File size: 18,394 Bytes
f348660 | 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 | """GRT-Small Model - from official codebase.
This implementation directly copies necessary modules from the official GRT codebase
(grt/deepradar/modules).
"""
import torch
import torch.nn as nn
from typing import Literal, Optional, Sequence
import numpy as np
from einops import rearrange
# ============================================================================
# Official GRT Modules (copied from grt/deepradar/modules/*.py)
# ============================================================================
class PatchMerge(nn.Module):
"""Merge patches with normalization and nominally reduced projection.
From: grt/deepradar/modules/patch.py
"""
def __init__(
self, d_in: int, d_out: int, scale: Sequence[int] = [], norm: bool = True
) -> None:
super().__init__()
self.scale = scale
d_merge = d_in * int(np.prod(scale))
self.linear = nn.Linear(d_merge, d_out, bias=False)
self.norm = nn.LayerNorm(d_merge) if norm else None
def _merge(self, x: torch.Tensor) -> torch.Tensor:
"""Perform patch merging."""
n, *t, c = x.shape
dims = sum(([d // s, s] for d, s in zip(t, self.scale)), start=[n])
order = (
[0]
+ [2 * i + 1 for i in range(len(self.scale))]
+ [2 * i + 2 for i in range(len(self.scale))]
+ [-1]
)
t2 = [d // s for d, s in zip(t, self.scale)]
return x.reshape(dims + [c]).permute(order).reshape(n, *t2, -1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Merge and project."""
merged = self._merge(x)
if self.norm is not None:
merged = self.norm(merged)
return self.linear(merged)
class Sinusoid(nn.Module):
"""Centered N-dimensional sinusoidal positional embedding.
From: grt/deepradar/modules/position.py
"""
def __init__(
self,
scale: Optional[Sequence[float]] = None,
global_scale: float = 1.0,
coef: float = 10000.0,
) -> None:
super().__init__()
if scale is None:
self.scale = [global_scale]
else:
self.scale = [s * global_scale for s in scale]
self.coef = coef
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Apply sinusoidal embedding."""
# w = coef ** (-i / c)
nd = len(x.shape) - 2
c = x.shape[-1] // 2 // nd
i = torch.arange(c, device=x.device)
w = self.coef ** (-i / c)
start_dim = 0
for axis, (d, scale) in enumerate(zip(x.shape[1:-1], self.scale * nd)):
# t = scale * (j - d/2) / (d/2) = scale * (2j / d - 1)
t = scale * (2 * (torch.arange(d, device=x.device) + 0.5) / d - 1)
wt = t[:, None] * w[None, :]
p_slice = [None] * (len(x.shape) - 1) + [slice(None)]
p_slice[axis + 1] = slice(None)
# pos[2 * i] = sin(w * t)
x_sin_slice = [slice(None)] * len(x.shape)
x_sin_slice[-1] = slice(start_dim, start_dim + c * 2, 2)
x_sin_slice = tuple(x_sin_slice)
p_slice_tuple = tuple(p_slice)
x[x_sin_slice] = x[x_sin_slice] + torch.sin(wt)[p_slice_tuple]
# pos[2 * i + 1] = cos(w * t)
x_cos_slice = [slice(None)] * len(x.shape)
x_cos_slice[-1] = slice(start_dim + 1, start_dim + c * 2 + 1, 2)
x_cos_slice = tuple(x_cos_slice)
x[x_cos_slice] = x[x_cos_slice] + torch.cos(wt)[p_slice_tuple]
start_dim += c * 2
return x
class Readout(nn.Module):
"""Add readout token (concatenating along the spatial axis).
From: grt/deepradar/modules/position.py
"""
def __init__(self, d_model: int = 512) -> None:
super().__init__()
self.readout = nn.Parameter(data=torch.normal(0, 0.02, (d_model,)))
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Concatenate readout token."""
readout = torch.tile(self.readout[None, None, :], (x.shape[0], 1, 1))
return torch.concatenate((x, readout), dim=1)
def transformer_mlp(
d_model: int = 512,
d_feedforward: int = 2048,
activation: str = "GELU",
dropout: float = 0.0,
eps: float = 1e-5,
) -> nn.Module:
"""Create transformer MLP.
