import torch from torch import nn import torch.nn.functional as F # coordinates system # ------------------------------> [ x: range=-1.0~1.0; w: range=0~W ] # | ----------------------------- # | | | # | | | # | | | # | | image | # | | | # | | | # | | | # | |---------------------------| # v # [ y: range=-1.0~1.0; h: range=0~H ] def simple_nms(scores, nms_radius: int): """ Fast Non-maximum suppression to remove nearby points """ assert (nms_radius >= 0) def max_pool(x): return torch.nn.functional.max_pool2d( x, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius) zeros = torch.zeros_like(scores) max_mask = scores == max_pool(scores) for _ in range(2): supp_mask = max_pool(max_mask.float()) > 0 supp_scores = torch.where(supp_mask, zeros, scores) new_max_mask = supp_scores == max_pool(supp_scores) max_mask = max_mask | (new_max_mask & (~supp_mask)) return torch.where(max_mask, scores, zeros) def sample_descriptor(descriptor_map, kpts, bilinear_interp=False): """ :param descriptor_map: BxCxHxW :param kpts: list, len=B, each is Nx2 (keypoints) [h,w] :param bilinear_interp: bool, whether to use bilinear interpolation :return: descriptors: list, len=B, each is NxD """ batch_size, channel, height, width = descriptor_map.shape descriptors = [] for index in range(batch_size): kptsi = kpts[index] # Nx2,(x,y) if bilinear_interp: descriptors_ = torch.nn.functional.grid_sample(descriptor_map[index].unsqueeze(0), kptsi.view(1, 1, -1, 2), mode='bilinear', align_corners=True)[0, :, 0, :] # CxN else: kptsi = (kptsi + 1) / 2 * kptsi.new_tensor([[width - 1, height - 1]]) kptsi = kptsi.long() descriptors_ = descriptor_map[index, :, kptsi[:, 1], kptsi[:, 0]] # CxN descriptors_ = torch.nn.functional.normalize(descriptors_, p=2, dim=0) descriptors.append(descriptors_.t()) return descriptors class DKD(nn.Module): def __init__(self, radius=2, top_k=0, scores_th=0.2, n_limit=20000): """ Args: radius: soft detection radius, kernel size is (2 * radius + 1) top_k: top_k > 0: return top k keypoints scores_th: top_k <= 0 threshold mode: scores_th > 0: return keypoints with scores>scores_th else: return keypoints with scores > scores.mean() n_limit: max number of keypoint in threshold mode """ super().__init__() self.radius = radius self.top_k = top_k self.scores_th = scores_th self.n_limit = n_limit self.kernel_size = 2 * self.radius + 1 self.temperature = 0.1 # tuned temperature self.unfold = nn.Unfold(kernel_size=self.kernel_size, padding=self.radius) # local xy grid x = torch.linspace(-self.radius, self.radius, self.kernel_size) # (kernel_size*kernel_size) x 2 : (w,h) self.hw_grid = torch.stack(torch.meshgrid([x, x])).view(2, -1).t()[:, [1, 0]] def detect_keypoints(self, scores_map, sub_pixel=True): b, c, h, w = scores_map.shape scores_nograd = scores_map.detach() # nms_scores = simple_nms(scores_nograd, self.radius) nms_scores = simple_nms(scores_nograd, 2) # remove border nms_scores[:, :, :self.radius + 1, :] = 0 nms_scores[:, :, :, :self.radius + 1] = 0 nms_scores[:, :, h - self.radius:, :] = 0 nms_scores[:, :, :, w - self.radius:] = 0 # detect keypoints without grad if self.top_k > 0: topk = torch.topk(nms_scores.view(b, -1), self.top_k) indices_keypoints = topk.indices # B x top_k else: if self.scores_th > 0: masks = nms_scores > self.scores_th