Add new regression and ensemble experiment configurations for V2-M and OrthoBridge
- Introduced multiple regression experiment configurations targeting vf_md, including: - cd_solo_reg_set.json: CD tower only regression setup. - img_solo_reg_set.json: Image tower only regression setup. - reg_head_epoch_sweep.json: Baseline regression sweeps at different epochs (50, 75, 100). - reg_head_set.json: Various regression setups including baseline and OrthoBridge configurations. - single_eye_reg.json: Single-eye regression setup for worst-eye aggregation analysis. - Added ensemble configurations for OrthoBridge with different inner bridges: - ortho_alts_ensemble.json: Ensemble tests with ConcatBridge, PairwiseAdditiveBridge, and GatedAdditiveBridge. - ortho_alts_tritower.json: Tritower tests with the same inner bridges. - Created V2-M specific configurations: - baseline_reg_nt50.json: Regression baseline with V2-M backbone. - geom_vec_gt.json and geom_vec_unet.json: Geometry vector injection experiments with V2-M. - single_l1_bridges.json: Single-eye ensemble experiments with various bridge types. - tritower_geom_gt.json: Tritower setup with GT contour-rasterized masks. - Promoted existing experiments to higher repetitions for robustness.
This commit is contained in:
rpotter6298
@@ -12,10 +12,11 @@ from torchvision import models, transforms
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_REPO_ROOT = Path(__file__).resolve().parents[3]
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REFUGELIKE_BACKBONE_PATH = _REPO_ROOT / "models/v2/refuge/refugelike_backbone.pt"
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REFUGE_DENSENET_PATH = _REPO_ROOT / "models/refuge/classifier/refuge_densenet_backbone.pt"
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REFUGE_EFFICIENT_B0_PATH = _REPO_ROOT / "models/refuge/classifier/refuge_efficient_b0_backbone.pt"
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REFUGE_EFFICIENT_B7_PATH = _REPO_ROOT / "models/refuge/classifier/refuge_efficient_b7_backbone.pt"
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REFUGELIKE_BACKBONE_PATH = _REPO_ROOT / "models/v2/refuge/refugelike_backbone.pt"
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REFUGE_DENSENET_PATH = _REPO_ROOT / "models/refuge/classifier/refuge_densenet_backbone.pt"
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REFUGE_EFFICIENTNET_B0_PATH = _REPO_ROOT / "models/refuge/classifier/refuge_efficientnet_b0_backbone.pt"
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REFUGE_EFFICIENTNET_B7_PATH = _REPO_ROOT / "models/refuge/classifier/refuge_efficientnet_b7_backbone.pt"
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REFUGE_EFFICIENTNET_V2_M_PATH = _REPO_ROOT / "models/refuge/classifier/refuge_efficientnet_v2_m_backbone.pt"
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@dataclass(frozen=True)
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@@ -109,6 +110,30 @@ BACKBONES: Dict[str, BackboneSpec] = {
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strip=_strip_efficientnet,
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blocks=_blocks_efficientnet,
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),
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"efficientnet_b7": BackboneSpec(
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ctor=models.efficientnet_b7,
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weights_default=models.EfficientNet_B7_Weights.DEFAULT,
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strip=_strip_efficientnet,
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blocks=_blocks_efficientnet,
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),
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"efficientnet_v2_s": BackboneSpec(
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ctor=models.efficientnet_v2_s,
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weights_default=models.EfficientNet_V2_S_Weights.DEFAULT,
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strip=_strip_efficientnet,
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blocks=_blocks_efficientnet,
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),
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"efficientnet_v2_m": BackboneSpec(
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ctor=models.efficientnet_v2_m,
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weights_default=models.EfficientNet_V2_M_Weights.DEFAULT,
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strip=_strip_efficientnet,
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blocks=_blocks_efficientnet,
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),
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"efficientnet_v2_l": BackboneSpec(
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ctor=models.efficientnet_v2_l,
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weights_default=models.EfficientNet_V2_L_Weights.DEFAULT,
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strip=_strip_efficientnet,
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blocks=_blocks_efficientnet,
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),
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"resnet18": BackboneSpec(
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ctor=models.resnet18,
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weights_default=models.ResNet18_Weights.DEFAULT,
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@@ -157,31 +182,94 @@ BACKBONES: Dict[str, BackboneSpec] = {
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strip=_strip_densenet,
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blocks=_blocks_densenet,
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),
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"refuge_efficient_b0": BackboneSpec(
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"refuge_efficientnet_b0": BackboneSpec(
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ctor=models.efficientnet_b0,
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weights_default=None,
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strip=_strip_efficientnet,
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blocks=_blocks_efficientnet,
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),
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"refuge_efficient_b7": BackboneSpec(
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"refuge_efficientnet_b7": BackboneSpec(
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ctor=models.efficientnet_b7,
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weights_default=None,
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strip=_strip_efficientnet,
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blocks=_blocks_efficientnet,
