def get_fragment(self, idx):
data_source, data_target, new_pair = self.get_raw_pair(idx)
if self.transform is not None:
data_source = self.transform(data_source)
data_target = self.transform(data_target)
if (hasattr(data_source, "multiscale")):
batch = MultiScalePair.make_pair(data_source, data_target)
else:
batch = Pair.make_pair(data_source, data_target)
if self.is_online_matching:
new_match = compute_overlap_and_matches(Data(pos=data_source.pos),
Data(pos=data_target.pos),
self.max_dist_overlap)
batch.pair_ind = torch.from_numpy(new_match["pair"].copy())
else:
pair = tracked_matches(data_source, data_target, new_pair)
batch.pair_ind = pair
num_pos_pairs = len(batch.pair_ind)
if self.num_pos_pairs < len(batch.pair_ind):
num_pos_pairs = self.num_pos_pairs
if not self.use_fps or (float(num_pos_pairs) / len(batch.pair_ind) >=
1):
rand_ind = torch.randperm(len(batch.pair_ind))[:num_pos_pairs]
else:
rand_ind = fps_sampling(batch.pair_ind, batch.pos, num_pos_pairs)
batch.pair_ind = batch.pair_ind[rand_ind]
batch.size_pair_ind = torch.tensor([num_pos_pairs])
if (len(batch.pair_ind) == 0):
print("Warning")
return batch.contiguous()
def get_fragment(self, idx):
match = np.load(osp.join(self.path_match,
'matches{:06d}.npy'.format(idx)),
allow_pickle=True).item()
data_source = torch.load(match['path_source'])
data_target = torch.load(match['path_target'])
# new_pair = compute_subsampled_matches(data_source, data_target,self.voxel_size_search,self.max_dist_overlap)
new_pair = torch.from_numpy(match['pair'])
if (self.transform is not None):
data_source = self.transform(data_source)
data_target = self.transform(data_target)
batch = Pair.make_pair(data_source, data_target)
if (self.is_online_matching):
new_match = compute_overlap_and_matches(Data(pos=data_source.pos),
Data(pos=data_target.pos),
self.max_dist_overlap)
batch.pair_ind = torch.from_numpy(new_match['pair'].copy())
else:
pair = tracked_matches(data_source, data_target, new_pair)
batch.pair_ind = pair
num_pos_pairs = len(batch.pair_ind)
if self.num_pos_pairs < len(batch.pair_ind):
num_pos_pairs = self.num_pos_pairs
rand_ind = torch.randperm(len(batch.pair_ind))[:num_pos_pairs]
batch.pair_ind = batch.pair_ind[rand_ind]
batch.size_pair_ind = torch.tensor([num_pos_pairs])
return batch.contiguous().to(torch.float)
def get_fragment(self, idx):
match = np.load(osp.join(self.path_match, "matches{:06d}.npy".format(idx)), allow_pickle=True).item()
data_source = torch.load(match["path_source"]).to(torch.float)
data_target = torch.load(match["path_target"]).to(torch.float)
new_pair = torch.from_numpy(match["pair"])
if self.transform is not None:
data_source = self.transform(data_source)
data_target = self.transform(data_target)
if(hasattr(data_source, "multiscale")):
batch = MultiScalePair.make_pair(data_source, data_target)
else:
batch = Pair.make_pair(data_source, data_target)
if self.is_online_matching:
new_match = compute_overlap_and_matches(
Data(pos=data_source.pos), Data(pos=data_target.pos), self.max_dist_overlap
)
batch.pair_ind = torch.from_numpy(new_match["pair"].copy())
else:
pair = tracked_matches(data_source, data_target, new_pair)
batch.pair_ind = pair
num_pos_pairs = len(batch.pair_ind)
if self.num_pos_pairs < len(batch.pair_ind):
num_pos_pairs = self.num_pos_pairs
rand_ind = torch.randperm(len(batch.pair_ind))[:num_pos_pairs]
batch.pair_ind = batch.pair_ind[rand_ind]
batch.size_pair_ind = torch.tensor([num_pos_pairs])
return batch.contiguous()
def test_simple(self):
ind_source = torch.tensor([1, 2, 5])
ind_target = torch.tensor([0, 5, 6])
data_s = Data(pos=torch.randn(3, 3), origin_id=ind_source)
data_t = Data(pos=torch.randn(3, 3), origin_id=ind_target)
pair = torch.tensor([[0, 2], [1, 3], [2, 0], [3, 1]])
res = tracked_matches(data_s, data_t, pair)
expected = np.array([[1, 0]])
npt.assert_array_almost_equal(res.detach().cpu().numpy(), expected)