File size: 20,991 Bytes
cf84204 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 | """
Tree Encoder Components for Neuron Morphology Analysis
This module contains TreeLSTM and related components for encoding tree structures.
"""
import torch
import torch.nn as nn
import dgl
class MultiNodeAggregation(nn.Module):
"""Attention-based aggregation over all tree nodes instead of just root"""
def __init__(self, h_size, aggregation_type="attention"):
super(MultiNodeAggregation, self).__init__()
self.h_size = h_size
self.aggregation_type = aggregation_type
if aggregation_type == "attention":
# Learnable attention over nodes
self.attention = nn.Sequential(
nn.Linear(h_size, h_size),
nn.Tanh(),
nn.Linear(h_size, 1)
)
elif aggregation_type == "weighted":
# Learnable weighted sum
self.weight_net = nn.Sequential(
nn.Linear(h_size, h_size // 2),
nn.ReLU(),
nn.Linear(h_size // 2, 1),
nn.Sigmoid()
)
def forward(self, g, node_features, offsets):
"""
Args:
g: DGL graph
node_features: (N, h_size) features for all nodes
offsets: indices of root nodes for each tree in batch
Returns:
aggregated: (batch_size, h_size) aggregated features
"""
batch_size = len(offsets)
aggregated = []
# For each tree in batch
for i in range(batch_size):
# Get start and end indices for this tree's nodes
if i < batch_size - 1:
start_idx = offsets[i] if i == 0 else offsets[i-1]
end_idx = offsets[i+1]
else:
start_idx = offsets[i-1] if i > 0 else 0
end_idx = len(node_features)
# Extract this tree's node features
tree_feats = node_features[start_idx:end_idx] # (num_nodes_i, h_size)
if self.aggregation_type == "attention":
# Attention weights
attn_scores = self.attention(tree_feats) # (num_nodes_i, 1)
attn_weights = torch.softmax(attn_scores, dim=0)
# Weighted sum
agg = (tree_feats * attn_weights).sum(dim=0) # (h_size,)
elif self.aggregation_type == "weighted":
# Simple learnable weighting
weights = self.weight_net(tree_feats) # (num_nodes_i, 1)
agg = (tree_feats * weights).sum(dim=0) # (h_size,)
elif self.aggregation_type == "mean":
agg = tree_feats.mean(dim=0) # (h_size,)
elif self.aggregation_type == "max":
agg = tree_feats.max(dim=0)[0] # (h_size,)
aggregated.append(agg)
return torch.stack(aggregated, dim=0) # (batch_size, h_size)
class TreeLSTMCell(nn.Module):
def __init__(self, x_size, h_size, mode="sum"):
super(TreeLSTMCell, self).__init__()
self.h_size, self.mode = h_size, mode
self.W_iou = nn.Linear(x_size, 3 * h_size, bias=False)
self.U_iou = nn.Linear(h_size, 3 * h_size, bias=False)
self.b_iou = nn.Parameter(torch.zeros(1, 3 * h_size))
self.U_f = nn.Linear(h_size, h_size)
def message_func(self, edges):
return {"h": edges.src["h"], "c": edges.src["c"]}
def reduce_func(self, nodes):
if self.mode == "sum":
h_cat = nodes.mailbox["h"].sum(dim=1)
elif self.mode == "max":
h_cat = nodes.mailbox["h"].max(dim=1)[0]
elif self.mode == "mean":
h_cat = nodes.mailbox["h"].mean(dim=1)
else:
raise NotImplementedError
f = torch.sigmoid(self.U_f(nodes.mailbox["h"]))
c = torch.sum(f * nodes.mailbox["c"], 1)
return {"iou": self.U_iou(h_cat), "c": c}
def apply_node_func(self, nodes):
iou = nodes.data["iou"] + self.b_iou
i, o, u = torch.chunk(iou, 3, 1)
i, o, u = torch.sigmoid(i), torch.sigmoid(o), torch.tanh(u)
c = i * u + nodes.data["c"]
h = o * torch.tanh(c)
return {"h": h, "c": c}