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),
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"refuge_efficientnet_v2_m": BackboneSpec(
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ctor=models.efficientnet_v2_m,
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weights_default=None,
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strip=_strip_efficientnet,
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blocks=_blocks_efficientnet,
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),
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}
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_TIMM_CONVNEXTV2_VARIANTS = (
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"convnextv2_atto",
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"convnextv2_femto",
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"convnextv2_pico",
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"convnextv2_nano",
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"convnextv2_tiny",
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"convnextv2_base",
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"convnextv2_large",
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"convnextv2_huge",
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)
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def _is_timm_backbone(name: str) -> bool:
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return name in _TIMM_CONVNEXTV2_VARIANTS
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def _build_timm_backbone(name: str, freeze_ratio: float) -> tuple[nn.Module, int, list]:
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"""Build a timm-sourced backbone (ConvNeXt-V2 family).
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Uses num_classes=0 + global_pool="avg" so the model returns a pooled
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(B, num_features) tensor directly — no head to strip. Freezable blocks
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are [stem, stage_0, stage_1, stage_2, stage_3].
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"""
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try:
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import timm
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except ImportError as e:
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raise ImportError(
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f"backbone {name!r} requires the `timm` package "
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"(pip install timm)"
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) from e
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m = timm.create_model(name, pretrained=True, num_classes=0, global_pool="avg")
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out_dim = int(getattr(m, "num_features", 0))
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if not out_dim:
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raise RuntimeError(f"timm model {name!r} did not expose num_features")
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blocks: list[nn.Module] = []
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if hasattr(m, "stem"):
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blocks.append(m.stem)
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if hasattr(m, "stages"):
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blocks.extend(list(m.stages))
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if not blocks:
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raise RuntimeError(
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f"timm model {name!r} did not expose .stem / .stages — "
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"extend _build_timm_backbone to support this architecture."
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)
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fr = max(0.0, min(1.0, float(freeze_ratio)))
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n_freeze = int(math.floor(len(blocks) * fr))
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for b in blocks[:n_freeze]:
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for p in b.parameters():
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p.requires_grad = False
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return m, out_dim, blocks
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def list_names() -> List[str]:
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return list(BACKBONES.keys())
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return list(BACKBONES.keys()) + list(_TIMM_CONVNEXTV2_VARIANTS)
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def load_backbone_weights(key: str, model: nn.Module) -> None:
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paths = {
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"refugelike": REFUGELIKE_BACKBONE_PATH,
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"refuge_densenet": REFUGE_DENSENET_PATH,
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"refuge_efficient_b0": REFUGE_EFFICIENT_B0_PATH,
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"refuge_efficient_b7": REFUGE_EFFICIENT_B7_PATH,
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"refugelike": REFUGELIKE_BACKBONE_PATH,
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"refuge_densenet": REFUGE_DENSENET_PATH,
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"refuge_efficientnet_b0": REFUGE_EFFICIENTNET_B0_PATH,
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"refuge_efficientnet_b7": REFUGE_EFFICIENTNET_B7_PATH,
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"refuge_efficientnet_v2_m": REFUGE_EFFICIENTNET_V2_M_PATH,
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}
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path = paths.get(key)
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if path is None:
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@@ -202,6 +290,8 @@ def build_backbone(name: str, freeze_ratio: float = 0.0) -> tuple[nn.Module, int
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freezable units — callers use it to dynamically adjust freeze_ratio later.
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"""
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key = (name or "").lower()
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if _is_timm_backbone(key):
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return _build_timm_backbone(key, freeze_ratio)
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if key not in BACKBONES:
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raise ValueError(f"Unknown backbone '{name}'. Available: {list_names()}")
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