| """Text encoder comparison + fusion under the final protocol (clean real TCGA texts only; synthetic / virtual = test only). |
| |
| Text encoders (frozen, cache/alltxt_text_<enc>.npy and cache/orion_gentext_text_<enc>.npy; 45_ / 45b_ / 08_): |
| biomedclip (current; raw features as in 42_), conchclip, plip, medcpt, biolord, pubmedbert, medembed, bge, qwen3-0.6b, |
| qwen3-4b, keep, quiltnet (whatever exists for both sets). Non-baseline inputs are z-scored with TCGA-train stats. |
| cat:<a>+<b> early fusion (z-scored blocks scaled by 1/sqrt(dim), one head) |
| ens:<a>+<b> late fusion (mean of the L2-normalised outputs of the single-encoder heads; same frozen UNI2 space) |
| Head: linear or MLP into the frozen UNI2 space, multi-positive InfoNCE (several texts per image), AdamW 3e-4 / wd 0.05 / |
| OneCycle, batch 256, 1000 steps, early stopping on val R@1 every 50 steps (= 42_ S4 text recipe). |
| Per fold k (splits/final/tcga_fold<k>.csv; one random text per test image, = 33_/42_): |
| trained H&E<->text TCGA test images (real text) |
| emergent mIF<->text(synthetic) ORION patient_5fold test tiles, mIF = S4 MLP head (runs/rnafm/other_fold<k>.pt, 44_) |
| emergent text<->RNA(virtual) TCGA test images, RNA = hvg linear head of fold k (runs/rnafm/rna_hvg_fold<k>.pt) |
| emergent text<->mIF(MIPHEI) TCGA test images, MIPHEI virtual mIF through the same mIF head |
| Usage: python 46_text_enc_eval.py [--encs ...] [--heads linear mlp] [--folds 0 1 2 3 4] |
| Output results/textenc_long.csv (resumable), results/textenc_summary.csv |
| """ |
| import argparse |
| import os |
|
|
| import numpy as np |
| import pandas as pd |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from scipy.optimize import linear_sum_assignment |
|
|
| from common import CACHE, ROOT, SPLITS |
|
|
| ap = argparse.ArgumentParser() |
| ap.add_argument("--encs", nargs="+", default=None) |
| ap.add_argument("--heads", nargs="+", default=["linear", "mlp"]) |
| ap.add_argument("--folds", nargs="+", type=int, default=[0, 1, 2, 3, 4]) |
| args = ap.parse_args() |
| dev = "cuda" |
| H = os.path.join(CACHE, "hest") |
| G100, DRAWS, BS, LR, EVAL_EVERY, STEPS = 100, 50, 256, 3e-4, 50, 1000 |
| f32 = lambda a: torch.from_numpy(np.asarray(a, dtype=np.float32)) |
| nz = lambda x: F.normalize(x, dim=-1) |
|
|
| HE_O = f32(np.load(os.path.join(CACHE, "he_uni2.npy"))).to(dev) |
| IF_O = f32(np.load(os.path.join(CACHE, "if_virtues_m16.npy"))).to(dev) |
| IF_V_T = f32(np.load(os.path.join(CACHE, "if_virtues_m16_virtual_miphei_tcga_text.npy"))[:, 0]).to(dev) |
| HE_H = f32(np.load(os.path.join(H, "he_uni2.npy"))[:, 0]).to(dev) |
| RNA_H = f32(np.load(os.path.join(H, "hvg.npy"))).to(dev) |
| HE_T = f32(np.load(os.path.join(CACHE, "allimg_he_uni2.npy"))).to(dev) |
| T = pd.read_csv(os.path.join(CACHE, "alltxt_meta.csv")) |
| T_IMG = torch.as_tensor(T.img_idx.values, device=dev) |