From: grt/deepradar/modules/transformer.py
"""
return nn.Sequential(
nn.LayerNorm(d_model, eps=eps, bias=True),
nn.Linear(d_model, d_feedforward, bias=True),
getattr(nn, activation)(),
nn.Dropout(dropout),
nn.Linear(d_feedforward, d_model, bias=True),
nn.Dropout(dropout),
)
class TransformerLayer(nn.Module):
"""Single transformer (encoder) layer.
Uses PyTorch's naming convention to match checkpoint:
- self_attn (not attn)
- linear1, linear2 (not feedforward.0, feedforward.4)
- norm1, norm2 (for attention and feedforward)
"""
def __init__(
self,
d_model: int = 512,
n_head: int = 8,
d_feedforward: int = 2048,
dropout: float = 0.0,
activation: str = "GELU",
) -> None:
super().__init__()
# Attention with PyTorch naming
self.self_attn = nn.MultiheadAttention(
d_model, n_head, dropout=dropout, bias=True, batch_first=True
)
self.dropout1 = nn.Dropout(dropout)
# Feedforward with PyTorch naming
self.linear1 = nn.Linear(d_model, d_feedforward, bias=True)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(d_feedforward, d_model, bias=True)
self.dropout2 = nn.Dropout(dropout)
# Norms
self.norm1 = nn.LayerNorm(d_model, eps=1e-5, bias=True)
self.norm2 = nn.LayerNorm(d_model, eps=1e-5, bias=True)
# Activation
self.activation = getattr(nn, activation)()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Apply transformer with pre-norm (norm_first=True style)."""
# Self attention block
x2 = self.norm1(x)
x2 = self.self_attn(x2, x2, x2, need_weights=False)[0]
x = x + self.dropout1(x2)
# Feedforward block
x2 = self.norm2(x)
x2 = self.linear1(x2)
x2 = self.activation(x2)
x2 = self.dropout(x2)
x2 = self.linear2(x2)
x = x + self.dropout2(x2)
return x
class TransformerDecoder(nn.Module):
"""Single transformer (decoder) layer.
Uses PyTorch's naming convention to match checkpoint:
- self_attn, multihead_attn (not attn, attn2)
- linear1, linear2 (not feedforward.0, feedforward.4)
- norm1, norm2, norm3 (for self-attn, cross-attn, and feedforward)
"""
def __init__(
self,
d_model: int = 512,
n_head: int = 8,
d_feedforward: int = 2048,
dropout: float = 0.0,
activation: str = "GELU",
) -> None:
super().__init__()
# Self attention with PyTorch naming
self.self_attn = nn.MultiheadAttention(
d_model, n_head, dropout=dropout, bias=True, batch_first=True
)
self.dropout1 = nn.Dropout(dropout)
# Cross attention with PyTorch naming (multihead_attn, not attn2)
self.multihead_attn = nn.MultiheadAttention(
d_model, n_head, dropout=dropout, bias=True, batch_first=True
)
self.dropout2 = nn.Dropout(dropout)
# Feedforward with PyTorch naming
self.linear1 = nn.Linear(d_model, d_feedforward, bias=True)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(d_feedforward, d_model, bias=True)
self.dropout3 = nn.Dropout(dropout)
# Norms (note: norm2 is for cross-attention)
self.norm1 = nn.LayerNorm(d_model, eps=1e-5, bias=True)
self.norm2 = nn.LayerNorm(d_model, eps=1e-5, bias=True)
self.norm3 = nn.LayerNorm(d_model, eps=1e-5, bias=True)
# Activation
self.activation = getattr(nn, activation)()
def forward(self, x: torch.Tensor, x_enc: torch.Tensor) -> torch.Tensor:
"""Apply transformer decoder with pre-norm."""
