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# %BANNER_BEGIN%
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# Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
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# Originating Authors: Paul-Edouard Sarlin
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# Adapted by Remi Pautrat, Philipp Lindenberger
import torch
from torch import nn
from .utils import ImagePreprocessor
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 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_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_conf = {
'descriptor_dim': 256,
'nms_radius': 4,
'max_num_keypoints': None,
'detection_threshold': 0.0005,
'remove_borders': 4,
}
preprocess_conf = {
**ImagePreprocessor.default_conf,
'resize': 1024,
'grayscale': True,
}
required_data_keys = ['image']
def __init__(self, **conf):
super().__init__()
self.conf = {**self.default_conf, **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)
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.conf['descriptor_dim'],
kernel_size=1, stride=1, padding=0)
url = "https://github.com/cvg/LightGlue/releases/download/v0.1_arxiv/superpoint_v1.pth"
self.load_state_dict(torch.hub.load_state_dict_from_url(url))
mk = self.conf['max_num_keypoints']
if mk is not None and mk <= 0:
raise ValueError('max_num_keypoints must be positive or None')
print('Loaded SuperPoint model')
def forward(self, data: dict) -> dict:
""" Compute keypoints, scores, descriptors for image """
for key in self.required_data_keys:
assert key in data, f'Missing key {key} in 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))
# 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.conf['nms_radius'])
# Discard keypoints near the image borders
if self.conf['remove_borders']:
pad = self.conf['remove_borders']
scores[:, :pad] = -1
scores[:, :, :pad] = -1
scores[:, -pad:] = -1
scores[:, :, -pad:] = -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
if self.conf['max_num_keypoints'] is not None:
keypoints, scores = list(zip(*[
top_k_keypoints(k, s, self.conf['max_num_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': torch.stack(keypoints, 0),
'keypoint_scores': torch.stack(scores, 0),
'descriptors': torch.stack(descriptors, 0).transpose(-1, -2),
}
def extract(self, img: torch.Tensor, **conf) -> dict:
""" Perform extraction with online resizing"""
if img.dim() == 3:
img = img[None] # add batch dim
assert img.dim() == 4 and img.shape[0] == 1
shape = img.shape[-2:][::-1]
img, scales = ImagePreprocessor(
**{**self.preprocess_conf, **conf})(img)
feats = self.forward({'image': img})
feats['image_size'] = torch.tensor(shape)[None].to(img).float()
feats['keypoints'] = (feats['keypoints'] + .5) / scales[None] - .5
return feats
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