File size: 13,426 Bytes
404d2af 8b973ee 404d2af e430362 8b973ee 8f7d727 8b973ee 404d2af 8b973ee 404d2af e430362 8b973ee 404d2af 8b973ee 404d2af 8b973ee e430362 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 |
import torch
import torch.nn as nn
eps = 1e-8
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def sinkhorn(M, r, c, iteration):
p = torch.softmax(M, dim=-1)
u = torch.ones_like(r)
v = torch.ones_like(c)
for _ in range(iteration):
u = r / ((p * v.unsqueeze(-2)).sum(-1) + eps)
v = c / ((p * u.unsqueeze(-1)).sum(-2) + eps)
p = p * u.unsqueeze(-1) * v.unsqueeze(-2)
return p
def sink_algorithm(M, dustbin, iteration):
M = torch.cat([M, dustbin.expand([M.shape[0], M.shape[1], 1])], dim=-1)
M = torch.cat([M, dustbin.expand([M.shape[0], 1, M.shape[2]])], dim=-2)
r = torch.ones([M.shape[0], M.shape[1] - 1], device=device)
r = torch.cat([r, torch.ones([M.shape[0], 1], device=device) * M.shape[1]], dim=-1)
c = torch.ones([M.shape[0], M.shape[2] - 1], device=device)
c = torch.cat([c, torch.ones([M.shape[0], 1], device=device) * M.shape[2]], dim=-1)
p = sinkhorn(M, r, c, iteration)
return p
def seeding(
nn_index1,
nn_index2,
x1,
x2,
topk,
match_score,
confbar,
nms_radius,
use_mc=True,
test=False,
):
# apply mutual check before nms
if use_mc:
mask_not_mutual = nn_index2.gather(dim=-1, index=nn_index1) != torch.arange(
nn_index1.shape[1], device=device
)
match_score[mask_not_mutual] = -1
# NMS
pos_dismat1 = (
(
(x1.norm(p=2, dim=-1) ** 2).unsqueeze_(-1)
+ (x1.norm(p=2, dim=-1) ** 2).unsqueeze_(-2)
- 2 * (x1 @ x1.transpose(1, 2))
)
.abs_()
.sqrt_()
)
x2 = x2.gather(index=nn_index1.unsqueeze(-1).expand(-1, -1, 2), dim=1)
pos_dismat2 = (
(
(x2.norm(p=2, dim=-1) ** 2).unsqueeze_(-1)
+ (x2.norm(p=2, dim=-1) ** 2).unsqueeze_(-2)
- 2 * (x2 @ x2.transpose(1, 2))
)
.abs_()
.sqrt_()
)
radius1, radius2 = nms_radius * pos_dismat1.mean(
dim=(1, 2), keepdim=True
), nms_radius * pos_dismat2.mean(dim=(1, 2), keepdim=True)
nms_mask = (pos_dismat1 >= radius1) & (pos_dismat2 >= radius2)
mask_not_local_max = (
match_score.unsqueeze(-1) >= match_score.unsqueeze(-2)
) | nms_mask
mask_not_local_max = ~(mask_not_local_max.min(dim=-1).values)
match_score[mask_not_local_max] = -1
# confidence bar
match_score[match_score < confbar] = -1
mask_survive = match_score > 0
if test:
topk = min(mask_survive.sum(dim=1)[0] + 2, topk)
_, topindex = torch.topk(match_score, topk, dim=-1) # b*k
seed_index1, seed_index2 = topindex, nn_index1.gather(index=topindex, dim=-1)
return seed_index1, seed_index2
class PointCN(nn.Module):
def __init__(self, channels, out_channels):
nn.Module.__init__(self)
self.shot_cut = nn.Conv1d(channels, out_channels, kernel_size=1)
self.conv = nn.Sequential(
nn.InstanceNorm1d(channels, eps=1e-3),
nn.SyncBatchNorm(channels),
nn.ReLU(),
nn.Conv1d(channels, channels, kernel_size=1),
nn.InstanceNorm1d(channels, eps=1e-3),
nn.SyncBatchNorm(channels),
nn.ReLU(),
nn.Conv1d(channels, out_channels, kernel_size=1),
)
def forward(self, x):
return self.conv(x) + self.shot_cut(x)
class attention_propagantion(nn.Module):
def __init__(self, channel, head):
nn.Module.__init__(self)
self.head = head
self.head_dim = channel // head
self.query_filter, self.key_filter, self.value_filter = (
nn.Conv1d(channel, channel, kernel_size=1),
nn.Conv1d(channel, channel, kernel_size=1),
nn.Conv1d(channel, channel, kernel_size=1),
)
self.mh_filter = nn.Conv1d(channel, channel, kernel_size=1)
self.cat_filter = nn.Sequential(
nn.Conv1d(2 * channel, 2 * channel, kernel_size=1),
