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Perceiver-based transformer for 3D roof wireframe prediction.
Architecture overview:
Input tokens [B, T, D]
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input_proj: Linear -> GELU -> Linear -> LayerNorm => [B, T, hidden]
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Perceiver latent bottleneck (N PerceiverLatentLayers):
Learnable latent embeddings [L, hidden] are broadcast to batch.
Each layer: cross-attn(latents <- tokens) -> self-attn(latents) -> FFN
Output: latents [B, L, hidden]
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Segment decoder (M SegmentDecoderLayers):
Learnable query embeddings [S, hidden] are broadcast to batch.
Each layer: cross-attn(queries <- latents) -> self-attn(queries) -> FFN
Output: queries [B, S, hidden]
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segment_head: Linear -> 6D -> (midpoint, half_vector)
+ query_offsets (learnable per-query bias)
endpoints = midpoint +/- half_vector -> [B, S, 2, 3]
"""
import torch
import torch.nn as nn
from .attention import MultiHeadSDPA, FeedForward
# ---------------------------------------------------------------------------
# Building blocks
# ---------------------------------------------------------------------------
class AttnResidual(nn.Module):
"""Pre-norm attention + residual + dropout."""
def __init__(
self,
d_model: int,
num_heads: int,
dropout: float = 0.0,
kv_heads: int | None = None,
norm_class=None,
qk_norm: bool = False,
qk_norm_type: str = "l2",
):
super().__init__()
norm_class = norm_class or nn.LayerNorm
self.norm = norm_class(d_model)
self.attn = MultiHeadSDPA(d_model, num_heads, kv_heads=kv_heads, qk_norm=qk_norm, qk_norm_type=qk_norm_type)
self.drop = nn.Dropout(dropout)
def forward(
self,
x: torch.Tensor,
memory: torch.Tensor,
memory_key_padding_mask: torch.Tensor | None = None,
) -> torch.Tensor:
res = x
x = self.norm(x)
x = self.attn(x, memory, key_padding_mask=memory_key_padding_mask)
return res + self.drop(x)
class FFNResidual(nn.Module):
"""Pre-norm feed-forward + residual + dropout."""
def __init__(
self,
d_model: int,
dim_ff: int,
dropout: float = 0.0,
activation: str = "gelu",
norm_class=None,
):
super().__init__()
norm_class = norm_class or nn.LayerNorm
self.norm = norm_class(d_model)
self.ffn = FeedForward(d_model, dim_ff, activation=activation)
self.drop = nn.Dropout(dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
res = x
x = self.norm(x)
x = self.ffn(x)
return res + self.drop(x)
# ---------------------------------------------------------------------------
# Perceiver encoder layer
# ---------------------------------------------------------------------------
class PerceiverLatentLayer(nn.Module):
"""Single Perceiver latent layer.
If use_cross=True: cross-attn(latents <- points) -> self-attn -> FFN
If use_cross=False: self-attn -> FFN (saves compute in deep stacks)
"""
def __init__(
self,
d_model: int,
num_heads: int,
dim_ff: int,
dropout: float = 0.0,
activation: str = "gelu",
kv_heads_cross: int | None = None,
kv_heads_self: int | None = None,
use_cross: bool = True,
norm_class=None,
qk_norm: bool = False,
qk_norm_type: str = "l2",
):
super().__init__()
self.use_cross = use_cross
if use_cross:
self.cross = AttnResidual(d_model, num_heads, dropout, kv_heads=kv_heads_cross, norm_class=norm_class, qk_norm=qk_norm, qk_norm_type=qk_norm_type)
self.self_attn = AttnResidual(d_model, num_heads, dropout, kv_heads=kv_heads_self, norm_class=norm_class, qk_norm=qk_norm, qk_norm_type=qk_norm_type)
self.ffn = FFNResidual(d_model, dim_ff, dropout, activation=activation, norm_class=norm_class)
def forward(
self,
latents: torch.Tensor,
points: torch.Tensor,
points_key_padding_mask: torch.Tensor | None = None,
) -> torch.Tensor:
if self.use_cross:
latents = self.cross(latents, points, memory_key_padding_mask=points_key_padding_mask)
latents = self.self_attn(latents, latents)
latents = self.ffn(latents)
return latents
# ---------------------------------------------------------------------------
# Segment decoder layer
# ---------------------------------------------------------------------------
class SegmentDecoderLayer(nn.Module):
"""Single segment decoder layer.
cross-attn(queries <- latents) -> [cross-attn(queries <- inputs)] -> self-attn(queries) -> FFN
If input_xattn=True, adds a second cross-attention that attends directly
to the projected input tokens (bypassing the latent bottleneck). This gives
queries access to fine-grained point-level detail for vertex precision.
