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N-level foundation model wrappers for Kukanja.
These extend the existing FM model classes to support N classification heads.
"""
import torch
import torch.nn as nn
class ClsDecoder(nn.Module):
"""Shared classification decoder head."""
def __init__(self, d_model: int, n_cls: int, nlayers: int = 3):
super().__init__()
layers = []
for _ in range(nlayers - 1):
layers += [nn.Linear(d_model, d_model), nn.LayerNorm(d_model), nn.LeakyReLU()]
layers.append(nn.Linear(d_model, n_cls))
self.net = nn.Sequential(*layers)
def forward(self, x):
return self.net(x)
class GeneformerNLevel(nn.Module):
"""
N-level Geneformer wrapper. Loads pretrained backbone from
GeneformerForAnnotation, replaces heads with N-level ModuleList.
"""
def __init__(self, code_dir, weights_path, gene_names,
output_num, dropout=0.2, freeze_backbone=False, max_seq_len=1024):
super().__init__()
from src.models.geneformer.geneformer_annotation import GeneformerForAnnotation
# Create base model with dummy 3-level output (just to load backbone)
dummy_out = output_num[:3] if len(output_num) >= 3 else output_num + [2] * (3 - len(output_num))
self._base = GeneformerForAnnotation(
code_dir=code_dir, weights_path=weights_path,
gene_names=gene_names, output_num=dummy_out,
dropout=dropout, freeze_backbone=freeze_backbone,
max_seq_len=max_seq_len,
)
d_model = self._base.d_model
# Replace with N-level heads
self.heads = nn.ModuleList([
ClsDecoder(d_model, n) for n in output_num
])
# Remove old heads to avoid confusion
del self._base.cls_head_class
del self._base.cls_head_subclass
del self._base.cls_head_supertype
def forward(self, X):
input_ids, attention_mask = self._base._tokenize(X)
outputs = self._base.backbone(input_ids=input_ids, attention_mask=attention_mask)
last_hidden = outputs.last_hidden_state
gene_attn_mask = attention_mask.clone()
gene_attn_mask[:, 0] = 0.0
count = gene_attn_mask.sum(dim=1, keepdim=True).clamp(min=1.0)
cell_emb = (last_hidden * gene_attn_mask.unsqueeze(-1)).sum(dim=1) / count
logits = [h(cell_emb) for h in self.heads]
return logits, cell_emb
class UCENLevel(nn.Module):
"""
N-level UCE wrapper. Loads pretrained backbone from UCEForAnnotation,
replaces heads with N-level ModuleList.
"""
def __init__(self, model_dir, gene_names, output_num,
dropout=0.2, freeze_backbone=False, species='human'):
super().__init__()
from src.models.uce.uce_annotation import UCEForAnnotation
dummy_out = output_num[:3] if len(output_num) >= 3 else output_num + [2] * (3 - len(output_num))
self._base = UCEForAnnotation(
model_dir=model_dir, gene_names=gene_names,
output_num=dummy_out, dropout=dropout,
freeze_backbone=freeze_backbone,
)
# For mouse species, we need to override the species filter
if species == 'mouse':
self._override_species_mouse(model_dir, gene_names)
d_model = self._base.d_model
self.heads = nn.ModuleList([
ClsDecoder(d_model, n) for n in output_num
])
del self._base.cls_head_class
del self._base.cls_head_subclass
del self._base.cls_head_supertype
def _override_species_mouse(self, model_dir, gene_names):
"""Override UCE's gene mapping to use mouse genes instead of human."""
