""" # %BANNER_BEGIN% # --------------------------------------------------------------------- # %COPYRIGHT_BEGIN% # # Magic Leap, Inc. ("COMPANY") CONFIDENTIAL # # Unpublished Copyright (c) 2020 # Magic Leap, Inc., All Rights Reserved. # # NOTICE: All information contained herein is, and remains the property # of COMPANY. The intellectual and technical concepts contained herein # are proprietary to COMPANY and may be covered by U.S. and Foreign # Patents, patents in process, and are protected by trade secret or # copyright law. Dissemination of this information or reproduction of # this material is strictly forbidden unless prior written permission is # obtained from COMPANY. Access to the source code contained herein is # hereby forbidden to anyone except current COMPANY employees, managers # or contractors who have executed Confidentiality and Non-disclosure # agreements explicitly covering such access. # # The copyright notice above does not evidence any actual or intended # publication or disclosure of this source code, which includes # information that is confidential and/or proprietary, and is a trade # secret, of COMPANY. ANY REPRODUCTION, MODIFICATION, DISTRIBUTION, # PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS # SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS # STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND # INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF THIS SOURCE # CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS # TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE, # USE, OR SELL ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART. # # %COPYRIGHT_END% # ---------------------------------------------------------------------- # %AUTHORS_BEGIN% # # Originating Authors: Paul-Edouard Sarlin # # %AUTHORS_END% # --------------------------------------------------------------------*/ # %BANNER_END% Described in: SuperPoint: Self-Supervised Interest Point Detection and Description, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich, CVPRW 2018. Original code: github.com/MagicLeapResearch/SuperPointPretrainedNetwork Adapted by Philipp Lindenberger (Phil26AT) """ import os.path import torch from torch import nn from gluefactory.models.base_model import BaseModel from gluefactory.models.utils.misc import pad_and_stack def simple_nms(scores, radius): """Perform non maximum suppression on the heatmap using max-pooling. This method does not suppress contiguous points that have the same score. Args: scores: the score heatmap of size `(B, H, W)`. radius: an integer scalar, the radius of the NMS window. """ def max_pool(x): return torch.nn.functional.max_pool2d( x, kernel_size=radius * 2 + 1, stride=1, padding=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 top_k_keypoints(keypoints, scores, k): if k >= len(keypoints): return keypoints, scores scores, indices = torch.topk(scores, k, dim=0, sorted=True) return keypoints[indices], scores def sample_k_keypoints(keypoints, scores, k): if k >= len(keypoints): return keypoints, scores indices = torch.multinomial(scores, k, replacement=False) return keypoints[indices], scores[indices] def soft_argmax_refinement(keypoints, scores, radius: int): width = 2 * radius + 1 sum_ = torch.nn.functional.avg_pool2d( scores[:, None], width, 1, radius, divisor_override=1 ) ar = torch.arange(-radius, radius + 1).to(scores) kernel_x = ar[None].expand(width, -1)[None, None] dx = torch.nn.functional.conv2d(scores[:, None], kernel_x, padding=radius) dy = torch.nn.functional.conv2d( scores[:, None], kernel_x.transpose(2, 3), padding=radius ) dydx = torch.stack([dy[:, 0], dx[:, 