class BidirectionalTreeLSTMCell(nn.Module):
"""Bidirectional TreeLSTM Cell that processes tree in both bottom-up and top-down directions"""
def __init__(self, x_size, h_size, mode="sum"):
super(BidirectionalTreeLSTMCell, self).__init__()
self.h_size = h_size
self.mode = mode
# Bottom-up (child to parent) parameters
self.W_iou_bu = nn.Linear(x_size, 3 * h_size, bias=False)
self.U_iou_bu = nn.Linear(h_size, 3 * h_size, bias=False)
self.b_iou_bu = nn.Parameter(torch.zeros(1, 3 * h_size))
self.U_f_bu = nn.Linear(h_size, h_size)
# Top-down (parent to child) parameters
self.W_iou_td = nn.Linear(x_size, 3 * h_size, bias=False)
self.U_iou_td = nn.Linear(h_size, 3 * h_size, bias=False)
self.b_iou_td = nn.Parameter(torch.zeros(1, 3 * h_size))
self.U_f_td = nn.Linear(h_size, h_size)
def message_func_bu(self, edges):
"""Bottom-up message function (from children to parent)"""
return {"h_bu": edges.src["h_bu"], "c_bu": edges.src["c_bu"]}
def reduce_func_bu(self, nodes):
"""Bottom-up reduce function"""
if self.mode == "sum":
h_cat = nodes.mailbox["h_bu"].sum(dim=1)
elif self.mode == "max":
h_cat = nodes.mailbox["h_bu"].max(dim=1)[0]
elif self.mode == "mean":
h_cat = nodes.mailbox["h_bu"].mean(dim=1)
else:
raise NotImplementedError
f = torch.sigmoid(self.U_f_bu(nodes.mailbox["h_bu"]))
c = torch.sum(f * nodes.mailbox["c_bu"], 1)
return {"iou_bu": self.U_iou_bu(h_cat), "c_bu": c}
def apply_node_func_bu(self, nodes):
"""Bottom-up apply node function"""
iou = nodes.data["iou_bu"] + self.b_iou_bu
i, o, u = torch.chunk(iou, 3, 1)
i, o, u = torch.sigmoid(i), torch.sigmoid(o), torch.tanh(u)
c = i * u + nodes.data["c_bu"]
h = o * torch.tanh(c)
return {"h_bu": h, "c_bu": c}
def message_func_td(self, edges):
"""Top-down message function (from parent to children)"""
return {"h_td": edges.dst["h_td"], "c_td": edges.dst["c_td"]}
def reduce_func_td(self, nodes):
"""Top-down reduce function"""
if nodes.mailbox["h_td"].shape[1] == 0:
# Root node has no parent
return {"iou_td": torch.zeros(nodes.batch_size(), 3 * self.h_size, device=nodes.mailbox["h_td"].device),
"c_td": torch.zeros(nodes.batch_size(), self.h_size, device=nodes.mailbox["h_td"].device)}
# For non-root nodes, aggregate parent information
h_parent = nodes.mailbox["h_td"][:, 0, :] # Take first (and only) parent
c_parent = nodes.mailbox["c_td"][:, 0, :]
return {"iou_td": self.U_iou_td(h_parent), "c_td": c_parent}
def apply_node_func_td(self, nodes):
"""Top-down apply node function"""
iou = nodes.data["iou_td"] + self.b_iou_td
i, o, u = torch.chunk(iou, 3, 1)
i, o, u = torch.sigmoid(i), torch.sigmoid(o), torch.tanh(u)
c = i * u + nodes.data["c_td"]
h = o * torch.tanh(c)
return {"h_td": h, "c_td": c}
class BidirectionalTreeLSTM(nn.Module):
"""Bidirectional TreeLSTM that combines bottom-up and top-down processing"""
def __init__(self, x_size, h_size, num_classes, mode="sum", fc=True, bn=False,
node_aggregation=None):
super(BidirectionalTreeLSTM, self).__init__()
self.x_size = x_size
self.h_size = h_size
self.node_aggregation = node_aggregation
# Input projection
if bn:
self.mlp1 = nn.Sequential(
nn.Linear(x_size, h_size),
nn.BatchNorm1d(h_size),
nn.ReLU(),
)
else:
self.mlp1 = nn.Sequential(
nn.Linear(x_size, h_size),
nn.ReLU(),
)
# Bidirectional TreeLSTM cell
self.cell = BidirectionalTreeLSTMCell(h_size, h_size, mode=mode)