"""
def __init__(
self,
d_model: int,
num_heads: int,
dim_ff: int,
dropout: float = 0.0,
activation: str = "gelu",
kv_heads_cross: int | None = None,
kv_heads_self: int | None = None,
norm_class=None,
input_xattn: bool = False,
qk_norm: bool = False,
qk_norm_type: str = "l2",
):
super().__init__()
self.cross = AttnResidual(d_model, num_heads, dropout, kv_heads=kv_heads_cross, norm_class=norm_class, qk_norm=qk_norm, qk_norm_type=qk_norm_type)
self.input_xattn = input_xattn
if input_xattn:
self.cross_input = AttnResidual(d_model, num_heads, dropout, kv_heads=kv_heads_cross, norm_class=norm_class, qk_norm=qk_norm, qk_norm_type=qk_norm_type)
self.self_attn = AttnResidual(d_model, num_heads, dropout, kv_heads=kv_heads_self, norm_class=norm_class, qk_norm=qk_norm, qk_norm_type=qk_norm_type)
self.ffn = FFNResidual(d_model, dim_ff, dropout, activation=activation, norm_class=norm_class)
def forward(
self,
queries: torch.Tensor,
latents: torch.Tensor,
src: torch.Tensor | None = None,
src_key_padding_mask: torch.Tensor | None = None,
) -> torch.Tensor:
queries = self.cross(queries, latents)
if self.input_xattn and src is not None:
queries = self.cross_input(queries, src, memory_key_padding_mask=src_key_padding_mask)
queries = self.self_attn(queries, queries)
queries = self.ffn(queries)
return queries
# ---------------------------------------------------------------------------
# Full model
# ---------------------------------------------------------------------------
class TokenTransformerSegments(nn.Module):
"""Perceiver transformer that predicts 3D roof wireframe segments.
Takes point-cloud tokens and outputs segment endpoints as [B, S, 2, 3]
where S is the number of segments and each segment has two 3D endpoints.
Args:
segments: Number of predicted segments (S).
in_dim: Dimensionality of input tokens.
hidden: Internal hidden dimension throughout the model.
num_heads: Number of attention heads.
kv_heads_cross: Grouped-query heads for cross-attention (None = standard MHA).
kv_heads_self: Grouped-query heads for self-attention (None = standard MHA).
dim_feedforward: FFN intermediate dimension.
dropout: Dropout rate applied after attention and FFN.
latent_tokens: Number of learnable latent embeddings (L) in the bottleneck.
latent_layers: Number of PerceiverLatentLayers (N).
decoder_layers: Number of SegmentDecoderLayers (M).
"""
def __init__(
self,
segments: int = 32,
in_dim: int = 128,
hidden: int = 128,
num_heads: int = 4,
kv_heads_cross: int | None = 2,
kv_heads_self: int | None = 0,
dim_feedforward: int = 256,
dropout: float = 0.01,
latent_tokens: int = 64,
latent_layers: int = 2,
decoder_layers: int = 2,
cross_attn_interval: int = 1,
norm_class=None,
activation: str = "gelu",
segment_conf: bool = False,
pre_encoder_layers: int = 0,
segment_param: str = "midpoint_halfvec",
length_floor: float = 0.0,
decoder_input_xattn: bool = False,
qk_norm: bool = False,
qk_norm_type: str = "l2",
):
super().__init__()
self.segments = segments
self.out_vertices = segments * 2
self.segment_param = segment_param
self.decoder_input_xattn = decoder_input_xattn
norm_class = norm_class or nn.LayerNorm
# Treat 0 as "use standard MHA"
if kv_heads_cross is not None and kv_heads_cross <= 0:
kv_heads_cross = None
if kv_heads_self is not None and kv_heads_self <= 0:
kv_heads_self = None
# -- Input projection --
self.input_proj = nn.Sequential(
nn.Linear(in_dim, dim_feedforward),
nn.GELU(),
nn.Linear(dim_feedforward, hidden),
norm_class(hidden),
)
# -- Optional pre-encoder: self-attention on full token sequence --
if pre_encoder_layers > 0:
self.pre_encoder = nn.ModuleList([
SelfAttentionEncoderLayer(
d_model=hidden,
num_heads=num_heads,
dim_ff=dim_feedforward,
dropout=dropout,
activation=activation,
kv_heads=kv_heads_self,
norm_class=norm_class,
qk_norm=qk_norm, qk_norm_type=qk_norm_type,
)
for _ in range(pre_encoder_layers)
])
else:
self.pre_encoder = None
# -- Perceiver latent bottleneck --
self.latent_embed = nn.Embedding(latent_tokens, hidden)
N = latent_layers
self.latent_layers = nn.ModuleList([