| T = T[~pd.read_csv(os.path.join(CACHE, "alltxt_junk.csv")).junk.values] |
| hvg = pd.read_csv(os.path.join(H, "hvg_genes.csv")).gene.values |
|
|
|
|
| def virtual_rna(st): |
| g = pd.read_csv(os.path.join(CACHE, "stpath", f"{st}_genes.csv")).gene.values |
| c = np.expm1(np.load(os.path.join(CACHE, "stpath", f"{st}_pred.npy")).astype(np.float32)).clip(0) |
| c = np.log1p(c / c.sum(1, keepdims=True) * 1e4) |
| return f32(c[:, pd.Series(range(len(g)), index=g).loc[hvg].values]).to(dev) |
|
|
|
|
| RNA_V_T = virtual_rna("tcga_text") |
|
|
|
|
| def load_enc(e): |
| e = e.split("@")[0] |
| return {s: f32(np.load(os.path.join(CACHE, f"{s}_text_{e}.npy"))[:, 0]).to(dev) for s in ["alltxt", "orion_gentext"]} |
|
|
|
|
| def mlp(din, dout=1536, hid=512): |
| return nn.Sequential(nn.Linear(din, hid), nn.GELU(), nn.Dropout(0.1), nn.Linear(hid, hid), nn.GELU(), |
| nn.LayerNorm(hid), nn.Linear(hid, dout)) |
|
|
|
|
| def one_text_per_image(t_ids, rng): |
| pick = T.loc[t_ids].groupby("img_idx").apply(lambda g: g.index[rng.integers(len(g))], include_groups=False) |
| return pick.index.values, pick.values |
|
|
|
|
| def g100(a, b, seed=0): |
| a, b = a.cpu(), b.cpu() |
| rng = np.random.default_rng(seed) |
| out = {m: [] for m in ["R@1", "R@5", "R@10", "MRR", "pair_acc"]} |
| for _ in range(DRAWS): |
| s = torch.from_numpy(rng.choice(len(a), min(G100, len(a)), replace=False)) |
| sim = (a[s] @ b[s].T).numpy(); d = np.diag(sim) |
| for S in (sim, sim.T): |
| rank = (S > d[:, None]).sum(1) |
| for k_ in (1, 5, 10): |
| out[f"R@{k_}"].append((rank < k_).mean() / 2) |
| out["MRR"].append((1 / (rank + 1)).mean() / 2) |
| ri, ci = linear_sum_assignment(-sim) |
| out["pair_acc"].append((ci[np.argsort(ri)] == np.arange(len(s))).mean()) |
| return {m: float(np.sum(v) / DRAWS) for m, v in out.items()} |
|
|
|
|
| def r1(a, b): |
| s = a @ b.T |
| return float((((s > s.diag()[:, None]).sum(1) == 0).float().mean() + ((s.T > s.diag()[:, None]).sum(1) == 0).float().mean()) / 2) |
|
|
|
|
| def train_head(X_tr, A_tr, X_va, A_va, kind, steps, seed, pos_tr=None): |
| torch.manual_seed(seed); rng = np.random.default_rng(seed) |
| h = (mlp(X_tr.shape[1]) if kind == "mlp" else nn.Linear(X_tr.shape[1], 1536)).to(dev) |
| logit = nn.Parameter(torch.tensor(np.log(1 / 0.07), dtype=torch.float32, device=dev)) |
| opt = torch.optim.AdamW(list(h.parameters()) + [logit], lr=LR, weight_decay=0.05) |
| sch = torch.optim.lr_scheduler.OneCycleLR(opt, max_lr=LR, total_steps=steps, pct_start=0.05) |
| za_va = nz(A_va[:, 0] if A_va.dim() == 3 else A_va) |
| best, state = -1, None |
| for step in range(1, steps + 1): |
| h.train() |
| b = torch.as_tensor(rng.choice(len(X_tr), BS, replace=False), device=dev) |
| a = A_tr[b, torch.randint(0, A_tr.shape[1], (BS,), device=dev)] if A_tr.dim() == 3 else A_tr[b] |
| lg = logit.exp().clamp(max=100) * nz(a) @ nz(h(X_tr[b])).T |
| if pos_tr is None: |
| t = torch.arange(BS, device=dev) |
