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import torch
from localization.base_model import BaseModel
def find_nn(sim, ratio_thresh, distance_thresh):
sim_nn, ind_nn = sim.topk(2 if ratio_thresh else 1, dim=-1, largest=True)
dist_nn = 2 * (1 - sim_nn)
mask = torch.ones(ind_nn.shape[:-1], dtype=torch.bool, device=sim.device)
if ratio_thresh:
mask = mask & (dist_nn[..., 0] <= (ratio_thresh ** 2) * dist_nn[..., 1])
if distance_thresh:
mask = mask & (dist_nn[..., 0] <= distance_thresh ** 2)
matches = torch.where(mask, ind_nn[..., 0], ind_nn.new_tensor(-1))
scores = torch.where(mask, (sim_nn[..., 0] + 1) / 2, sim_nn.new_tensor(0))
return matches, scores
def mutual_check(m0, m1):
inds0 = torch.arange(m0.shape[-1], device=m0.device)
loop = torch.gather(m1, -1, torch.where(m0 > -1, m0, m0.new_tensor(0)))
ok = (m0 > -1) & (inds0 == loop)
m0_new = torch.where(ok, m0, m0.new_tensor(-1))
return m0_new
class NearestNeighbor(BaseModel):
default_conf = {
'ratio_threshold': None,
'distance_threshold': None,
'do_mutual_check': True,
}
required_inputs = ['descriptors0', 'descriptors1']
def _init(self, conf):
pass
def _forward(self, data):
sim = torch.einsum(
'bdn,bdm->bnm', data['descriptors0'], data['descriptors1'])
matches0, scores0 = find_nn(
sim, self.conf['ratio_threshold'], self.conf['distance_threshold'])
# matches1, scores1 = find_nn(
# sim.transpose(1, 2), self.conf['ratio_threshold'],
# self.conf['distance_threshold'])
if self.conf['do_mutual_check']:
# print("with mutual check")
matches1, scores1 = find_nn(
sim.transpose(1, 2), self.conf['ratio_threshold'],
self.conf['distance_threshold'])
matches0 = mutual_check(matches0, matches1)
# else:
# print("no mutual check")
return {
'matches0': matches0,
'matching_scores0': scores0,
}
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