import os, pickle
import pandas as pd
import torch
import numpy as np
chrom_csv = os.path.join(model_dir, 'species_chrom.csv')
offsets_pkl = os.path.join(model_dir, 'species_offsets.pkl')
chrom_df = pd.read_csv(chrom_csv)
# Reconstruct spec_chrom codes the same way as base class
chrom_df["spec_chrom"] = pd.Categorical(
chrom_df["species"] + "_" + chrom_df["chromosome"].astype(str)
)
mouse_df = chrom_df[chrom_df['species'] == 'mouse'].reset_index(drop=True)
if mouse_df.empty:
print("[UCE] WARNING: No mouse genes found in species_chrom.csv")
return
with open(offsets_pkl, 'rb') as f:
offsets = pickle.load(f)
mouse_offset = offsets.get('mouse', 0)
# Build gene lookup (case-insensitive: UCE uses UPPERCASE, EAE uses Title Case)
gene_to_info = {}
for i, row in mouse_df.iterrows():
gene = row['gene_symbol']
gene_to_info[gene.upper()] = {
"token_idx": mouse_offset + i,
"chrom_code": int(mouse_df["spec_chrom"].cat.codes[i]),
"start": int(row["start"]),
}
# Rebuild all four buffers
from src.models.uce.uce_annotation import PAD_TOKEN_IDX
token_idxs = []
chrom_codes = []
starts = []
valid_mask = []
for g in gene_names:
info = gene_to_info.get(g.upper())
if info is None:
token_idxs.append(PAD_TOKEN_IDX)
chrom_codes.append(-1)
starts.append(0)
valid_mask.append(False)
else:
token_idxs.append(info["token_idx"])
chrom_codes.append(info["chrom_code"])
starts.append(info["start"])
valid_mask.append(True)
valid_count = sum(valid_mask)
print(f"[UCE-mouse] Gene coverage: {valid_count}/{len(gene_names)}")
# Replace the base class buffers
self._base._token_idxs = torch.tensor(token_idxs, dtype=torch.long)
self._base._chrom_codes = torch.tensor(chrom_codes, dtype=torch.long)
self._base._starts = torch.tensor(starts, dtype=torch.long)
self._base._valid_mask = torch.tensor(valid_mask, dtype=torch.bool)
self._base.register_buffer("_token_idxs", self._base._token_idxs)
self._base.register_buffer("_chrom_codes", self._base._chrom_codes)
self._base.register_buffer("_starts", self._base._starts)
self._base.register_buffer("_valid_mask", self._base._valid_mask)
def forward(self, X):
# Bypass base forward (heads deleted), call backbone directly
import torch.nn.functional as F
sentences, mask = self._base._build_sentences(X)
emb = self._base.pe_embedding(sentences)
emb = F.normalize(emb, dim=2)
_, cell_emb = self._base.backbone(emb, mask=mask)
logits = [h(cell_emb) for h in self.heads]
return logits, cell_emb
class ScGPTNLevel(nn.Module):
"""
N-level scGPT wrapper. Converts gene_names to vocab IDs, loads pretrained
backbone from scGPTForAnnotation, replaces heads with N-level ModuleList.
"""
def __init__(self, ckpt_dir, gene_names, output_num,
dropout=0.2, freeze_backbone=False):
super().__init__()
import json
from pathlib import Path
from src.models.scGPT.scGPT_annotation import scGPTForAnnotation
# Convert gene names to vocab IDs
ckpt_path = Path(ckpt_dir)
with open(ckpt_path / "vocab.json") as f:
vocab = json.load(f)
gene_ids = []
valid = 0
for g in gene_names:
if g in vocab:
gene_ids.append(vocab[g])
valid += 1
else:
gene_ids.append(vocab.get("<pad>", 0))
print(f"[scGPT] Gene coverage: {valid}/{len(gene_names)}")
gene_ids_tensor = torch.tensor(gene_ids, dtype=torch.long)
dummy_out = output_num[:3] if len(output_num) >= 3 else output_num + [2] * (3 - len(output_num))
self._base = scGPTForAnnotation(
checkpoint_dir=ckpt_dir, gene_ids=gene_ids_tensor,
output_num=dummy_out, dropout=dropout,
freeze_backbone=freeze_backbone,
)
d_model = self._base.d_model
self.heads = nn.ModuleList([
ClsDecoder(d_model, n) for n in output_num
])
del self._base.cls_head_class
del self._base.cls_head_subclass
del self._base.cls_head_supertype
def forward(self, X):
# Bypass base forward (heads deleted), call backbone directly
from src.models.scGPT.binning import scgpt_binning_torch
batch_size = X.shape[0]
device = X.device
X_norm = torch.log1p(X)
X_binned = scgpt_binning_torch(X_norm, n_bins=self._base.n_bins).float()
cls_ids = X.new_full((batch_size, 1), self._base.cls_token_id, dtype=torch.long)
cls_vals = X.new_zeros(batch_size, 1)
gene_ids_exp = self._base.gene_ids.unsqueeze(0).expand(batch_size, -1)
src = torch.cat([cls_ids, gene_ids_exp], dim=1)
values = torch.cat([cls_vals, X_binned], dim=1)
src_key_padding_mask = torch.zeros(
batch_size, src.shape[1], dtype=torch.bool, device=device
)
output = self._base.backbone(src, values, src_key_padding_mask)
cell_emb = output["cell_emb"]
logits = [h(cell_emb) for h in self.heads]
return logits, cell_emb
class StackNLevel(nn.Module):
"""
N-level Stack wrapper. Loads pretrained backbone from StackForAnnotation,
replaces heads with N-level ModuleList.
