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# %BANNER_BEGIN%
# ---------------------------------------------------------------------
# %COPYRIGHT_BEGIN%
#
# Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
#
# Unpublished Copyright (c) 2020
# Magic Leap, Inc., All Rights Reserved.
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# Patents, patents in process, and are protected by trade secret or
# copyright law. Dissemination of this information or reproduction of
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# 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
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# Originating Authors: Paul-Edouard Sarlin
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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,
}
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