File size: 6,634 Bytes
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 | """train PANDA on the pancreas corpus."""
from __future__ import annotations
from pathlib import Path
import warnings, json, sys, time
warnings.filterwarnings("ignore")
import numpy as np, anndata as ad, torch, 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/pancreas/harmonized/corpus.h5ad"))
OUT = Path(str(PANDA_ROOT / "checkpoints/pancreas"))
OUT.mkdir(parents=True, exist_ok=True)
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
GUARANTEED_PER_CLASS = 6
NATURAL_SLOTS = 96
class Ds(Dataset):
def __init__(self, X, y, d, mhf, logc):
self.X, self.y, self.d = X.astype(np.float32), y.astype(np.int64), d.astype(np.int64)
self.mhf, self.logc = mhf.astype(np.float32), 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, self.d = np.asarray(y), 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}
counts = np.bincount(self.y, minlength=int(self.classes.max())+1)
self.p = counts[self.classes] / counts[self.classes].sum()
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:
pick = self.rng.choice(idx, size=take, replace=(len(idx) < take))
batch.extend(pick.tolist())
for _ in range(NATURAL_SLOTS):
c = self.rng.choice(self.classes, p=self.p)
batch.append(int(self.rng.choice(self.by_cls[c])))
yield batch
def __len__(self): return self.n_batches
def main():
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())
c2i = {c: i for i, c in enumerate(classes)}
d2i = {d: i for i, d in enumerate(datasets)}
y = np.array([c2i[c] for c in a.obs["canonical_label"].astype(str)])
d = np.array([d2i[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
counts = a.obs["total_counts"].astype(float).values if "total_counts" in a.obs.columns \
else np.asarray(a.X.sum(axis=1)).ravel()
logc = np.log10(counts + 1); logc = (logc - logc.mean()) / (logc.std() + 1e-6)
counts_per = np.bincount(y, minlength=len(classes))
print(f"[train] {a.shape}, n_classes={len(classes)}, "
f"class_counts={dict(zip(classes, counts_per.tolist()))}", flush=True)
with open(OUT / "label_encoding.json", "w") as f:
json.dump({"classes": classes, "datasets": datasets}, f, indent=2)
inv_sqrt = 1.0 / np.sqrt(counts_per + 1); inv_sqrt = inv_sqrt / inv_sqrt.mean()
class_w = torch.tensor(0.5 * inv_sqrt + 0.5 * np.ones_like(inv_sqrt),
dtype=torch.float32).to(DEVICE)
ds = Ds(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)
opt = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-4)
stage_epochs = [15, 25, 40, 40]
for stage in range(4):
n_ep = stage_epochs[stage]
print(f"\n=== stage {stage} ({n_ep}) ===", flush=True)
for e in range(n_ep):
t0 = time.time()
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_sup = 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_sup + 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
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
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)
if e % 5 == 0:
print(f"[s{stage}][ep {e}] 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"[done] saved {OUT}/panda_final.pt", flush=True)
if __name__ == "__main__":
main()
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