PerceiverLatentLayer(
d_model=hidden,
num_heads=num_heads,
dim_ff=dim_feedforward,
dropout=dropout,
activation=activation,
kv_heads_cross=kv_heads_cross,
kv_heads_self=kv_heads_self,
use_cross=(i == 0) or (i == N - 1) or (i % cross_attn_interval == 0),
norm_class=norm_class,
qk_norm=qk_norm, qk_norm_type=qk_norm_type,
)
for i in range(N)
])
# -- Segment decoder --
self.query_embed = nn.Embedding(segments, hidden)
self.decoder_layers = nn.ModuleList([
SegmentDecoderLayer(
d_model=hidden,
num_heads=num_heads,
dim_ff=dim_feedforward,
dropout=dropout,
activation=activation,
kv_heads_cross=kv_heads_cross,
kv_heads_self=kv_heads_self,
norm_class=norm_class,
input_xattn=decoder_input_xattn,
qk_norm=qk_norm, qk_norm_type=qk_norm_type,
)
for _ in range(decoder_layers)
])
# -- Output head --
if segment_param == "midpoint_dir_len":
self.segment_head = nn.Linear(hidden, 7) # mid(3) + dir(3) + len(1)
else:
self.segment_head = nn.Linear(hidden, 6) # mid(3) + half(3)
self.query_offsets = nn.Parameter(torch.zeros(segments, 2, 3))
nn.init.trunc_normal_(self.segment_head.weight, mean=0.0, std=1e-3)
if self.segment_head.bias is not None:
nn.init.zeros_(self.segment_head.bias)
if segment_param == "midpoint_dir_len":
# softplus(0.5) * 0.1 ≈ 0.097 default length in normalized space
self.segment_head.bias.data[6] = 0.5
nn.init.normal_(self.query_offsets, mean=0.0, std=0.05)
# -- Optional confidence head --
self.segment_conf = segment_conf
if segment_conf:
self.conf_head = nn.Linear(hidden, 1)
nn.init.zeros_(self.conf_head.bias)
def forward(
self,
tokens: torch.Tensor,
mask: torch.Tensor | None = None,
) -> dict[str, torch.Tensor | list]:
"""
Args:
tokens: Input point-cloud tokens [B, T, in_dim].
mask: Boolean validity mask [B, T]. True = valid token.
Returns:
Dict with keys:
"vertices": [B, S*2, 3] flattened endpoints.
"segments": [B, S, 2, 3] segment endpoints.
"edges": Per-batch list of (start, end) index pairs into vertices.
"conf": [B, S] logits (only if segment_conf=True).
"""
B = tokens.shape[0]
# Project input tokens
src = self.input_proj(tokens) # [B, T, hidden]
# Padding mask (True where padded) for cross-attention
pad_mask = ~mask.bool() if mask is not None else None
# Optional pre-encoder: self-attention on full token sequence
if self.pre_encoder is not None:
for layer in self.pre_encoder:
src = layer(src, key_padding_mask=pad_mask)
# Perceiver latent bottleneck
latents = self.latent_embed.weight.unsqueeze(0).expand(B, -1, -1)
for layer in self.latent_layers:
latents = layer(latents, src, points_key_padding_mask=pad_mask)
# Segment decoder
queries = self.query_embed.weight.unsqueeze(0).expand(B, -1, -1)
for layer in self.decoder_layers:
queries = layer(queries, latents,
src=src if self.decoder_input_xattn else None,
src_key_padding_mask=pad_mask if self.decoder_input_xattn else None)
# Predict segments -> endpoints
if self.segment_param == "midpoint_dir_len":
raw = self.segment_head(queries) # [B, S, 7]
mid = raw[:, :, :3] + self.query_offsets[:, 0, :].unsqueeze(0)
direction = torch.nn.functional.normalize(raw[:, :, 3:6], dim=-1)
length = torch.nn.functional.softplus(raw[:, :, 6:7]) * 0.1
half = direction * length * 0.5
else:
raw = self.segment_head(queries).view(B, self.segments, 2, 3)
raw = raw + self.query_offsets.unsqueeze(0)
mid, half = raw[:, :, 0], raw[:, :, 1]
seg_params = torch.stack([mid - half, mid + half], dim=2)
vertices = seg_params.reshape(B, self.out_vertices, 3)
edges = [[(2 * i, 2 * i + 1) for i in range(self.segments)] for _ in range(B)]
out = {"vertices": vertices, "segments": seg_params, "edges": edges,
"src": src, "pad_mask": pad_mask, "queries": queries}
if self.segment_conf:
out["conf"] = self.conf_head(queries).squeeze(-1) # [B, S]
return out
# ---------------------------------------------------------------------------
# Encoder-only layer (self-attention on full token sequence)
# ---------------------------------------------------------------------------
class SelfAttentionEncoderLayer(nn.Module):
"""Single self-attention layer: self-attn(tokens) -> FFN."""