# Self attention block
x2 = self.norm1(x)
x2 = self.self_attn(x2, x2, x2, need_weights=False)[0]
x = x + self.dropout1(x2)
# Cross attention block
x2 = self.norm2(x)
x2 = self.multihead_attn(x2, x_enc, x_enc, need_weights=False)[0]
x = x + self.dropout2(x2)
# Feedforward block
x2 = self.norm3(x)
x2 = self.linear1(x2)
x2 = self.activation(x2)
x2 = self.dropout(x2)
x2 = self.linear2(x2)
x = x + self.dropout3(x2)
return x
class BasisChange(nn.Module):
"""Create "change-of-basis" query.
From: grt/deepradar/modules/transformer.py
"""
def __init__(
self,
shape: Sequence[int] = [],
flatten: bool = True,
scale: Optional[Sequence[float]] = None,
global_scale: float = 1.0,
) -> None:
super().__init__()
self.pos = Sinusoid(scale=scale, global_scale=global_scale)
self.shape = shape
self.flatten = flatten
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Apply change of basis."""
idxs = tuple([slice(None)] + [None] * len(self.shape) + [slice(None)])
query = self.pos(torch.tile(x[idxs], (1, *self.shape, 1)))
if self.flatten:
query = query.reshape(x.shape[0], -1, x.shape[-1])
return query
class Unpatch(nn.Module):
"""Unpatch data.
Args:
output_size: output 2D shape.
features: number of input features; should be `>= size * size`.
size: patch size as (width, height, channels).
"""
def __init__(
self,
output_size: Sequence[int],
features: int = 512,
size: Sequence[int] = (16, 16),
) -> None:
super().__init__()
self.linear = nn.Linear(features, output_size[-1] * int(np.prod(size)))
self.size = size
self.output_size = output_size
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Perform 2D unpatching.
Operates in batch-spatial-feature order; spatial axes are flattened on
the input, and unflattened in the output.
"""
embedding = self.linear(x)
if len(self.size) == 2:
return rearrange(
embedding,
"n (x1 x2) (s1 s2 c) -> n (x1 s1) (x2 s2) c",
x1=self.output_size[0] // self.size[0],
x2=self.output_size[1] // self.size[1],
s1=self.size[0],
s2=self.size[1],
c=self.output_size[-1],
)
elif len(self.size) == 3:
return rearrange(
embedding,
"n (x1 x2 x3) (s1 s2 s3 c) -> n (x1 s1) (x2 s2) (x3 s3) c",
x1=self.output_size[0] // self.size[0],
x2=self.output_size[1] // self.size[1],
x3=self.output_size[2] // self.size[2],
s1=self.size[0],
s2=self.size[1],
s3=self.size[2],
c=self.output_size[-1],
)
else:
raise ValueError("Unpatch is only implemented for 2D and 3D tensors.")
# ============================================================================
# GRT Model Components
# ============================================================================
class GRTEncoder(nn.Module):
"""GRT Transformer Encoder matching official implementation."""
def __init__(
self,
layers: int = 4,
dim: int = 512,
ff_ratio: float = 4.0,
head_dim: int = 64,
dropout: float = 0.1,
activation: str = "GELU",
patch: list[int] = [2, 8, 2, 4],
pos_scale: list[float] = [1.0, 1.0, 1.0, 1.0],
global_scale: float = 16.0,
input_channels: int = 2,
positions: Literal["flat", "nd"] = "nd",
):
super().__init__()
# Patch embedding
self.patch = PatchMerge(d_in=input_channels, d_out=dim, scale=patch, norm=False)
# Position embedding
self.positions = positions
self.pos = Sinusoid(scale=pos_scale, global_scale=global_scale)
# Readout token
self.readout = Readout(d_model=dim)
# Encoder layers
self.layers = nn.ModuleList(
[
TransformerLayer(
d_feedforward=int(ff_ratio * dim),
d_model=dim,
n_head=dim // head_dim,
dropout=dropout,
activation=activation,
)
for _ in range(layers)
]
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward pass."""
