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"""
LookingGlass Classifiers - Fine-tuned DNA sequence classifiers
Pure PyTorch implementation of LookingGlass classifiers from the paper.
Uses LookingGlass encoder with classification head.
Usage:
from lookingglass_classifier import LookingGlassClassifier, LookingGlassTokenizer
model = LookingGlassClassifier.from_pretrained('.')
tokenizer = LookingGlassTokenizer()
inputs = tokenizer(["GATTACA"], return_tensors=True)
logits = model(inputs['input_ids']) # (batch, num_classes)
predictions = logits.argmax(dim=-1)
"""
import json
import os
from dataclasses import dataclass, asdict, field
from typing import Optional, List
import torch
import torch.nn as nn
from lookingglass import (
LookingGlassConfig,
LookingGlassTokenizer,
_AWDLSTMEncoder,
_is_hf_hub_id,
_download_from_hub,
)
__version__ = "1.1.0"
__all__ = ["LookingGlassClassifierConfig", "LookingGlassClassifier", "LookingGlassTokenizer"]
@dataclass
class LookingGlassClassifierConfig(LookingGlassConfig):
"""Configuration for LookingGlass classifier."""
num_classes: int = 2
classifier_hidden: int = 50
classifier_dropout: float = 0.0
class_names: List[str] = field(default_factory=list)
def save_pretrained(self, save_directory: str):
os.makedirs(save_directory, exist_ok=True)
with open(os.path.join(save_directory, "config.json"), 'w') as f:
json.dump(self.to_dict(), f, indent=2)
@classmethod
def from_pretrained(cls, pretrained_path: str) -> "LookingGlassClassifierConfig":
if _is_hf_hub_id(pretrained_path):
try:
config_path = _download_from_hub(pretrained_path, "config.json")
except Exception:
return cls()
elif os.path.isdir(pretrained_path):
config_path = os.path.join(pretrained_path, "config.json")
else:
config_path = pretrained_path
if os.path.exists(config_path):
with open(config_path, 'r') as f:
config_dict = json.load(f)
valid_fields = {f.name for f in cls.__dataclass_fields__.values()}
return cls(**{k: v for k, v in config_dict.items() if k in valid_fields})
return cls()
class LookingGlassClassifier(nn.Module):
"""
LookingGlass with classification head.
Uses concat pooling (max + mean + last) followed by classification layers.
Example:
>>> model = LookingGlassClassifier.from_pretrained('.')
>>> tokenizer = LookingGlassTokenizer()
>>> inputs = tokenizer("GATTACA", return_tensors=True)
>>> logits = model(inputs['input_ids']) # (1, num_classes)
>>> prediction = logits.argmax(dim=-1)
"""
def __init__(self, config: Optional[LookingGlassClassifierConfig] = None):
super().__init__()
self.config = config or LookingGlassClassifierConfig()
self.encoder = _AWDLSTMEncoder(self.config)
# Concat pooling: max + mean + last = 3 * hidden_size
pooled_size = 3 * self.config.hidden_size
# Classification head: BatchNorm -> Linear -> ReLU -> BatchNorm -> Linear
self.classifier = nn.Sequential(
nn.BatchNorm1d(pooled_size),
nn.Dropout(self.config.classifier_dropout),
nn.Linear(pooled_size, self.config.classifier_hidden),
nn.ReLU(),
nn.BatchNorm1d(self.config.classifier_hidden),
nn.Dropout(self.config.classifier_dropout),
nn.Linear(self.config.classifier_hidden, self.config.num_classes),
)
def forward(self, input_ids: torch.LongTensor) -> torch.Tensor:
"""
Forward pass returning classification logits.
Args:
input_ids: Token indices (batch, seq_len)
Returns:
Logits (batch, num_classes)
"""
self.encoder.reset()
hidden = self.encoder(input_ids) # (batch, seq_len, hidden_size)
# Concat pooling: max, mean, last
max_pool = hidden.max(dim=1).values
mean_pool = hidden.mean(dim=1)
last_pool = hidden[:, -1]
pooled = torch.cat([max_pool, mean_pool, last_pool], dim=-1)
return self.classifier(pooled)
def predict(self, input_ids: torch.LongTensor) -> torch.Tensor:
"""Return predicted class indices."""
logits = self.forward(input_ids)
return logits.argmax(dim=-1)
def predict_proba(self, input_ids: torch.LongTensor) -> torch.Tensor:
"""Return class probabilities."""