# Node aggregation
if node_aggregation:
self.node_agg = MultiNodeAggregation(h_size * 2, aggregation_type=node_aggregation)
# Classification head
self.fc = fc
if fc:
self.linear = nn.Linear(h_size * 2, num_classes)
def forward(self, batch):
"""Forward pass combining bottom-up and top-down TreeLSTM"""
g = batch.graph.to(torch.device("cuda"))
g = dgl.graph(g.edges())
n = g.number_of_nodes()
# Input projection
feats = self.mlp1(batch.feats.cuda())
# Initialize node states for both directions
g.ndata["iou_bu"] = self.cell.W_iou_bu(feats)
g.ndata["iou_td"] = self.cell.W_iou_td(feats)
g.ndata["h_bu"] = torch.zeros((n, self.h_size)).cuda()
g.ndata["c_bu"] = torch.zeros((n, self.h_size)).cuda()
g.ndata["h_td"] = torch.zeros((n, self.h_size)).cuda()
g.ndata["c_td"] = torch.zeros((n, self.h_size)).cuda()
# Bottom-up pass
dgl.prop_nodes_topo(
g,
message_func=self.cell.message_func_bu,
reduce_func=self.cell.reduce_func_bu,
apply_node_func=self.cell.apply_node_func_bu,
)
# Top-down pass (reverse topological order)
dgl.prop_nodes_topo(
g,
message_func=self.cell.message_func_td,
reduce_func=self.cell.reduce_func_td,
apply_node_func=self.cell.apply_node_func_td,
reverse=True,
)
# Combine bottom-up and top-down representations
h_combined = torch.cat([g.ndata.pop("c_bu"), g.ndata.pop("c_td")], dim=1)
# Aggregate node features
if self.node_aggregation:
h = self.node_agg(g, h_combined, batch.offset.long())
else:
h = h_combined[batch.offset.long()]
# Classification
if self.fc:
return self.linear(h)
return h
class TreeLSTM(nn.Module):
def __init__(self, x_size, h_size, num_classes, mode="sum", fc=True, bn=False,
node_aggregation=None):
super(TreeLSTM, self).__init__()
self.x_size = x_size
self.h_size = h_size
self.node_aggregation = node_aggregation
if bn:
self.mlp1 = nn.Sequential(
nn.Linear(x_size, h_size),
nn.BatchNorm1d(h_size),
nn.ReLU(),
)
else:
self.mlp1 = nn.Sequential(
nn.Linear(x_size, h_size),
nn.ReLU(),
)
self.cell = TreeLSTMCell(h_size, h_size, mode=mode)
if node_aggregation:
self.node_agg = MultiNodeAggregation(h_size, aggregation_type=node_aggregation)
self.fc = fc
if fc:
self.linear = nn.Linear(h_size, num_classes)
def forward(self, batch):
g = batch.graph.to(torch.device("cuda"))
g = dgl.graph(g.edges())
n = g.number_of_nodes()
feats = self.mlp1(batch.feats.cuda())
g.ndata["iou"] = self.cell.W_iou(feats)
g.ndata["h"] = torch.zeros((n, self.h_size)).cuda()
g.ndata["c"] = torch.zeros((n, self.h_size)).cuda()
dgl.prop_nodes_topo(
g,
message_func=self.cell.message_func,
reduce_func=self.cell.reduce_func,
apply_node_func=self.cell.apply_node_func,
)
h = g.ndata.pop("c")
if self.node_aggregation:
h = self.node_agg(g, h, batch.offset.long())
else:
h = h[batch.offset.long()]
if self.fc:
return self.linear(h)
return h
class TreeLSTM_wo_MLP(nn.Module):
"""TreeLSTM without initial MLP projection"""
def __init__(self, x_size, h_size, num_classes, mode="sum", fc=True):
super(TreeLSTM_wo_MLP, self).__init__()
self.x_size = x_size
self.h_size = h_size
self.cell = TreeLSTMCell(x_size, h_size, mode=mode)
self.fc = fc
if fc:
self.linear = nn.Linear(h_size, num_classes)
def forward(self, batch):
g = batch.graph.to(torch.device("cuda"))
g = dgl.graph(g.edges())
n = g.number_of_nodes()
feats = batch.feats.cuda()