0]], -1) / sum_[:, 0, :, :, None] refined_keypoints = [] for i, kpts in enumerate(keypoints): delta = dydx[i][tuple(kpts.t())] refined_keypoints.append(kpts.float() + delta) return refined_keypoints # Legacy (broken) sampling of the descriptors def sample_descriptors(keypoints, descriptors, s): b, c, h, w = descriptors.shape keypoints = keypoints - s / 2 + 0.5 keypoints /= torch.tensor( [(w * s - s / 2 - 0.5), (h * s - s / 2 - 0.5)], ).to( keypoints )[None] keypoints = keypoints * 2 - 1 # normalize to (-1, 1) args = {"align_corners": True} if torch.__version__ >= "1.3" else {} descriptors = torch.nn.functional.grid_sample( descriptors, keypoints.view(b, 1, -1, 2), mode="bilinear", **args ) descriptors = torch.nn.functional.normalize( descriptors.reshape(b, c, -1), p=2, dim=1 ) return descriptors # The original keypoint sampling is incorrect. We patch it here but # keep the original one above for legacy. def sample_descriptors_fix_sampling(keypoints, descriptors, s: int = 8): """Interpolate descriptors at keypoint locations""" b, c, h, w = descriptors.shape keypoints = keypoints / (keypoints.new_tensor([w, h]) * s) keypoints = keypoints * 2 - 1 # normalize to (-1, 1) descriptors = torch.nn.functional.grid_sample( descriptors, keypoints.view(b, 1, -1, 2), mode="bilinear", align_corners=False ) descriptors = torch.nn.functional.normalize( descriptors.reshape(b, c, -1), p=2, dim=1 ) return descriptors class SuperPoint(BaseModel): default_conf = { "has_detector": True, "has_descriptor": True, "descriptor_dim": 256, # Inference "sparse_outputs": True, "dense_outputs": False, "nms_radius": 4, "refinement_radius": 0, "detection_threshold": 0.005, "max_num_keypoints": -1, "max_num_keypoints_val": None, "force_num_keypoints": False, "randomize_keypoints_training": False, "remove_borders": 4, "legacy_sampling": True, # True to use the old broken sampling } required_data_keys = ["image"] checkpoint_url = "https://github.com/magicleap/SuperGluePretrainedNetwork/raw/master/models/weights/superpoint_v1.pth" # noqa: E501 def _init(self, conf): self.relu = nn.ReLU(inplace=True) self.pool = nn.MaxPool2d(kernel_size=2, stride=2) c1, c2, c3, c4, c5 = 64, 64, 128, 128, 256 self.conv1a = nn.Conv2d(1, c1, kernel_size=3, stride=1, padding=1) self.conv1b = nn.Conv2d(c1, c1, kernel_size=3, stride=1, padding=1) self.conv2a = nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1) self.conv2b = nn.Conv2d(c2, c2, kernel_size=3, stride=1, padding=1) self.conv3a = nn.Conv2d(c2, c3, kernel_size=3, stride=1, padding=1) self.conv3b = nn.Conv2d(c3, c3, kernel_size=3, stride=1, padding=1) self.conv4a = nn.Conv2d(c3, c4, kernel_size=3, stride=1, padding=1) self.conv4b = nn.Conv2d(c4, c4, kernel_size=3, stride=1, padding=1) if conf.has_detector: self.convPa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) self.convPb = nn.Conv2d(c5, 65, kernel_size=1, stride=1, padding=0) for param in self.convPa.parameters(): param.requires_grad = False for param in self.convPb.parameters(): param.requires_grad = False if conf.has_descriptor: self.convDa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) self.convDb = nn.Conv2d( c5, conf.descriptor_dim, kernel_size=1, stride=1, padding=0 ) self.load_state_dict(torch.load(os.path.join('weights', 'superpoint_v1.pth'))) def _forward(self, data): image = data["image"] if image.shape[1] == 3: # RGB scale = image.new_tensor([0.299, 0.587, 0.114]).view(1, 3, 1, 1) image = (image * scale).sum(1, keepdim=True) # Shared Encoder x = self.relu(self.conv1a(image)) x = self.relu(self.conv1b(x)) x = self.pool(x) x = self.relu(self.conv2a(x)) x = self.relu(self.conv2b(x)) x = self.pool(x) x = self.relu(self.conv3a(x)) x = self.relu(self.conv3b(x)) x = self.pool(x) x = self.relu(self.conv4a(x)) x = self.relu(self.conv4b(x)) pred = {} if self.conf.has_detector: # Compute the dense keypoint scores cPa = self.relu(self.convPa(x)) scores = self.convPb(cPa) scores = torch.nn.functional.softmax(scores, 1)[:, :-1] b, c, h, w = scores.shape scores = scores.permute(0, 2, 3, 1).reshape(b, h, w, 8, 8) scores = scores.permute(0, 1, 3, 2, 4).reshape(b, h * 8, w * 8) pred["keypoint_scores"] = dense_scores = scores if self.conf.has_descriptor: # Compute the dense descriptors cDa = self.relu(self.convDa(x)) dense_desc = self.convDb(cDa) dense_desc = torch.nn.functional.normalize(dense_desc, p=2, dim=1) pred["descriptors"] = dense_desc if self.conf.sparse_outputs: assert self.conf.has_detector and self.conf.has_descriptor scores = simple_nms(scores, self.conf.nms_radius) # Discard keypoints near the image borders if self.conf.remove_borders: scores[:, : self.conf.remove_borders] = -1 scores[:, :, : self.conf.remove_borders] = -1 if "image_size" in data: for i in range(scores.shape[0]): w, h = data["image_size"][i] scores[i, int(h.item()) - self.conf.remove_borders :] = -1 scores[i, :, int(w.item()) - self.conf.remove_borders :] = -1 else: scores[:, -self.conf.remove_borders :] = -1 scores[:, :, -self.conf.remove_borders :] = -1 # Extract keypoints best_kp = torch.where(scores > self.conf.detection_threshold) scores = scores[best_kp] # Separate into batches keypoints = [ torch.stack(best_kp[1:3], dim=-1)[best_kp[0] == i] for i in range(b) ] scores = [scores[best_kp[0] == i] for i in range(b)] # Keep the k keypoints with highest score max_kps = self.conf.max_num_keypoints # for val we allow different if not self.training and self.conf.max_num_keypoints_val is not None: max_kps = self.conf.max_num_keypoints_val # Keep the k keypoints with highest score if max_kps > 0: if self.conf.randomize_keypoints_training and self.training: # instead of selecting top-k, sample k by score weights keypoints, scores = list( zip( *[ sample_k_keypoints(k, s, max_kps) for k, s in zip(keypoints, scores) ] ) ) else: keypoints, scores = list( zip( *[ top_k_keypoints(k, s, max_kps) for k, s in zip(keypoints, scores) ] ) ) keypoints, scores = list(keypoints), list(scores) if self.conf["refinement_radius"] > 0: keypoints = soft_argmax_refinement( keypoints, dense_scores, self.conf["refinement_radius"] ) # Convert (h, w) to (x, y) keypoints = [torch.flip(k, [1]).float() for k in keypoints] if self.conf.force_num_keypoints: keypoints = pad_and_stack( keypoints, max_kps, -2, mode="random_c", bounds=( 0, data.get("image_size", torch.tensor(image.shape[-2:])) .min() .item(), ), ) scores = pad_and_stack(scores, max_kps, -1, mode="zeros") else: keypoints = torch.stack(keypoints, 0) scores = torch.stack(scores, 0) # Extract descriptors if (len(keypoints) == 1) or self.conf.force_num_keypoints: # Batch sampling of the descriptors if self.conf.legacy_sampling: desc = sample_descriptors(keypoints, dense_desc, 8) else: desc = sample_descriptors_fix_sampling(keypoints, dense_desc, 8) else: if self.conf.legacy_sampling: desc = [ sample_descriptors(k[None], d[None], 8)[0] for k, d in zip(keypoints, dense_desc) ] else: desc = [ sample_descriptors_fix_sampling(k[None], d[None], 8)[0] for k, d in zip(keypoints, dense_desc) ] pred = { "keypoints": keypoints + 0.5, "descriptors": desc.transpose(-1, -2), } if self.conf.dense_outputs: pred["dense_descriptors"] = dense_desc return pred def loss(self, pred, data): raise NotImplementedError def metrics(self, pred, data): raise NotImplementedError