu().contiguous().view(-1, 4,
4) # --> [1, 4, 4]
dg = g_hat.bmm(ig_gt) # if correct, dg == identity matrix.
dx = se3.log(
dg) # --> [1, 6] (if corerct, dx == zero vector)
dn = dx.norm(p=2, dim=1) # --> [1]
dm = dn.mean()
self.eval_1__write(fout, ig_gt, g_hat)
print('test, %d/%d, %f' % (i, len(testloader), dm))
def main(args):
# dataset
testset = get_datasets(args)
batch_size = len(testset)
amp = args.deg * math.pi / 180.0
w = torch.randn(batch_size, 3)
w = w / w.norm(p=2, dim=1, keepdim=True) * amp
t = torch.rand(batch_size, 3) * args.max_trans
if args.format == 'wv':
# the output: twist vectors.
R = so3.exp(w) # (N, 3) --> (N, 3, 3)
G = torch.zeros(batch_size, 4, 4)
G[:, 3, 3] = 1
G[:, 0:3, 0:3] = R
G[:, 0:3, 3] = t
x = se3.log(G) # --> (N, 6)
else:
# rotation-vector and translation-vector
x = torch.cat((w, t), dim=1)
numpy.savetxt(args.outfile, x, delimiter=',')