g.ndata["iou"] = self.cell.W_iou(feats)
g.ndata["h"] = torch.zeros((n, self.h_size)).cuda()
g.ndata["c"] = torch.zeros((n, self.h_size)).cuda()
dgl.prop_nodes_topo(
g,
message_func=self.cell.message_func,
reduce_func=self.cell.reduce_func,
apply_node_func=self.cell.apply_node_func,
)
h = g.ndata.pop("c")[batch.offset.long()]
if self.fc:
return self.linear(h)
return h
class TreeLSTMCellv2(nn.Module):
"""TreeLSTM Cell with alternative architecture"""
def __init__(self, x_size, h_size, mode="sum"):
super(TreeLSTMCellv2, self).__init__()
self.h_size = h_size
self.mode = mode
self.W_iou = nn.Linear(x_size, 3 * h_size, bias=False)
self.U_iou = nn.Linear(2 * h_size, 3 * h_size, bias=False)
self.b_iou = nn.Parameter(torch.zeros(1, 3 * h_size))
self.U_f = nn.Linear(2 * h_size, h_size)
def message_func(self, edges):
return {"h": edges.src["h"], "c": edges.src["c"]}
def reduce_func(self, nodes):
if self.mode == "sum":
h_cat = nodes.mailbox["h"].sum(dim=1)
elif self.mode == "max":
h_cat = nodes.mailbox["h"].max(dim=1)[0]
elif self.mode == "mean":
h_cat = nodes.mailbox["h"].mean(dim=1)
h_max = nodes.mailbox["h"].max(dim=1)[0]
h_combined = torch.cat([h_cat, h_max], dim=1)
f = torch.sigmoid(self.U_f(h_combined.unsqueeze(1).expand(-1, nodes.mailbox["h"].shape[1], -1)))
c = torch.sum(f * nodes.mailbox["c"], 1)
return {"iou": self.U_iou(h_combined), "c": c}
def apply_node_func(self, nodes):
iou = nodes.data["iou"] + self.b_iou
i, o, u = torch.chunk(iou, 3, 1)
i, o, u = torch.sigmoid(i), torch.sigmoid(o), torch.tanh(u)
c = i * u + nodes.data["c"]
h = o * torch.tanh(c)
return {"h": h, "c": c}
class TreeLSTMDoubleCell(nn.Module):
"""TreeLSTM Cell with double hidden state (original implementation)"""
def __init__(self, x_size, h_size, mode="sum"):
super(TreeLSTMDoubleCell, self).__init__()
self.W1_iouf = nn.Linear(x_size, 4 * h_size)
self.U1_iouf = nn.Linear(h_size, 4 * h_size)
self.W2_iouf = nn.Linear(h_size, 4 * h_size)
self.U2_iouf = nn.Linear(h_size, 4 * h_size)
self.mode = mode
self.init_state = True
self.h_size = h_size
def message_func(self, edges):
return {
"h1": edges.src["h1"],
"c1": edges.src["c1"],
"h2": edges.src["h2"],
"c2": edges.src["c2"],
}
def reduce_func(self, nodes):
h1, c1 = nodes.mailbox["h1"], nodes.mailbox["c1"]
h2, c2 = nodes.mailbox["h2"], nodes.mailbox["c2"]
if self.mode == "sum":
h1, c1, h2, c2 = h1.sum(-2), c1.sum(-2), h2.sum(-2), c2.sum(-2)
elif self.mode == "mean":
h1, c1, h2, c2 = h1.mean(-2), c1.mean(-2), h2.mean(-2), c2.mean(-2)
else:
raise ValueError("must in [sum, mean]")
x_iouf = nodes.data["iouf"]
xi, xo, xu, xf = torch.chunk(x_iouf, 4, 1)
h_iouf1 = self.U1_iouf(h1)
hi1, ho1, hu1, hf1 = torch.chunk(h_iouf1, 4, 1)
i = torch.sigmoid(xi + hi1)
f = torch.sigmoid(xf + hf1)
o = torch.sigmoid(xo + ho1)
u = torch.tanh(xu + hu1)
c1 = i * u + f * c1
h1 = o * torch.tanh(c1)
x_iouf2 = self.W2_iouf(c1)
xi, xo, xu, xf = torch.chunk(x_iouf2, 4, 1)
h_iouf2 = self.U2_iouf(h2)
hi2, ho2, hu2, hf2 = torch.chunk(h_iouf2, 4, 1)
i = torch.sigmoid(xi + hi2)
f = torch.sigmoid(xf + hf2)
o = torch.sigmoid(xo + ho2)
u = torch.tanh(xu + hu2)
c2 = i * u + f * c2
h2 = o * torch.tanh(c2)
return {"h1": h1, "c1": c1, "h2": h2, "c2": c2}
def apply_node_func(self, nodes):
if self.init_state:
iouf = nodes.data["iouf"]
i, o, u, f = torch.chunk(iouf, 4, 1)