| loss = (F.cross_entropy(lg, t) + F.cross_entropy(lg.T, t)) / 2 |
| else: |
| ii = pos_tr[b]; pos = (ii[:, None] == ii[None, :]).float(); tgt = pos / pos.sum(1, keepdim=True) |
| loss = (torch.sum(-tgt * F.log_softmax(lg, 1), 1).mean() + torch.sum(-tgt * F.log_softmax(lg.T, 1), 1).mean()) / 2 |
| opt.zero_grad(set_to_none=True); loss.backward(); opt.step(); sch.step() |
| if step % EVAL_EVERY == 0 or step == steps: |
| h.eval() |
| with torch.no_grad(): |
| v = r1(za_va, nz(h(X_va))) |
| if v > best: |
| best, state = v, {k_: x.detach().clone() for k_, x in h.state_dict().items()} |
| h.load_state_dict(state); h.eval() |
| return h, best |
|
|
|
|
| class Fold: |
| def __init__(self, k): |
| s = pd.read_csv(os.path.join(SPLITS, "patient_5fold", f"fold{k}.csv")) |
| self.o = {x: s.tile_id[s.split == x].values for x in ["train", "val", "test"]} |
| self.if_mu, self.if_sd = IF_O[self.o["train"]].mean((0, 1)), IF_O[self.o["train"]].std((0, 1)) + 1e-3 |
| h = pd.read_csv(os.path.join(SPLITS, "final", f"hest_fold{k}.csv")) |
| self.h = {x: h.spot_idx[h.split == x].values for x in ["train", "val", "test"]} |
| sp = pd.read_csv(os.path.join(SPLITS, "final", f"tcga_fold{k}.csv")).set_index("img_idx").split |
| tsp = sp.loc[T.img_idx].values |
| self.t = {x: T.index[tsp == x].values for x in ["train", "val", "test"]} |
| self.t_va = one_text_per_image(self.t["val"], np.random.default_rng(0)) |
| self.t_te = one_text_per_image(self.t["test"], np.random.default_rng(k)) |
|
|
| def nif(self, x): |
| return (x - self.if_mu) / self.if_sd |
|
|
| def mif_head(self, k): |
| f = os.path.join(ROOT, "runs", "rnafm", f"other_fold{k}.pt") |
| hm = mlp(512).to(dev) |
| hm.load_state_dict(torch.load(f, map_location=dev)["mif"]) |
| return hm.eval() |
|
|
| def rna_head(self, k): |
| f = os.path.join(ROOT, "runs", "rnafm", f"rna_hvg_fold{k}.pt") |
| hr = nn.Linear(2000, 1536).to(dev) |
| if os.path.exists(f): |
| hr.load_state_dict(torch.load(f, map_location=dev)) |
| return hr.eval() |
| hr, _ = train_head(RNA_H[self.h["train"]], HE_H[self.h["train"]], RNA_H[self.h["val"]], HE_H[self.h["val"]], |
| "linear", 2700, k) |
| torch.save(hr.state_dict(), f) |
| return hr |
|
|
|
|
| def build(enc, tr): |
| parts = enc.split(":", 1)[1].split("+") if enc.startswith("cat:") else [enc] |
| blocks = [] |
| for p in parts: |
| X = CACHE_ENC[p] |
| if p.split("@")[0] == "biomedclip" and len(parts) == 1: |
| blocks.append(X); continue |
| mu, sd = X["alltxt"][tr].mean(0), X["alltxt"][tr].std(0) + 1e-6 |
| w = 1 / np.sqrt(X["alltxt"].shape[1]) if len(parts) > 1 else 1.0 |
| blocks.append({s: (v - mu) / sd * w for s, v in X.items()}) |
| return {s: torch.cat([b[s] for b in blocks], 1) for s in blocks[0]} |
|
|
|
|
| avail = sorted({f[len("alltxt_text_"):-4] for f in os.listdir(CACHE) if f.startswith("alltxt_text_") and f.endswith(".npy") |
| and os.path.exists(os.path.join(CACHE, "orion_gentext_text_" + f[len("alltxt_text_"):]))}) |
