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141bacd | 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 | """v3c PANDA-MLP training. hybrid sampler: guaranteed per-class + natural-freq fill."""
from __future__ import annotations
from pathlib import Path
import warnings, json, sys, time
warnings.filterwarnings("ignore")
import numpy as np
import anndata as ad
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import os as _os
from pathlib import Path as _Path
PANDA_ROOT = _Path(_os.environ.get("PANDA_ROOT", str(_Path(__file__).resolve().parents[2])))
sys.path.insert(0, str(PANDA_ROOT))
from panda.pan_skin.model import (
PANDAEncoder, supcon_loss, vicreg_loss, hsic_biased, prototype_infonce
)
CORPUS = Path(str(PANDA_ROOT / "data/corpus/pan_skin/harmonized/corpus.h5ad"))
OUT = Path(str(PANDA_ROOT / "checkpoints/pan_skin"))
V2_CK = Path(str(PANDA_ROOT / "checkpoints/pan_skin_v2_kept/panda_final.pt"))
OUT.mkdir(parents=True, exist_ok=True)
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
BALANCE_MIX = 0.5 # 0 = uniform CE weights, 1 = full inverse-sqrt-frequency
GUARANTEED_PER_CLASS = 6 # rare class cells per batch (if class has >=6 cells)
NATURAL_SLOTS = 96 # additional slots at natural frequency
class CorpusDataset(Dataset):
def __init__(self, X, y, d, mhf, logc):
self.X = X.astype(np.float32); self.y = y.astype(np.int64)
self.d = d.astype(np.int64); self.mhf = mhf.astype(np.float32)
self.logc = logc.astype(np.float32)
def __len__(self): return self.X.shape[0]
def __getitem__(self, i):
return (torch.from_numpy(self.X[i]),
torch.tensor(self.y[i]),
torch.tensor(self.d[i]),
torch.tensor([self.mhf[i], self.logc[i]], dtype=torch.float32))
class HybridSampler:
def __init__(self, y, d, n_batches=100, seed=0):
self.y = np.asarray(y); self.d = np.asarray(d)
self.n_batches = n_batches
self.rng = np.random.default_rng(seed)
self.classes = np.unique(self.y)
self.by_cls = {c: np.where(self.y == c)[0] for c in self.classes}
self.class_counts = np.bincount(self.y, minlength=len(self.classes))
p = self.class_counts / self.class_counts.sum()
self.natural_p = p
def __iter__(self):
for _ in range(self.n_batches):
batch = []
for c in self.classes:
idx = self.by_cls[c]
take = min(GUARANTEED_PER_CLASS, len(idx))
if take > 0:
pick = self.rng.choice(idx, size=take, replace=(len(idx) < take))
batch.extend(pick.tolist())
n_extra = NATURAL_SLOTS
for _ in range(n_extra):
c_pick = self.rng.choice(self.classes, p=self.natural_p)
idx = self.by_cls[c_pick]
batch.append(int(self.rng.choice(idx)))
yield batch
def __len__(self): return self.n_batches
def rankme(z):
with torch.no_grad():
_, s, _ = torch.svd(z - z.mean(0, keepdim=True))
p = s / s.sum().clamp_min(1e-12)
H = -(p * (p + 1e-12).log()).sum()
return float(H.exp().item())
def main():
print(f"[train] device: {DEVICE}", flush=True)
a = ad.read_h5ad(CORPUS)
keep = (a.obs["canonical_label"].astype(str) != "UNK").values
a = a[keep].copy()
classes = sorted(a.obs["canonical_label"].astype(str).unique())
datasets = sorted(a.obs["dataset"].astype(str).unique())
cls_ix = {c: i for i, c in enumerate(classes)}
ds_ix = {d: i for i, d in enumerate(datasets)}
y = np.array([cls_ix[c] for c in a.obs["canonical_label"].astype(str)])
d = np.array([ds_ix[dd] for dd in a.obs["dataset"].astype(str)])
X = np.asarray(a.obsm["X_pca"])
mhf = a.obs.get("missing_hvg_frac", np.zeros(len(a))).astype(np.float32).values
if "total_counts" in a.obs.columns:
counts = a.obs["total_counts"].astype(float).values
else:
counts = np.asarray(a.X.sum(axis=1)).ravel()
logc = np.log10(counts + 1); logc = (logc - logc.mean()) / (logc.std() + 1e-6)
with open(OUT / "label_encoding.json", "w") as f:
json.dump({"classes": classes, "datasets": datasets}, f, indent=2)
counts_per = np.bincount(y, minlength=len(classes))
