Spaces:
Running
Running
File size: 8,142 Bytes
6ba5875 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 |
# %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# Unpublished Copyright (c) 2020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%
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):
""" Compute keypoints, scores, descriptors for image """
# 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,
}
|