File size: 1,376 Bytes
dadf189 | 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 | from __future__ import annotations
import torch
import torch.nn as nn
class SIFQ(nn.Module):
"""Full SIFQ model wrapper."""
def __init__(
self,
backbone: nn.Module,
concept_head: nn.Module,
aggregator: nn.Module,
sensor_disc: nn.Module,
):
super().__init__()
self.backbone = backbone
self.concept_head = concept_head
self.aggregator = aggregator
self.sensor_disc = sensor_disc
def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
if getattr(self.concept_head, "uses_spatial", False):
# SpatialConceptHead: pass full 14×14 token map [B, N, D]
spatial = self.backbone.forward_spatial(x) # [B, N, D]
features = spatial.mean(dim=1) # [B, D] — for sensor_disc & L_mat
concepts = self.concept_head(spatial) # [B, k]
else:
# ConceptHead (legacy): pass globally-pooled vector [B, D]
features = self.backbone(x) # [B, D]
concepts = self.concept_head(features) # [B, k]
score = self.aggregator(concepts)
sensor_logits = self.sensor_disc(features)
return {
"score": score,
"concepts": concepts,
"sensor_logits": sensor_logits,
"features": features,
}
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