if masks.sum() == 0: th = scores_nograd.reshape(b, -1).mean(dim=1) # th = self.scores_th masks = nms_scores > th.reshape(b, 1, 1, 1) else: th = scores_nograd.reshape(b, -1).mean(dim=1) # th = self.scores_th masks = nms_scores > th.reshape(b, 1, 1, 1) masks = masks.reshape(b, -1) indices_keypoints = [] # list, B x (any size) scores_view = scores_nograd.reshape(b, -1) for mask, scores in zip(masks, scores_view): indices = mask.nonzero(as_tuple=False)[:, 0] if len(indices) > self.n_limit: kpts_sc = scores[indices] sort_idx = kpts_sc.sort(descending=True)[1] sel_idx = sort_idx[:self.n_limit] indices = indices[sel_idx] indices_keypoints.append(indices) keypoints = [] scoredispersitys = [] kptscores = [] if sub_pixel: # detect soft keypoints with grad backpropagation patches = self.unfold(scores_map) # B x (kernel**2) x (H*W) self.hw_grid = self.hw_grid.to(patches) # to device for b_idx in range(b): patch = patches[b_idx].t() # (H*W) x (kernel**2) indices_kpt = indices_keypoints[b_idx] # one dimension vector, say its size is M patch_scores = patch[indices_kpt] # M x (kernel**2) # max is detached to prevent undesired backprop loops in the graph max_v = patch_scores.max(dim=1).values.detach()[:, None] x_exp = ((patch_scores - max_v) / self.temperature).exp() # M * (kernel**2), in [0, 1] # \frac{ \sum{(i,j) \times \exp(x/T)} }{ \sum{\exp(x/T)} } xy_residual = x_exp @ self.hw_grid / x_exp.sum(dim=1)[:, None] # Soft-argmax, Mx2 hw_grid_dist2 = torch.norm((self.hw_grid[None, :, :] - xy_residual[:, None, :]) / self.radius, dim=-1) ** 2 scoredispersity = (x_exp * hw_grid_dist2).sum(dim=1) / x_exp.sum(dim=1) # compute result keypoints keypoints_xy_nms = torch.stack([indices_kpt % w, indices_kpt // w], dim=1) # Mx2 keypoints_xy = keypoints_xy_nms + xy_residual keypoints_xy = keypoints_xy / keypoints_xy.new_tensor( [w - 1, h - 1]) * 2 - 1 # (w,h) -> (-1~1,-1~1) kptscore = torch.nn.functional.grid_sample(scores_map[b_idx].unsqueeze(0), keypoints_xy.view(1, 1, -1, 2), mode='bilinear', align_corners=True)[0, 0, 0, :] # CxN keypoints.append(keypoints_xy) scoredispersitys.append(scoredispersity) kptscores.append(kptscore) else: for b_idx in range(b): indices_kpt = indices_keypoints[b_idx] # one dimension vector, say its size is M keypoints_xy_nms = torch.stack([indices_kpt % w, indices_kpt // w], dim=1) # Mx2 keypoints_xy = keypoints_xy_nms / keypoints_xy_nms.new_tensor( [w - 1, h - 1]) * 2 - 1 # (w,h) -> (-1~1,-1~1) kptscore = torch.nn.functional.grid_sample(scores_map[b_idx].unsqueeze(0), keypoints_xy.view(1, 1, -1, 2), mode='bilinear', align_corners=True)[0, 0, 0, :] # CxN keypoints.append(keypoints_xy) scoredispersitys.append(None) kptscores.append(kptscore) return keypoints, scoredispersitys, kptscores def forward(self, scores_map, descriptor_map, sub_pixel=False): """ :param scores_map: Bx1xHxW :param descriptor_map: BxCxHxW :param sub_pixel: whether to use sub-pixel keypoint detection :return: kpts: list[Nx2,...]; kptscores: list[N,....] normalised position: -1.0 ~ 1.0 """ keypoints, scoredispersitys, kptscores = self.detect_keypoints(scores_map, sub_pixel) descriptors = sample_descriptor(descriptor_map, keypoints, sub_pixel) # keypoints: B M 2 # descriptors: B M D # scoredispersitys: return keypoints, descriptors, kptscores, scoredispersitys