| encs = sorted(args.encs or avail, key=lambda e: e.startswith("ens:")) |
| singles = sorted({p for e in encs for p in (e.split(":", 1)[1].split("+") if ":" in e else [e])}) |
| CACHE_ENC = {e: load_enc(e) for e in singles} |
| print("encoders:", {e: CACHE_ENC[e]["alltxt"].shape[1] for e in singles}, flush=True) |
| f_out = os.path.join(ROOT, "results", "textenc_long.csv") |
| rows = pd.read_csv(f_out).to_dict("records") if os.path.exists(f_out) else [] |
| done = {(r["enc"], r["head"], r["fold"]) for r in rows} |
| for k in args.folds: |
| F_ = Fold(k) |
| hm, hr = F_.mif_head(k), F_.rna_head(k) |
| te_o = F_.o["test"]; ii, tt = (torch.as_tensor(x, device=dev) for x in F_.t_te) |
| vi, vt = (torch.as_tensor(x, device=dev) for x in F_.t_va) |
| trt = torch.as_tensor(F_.t["train"], device=dev) |
| with torch.no_grad(): |
| z_mif = nz(hm(F_.nif(IF_O[te_o, 0]))); z_rna = nz(hr(RNA_V_T[ii])); z_vmif = nz(hm(F_.nif(IF_V_T[ii]))) |
| z_he = nz(HE_T[ii, 0]) |
| heads_out = {} |
| for enc in encs: |
| for hk in args.heads: |
| parts = enc.split(":", 1)[1].split("+") if enc.startswith("ens:") else None |
| need = any(enc in e.split(":", 1)[1].split("+") for e in encs if e.startswith("ens:")) |
| if (enc, hk, k) in done and not (parts is None and need): |
| continue |
| if parts is not None: |
| if not all((p, hk) in heads_out for p in parts): |
| continue |
| Z = {s: nz(sum(heads_out[(p, hk)][s] for p in parts)) for s in heads_out[(parts[0], hk)]} |
| best = float("nan") |
| else: |
| X = build(enc, trt) |
| sd_ = k + 1000 * int(enc.split("@")[1]) if "@" in enc else k |
| h, best = train_head(X["alltxt"][trt], HE_T[T_IMG[trt]], X["alltxt"][vt], HE_T[vi, 0], hk, STEPS, sd_, |
| pos_tr=T_IMG[trt]) |
| with torch.no_grad(): |
| Z = {"tcga": nz(h(X["alltxt"][tt])), "orion": nz(h(X["orion_gentext"][te_o]))} |
| heads_out[(enc, hk)] = Z |
| if (enc, hk, k) in done: |
| continue |
| res = {("H&E<->text (TCGA)", "trained"): g100(z_he, Z["tcga"]), |
| ("mIF<->text(synthetic) (ORION)", "emergent"): g100(z_mif, Z["orion"]), |
| ("text<->RNA(virtual) (TCGA)", "emergent"): g100(Z["tcga"], z_rna), |
| ("text<->mIF(MIPHEI) (TCGA)", "emergent"): g100(Z["tcga"], z_vmif)} |
| for (pair, kind), r in res.items(): |
| rows.append({"enc": enc, "head": hk, "fold": k, "pair": pair, "kind": kind, "val_R@1": best, **r}) |
| pd.DataFrame(rows).to_csv(f_out, index=False) |
| print(f"fold {k} {enc:<34} {hk:<6} val {best:.3f} " + |
| " ".join(f"{p.split(' (')[0]} {r['R@1']:.3f}" for (p, _), r in res.items()), flush=True) |
|
|
| R = pd.DataFrame(rows) |
| S = R.groupby(["enc", "head", "kind", "pair"], sort=False)[["R@1", "R@10", "pair_acc"]].agg(["mean", "std"]).round(4) |
| S.to_csv(os.path.join(ROOT, "results", "textenc_summary.csv")) |
| pd.set_option("display.width", 250) |
| print(R.pivot_table(index=["enc", "head"], columns="pair", values="R@1", sort=False).round(3).to_string()) |
|
|
