rpotter6298
84 lines
3.2 KiB
Python
84 lines
3.2 KiB
Python
"""image_tower — ImageEncoder for v4.
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Self-contained: no v3 dependencies.
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Inherits get_sample dispatch from TowerBase.
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"""
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from __future__ import annotations
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import math
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import torch
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from torch import nn
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from v4.classes.towerbase import TowerBase
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from v4.classes.accessory.backbones import build_backbone
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from v4.classes.accessory.se_block import SEBlock
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from v4.classes.accessory.transforms import build_backbone_transform, build_eval_transform
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class ImageEncoder(TowerBase):
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"""Vision backbone → pooled feature vector.
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image_data : ImageDataView — provides load_image(*ids) and side_map
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backbone : backbone key (see accessory/backbones.py)
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freeze_ratio : fraction of early blocks to freeze in [0, 1]
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use_se : apply SE attention over the pooled feature vector
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augment : include random flip/rotation/jitter in the train transform
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"""
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def __init__(
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self,
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image_data,
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backbone: str = "efficientnet_b0",
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freeze_ratio: float = 0.0,
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use_se: bool = False,
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se_reduction: int = 16,
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se_pre_norm: bool = True,
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augment: bool = True,
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):
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super().__init__()
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self.image_data = image_data
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self._name = backbone
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self.backbone, self._base_dim, self._blocks = build_backbone(backbone, freeze_ratio)
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self.transform = build_backbone_transform(backbone, augment=augment)
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self.eval_transform = build_eval_transform(backbone)
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self.tower_ln = nn.LayerNorm(self._base_dim) if se_pre_norm else nn.Identity()
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self.tower_se = SEBlock(self._base_dim, reduction=se_reduction, residual=True) if use_se else None
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# ── TowerBase interface ──────────────────────────────────────────────────
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@property
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def out_dim(self) -> int:
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return self._base_dim
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@property
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def _side_map(self) -> dict[str, str]:
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return self.image_data.side_map
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def _get(self, *ids) -> torch.Tensor:
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img = self.image_data.load_image(*ids)
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t = self.transform if self.training else self.eval_transform
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return t(img)
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# ── nn.Module forward ────────────────────────────────────────────────────
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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y = self.backbone(x)
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if self.tower_se is not None:
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y, _ = self.tower_se(self.tower_ln(y))
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return y
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# ── Utilities ────────────────────────────────────────────────────────────
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def set_freeze_ratio(self, ratio: float) -> None:
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"""Dynamically freeze the earliest floor(N * ratio) backbone blocks."""
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r = max(0.0, min(1.0, float(ratio)))
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n_freeze = int(math.floor(len(self._blocks) * r))
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for b in self._blocks:
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for p in b.parameters():
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p.requires_grad = True
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for b in self._blocks[:n_freeze]:
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for p in b.parameters():
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p.requires_grad = False
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