Spaces:
Sleeping
Sleeping
File size: 9,185 Bytes
a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 |
import torch
from torch import nn
import torch.nn.functional as F
# coordinates system
# ------------------------------> [ x: range=-1.0~1.0; w: range=0~W ]
# | -----------------------------
# | | |
# | | |
# | | |
# | | image |
# | | |
# | | |
# | | |
# | |---------------------------|
# v
# [ y: range=-1.0~1.0; h: range=0~H ]
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 sample_descriptor(descriptor_map, kpts, bilinear_interp=False):
"""
:param descriptor_map: BxCxHxW
:param kpts: list, len=B, each is Nx2 (keypoints) [h,w]
:param bilinear_interp: bool, whether to use bilinear interpolation
:return: descriptors: list, len=B, each is NxD
"""
batch_size, channel, height, width = descriptor_map.shape
descriptors = []
for index in range(batch_size):
kptsi = kpts[index] # Nx2,(x,y)
if bilinear_interp:
descriptors_ = torch.nn.functional.grid_sample(
descriptor_map[index].unsqueeze(0),
kptsi.view(1, 1, -1, 2),
mode="bilinear",
align_corners=True,
)[
0, :, 0, :
] # CxN
else:
kptsi = (kptsi + 1) / 2 * kptsi.new_tensor([[width - 1, height - 1]])
kptsi = kptsi.long()
descriptors_ = descriptor_map[index, :, kptsi[:, 1], kptsi[:, 0]] # CxN
descriptors_ = torch.nn.functional.normalize(descriptors_, p=2, dim=0)
descriptors.append(descriptors_.t())
return descriptors
class DKD(nn.Module):
def __init__(self, radius=2, top_k=0, scores_th=0.2, n_limit=20000):
"""
Args:
radius: soft detection radius, kernel size is (2 * radius + 1)
top_k: top_k > 0: return top k keypoints
scores_th: top_k <= 0 threshold mode: scores_th > 0: return keypoints with scores>scores_th
else: return keypoints with scores > scores.mean()
n_limit: max number of keypoint in threshold mode
"""
super().__init__()
self.radius = radius
self.top_k = top_k
self.scores_th = scores_th
self.n_limit = n_limit
self.kernel_size = 2 * self.radius + 1
self.temperature = 0.1 # tuned temperature
self.unfold = nn.Unfold(kernel_size=self.kernel_size, padding=self.radius)
# local xy grid
x = torch.linspace(-self.radius, self.radius, self.kernel_size)
# (kernel_size*kernel_size) x 2 : (w,h)
self.hw_grid = torch.stack(torch.meshgrid([x, x])).view(2, -1).t()[:, [1, 0]]
def detect_keypoints(self, scores_map, sub_pixel=True):
b, c, h, w = scores_map.shape
scores_nograd = scores_map.detach()
# nms_scores = simple_nms(scores_nograd, self.radius)
nms_scores = simple_nms(scores_nograd, 2)
# remove border
nms_scores[:, :, : self.radius + 1, :] = 0
nms_scores[:, :, :, : self.radius + 1] = 0
nms_scores[:, :, h - self.radius :, :] = 0
nms_scores[:, :, :, w - self.radius :] = 0
# detect keypoints without grad
if self.top_k > 0:
topk = torch.topk(nms_scores.view(b, -1), self.top_k)
indices_keypoints = topk.indices # B x top_k
else:
if self.scores_th > 0:
masks = nms_scores > self.scores_th
if masks.sum() == 0:
th = scores_nograd.reshape(b, -1).mean(dim=1) # th = self.scores_th
masks = nms_scores > th.reshape(b, 1, 1, 1)
else:
th = scores_nograd.reshape(b, -1).mean(dim=1) # th = self.scores_th
