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fe5a903 | 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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | """
Classifier head for protein localization from precomputed embeddings.
"""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any, Dict, List, Mapping, Sequence
import torch
from torch import Tensor, nn
ROOT = Path(__file__).resolve().parent.parent.parent
DEFAULT_LABEL_COLUMNS_JSON = ROOT / "data" / "processed" / "embeddings" / "esm2_t33_650M" / "label_columns.json"
FALLBACK_LABEL_NAMES: List[str] = [
"Membrane",
"Cytoplasm",
"Nucleus",
"Extracellular",
"Cell membrane",
"Mitochondrion",
"Plastid",
"Endoplasmic reticulum",
"Lysosome/Vacuole",
"Golgi apparatus",
"Peroxisome",
]
def _load_label_names_from_json(path: Path) -> List[str] | None:
if not path.is_file():
return None
with path.open("r", encoding="utf-8") as f:
payload: Any = json.load(f)
if isinstance(payload, dict) and isinstance(payload.get("label_columns"), list):
names = [str(x) for x in payload["label_columns"]]
if names:
return names
return None
class ProteinLocalizationClassifier(nn.Module):
def __init__(
self,
embedding_dim: int,
num_labels: int | None = None,
dropout_rates: Sequence[float] = (0.3, 0.3, 0.2),
hidden_dims: Sequence[int] = (512, 256, 128),
label_names: Sequence[str] | None = None,
label_columns_path: str | Path | None = None,
) -> None:
super().__init__()
if len(dropout_rates) != 3:
raise ValueError(f"Expected 3 dropout rates, got {len(dropout_rates)}")
if len(hidden_dims) != 3:
raise ValueError(f"Expected 3 hidden dims, got {len(hidden_dims)}")
if embedding_dim <= 0:
raise ValueError("embedding_dim must be > 0")
if label_names is None:
if label_columns_path is None:
label_columns_file = DEFAULT_LABEL_COLUMNS_JSON
else:
label_columns_file = Path(label_columns_path).expanduser().resolve()
resolved = _load_label_names_from_json(label_columns_file)
label_names = resolved if resolved is not None else FALLBACK_LABEL_NAMES
inferred_num_labels = len(label_names)
if num_labels is None:
self.num_labels = inferred_num_labels
else:
if num_labels <= 0:
raise ValueError("num_labels must be > 0")
self.num_labels = int(num_labels)
if self.num_labels != inferred_num_labels:
raise ValueError(
f"num_labels={self.num_labels} must match len(label_names)={inferred_num_labels}"
)
self.label_names = list(label_names)
h1, h2, h3 = [int(h) for h in hidden_dims]
d1, d2, d3 = [float(d) for d in dropout_rates]
self.net = nn.Sequential(
nn.Linear(embedding_dim, h1),
nn.BatchNorm1d(h1),
nn.ReLU(inplace=True),
nn.Dropout(d1),
nn.Linear(h1, h2),
nn.BatchNorm1d(h2),
nn.ReLU(inplace=True),
nn.Dropout(d2),
nn.Linear(h2, h3),
nn.BatchNorm1d(h3),
nn.ReLU(inplace=True),
nn.Dropout(d3),
nn.Linear(h3, self.num_labels),
)
self._init_weights()
def _init_weights(self) -> None:
for module in self.modules():
if isinstance(module, nn.Linear):
nn.init.kaiming_normal_(module.weight, mode="fan_in", nonlinearity="relu")
if module.bias is not None:
nn.init.zeros_(module.bias)
def forward(self, x: Tensor) -> Tensor:
# No sigmoid here; use BCEWithLogitsLoss during training.
return self.net(x)
def _ensure_batch(self, embedding: Tensor) -> tuple[Tensor, bool]:
if embedding.dim() == 1:
return embedding.unsqueeze(0), True
if embedding.dim() == 2:
return embedding, False
raise ValueError(f"Expected tensor with dim 1 or 2, got shape {tuple(embedding.shape)}")
def predict_proba(self, embedding: Tensor) -> Dict[str, float] | List[Dict[str, float]]:
was_training = self.training
self.eval()
with torch.no_grad():
x, single = self._ensure_batch(embedding)
probs = torch.sigmoid(self.forward(x))
probs_cpu = probs.detach().cpu().tolist()
if was_training:
self.train()
output = [
{name: float(row[i]) for i, name in enumerate(self.label_names)}
for row in probs_cpu
]
return output[0] if single else output
def predict(
self,
embedding: Tensor,
thresholds: Dict[str, float] | Tensor | None = None,
) -> Dict[str, int] | List[Dict[str, int]]:
was_training = self.training
self.eval()
with torch.no_grad():
x, single = self._ensure_batch(embedding)
probs = torch.sigmoid(self.forward(x))
if thresholds is None:
th = torch.full((self.num_labels,), 0.5, dtype=probs.dtype, device=probs.device)
elif isinstance(thresholds, dict):
th_vals = [float(thresholds.get(name, 0.5)) for name in self.label_names]
th = torch.tensor(th_vals, dtype=probs.dtype, device=probs.device)
elif isinstance(thresholds, Tensor):
if thresholds.numel() != self.num_labels:
raise ValueError(
f"threshold tensor must have {self.num_labels} values, got {thresholds.numel()}"
)
th = thresholds.to(device=probs.device, dtype=probs.dtype).reshape(-1)
else:
raise TypeError("thresholds must be None, dict, or torch.Tensor")
binary = (probs >= th.unsqueeze(0)).to(torch.int64).detach().cpu().tolist()
if was_training:
self.train()
output = [
{name: int(row[i]) for i, name in enumerate(self.label_names)}
for row in binary
]
return output[0] if single else output
def count_parameters(model: nn.Module) -> None:
total = sum(p.numel() for p in model.parameters())
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Total parameters: {total:,}")
print(f"Trainable parameters: {trainable:,}")
def load_model(
path: str | Path,
embedding_dim: int,
num_labels: int | None,
device: torch.device | str,
) -> ProteinLocalizationClassifier:
device = torch.device(device)
ckpt_path = Path(path).expanduser().resolve()
checkpoint = torch.load(ckpt_path, map_location=device)
label_names: Sequence[str] | None = None
if isinstance(checkpoint, dict) and "label_names" in checkpoint:
label_names = checkpoint["label_names"]
if isinstance(checkpoint, dict) and "state_dict" in checkpoint:
state_dict = checkpoint["state_dict"]
elif isinstance(checkpoint, Mapping):
state_dict = checkpoint
else:
raise ValueError("Unsupported checkpoint format: expected dict or dict with 'state_dict'.")
if num_labels is None:
if label_names is not None:
num_labels = len(label_names)
else:
classifier_weight = state_dict.get("net.12.weight")
if classifier_weight is None:
raise ValueError("Could not infer num_labels from checkpoint; pass num_labels explicitly.")
num_labels = int(classifier_weight.shape[0])
dropout_rates: Sequence[float] | None = None
hidden_dims: Sequence[int] | None = None
if isinstance(checkpoint, dict):
if "dropout_rates" in checkpoint:
dropout_rates = tuple(checkpoint["dropout_rates"]) # type: ignore[assignment]
if "hidden_dims" in checkpoint:
hidden_dims = tuple(int(x) for x in checkpoint["hidden_dims"]) # type: ignore[assignment]
model = ProteinLocalizationClassifier(
embedding_dim=embedding_dim,
num_labels=num_labels,
label_names=label_names,
dropout_rates=dropout_rates if dropout_rates is not None else (0.3, 0.3, 0.2),
hidden_dims=hidden_dims if hidden_dims is not None else (512, 256, 128),
)
model.load_state_dict(state_dict)
model.to(device)
model.eval()
return model
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