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| # %BANNER_BEGIN% | |
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| # Magic Leap, Inc. ("COMPANY") CONFIDENTIAL | |
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| # %AUTHORS_BEGIN% | |
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| # Originating Authors: Paul-Edouard Sarlin | |
| # | |
| # %AUTHORS_END% | |
| # --------------------------------------------------------------------*/ | |
| # %BANNER_END% | |
| from pathlib import Path | |
| import torch | |
| from torch import nn | |
| 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 remove_borders(keypoints, scores, border: int, height: int, width: int): | |
| """Removes keypoints too close to the border""" | |
| mask_h = (keypoints[:, 0] >= border) & (keypoints[:, 0] < (height - border)) | |
| mask_w = (keypoints[:, 1] >= border) & (keypoints[:, 1] < (width - border)) | |
| mask = mask_h & mask_w | |
| return keypoints[mask], scores[mask] | |
| def top_k_keypoints(keypoints, scores, k: int): | |
| if k >= len(keypoints): | |
| return keypoints, scores | |
| scores, indices = torch.topk(scores, k, dim=0) | |
| return keypoints[indices], scores | |
| def sample_descriptors(keypoints, descriptors, s: int = 8): | |
| """Interpolate descriptors at keypoint locations""" | |
| 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 | |
| class SuperPoint(nn.Module): | |
| """SuperPoint Convolutional Detector and Descriptor | |
| SuperPoint: Self-Supervised Interest Point Detection and | |
| Description. Daniel DeTone, Tomasz Malisiewicz, and Andrew | |
| Rabinovich. In CVPRW, 2019. https://arxiv.org/abs/1712.07629 | |
| """ | |
| default_config = { | |
| "descriptor_dim": 256, | |
| "nms_radius": 4, | |
| "keypoint_threshold": 0.005, | |
| "max_keypoints": -1, | |
| "remove_borders": 4, | |
| } | |
| def __init__(self, config): | |
| super().__init__() | |
| self.config = {**self.default_config, **config} | |
| 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) | |
| 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) | |
| self.convDa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) | |
| self.convDb = nn.Conv2d( | |
| c5, | |
| self.config["descriptor_dim"], | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| ) | |
| path = Path(__file__).parent / "weights/superpoint_v1.pth" | |
| self.load_state_dict(torch.load(str(path))) | |
| mk = self.config["max_keypoints"] | |
| if mk == 0 or mk < -1: | |
| raise ValueError('"max_keypoints" must be positive or "-1"') | |
| print("Loaded SuperPoint model") | |
| def forward(self, data, cfg={}): | |
| """Compute keypoints, scores, descriptors for image""" | |
| self.config = { | |
| **self.config, | |
| **cfg, | |
| } | |
| # Shared Encoder | |
| x = self.relu(self.conv1a(data["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)) | |
| # Compute the dense keypoint scores | |
| cPa = self.relu(self.convPa(x)) | |
| scores = self.convPb(cPa) | |
| scores = torch.nn.functional.softmax(scores, 1)[:, :-1] | |
| b, _, 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) | |
| scores = simple_nms(scores, self.config["nms_radius"]) | |
| # Extract keypoints | |
| keypoints = [ | |
| torch.nonzero(s > self.config["keypoint_threshold"]) for s in scores | |
| ] | |
| scores = [s[tuple(k.t())] for s, k in zip(scores, keypoints)] | |
| # Discard keypoints near the image borders | |
| keypoints, scores = list( | |
| zip( | |
| *[ | |
| remove_borders( | |
| k, s, self.config["remove_borders"], h * 8, w * 8 | |
| ) | |
| for k, s in zip(keypoints, scores) | |
| ] | |
| ) | |
| ) | |
| # Keep the k keypoints with highest score | |
| if self.config["max_keypoints"] >= 0: | |
| keypoints, scores = list( | |
| zip( | |
| *[ | |
| top_k_keypoints(k, s, self.config["max_keypoints"]) | |
| for k, s in zip(keypoints, scores) | |
| ] | |
| ) | |
| ) | |
| # Convert (h, w) to (x, y) | |
| keypoints = [torch.flip(k, [1]).float() for k in keypoints] | |
| # Compute the dense descriptors | |
| cDa = self.relu(self.convDa(x)) | |
| descriptors = self.convDb(cDa) | |
| descriptors = torch.nn.functional.normalize(descriptors, p=2, dim=1) | |
| # Extract descriptors | |
| descriptors = [ | |
| sample_descriptors(k[None], d[None], 8)[0] | |
| for k, d in zip(keypoints, descriptors) | |
| ] | |
| return { | |
| "keypoints": keypoints, | |
| "scores": scores, | |
| "descriptors": descriptors, | |
| } | |