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UCE fine-tuning wrapper for hierarchical cell-type annotation.
Pipeline per forward call:
raw X (N×140) → build chromosome-ordered gene sentences
→ pe_embedding lookup + L2-norm → TransformerModel backbone (4-layer)
→ CLS embedding (position 0) → 3 × ClsDecoder heads
Gene sentence structure (per cell, using non-zero genes):
[CLS] [CHROM_OPEN_A] gene_a1 gene_a2 ... [CHROM_CLOSE] [CHROM_OPEN_B] ...
Chromosomes are visited in shuffled order; genes within each chromosome
are sorted by genomic start position.
"""
import sys
import pickle
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
sys.path.insert(0, "/data2/yang/UCE")
from model import TransformerModel
# UCE special token indices (as defined in eval_data.py)
CLS_TOKEN_IDX = 3
PAD_TOKEN_IDX = 0
CHROM_TOKEN_RIGHT = 2 # closing bracket for a chromosome block
CHROM_TOKEN_OFFSET = 143574 # chrom_code + CHROM_TOKEN_OFFSET = chrom open token
class ClsDecoder(nn.Module):
"""Two-layer classification head (matches scGPT's ClsDecoder structure)."""
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 UCEForAnnotation(nn.Module):
"""
Pre-trained UCE (4-layer) fine-tuned for hierarchical cell-type
annotation on MERFISH spatial-transcriptomics data.
Args:
model_dir: path to directory containing 4layer_model.torch,
all_tokens.torch, species_chrom.csv,
species_offsets.pkl.
gene_names: list of MERFISH gene symbols (same order as the
data matrix columns).
output_num: [n_class, n_subclass, n_supertype].
dropout: dropout for the classification heads (unused here,
kept for interface parity).
freeze_backbone: freeze transformer weights; train heads only.
"""
def __init__(
self,
model_dir: str,
gene_names: list,
output_num: list = None,
dropout: float = 0.2,
freeze_backbone: bool = False,
):
super().__init__()
if output_num is None:
output_num = [3, 24, 137]
self.gene_names = gene_names
self.n_genes = len(gene_names)
self.d_model = 1280
# ── backbone ──────────────────────────────────────────────────────────
self.backbone = TransformerModel(
token_dim = 5120,
d_model = 1280,
nhead = 20,
d_hid = 5120,
nlayers = 4,
output_dim = 1280,
dropout = 0.05,
)
full_state = torch.load(f"{model_dir}/4layer_model.torch", map_location="cpu")
# 4layer_model.torch includes pe_embedding.weight; exclude it for the backbone
backbone_state = {k: v for k, v in full_state.items()
if not k.startswith("pe_embedding")}
missing, unexpected = self.backbone.load_state_dict(backbone_state, strict=False)
print(f"[UCE] Loaded backbone from {model_dir}/4layer_model.torch"
f" | missing: {len(missing)} | unexpected: {len(unexpected)}")
# ── pe_embedding: lookup table for all UCE tokens ─────────────────────
all_tokens = torch.load(f"{model_dir}/all_tokens.torch", map_location="cpu")
self.pe_embedding = nn.Embedding.from_pretrained(all_tokens, freeze=True)
print(f"[UCE] pe_embedding shape: {all_tokens.shape}")
# ── precompute per-gene metadata ──────────────────────────────────────
chrom_df = pd.read_csv(f"{model_dir}/species_chrom.csv")
# categorical codes must be computed over the full DF to match pretraining
chrom_df["spec_chrom"] = pd.Categorical(
chrom_df["species"] + "_" + chrom_df["chromosome"].astype(str)
)
human_df = chrom_df[chrom_df["species"] == "human"].reset_index(drop=True)
with open(f"{model_dir}/species_offsets.pkl", "rb") as f:
offsets = pickle.load(f)
human_offset = offsets["human"] # 13466
gene_to_info = {
row["gene_symbol"]: {
"token_idx": human_offset + i,
"chrom_code": int(human_df["spec_chrom"].cat.codes[i]),
"start": int(row["start"]),
}
for i, row in human_df.iterrows()
}
token_idxs = []
chrom_codes = []
starts = []
valid_mask = []
for g in gene_names:
info = gene_to_info.get(g)
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)
self.register_buffer("_token_idxs", torch.tensor(token_idxs, dtype=torch.long))
self.register_buffer("_chrom_codes", torch.tensor(chrom_codes, dtype=torch.long))
self.register_buffer("_starts", torch.tensor(starts, dtype=torch.long))
self.register_buffer("_valid_mask", torch.tensor(valid_mask, dtype=torch.bool))
n_mapped = self._valid_mask.sum().item()
print(f"[UCE] Gene coverage: {n_mapped}/{self.n_genes}")
if freeze_backbone:
for p in self.backbone.parameters():
p.requires_grad = False
print("[UCE] Backbone weights frozen.")