# Patch embedding
embedded = self.patch(x)
# Apply positional encoding
if self.positions == "nd":
embedded = self.pos(embedded)
# Flatten spatial dimensions
flat = embedded.reshape(embedded.shape[0], -1, embedded.shape[-1])
# Apply flat positional encoding if needed
if self.positions == "flat":
flat = self.pos(flat)
# Add readout token
x = self.readout(flat)
# Apply encoder layers
for layer in self.layers:
x = layer(x)
return x
class GRTDecoder3D(nn.Module):
"""GRT 3D Transformer Decoder matching official implementation."""
def __init__(
self,
key: str = "map",
layers: int = 4,
dim: int = 512,
ff_ratio: float = 4.0,
head_dim: int = 64,
dropout: float = 0.1,
activation: str = "GELU",
shape: list[int] = [64, 128, 64],
pos_scale: list[float] = [1.0, 1.0, 1.0],
global_scale: float = 16.0,
patch: list[int] = [8, 8, 8],
out_dim: int = 0,
positions: Literal["flat", "nd"] = "nd",
mode: Literal["last", "pool"] = "last",
):
super().__init__()
self.key = key
self.out_dim = out_dim
self.mode = mode
# Decoder layers
self.layers = nn.ModuleList(
[
TransformerDecoder(
d_feedforward=int(ff_ratio * dim),
d_model=dim,
n_head=dim // head_dim,
dropout=dropout,
activation=activation,
)
for _ in range(layers)
]
)
# Query generation with position encoding
query_shape = [s // p for s, p in zip(shape, patch)]
if positions == "flat":
query_shape = [int(np.prod(query_shape))]
self.query = BasisChange(
shape=query_shape, scale=pos_scale, global_scale=global_scale, flatten=True
)
# Unpatch to reconstruct output
self.unpatch = Unpatch(
output_size=(*shape, max(1, self.out_dim)), features=dim, size=patch
)
def forward(self, encoded: torch.Tensor) -> dict[str, torch.Tensor]:
"""Forward pass."""
# Extract readout token or pool
if self.mode == "last":
x = encoded[:, -1, :]
else:
x = torch.mean(encoded, dim=1)
# Generate query with positional encoding
x = self.query(x)
# Encoded features without readout token
enc = encoded[:, :-1, :]
# Apply decoder layers
for layer in self.layers:
x = layer(x, enc)
# Unpatch to 3D output
out = self.unpatch(x)
# Squeeze channel dimension if binary output
if self.out_dim == 0:
out = out[..., 0]
return {self.key: out}
# ============================================================================
# Complete GRT-Small Model
# ============================================================================
class GRTSmall(nn.Module):
"""GRT-Small model for 3D occupancy mapping.
Input: (batch, doppler, azimuth, elevation, range, 2)
- doppler: 64
- azimuth: 8
- elevation: 2
- range: 256
- channels: 2 (I/Q)
Output: (batch, elevation, azimuth, range)
- elevation: 64
- azimuth: 128
- range: 64
~29M parameters for GRT-small variant.
"""
def __init__(self):
super().__init__()
dim = 512
layers = 4
# Create encoder - stored as "tokenizer" + "encoder" in checkpoint
# But we organize logically here and handle mapping in load_checkpoint
self.tokenizer = GRTEncoder(
layers=layers,
dim=dim,
ff_ratio=4.0,
head_dim=64,
dropout=0.1,
activation="GELU",
patch=[2, 8, 2, 4],
pos_scale=[1.0, 1.0, 1.0, 1.0],
global_scale=16.0,
input_channels=2,
positions="nd",
)
# Create decoder wrapper
self.decoder = nn.Module()
self.decoder.occ3d = GRTDecoder3D(
key="map",
layers=layers,
dim=dim,
ff_ratio=4.0,
head_dim=64,
dropout=0.1,
activation="GELU",
shape=[64, 128, 64],
pos_scale=[1.0, 1.0, 1.0],
global_scale=16.0,
patch=[8, 8, 8],
out_dim=0,
positions="nd",
mode="last",
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward pass."""
# Encode
encoded = self.tokenizer(x)
# Decode
output = self.decoder.occ3d(encoded)
# Return just the occupancy map tensor
return output["map"]
|