nn.SyncBatchNorm(2 * channel),
nn.ReLU(),
nn.Conv1d(2 * channel, channel, kernel_size=1),
)
def forward(self, desc1, desc2, weight_v=None):
# desc1(q) attend to desc2(k,v)
batch_size = desc1.shape[0]
query, key, value = (
self.query_filter(desc1).view(batch_size, self.head, self.head_dim, -1),
self.key_filter(desc2).view(batch_size, self.head, self.head_dim, -1),
self.value_filter(desc2).view(batch_size, self.head, self.head_dim, -1),
)
if weight_v is not None:
value = value * weight_v.view(batch_size, 1, 1, -1)
score = torch.softmax(
torch.einsum("bhdn,bhdm->bhnm", query, key) / self.head_dim**0.5, dim=-1
)
add_value = torch.einsum("bhnm,bhdm->bhdn", score, value).reshape(
batch_size, self.head_dim * self.head, -1
)
add_value = self.mh_filter(add_value)
desc1_new = desc1 + self.cat_filter(torch.cat([desc1, add_value], dim=1))
return desc1_new
class hybrid_block(nn.Module):
def __init__(self, channel, head):
nn.Module.__init__(self)
self.head = head
self.channel = channel
self.attention_block_down = attention_propagantion(channel, head)
self.cluster_filter = nn.Sequential(
nn.Conv1d(2 * channel, 2 * channel, kernel_size=1),
nn.SyncBatchNorm(2 * channel),
nn.ReLU(),
nn.Conv1d(2 * channel, 2 * channel, kernel_size=1),
)
self.cross_filter = attention_propagantion(channel, head)
self.confidence_filter = PointCN(2 * channel, 1)
self.attention_block_self = attention_propagantion(channel, head)
self.attention_block_up = attention_propagantion(channel, head)
def forward(self, desc1, desc2, seed_index1, seed_index2):
cluster1, cluster2 = desc1.gather(
dim=-1, index=seed_index1.unsqueeze(1).expand(-1, self.channel, -1)
), desc2.gather(
dim=-1, index=seed_index2.unsqueeze(1).expand(-1, self.channel, -1)
)
# pooling
cluster1, cluster2 = self.attention_block_down(
cluster1, desc1
), self.attention_block_down(cluster2, desc2)
concate_cluster = self.cluster_filter(torch.cat([cluster1, cluster2], dim=1))
# filtering
cluster1, cluster2 = self.cross_filter(
concate_cluster[:, : self.channel], concate_cluster[:, self.channel :]
), self.cross_filter(
concate_cluster[:, self.channel :], concate_cluster[:, : self.channel]
)
cluster1, cluster2 = self.attention_block_self(
cluster1, cluster1
), self.attention_block_self(cluster2, cluster2)
# unpooling
seed_weight = self.confidence_filter(torch.cat([cluster1, cluster2], dim=1))
seed_weight = torch.sigmoid(seed_weight).squeeze(1)
desc1_new, desc2_new = self.attention_block_up(
desc1, cluster1, seed_weight
), self.attention_block_up(desc2, cluster2, seed_weight)
return desc1_new, desc2_new, seed_weight
class matcher(nn.Module):
def __init__(self, config):
nn.Module.__init__(self)
self.seed_top_k = config.seed_top_k
self.conf_bar = config.conf_bar
self.seed_radius_coe = config.seed_radius_coe
self.use_score_encoding = config.use_score_encoding
self.detach_iter = config.detach_iter
self.seedlayer = config.seedlayer
self.layer_num = config.layer_num
self.sink_iter = config.sink_iter
self.position_encoder = nn.Sequential(
nn.Conv1d(3, 32, kernel_size=1)
if config.use_score_encoding
else nn.Conv1d(2, 32, kernel_size=1),
nn.SyncBatchNorm(32),
nn.ReLU(),
nn.Conv1d(32, 64, kernel_size=1),
nn.SyncBatchNorm(64),
nn.ReLU(),
nn.Conv1d(64, 128, kernel_size=1),
nn.SyncBatchNorm(128),
nn.ReLU(),
nn.Conv1d(128, 256, kernel_size=1),
nn.SyncBatchNorm(256),
nn.ReLU(),
nn.Conv1d(256, config.net_channels, kernel_size=1),
)
self.hybrid_block = nn.Sequential(
*[
hybrid_block(config.net_channels, config.head)
for _ in range(config.layer_num)
]
)
self.final_project = nn.Conv1d(