print(f"[train] shape {a.shape}, n_classes {len(classes)}, n_datasets {len(datasets)}",
flush=True)
print(f"[train] class counts: {dict(zip(classes, counts_per.tolist()))}", flush=True)
inv_sqrt = 1.0 / np.sqrt(counts_per + 1)
inv_sqrt = inv_sqrt / inv_sqrt.mean()
class_w = BALANCE_MIX * inv_sqrt + (1 - BALANCE_MIX) * np.ones_like(inv_sqrt)
class_w = torch.tensor(class_w, dtype=torch.float32).to(DEVICE)
ds = CorpusDataset(X, y, d, mhf, logc)
sampler = HybridSampler(y, d, n_batches=100)
loader = DataLoader(ds, batch_sampler=sampler, num_workers=0)
model = PANDAEncoder(n_pca=X.shape[1], n_classes=len(classes),
n_datasets=len(datasets)).to(DEVICE)
if V2_CK.exists():
try:
ck = torch.load(V2_CK, map_location=DEVICE, weights_only=False)
if "classes" in ck:
# copy prototypes for classes shared with v2
idx_v2 = {c: i for i, c in enumerate(ck["classes"])}
for c in classes:
if c in idx_v2:
model.prototypes[cls_ix[c]].copy_(
torch.tensor(ck["prototypes"][idx_v2[c]]).to(DEVICE))
print(f"[warmstart] copied {sum(1 for c in classes if c in idx_v2)}/{len(classes)}"
f" prototypes from v2", flush=True)
except Exception as exc:
print(f"[warmstart] skipped ({exc})")
opt = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-4)
stage_epochs = [15, 25, 40, 40]
def train_one_epoch(stage):
L = {"supcon": [], "vic": [], "ce": [], "proto": [], "dom": [], "depth": [], "hsic": []}
for X_b, y_b, d_b, aux_b in loader:
X_b, y_b, d_b, aux_b = X_b.to(DEVICE), y_b.to(DEVICE), d_b.to(DEVICE), aux_b.to(DEVICE)
if stage >= 2:
jitter = torch.empty_like(aux_b[:, 1:2]).uniform_(-2, 0)
aux_b = aux_b.clone(); aux_b[:, 1:2] = aux_b[:, 1:2] + jitter
lam = 1.0 if stage >= 2 else 0.0
out = model(X_b, aux_b, lam_dann=lam)
L_supcon = supcon_loss(out["z"], y_b)
L_vic = vicreg_loss(out["z"])
L_ce = F.cross_entropy(out["logits"], y_b, weight=class_w, label_smoothing=0.05)
total = L_supcon + 1.0 * L_vic + 0.4 * L_ce
if stage >= 1:
proto_ref = model.prototypes.detach().clone()
L_p = prototype_infonce(out["z"], y_b, proto_ref)
total = total + 0.6 * L_p
L["proto"].append(float(L_p))
if stage >= 2:
L_d = F.cross_entropy(out["dom"], d_b)
L_dep = F.mse_loss(out["depth"].squeeze(1), aux_b[:, 1])
L_h = hsic_biased(out["repr"], aux_b[:, 1:2])
total = total + L_d + 0.3 * L_dep + 0.05 * L_h
L["dom"].append(float(L_d)); L["depth"].append(float(L_dep)); L["hsic"].append(float(L_h))
L["supcon"].append(float(L_supcon)); L["vic"].append(float(L_vic)); L["ce"].append(float(L_ce))
opt.zero_grad(); total.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)
opt.step()
if stage >= 1:
model.update_prototypes(out["z"].detach(), y_b)
return {k: float(np.mean(v)) if v else 0.0 for k, v in L.items()}
global_ep = 0
for stage in range(4):
n = stage_epochs[stage]
print(f"\n=== stage {stage} ({n} epochs) ===", flush=True)
for e in range(n):
t0 = time.time()
m = train_one_epoch(stage)
global_ep += 1
if global_ep % 3 == 0 or e == n - 1:
X_b, *_ = next(iter(loader))
with torch.no_grad():
z_b = model(X_b.to(DEVICE),
torch.zeros(len(X_b), 2, device=DEVICE))["z"]
rk = rankme(z_b)
print(f"[s{stage}][ep {global_ep}] sup={m['supcon']:.3f} vic={m['vic']:.3f} "
f"ce={m['ce']:.3f} pro={m['proto']:.3f} dom={m['dom']:.3f} "
f"depth={m['depth']:.3f} hsic={m['hsic']:.3f} | "
f"RankMe={rk:.1f} dt={time.time()-t0:.1f}s", flush=True)
torch.save({"model": model.state_dict(), "classes": classes, "datasets": datasets},
OUT / f"panda_stage{stage}.pt")
torch.save({"model": model.state_dict(), "classes": classes, "datasets": datasets,
"prototypes": model.prototypes.detach().cpu().numpy()},
OUT / "panda_final.pt")
np.save(OUT / "prototypes.npy", model.prototypes.detach().cpu().numpy())
print(f"\n[done] saved {OUT}/panda_final.pt", flush=True)
if __name__ == "__main__":
main()
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