rpotter6298
512ebd13b2
- Introduced `protocol.py` for shared data models used in server/client communication, including request and response schemas for registration, job submission, and status updates. - Implemented `server.py` to manage a SQLite job queue and client registry, handling job polling, status updates, and job completion. - Created a cheat sheet for server usage, detailing commands for starting the server, submitting jobs, and monitoring clients. - Added several experiment configuration files for various training setups, including geometry vector injections and baseline ensembles.
135 lines
5.1 KiB
Python
135 lines
5.1 KiB
Python
"""clinical_tower — ClinicalEncoder 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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Geometry injection (EPC consumption)
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--------------------------------------
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When geom_dim > 0, ClinicalEncoder requests the "geometry_vectors" key from EPC
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during early_pass and appends the geometry features to every clinical vector.
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The input layer is sized to clinical_data.feature_dim + geom_dim automatically.
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Config example (cd tower consuming geometry):
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{
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"name": "cd",
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"module": "v4.classes.towers.clinical_tower",
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"class": "ClinicalEncoder",
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"data_source": "matrix",
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"epc_requests": ["geometry_vectors"],
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"args": {
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"hidden_dim": 128,
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"geom_dim": 5
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}
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}
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"""
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from __future__ import annotations
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import numpy as np
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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.se_block import SEBlock
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class ClinicalEncoder(TowerBase):
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"""MLP over tabular clinical features, with optional geometry vector injection.
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clinical_data : ClinicalDataView — provides feature_dim, vectorize_entity, side_map
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hidden_dim : output embedding dimensionality
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dropout : applied after the first linear block
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use_se : wrap output with SEBlock channel gating
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se_reduction : SEBlock bottleneck factor
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se_pre_norm : apply LayerNorm before SEBlock
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geom_dim : number of geometry features to append from EPC (0 = disabled)
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requires epc_requests: ["geometry_vectors"] in tower config
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"""
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EPC_GEOMETRY_KEY = "geometry_vectors"
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def __init__(
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self,
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clinical_data,
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hidden_dim: int = 128,
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dropout: float = 0.1,
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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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geom_dim: int = 0,
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):
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super().__init__()
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self.clinical_data = clinical_data
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self._out_dim = hidden_dim
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self._geom_dim = geom_dim
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self._geom_vectors: dict | None = None # filled by early_pass when geom_dim > 0
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feature_dim = clinical_data.feature_dim + geom_dim
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self.block0 = nn.Sequential(
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nn.Linear(feature_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.ReLU(inplace=True),
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nn.Dropout(dropout),
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)
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self.block1 = nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim),
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nn.ReLU(inplace=True),
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)
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self.net = nn.Sequential(self.block0, self.block1)
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self.tower_ln = nn.LayerNorm(hidden_dim) if se_pre_norm else nn.Identity()
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self.tower_se = SEBlock(hidden_dim, reduction=se_reduction, residual=True) if use_se else None
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# ── EPC early_pass ───────────────────────────────────────────────────────
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def early_pass(self, context) -> None:
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if self._geom_dim > 0:
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self._geom_vectors = context.require(self.EPC_GEOMETRY_KEY)
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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._out_dim
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@property
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def _side_map(self) -> dict[str, str]:
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return self.clinical_data.side_map
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def _get(self, *ids) -> torch.Tensor:
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arr = self.clinical_data.vectorize_entity(*ids)
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if self._geom_dim > 0 and self._geom_vectors is not None:
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pid = int(ids[0])
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eye = str(ids[1]) if len(ids) > 1 else "OD"
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geom = self._geom_vectors.get(
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(pid, eye),
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np.zeros(self._geom_dim, dtype=np.float32),
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)
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arr = np.concatenate([arr, geom[: self._geom_dim]])
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return torch.from_numpy(arr.astype(np.float32, copy=False))
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# ── nn.Module forward ────────────────────────────────────────────────────
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def forward(self, x) -> torch.Tensor:
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if not isinstance(x, torch.Tensor):
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x = torch.as_tensor(x, dtype=torch.float32)
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h = self.net(x)
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if self.tower_se is not None:
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h, _ = self.tower_se(self.tower_ln(h))
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return h
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# ── Utilities ────────────────────────────────────────────────────────────
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def set_freeze_ratio(self, ratio: float) -> None:
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"""Freeze the earliest MLP block proportionally."""
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r = max(0.0, min(1.0, float(ratio)))
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for p in self.block0.parameters():
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p.requires_grad = True
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for p in self.block1.parameters():
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p.requires_grad = True
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if r >= 0.5:
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for p in self.block0.parameters():
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p.requires_grad = False
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if r >= 1.0:
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for p in self.block1.parameters():
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p.requires_grad = False
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