| import torch | |
| from ..utils.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): | |
| if ( | |
| data["descriptors0"].size(-1) == 0 | |
| or data["descriptors1"].size(-1) == 0 | |
| ): | |
| matches0 = torch.full( | |
| data["descriptors0"].shape[:2], | |
| -1, | |
| device=data["descriptors0"].device, | |
| ) | |
| return { | |
| "matches0": matches0, | |
| "matching_scores0": torch.zeros_like(matches0), | |
| } | |
| ratio_threshold = self.conf["ratio_threshold"] | |
| if ( | |
| data["descriptors0"].size(-1) == 1 | |
| or data["descriptors1"].size(-1) == 1 | |
| ): | |
| ratio_threshold = None | |
| sim = torch.einsum( | |
| "bdn,bdm->bnm", data["descriptors0"], data["descriptors1"] | |
| ) | |
| matches0, scores0 = find_nn( | |
| sim, ratio_threshold, self.conf["distance_threshold"] | |
| ) | |
| if self.conf["do_mutual_check"]: | |
| matches1, scores1 = find_nn( | |
| sim.transpose(1, 2), | |
| ratio_threshold, | |
| self.conf["distance_threshold"], | |
| ) | |
| matches0 = mutual_check(matches0, matches1) | |
| return { | |
| "matches0": matches0, | |
| "matching_scores0": scores0, | |
| } | |