logits = self.forward(input_ids)
return torch.softmax(logits, dim=-1)
def get_embeddings(self, input_ids: torch.LongTensor) -> torch.Tensor:
"""Get sequence embeddings (last token) from encoder."""
self.encoder.reset()
hidden = self.encoder(input_ids)
return hidden[:, -1]
def save_pretrained(self, save_directory: str):
os.makedirs(save_directory, exist_ok=True)
self.config.save_pretrained(save_directory)
torch.save(self.state_dict(), os.path.join(save_directory, "pytorch_model.bin"))
@classmethod
def from_pretrained(
cls, pretrained_path: str, config: Optional[LookingGlassClassifierConfig] = None
) -> "LookingGlassClassifier":
config = config or LookingGlassClassifierConfig.from_pretrained(pretrained_path)
model = cls(config)
if _is_hf_hub_id(pretrained_path):
model_path = _download_from_hub(pretrained_path, "pytorch_model.bin")
else:
model_path = os.path.join(pretrained_path, "pytorch_model.bin")
if os.path.exists(model_path):
state_dict = torch.load(model_path, map_location='cpu')
model.load_state_dict(state_dict, strict=False)
return model
def convert_classifier_weights(
original_path: str,
output_dir: str,
num_classes: int,
class_names: Optional[List[str]] = None,
):
"""
Convert original fastai classifier weights to pure PyTorch format.
Args:
original_path: Path to original .pth file
output_dir: Output directory for converted model
num_classes: Number of output classes
class_names: Optional list of class names
"""
print(f"Loading weights from {original_path}...")
original = torch.load(original_path, map_location='cpu')
if 'model' in original:
original = original['model']
# Create config
config = LookingGlassClassifierConfig(
num_classes=num_classes,
classifier_hidden=50,
class_names=class_names or [],
)
# Create model
model = LookingGlassClassifier(config)
# Map weights
new_state = {}
# Encoder weights
weight_map = {
'0.module.encoder.weight': 'encoder.embed_tokens.weight',
'0.module.encoder_dp.emb.weight': 'encoder.embed_dropout.embedding.weight',
}
for i in range(3):
weight_map.update({
f'0.module.rnns.{i}.weight_hh_l0_raw': f'encoder.layers.{i}.weight_hh_l0_raw',
f'0.module.rnns.{i}.module.weight_ih_l0': f'encoder.layers.{i}.module.weight_ih_l0',
f'0.module.rnns.{i}.module.weight_hh_l0': f'encoder.layers.{i}.module.weight_hh_l0',
f'0.module.rnns.{i}.module.bias_ih_l0': f'encoder.layers.{i}.module.bias_ih_l0',
f'0.module.rnns.{i}.module.bias_hh_l0': f'encoder.layers.{i}.module.bias_hh_l0',
})
# Classifier head weights
# Original: 1.layers.{0,2,4,6} -> our Sequential indices
classifier_map = {
'1.layers.0.weight': 'classifier.0.weight',
'1.layers.0.bias': 'classifier.0.bias',
'1.layers.0.running_mean': 'classifier.0.running_mean',
'1.layers.0.running_var': 'classifier.0.running_var',
'1.layers.0.num_batches_tracked': 'classifier.0.num_batches_tracked',
'1.layers.2.weight': 'classifier.2.weight',
'1.layers.2.bias': 'classifier.2.bias',
'1.layers.4.weight': 'classifier.4.weight',
'1.layers.4.bias': 'classifier.4.bias',
'1.layers.4.running_mean': 'classifier.4.running_mean',
'1.layers.4.running_var': 'classifier.4.running_var',
'1.layers.4.num_batches_tracked': 'classifier.4.num_batches_tracked',
'1.layers.6.weight': 'classifier.6.weight',
'1.layers.6.bias': 'classifier.6.bias',
}
weight_map.update(classifier_map)
for old_key, new_key in weight_map.items():
if old_key in original:
new_state[new_key] = original[old_key]
# Load and save
model.load_state_dict(new_state, strict=False)
os.makedirs(output_dir, exist_ok=True)
config.save_pretrained(output_dir)
torch.save(model.state_dict(), os.path.join(output_dir, "pytorch_model.bin"))
print(f"Saved to {output_dir}")
return model
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Convert LookingGlass classifier weights")
parser.add_argument("--input", required=True, help="Path to original .pth file")
parser.add_argument("--output", required=True, help="Output directory")
parser.add_argument("--num-classes", type=int, required=True, help="Number of classes")
parser.add_argument("--class-names", nargs="+", help="Class names")
args = parser.parse_args()
convert_classifier_weights(args.input, args.output, args.num_classes, args.class_names)
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