rpotter6298
62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
"""htfusion — HTFusion: N named towers fused through a FusionBridge."""
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from __future__ import annotations
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import torch
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import torch.nn as nn
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from v4.classes.bridges.fusion_bridge import FusionBridge
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from v4.classes.vehicles.htbase import HTBase
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class HTFusion(HTBase):
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"""General N-tower fusion vehicle.
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Each named encoder is registered as a submodule; the FusionBridge
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projects and Hadamard-fuses their embeddings.
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Parameters
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----------
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towers : ordered dict ``{name: encoder}``. Each encoder must
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expose ``.out_dim``.
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num_classes : output classes
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fusion_dim : bridge projection dimensionality
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dropout : bridge dropout
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use_se : SE gate on the fused vector
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Forward contract
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----------------
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``forward(embeddings)`` takes a ``dict[str, Tensor]`` of pre-computed
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per-tower embeddings and returns ``(logits_fused, aux_dict)`` where
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``aux_dict`` maps each tower name to its auxiliary head logits.
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"""
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def __init__(
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self,
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towers: dict[str, nn.Module],
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num_classes: int,
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fusion_dim: int = 256,
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dropout: float = 0.5,
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use_se: bool = False,
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):
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super().__init__()
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self.towers = nn.ModuleDict(towers)
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self.bridge = FusionBridge(
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tower_dims=[t.out_dim for t in self.towers.values()],
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num_classes=num_classes,
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fusion_dim=fusion_dim,
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dropout=dropout,
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use_se=use_se,
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)
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def encode(self, embeddings: dict[str, torch.Tensor]) -> torch.Tensor:
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"""Return z_fused (pre-classifier) from a dict of per-tower embeddings."""
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return self.bridge.encode([embeddings[name] for name in self.towers])
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def forward(
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self,
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embeddings: dict[str, torch.Tensor],
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) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
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ordered = [embeddings[name] for name in self.towers]
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logits, aux = self.bridge.fuse(ordered)
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return logits, {name: aux[i] for i, name in enumerate(self.towers)}
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