i, o, u, f = torch.sigmoid(i), torch.sigmoid(o), torch.tanh(u), torch.sigmoid(f)
c1 = i * u + f * nodes.data["c1"]
h1 = o * torch.tanh(c1)
iouf2 = self.W2_iouf(c1)
i, o, u, f = torch.chunk(iouf2, 4, 1)
i, o, u, f = torch.sigmoid(i), torch.sigmoid(o), torch.tanh(u), torch.sigmoid(f)
c2 = i * u + f * nodes.data["c2"]
h2 = o * torch.tanh(c2)
self.init_state = False
return {"h1": h1, "c1": c1, "h2": h2, "c2": c2}
else:
return {
"h1": nodes.data["h1"],
"c1": nodes.data["c1"],
"h2": nodes.data["h2"],
"c2": nodes.data["c2"],
}
class TreeLSTMDouble(nn.Module):
"""TreeLSTM with double hidden state aggregation"""
def __init__(self, x_size, h_size, num_classes, mode="sum", fc=True, bn=False, node_aggregation=None):
super(TreeLSTMDouble, self).__init__()
self.x_size, self.h_size = x_size, h_size
self.node_aggregation = node_aggregation
if bn:
self.mlp1 = nn.Sequential(
nn.Linear(x_size, h_size),
nn.BatchNorm1d(h_size),
nn.ReLU(),
nn.Linear(h_size, 2 * h_size),
nn.BatchNorm1d(2 * h_size),
nn.ReLU(),
nn.Linear(2 * h_size, h_size),
)
else:
self.mlp1 = nn.Sequential(
nn.Linear(x_size, h_size),
nn.ReLU(),
nn.Linear(h_size, 2 * h_size),
nn.ReLU(),
nn.Linear(2 * h_size, h_size),
)
self.cell = TreeLSTMDoubleCell(h_size, h_size, mode=mode)
if node_aggregation:
self.node_agg = MultiNodeAggregation(h_size, aggregation_type=node_aggregation)
self.fc = fc
if fc:
self.linear = nn.Linear(h_size, num_classes)
def forward_backbone(self, batch):
g = batch.graph.to(torch.device("cuda"))
# to heterogenous graph
g = dgl.graph(g.edges())
n = g.number_of_nodes()
# feed embedding
feats = self.mlp1(batch.feats.cuda())
g.ndata["iouf"] = self.cell.W1_iouf(feats)
g.ndata["h1"] = torch.zeros((n, self.h_size)).cuda()
g.ndata["c1"] = torch.zeros((n, self.h_size)).cuda()
g.ndata["h2"] = torch.zeros((n, self.h_size)).cuda()
g.ndata["c2"] = torch.zeros((n, self.h_size)).cuda()
# propagate
dgl.prop_nodes_topo(
g,
message_func=self.cell.message_func,
reduce_func=self.cell.reduce_func,
apply_node_func=self.cell.apply_node_func,
)
logits = g.ndata.pop("c2")[batch.offset.long()]
return logits
def forward(self, batch):
logits = self.forward_backbone(batch)
if self.fc:
logits = self.linear(logits)
return logits
else:
return logits
class TreeLSTMv2(nn.Module):
"""TreeLSTM variant with improved architecture"""
def __init__(self, x_size, h_size, num_classes, mode="sum", fc=True, bn=False):
super(TreeLSTMv2, self).__init__()
self.x_size, self.h_size = x_size, h_size
if bn:
self.mlp1 = nn.Sequential(
nn.Linear(x_size, h_size),
nn.BatchNorm1d(h_size),
nn.ReLU(),
)
else:
self.mlp1 = nn.Sequential(
nn.Linear(x_size, h_size),
nn.ReLU(),
)
self.cell = TreeLSTMCellv2(h_size, h_size, mode=mode)
self.fc = fc
if fc:
self.linear = nn.Linear(h_size, num_classes)
def forward(self, batch):
g = batch.graph.to(torch.device("cuda"))
g = dgl.graph(g.edges())
n = g.number_of_nodes()
feats = self.mlp1(batch.feats.cuda())
g.ndata["iou"] = self.cell.W_iou(feats)
g.ndata["h"] = torch.zeros((n, self.h_size)).cuda()
g.ndata["c"] = torch.zeros((n, self.h_size)).cuda()
dgl.prop_nodes_topo(
g,
message_func=self.cell.message_func,
reduce_func=self.cell.reduce_func,
apply_node_func=self.cell.apply_node_func,
)
h = g.ndata.pop("c")[batch.offset.long()]
if self.fc:
return self.linear(h)
return h
|