"""
def __init__(self, checkpoint_path, gene_list_path, gene_names,
output_num, dropout=0.2, freeze_backbone=False):
super().__init__()
from src.models.stack_model.stack_annotation import StackForAnnotation
dummy_out = output_num[:3] if len(output_num) >= 3 else output_num + [2] * (3 - len(output_num))
self._base = StackForAnnotation(
checkpoint_path=checkpoint_path,
gene_list_path=gene_list_path,
gene_names=gene_names,
output_num=dummy_out,
dropout=dropout,
freeze_backbone=freeze_backbone,
)
d_model = self._base.embed_dim
self.heads = nn.ModuleList([
ClsDecoder(d_model, n) for n in output_num
])
del self._base.cls_head_class
del self._base.cls_head_subclass
del self._base.cls_head_supertype
def forward(self, X):
features = self._base._map_genes(X)
features_log = torch.log1p(features)
if self._base.freeze_backbone:
with torch.no_grad():
tokens = self._base.backbone._reduce_and_tokenize(features_log)
x = self._base.backbone._run_attention_layers(tokens)
else:
tokens = self._base.backbone._reduce_and_tokenize(features_log)
x = self._base.backbone._run_attention_layers(tokens)
cell_emb = x.reshape(X.shape[0], -1)
logits = [h(cell_emb) for h in self.heads]
return logits, cell_emb
class ScSimilarityNLevel(nn.Module):
"""
N-level scSimilarity wrapper trained from scratch on the current gene panel.
"""
def __init__(self, n_genes, output_num, latent_dim=128, hidden_dim=None,
dropout=0.5, input_dropout=0.4, freeze_backbone=False):
super().__init__()
from scimilarity.nn_models import Encoder
if hidden_dim is None:
hidden_dim = [512, 512]
self.encoder = Encoder(
n_genes=n_genes,
latent_dim=latent_dim,
hidden_dim=hidden_dim,
dropout=dropout,
input_dropout=input_dropout,
)
self.freeze_backbone = freeze_backbone
if freeze_backbone:
for p in self.encoder.parameters():
p.requires_grad = False
self.heads = nn.ModuleList([
ClsDecoder(latent_dim, n) for n in output_num
])
def forward(self, X):
X_norm = torch.log1p(X)
if self.freeze_backbone:
with torch.no_grad():
cell_emb = self.encoder(X_norm)
else:
cell_emb = self.encoder(X_norm)
logits = [h(cell_emb) for h in self.heads]
return logits, cell_emb
class NicheformerNLevel(nn.Module):
"""
N-level Nicheformer wrapper for human Kukanja datasets.
"""
def __init__(self, checkpoint_path, vocab_path, merfish_mean_path,
gene_name_to_ens_path, gene_names, output_num,
dropout=0.2, freeze_backbone=False, specie_token=5):
super().__init__()
from src.models.nicheformer.nicheformer_annotation import NicheformerForAnnotation
dummy_out = output_num[:3] if len(output_num) >= 3 else output_num + [2] * (3 - len(output_num))
self._base = NicheformerForAnnotation(
checkpoint_path=checkpoint_path,
vocab_path=vocab_path,
merfish_mean_path=merfish_mean_path,
gene_name_to_ens_path=gene_name_to_ens_path,
gene_names=gene_names,
output_num=dummy_out,
dropout=dropout,
freeze_backbone=freeze_backbone,
specie_token=specie_token,
)
for head in [self._base.cls_head_class, self._base.cls_head_subclass, self._base.cls_head_supertype]:
for p in head.parameters():
p.requires_grad = False
d_model = self._base.d_model
self.heads = nn.ModuleList([
ClsDecoder(d_model, n) for n in output_num
])
def forward(self, X):
_, cell_emb = self._base(X)
logits = [h(cell_emb) for h in self.heads]
return logits, cell_emb
class ScFoundationNLevel(nn.Module):
"""
N-level scFoundation wrapper.
"""
def __init__(self, model_path, config_path, gene_names, output_num,
dropout=0.2, freeze_backbone=False, pool_type='all'):
super().__init__()
from src.models.scfoundation.scfoundation_annotation import ScFoundationForAnnotation
dummy_out = output_num[:3] if len(output_num) >= 3 else output_num + [2] * (3 - len(output_num))
self._base = ScFoundationForAnnotation(
model_path=model_path,
config_path=config_path,
gene_names=gene_names,
output_num=dummy_out,
dropout=dropout,
freeze_backbone=freeze_backbone,
pool_type=pool_type,
)
for head in [self._base.cls_head_class, self._base.cls_head_subclass, self._base.cls_head_supertype]:
for p in head.parameters():
p.requires_grad = False
d_model = self._base.embed_dim
self.heads = nn.ModuleList([
ClsDecoder(d_model, n) for n in output_num
])
def forward(self, X):
cell_emb = self._base._encode(X)
cell_emb = self._base.emb_norm(cell_emb)
logits = [h(cell_emb) for h in self.heads]
return logits, cell_emb
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