config.net_channels, config.net_channels, kernel_size=1
)
self.dustbin = nn.Parameter(torch.tensor(1.5, dtype=torch.float32))
# if reseeding
if len(config.seedlayer) != 1:
self.mid_dustbin = nn.ParameterDict(
{
str(i): nn.Parameter(torch.tensor(2, dtype=torch.float32))
for i in config.seedlayer[1:]
}
)
self.mid_final_project = nn.Conv1d(
config.net_channels, config.net_channels, kernel_size=1
)
def forward(self, data, test_mode=True):
x1, x2, desc1, desc2 = (
data["x1"][:, :, :2],
data["x2"][:, :, :2],
data["desc1"],
data["desc2"],
)
desc1, desc2 = torch.nn.functional.normalize(
desc1, dim=-1
), torch.nn.functional.normalize(desc2, dim=-1)
if test_mode:
encode_x1, encode_x2 = data["x1"], data["x2"]
else:
encode_x1, encode_x2 = data["aug_x1"], data["aug_x2"]
# preparation
desc_dismat = (2 - 2 * torch.matmul(desc1, desc2.transpose(1, 2))).sqrt_()
values, nn_index = torch.topk(
desc_dismat, k=2, largest=False, dim=-1, sorted=True
)
nn_index2 = torch.min(desc_dismat, dim=1).indices.squeeze(1)
inverse_ratio_score, nn_index1 = (
values[:, :, 1] / values[:, :, 0],
nn_index[:, :, 0],
) # get inverse score
# initial seeding
seed_index1, seed_index2 = seeding(
nn_index1,
nn_index2,
x1,
x2,
self.seed_top_k[0],
inverse_ratio_score,
self.conf_bar[0],
self.seed_radius_coe,
test=test_mode,
)
# position encoding
desc1, desc2 = desc1.transpose(1, 2), desc2.transpose(1, 2)
if not self.use_score_encoding:
encode_x1, encode_x2 = encode_x1[:, :, :2], encode_x2[:, :, :2]
encode_x1, encode_x2 = encode_x1.transpose(1, 2), encode_x2.transpose(1, 2)
x1_pos_embedding, x2_pos_embedding = self.position_encoder(
encode_x1
), self.position_encoder(encode_x2)
aug_desc1, aug_desc2 = x1_pos_embedding + desc1, x2_pos_embedding + desc2
seed_weight_tower, mid_p_tower, seed_index_tower, nn_index_tower = (
[],
[],
[],
[],
)
seed_index_tower.append(torch.stack([seed_index1, seed_index2], dim=-1))
nn_index_tower.append(nn_index1)
seed_para_index = 0
for i in range(self.layer_num):
# mid seeding
if i in self.seedlayer and i != 0:
seed_para_index += 1
aug_desc1, aug_desc2 = self.mid_final_project(
aug_desc1
), self.mid_final_project(aug_desc2)
M = torch.matmul(aug_desc1.transpose(1, 2), aug_desc2)
p = sink_algorithm(
M, self.mid_dustbin[str(i)], self.sink_iter[seed_para_index - 1]
)
mid_p_tower.append(p)
# rematching with p
values, nn_index = torch.topk(p[:, :-1, :-1], k=1, dim=-1)
nn_index2 = torch.max(p[:, :-1, :-1], dim=1).indices.squeeze(1)
p_match_score, nn_index1 = values[:, :, 0], nn_index[:, :, 0]
# reseeding
seed_index1, seed_index2 = seeding(
nn_index1,
nn_index2,
x1,
x2,
self.seed_top_k[seed_para_index],
p_match_score,
self.conf_bar[seed_para_index],
self.seed_radius_coe,
test=test_mode,
)
seed_index_tower.append(
torch.stack([seed_index1, seed_index2], dim=-1)
), nn_index_tower.append(nn_index1)
if not test_mode and data["step"] < self.detach_iter:
aug_desc1, aug_desc2 = aug_desc1.detach(), aug_desc2.detach()
aug_desc1, aug_desc2, seed_weight = self.hybrid_block[i](
aug_desc1, aug_desc2, seed_index1, seed_index2
)
seed_weight_tower.append(seed_weight)
aug_desc1, aug_desc2 = self.final_project(aug_desc1), self.final_project(
aug_desc2
)
cmat = torch.matmul(aug_desc1.transpose(1, 2), aug_desc2)
p = sink_algorithm(cmat, self.dustbin, self.sink_iter[-1])
# seed_weight_tower: l*b*k
# seed_index_tower: l*b*k*2
# nn_index_tower: seed_l*b
return {
"p": p,
"seed_conf": seed_weight_tower,
"seed_index": seed_index_tower,
"mid_p": mid_p_tower,
"nn_index": nn_index_tower,
}
|