masks = nms_scores > th.reshape(b, 1, 1, 1)
masks = masks.reshape(b, -1)
indices_keypoints = [] # list, B x (any size)
scores_view = scores_nograd.reshape(b, -1)
for mask, scores in zip(masks, scores_view):
indices = mask.nonzero(as_tuple=False)[:, 0]
if len(indices) > self.n_limit:
kpts_sc = scores[indices]
sort_idx = kpts_sc.sort(descending=True)[1]
sel_idx = sort_idx[: self.n_limit]
indices = indices[sel_idx]
indices_keypoints.append(indices)
keypoints = []
scoredispersitys = []
kptscores = []
if sub_pixel:
# detect soft keypoints with grad backpropagation
patches = self.unfold(scores_map) # B x (kernel**2) x (H*W)
self.hw_grid = self.hw_grid.to(patches) # to device
for b_idx in range(b):
patch = patches[b_idx].t() # (H*W) x (kernel**2)
indices_kpt = indices_keypoints[
b_idx
] # one dimension vector, say its size is M
patch_scores = patch[indices_kpt] # M x (kernel**2)
# max is detached to prevent undesired backprop loops in the graph
max_v = patch_scores.max(dim=1).values.detach()[:, None]
x_exp = (
(patch_scores - max_v) / self.temperature
).exp() # M * (kernel**2), in [0, 1]
# \frac{ \sum{(i,j) \times \exp(x/T)} }{ \sum{\exp(x/T)} }
xy_residual = (
x_exp @ self.hw_grid / x_exp.sum(dim=1)[:, None]
) # Soft-argmax, Mx2
hw_grid_dist2 = (
torch.norm(
(self.hw_grid[None, :, :] - xy_residual[:, None, :])
/ self.radius,
dim=-1,
)
** 2
)
scoredispersity = (x_exp * hw_grid_dist2).sum(dim=1) / x_exp.sum(dim=1)
# compute result keypoints
keypoints_xy_nms = torch.stack(
[indices_kpt % w, indices_kpt // w], dim=1
) # Mx2
keypoints_xy = keypoints_xy_nms + xy_residual
keypoints_xy = (
keypoints_xy / keypoints_xy.new_tensor([w - 1, h - 1]) * 2 - 1
) # (w,h) -> (-1~1,-1~1)
kptscore = torch.nn.functional.grid_sample(
scores_map[b_idx].unsqueeze(0),
keypoints_xy.view(1, 1, -1, 2),
mode="bilinear",
align_corners=True,
)[
0, 0, 0, :
] # CxN
keypoints.append(keypoints_xy)
scoredispersitys.append(scoredispersity)
kptscores.append(kptscore)
else:
for b_idx in range(b):
indices_kpt = indices_keypoints[
b_idx
] # one dimension vector, say its size is M
keypoints_xy_nms = torch.stack(
[indices_kpt % w, indices_kpt // w], dim=1
) # Mx2
keypoints_xy = (
keypoints_xy_nms / keypoints_xy_nms.new_tensor([w - 1, h - 1]) * 2
- 1
) # (w,h) -> (-1~1,-1~1)
kptscore = torch.nn.functional.grid_sample(
scores_map[b_idx].unsqueeze(0),
keypoints_xy.view(1, 1, -1, 2),
mode="bilinear",
align_corners=True,
)[
0, 0, 0, :
] # CxN
keypoints.append(keypoints_xy)
scoredispersitys.append(None)
kptscores.append(kptscore)
return keypoints, scoredispersitys, kptscores
def forward(self, scores_map, descriptor_map, sub_pixel=False):
"""
:param scores_map: Bx1xHxW
:param descriptor_map: BxCxHxW
:param sub_pixel: whether to use sub-pixel keypoint detection
:return: kpts: list[Nx2,...]; kptscores: list[N,....] normalised position: -1.0 ~ 1.0
"""
keypoints, scoredispersitys, kptscores = self.detect_keypoints(
scores_map, sub_pixel
)
descriptors = sample_descriptor(descriptor_map, keypoints, sub_pixel)
# keypoints: B M 2
# descriptors: B M D
# scoredispersitys:
return keypoints, descriptors, kptscores, scoredispersitys
|