# ── hierarchical classification heads ─────────────────────────────────
self.cls_head_class = ClsDecoder(self.d_model, output_num[0])
self.cls_head_subclass = ClsDecoder(self.d_model, output_num[1])
self.cls_head_supertype = ClsDecoder(self.d_model, output_num[2])
# ─────────────────────────────────────────────────────────────────────────
def _build_sentences(self, X: torch.Tensor):
"""
Build chromosome-ordered gene sentences for a batch of cells.
Args:
X: (B, G) raw expression (non-negative).
Returns:
sentences: LongTensor (seq_len, B)
mask: FloatTensor (B, seq_len) – 1 valid, 0 pad
"""
B = X.shape[0]
device = X.device
X_np = X.detach().cpu().numpy()
token_idxs = self._token_idxs.cpu().numpy()
chrom_codes = self._chrom_codes.cpu().numpy()
starts_np = self._starts.cpu().numpy()
valid_mask = self._valid_mask.cpu().numpy()
all_sentences = []
max_len = 0
for b in range(B):
expr = X_np[b]
sel = valid_mask & (expr > 0)
sel_idx = np.where(sel)[0]
sent = [CLS_TOKEN_IDX]
if len(sel_idx) > 0:
uq_chroms = np.unique(chrom_codes[sel_idx])
np.random.shuffle(uq_chroms) # shuffled chrom order (matches pretraining)
for chrom in uq_chroms:
g_in_chrom = sel_idx[chrom_codes[sel_idx] == chrom]
order = np.argsort(starts_np[g_in_chrom])
g_in_chrom = g_in_chrom[order]
sent.append(int(chrom) + CHROM_TOKEN_OFFSET) # open bracket
for gi in g_in_chrom:
sent.append(int(token_idxs[gi]))
sent.append(CHROM_TOKEN_RIGHT) # close bracket
all_sentences.append(sent)
max_len = max(max_len, len(sent))
# pad and stack
sentences_padded = torch.full((B, max_len), PAD_TOKEN_IDX, dtype=torch.long)
mask_padded = torch.zeros(B, max_len, dtype=torch.float)
for b, sent in enumerate(all_sentences):
n = len(sent)
sentences_padded[b, :n] = torch.tensor(sent, dtype=torch.long)
mask_padded[b, :n] = 1.0
# backbone expects (seq_len, B) layout
sentences_padded = sentences_padded.t().contiguous().to(device)
mask_padded = mask_padded.to(device)
return sentences_padded, mask_padded
def forward(self, X: torch.Tensor):
"""
Args:
X: (B, G) raw gene expression, non-negative float32.
Returns:
logits: list of [(B, 3), (B, 24), (B, 137)]
cell_emb: (B, 1280)
"""
# 1. Build token sentences
sentences, mask = self._build_sentences(X) # (seq_len, B), (B, seq_len)
# 2. Embedding lookup + L2-normalize
emb = self.pe_embedding(sentences) # (seq_len, B, 5120)
emb = nn.functional.normalize(emb, dim=2)
# 3. Backbone → CLS embedding at position 0
_, cell_emb = self.backbone(emb, mask=mask) # (B, 1280)
# 4. Classification heads
logit_class = self.cls_head_class(cell_emb)
logit_subclass = self.cls_head_subclass(cell_emb)
logit_supertype = self.cls_head_supertype(cell_emb)
return [logit_class, logit_subclass, logit_supertype], cell_emb
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