def __init__(
self,
d_model: int,
num_heads: int,
dim_ff: int,
dropout: float = 0.0,
activation: str = "gelu",
kv_heads: int | None = None,
norm_class=None,
qk_norm: bool = False,
qk_norm_type: str = "l2",
):
super().__init__()
self.self_attn = AttnResidual(d_model, num_heads, dropout, kv_heads=kv_heads, norm_class=norm_class, qk_norm=qk_norm, qk_norm_type=qk_norm_type)
self.ffn = FFNResidual(d_model, dim_ff, dropout, activation=activation, norm_class=norm_class)
def forward(self, x: torch.Tensor, key_padding_mask: torch.Tensor | None = None) -> torch.Tensor:
x = self.self_attn(x, x, memory_key_padding_mask=key_padding_mask)
x = self.ffn(x)
return x
# ---------------------------------------------------------------------------
# End-to-end model: tokenizer embeddings + perceiver
# ---------------------------------------------------------------------------
class EdgeDepthSegmentsModel(nn.Module):
"""Tokenizer embeddings + transformer for 3D roof wireframes.
Supports two architectures via the `arch` parameter:
- "perceiver": Perceiver latent bottleneck (default, O(L*T) attention)
- "transformer": Standard self-attention encoder (O(T^2) attention)
Both share the same decoder, output head, and tokenizer.
"""
def __init__(
self,
seq_cfg,
segments: int = 32,
hidden: int = 128,
num_heads: int = 4,
kv_heads_cross: int | None = 2,
kv_heads_self: int | None = 0,
dim_feedforward: int = 256,
dropout: float = 0.1,
latent_tokens: int = 64,
latent_layers: int = 1,
decoder_layers: int = 2,
label_emb_dim: int = 16,
src_emb_dim: int = 2,
behind_emb_dim: int = 8,
fourier_seed: int = 0,
cross_attn_interval: int = 1,
norm_class=None,
activation: str = "gelu",
segment_conf: bool = False,
use_vote_features: bool = False,
arch: str = "perceiver",
encoder_layers: int = 4,
pre_encoder_layers: int = 0,
segment_param: str = "midpoint_halfvec",
length_floor: float = 0.0,
decoder_input_xattn: bool = False,
qk_norm: bool = False,
qk_norm_type: str = "l2",
learnable_fourier: bool = False,
):
super().__init__()
self.seq_cfg = seq_cfg
from .tokenizer import EdgeDepthSequenceBuilder
self.tokenizer = EdgeDepthSequenceBuilder(
seq_cfg,
label_emb_dim=label_emb_dim,
src_emb_dim=src_emb_dim,
behind_emb_dim=behind_emb_dim,
fourier_seed=fourier_seed,
use_vote_features=use_vote_features,
learnable_fourier=learnable_fourier,
)
if arch == "transformer":
raise ValueError(
"arch='transformer' is no longer supported. "
"TransformerSegments has been removed; use arch='perceiver'.")
else:
self.segmenter = TokenTransformerSegments(
segments=segments,
in_dim=self.tokenizer.out_dim,
hidden=hidden,
num_heads=num_heads,
kv_heads_cross=kv_heads_cross,
kv_heads_self=kv_heads_self,
dim_feedforward=dim_feedforward,
dropout=dropout,
latent_tokens=latent_tokens,
latent_layers=latent_layers,
decoder_layers=decoder_layers,
cross_attn_interval=cross_attn_interval,
norm_class=norm_class,
activation=activation,
segment_conf=segment_conf,
pre_encoder_layers=pre_encoder_layers,
segment_param=segment_param,
length_floor=length_floor,
decoder_input_xattn=decoder_input_xattn,
qk_norm=qk_norm, qk_norm_type=qk_norm_type,
)
def forward_tokens(self, tokens: torch.Tensor, mask: torch.Tensor):
"""Run the segmenter on pre-built token tensors."""
return self.segmenter(tokens, mask)
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