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"""
Inference model of SuperPoint, a feature detector and descriptor.
Described in:
SuperPoint: Self-Supervised Interest Point Detection and Description,
Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich, CVPRW 2018.
Original code: github.com/MagicLeapResearch/SuperPointPretrainedNetwork
"""
import torch
from torch import nn
from .. import GLUESTICK_ROOT
from ..models.base_model import BaseModel
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)`.
size: an interger 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 remove_borders(keypoints, scores, b, h, w):
mask_h = (keypoints[:, 0] >= b) & (keypoints[:, 0] < (h - b))
mask_w = (keypoints[:, 1] >= b) & (keypoints[:, 1] < (w - b))
mask = mask_h & mask_w
return keypoints[mask], scores[mask]
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):
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(BaseModel):
default_conf = {
"has_detector": True,
"has_descriptor": True,
"descriptor_dim": 256,
# Inference
"return_all": False,
"sparse_outputs": True,
"nms_radius": 4,
"detection_threshold": 0.005,
"max_num_keypoints": -1,
"force_num_keypoints": False,
"remove_borders": 4,
}
required_data_keys = ["image"]
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)
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
)
path = GLUESTICK_ROOT / "resources" / "weights" / "superpoint_v1.pth"
self.load_state_dict(torch.load(str(path)), strict=False)
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 and self.conf.max_num_keypoints != 0:
# 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))
all_desc = self.convDb(cDa)
all_desc = torch.nn.functional.normalize(all_desc, p=2, dim=1)
pred["descriptors"] = all_desc
if self.conf.max_num_keypoints == 0: # Predict dense descriptors only
b_size = len(image)
device = image.device
return {
"keypoints": torch.empty(b_size, 0, 2, device=device),
"keypoint_scores": torch.empty(b_size, 0, device=device),
"descriptors": torch.empty(
b_size, self.conf.descriptor_dim, 0, device=device
),
"all_descriptors": all_desc,
}
if self.conf.sparse_outputs:
assert self.conf.has_detector and self.conf.has_descriptor
scores = simple_nms(scores, self.conf.nms_radius)
# Extract keypoints
keypoints = [
torch.nonzero(s > self.conf.detection_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.conf.remove_borders, h * 8, w * 8)
for k, s in zip(keypoints, scores)
]
)
)
# Keep the k keypoints with highest score
if self.conf.max_num_keypoints > 0:
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]
if self.conf.force_num_keypoints:
_, _, h, w = data["image"].shape
assert self.conf.max_num_keypoints > 0
scores = list(scores)
for i in range(len(keypoints)):
k, s = keypoints[i], scores[i]
missing = self.conf.max_num_keypoints - len(k)
if missing > 0:
new_k = torch.rand(missing, 2).to(k)
new_k = new_k * k.new_tensor([[w - 1, h - 1]])
new_s = torch.zeros(missing).to(s)
keypoints[i] = torch.cat([k, new_k], 0)
scores[i] = torch.cat([s, new_s], 0)
# Extract descriptors
desc = [
sample_descriptors(k[None], d[None], 8)[0]
for k, d in zip(keypoints, all_desc)
]
if (len(keypoints) == 1) or self.conf.force_num_keypoints:
keypoints = torch.stack(keypoints, 0)
scores = torch.stack(scores, 0)
desc = torch.stack(desc, 0)
pred = {
"keypoints": keypoints,
"keypoint_scores": scores,
"descriptors": desc,
}
if self.conf.return_all:
pred["all_descriptors"] = all_desc
pred["dense_score"] = dense_scores
else:
del all_desc
torch.cuda.empty_cache()
return pred
def loss(self, pred, data):
raise NotImplementedError
def metrics(self, pred, data):
raise NotImplementedError
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