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PRIORITY 3: Transfer matrix across all available scenarios.
Property-scenario availability:
restitution: collision, ramp, flat_drop, elasticity, ramp_3prop (5)
friction: ramp, flat_drop, ramp_3prop (3)
mass: collision (elasticity has constant mass → skip) (1)
For each property with >=2 scenarios: train on one, test on all.
Subsample all features to [N, 4, D] (fpa=1, 4 agents).
Modes:
zero-shot — apply source-trained receiver to target codes
16-shot — train new receiver on 16 stratified target examples
Backbones: vjepa2, dinov2, clip. Seeds: 2.
"""
import json, time, sys, os, math
from pathlib import Path
from datetime import datetime, timezone
import numpy as np
import torch
import torch.nn.functional as F
sys.path.insert(0, os.path.dirname(__file__))
from _kinematics_train import (
ClassifierReceiver,
HIDDEN_DIM, VOCAB_SIZE, N_HEADS, N_AGENTS, MSG_DIM, BATCH_SIZE,
SENDER_LR, RECEIVER_LR, EARLY_STOP_PATIENCE, DEVICE,
)
from _killer_experiment import (
TemporalEncoder, DiscreteSender, DiscreteMultiSender,
)
from _overnight_p1_transfer import (
build_sender, train_base, eval_zero_shot, train_receiver_frozen_sender,
make_splits, N_FRAMES_SUBSAMPLE,
)
OUT = Path("results/cross_scenario_transfer")
OUT.mkdir(parents=True, exist_ok=True)
LOG = Path("results/overnight_log.txt")
N_EPOCHS = 150
N_SEEDS = 2
# Feature file locations per (dataset, backbone)
FEATURE_FILES = {
("collision", "vjepa2"): "results/vjepa2_collision_pooled.pt",
("collision", "dinov2"): "results/collision_dinov2_features.pt",
("collision", "clip"): "results/kinematics_vs_mechanics/clip_collision_features.pt",
("ramp", "vjepa2"): "results/vjepa2_ramp_temporal.pt",
("ramp", "dinov2"): "results/phase54b_dino_features.pt",
("ramp", "clip"): "results/kinematics_vs_mechanics/clip_ramp_features.pt",
("flat_drop", "vjepa2"): "results/kinematics_vs_mechanics/feat_vjepa2_flat_drop.pt",
("flat_drop", "dinov2"): "results/kinematics_vs_mechanics/feat_dinov2_flat_drop.pt",
("flat_drop", "clip"): "results/kinematics_vs_mechanics/feat_clip_flat_drop.pt",
("elasticity", "vjepa2"): "results/kinematics_vs_mechanics/feat_vjepa2_elasticity.pt",
("elasticity", "dinov2"): "results/kinematics_vs_mechanics/feat_dinov2_elasticity.pt",
("elasticity", "clip"): "results/kinematics_vs_mechanics/feat_clip_elasticity.pt",
("ramp_3prop", "vjepa2"): "results/kinematics_vs_mechanics/feat_vjepa2_ramp_3prop.pt",
("ramp_3prop", "dinov2"): "results/kinematics_vs_mechanics/feat_dinov2_ramp_3prop.pt",
("ramp_3prop", "clip"): "results/kinematics_vs_mechanics/feat_clip_ramp_3prop.pt",
}
LABEL_FILES = {
"collision": "results/kinematics_vs_mechanics/labels_collision.npz",
"ramp": "results/kinematics_vs_mechanics/labels_ramp.npz",
"flat_drop": "results/kinematics_vs_mechanics/labels_flat_drop.npz",
"elasticity": "results/kinematics_vs_mechanics/labels_elasticity.npz",
"ramp_3prop": "results/kinematics_vs_mechanics/labels_ramp_3prop.npz",
}
# Property availability
PROPERTY_SCENARIOS = {
"restitution": ["collision", "ramp", "flat_drop", "elasticity", "ramp_3prop"],
"friction": ["ramp", "flat_drop", "ramp_3prop"],
}
def log(msg):
ts = datetime.now(timezone.utc).strftime("%H:%M:%SZ")
line = f"[{ts}] P3-matrix: {msg}"
print(line, flush=True)
with open(LOG, "a") as f: f.write(line + "\n")
def load_feat_subsampled(dataset, backbone):
"""Return [N, 4, D]. Subsample evenly or duplicate-pad to 4 temporal positions."""
path = FEATURE_FILES[(dataset, backbone)]
d = torch.load(path, weights_only=False, map_location="cpu")
feat = d["features"].float()
T = feat.shape[1]
if T >= N_FRAMES_SUBSAMPLE:
idx = np.linspace(0, T - 1, N_FRAMES_SUBSAMPLE).astype(int)
feat = feat[:, idx, :].contiguous()
else:
# Duplicate-pad. Repeat each position ceil(4/T) times and slice first 4.
reps = (N_FRAMES_SUBSAMPLE + T - 1) // T
feat = feat.repeat(1, reps, 1)[:, :N_FRAMES_SUBSAMPLE, :].contiguous()
return feat
def load_labels(dataset, target):
z = np.load(LABEL_FILES[dataset])
key = f"{target}_bin"
if key not in z:
return None
return z[key].astype(np.int64)
def main():
t0_all = time.time()
log(f"=== PRIORITY 3: Transfer Matrix ===")
# Load + cache all features
feats = {}
for key, path in FEATURE_FILES.items():
if Path(path).exists():
feats[key] = load_feat_subsampled(*key)
log(f" {key[0]}/{key[1]}: shape={tuple(feats[key].shape)}")
else:
log(f" MISSING: {path}")
# Load + validate labels
labels_cache = {}
for prop, scenarios in PROPERTY_SCENARIOS.items():
for ds in scenarios:
lbl = load_labels(ds, prop)
if lbl is not None:
labels_cache[(ds, prop)] = lbl
log(f" labels {ds}/{prop}: {np.bincount(lbl, minlength=3).tolist()}")
else:
log(f" labels {ds}/{prop} MISSING")
# Train all base senders (property, ds, bb, seed) once
log("\n--- Training base senders ---")
bases = {}
for prop, scenarios in PROPERTY_SCENARIOS.items():
for ds in scenarios:
if (ds, prop) not in labels_cache: continue
labels = labels_cache[(ds, prop)]
for bb in ("vjepa2", "dinov2", "clip"):
if (ds, bb) not in feats: continue
for seed in range(N_SEEDS):
t0 = time.time()
b = train_base(feats[(ds, bb)], labels, seed, n_epochs=N_EPOCHS)
bases[(prop, ds, bb, seed)] = b
log(f" {prop}/{ds}/{bb}/seed{seed}: within_acc={b['task_acc']:.3f} [{time.time()-t0:.0f}s]")
# Build transfer matrix
log("\n--- Transfer matrix evaluation ---")
results = []
for prop, scenarios in PROPERTY_SCENARIOS.items():
for src in scenarios:
if (src, prop) not in labels_cache: continue
for tgt in scenarios:
if (tgt, prop) not in labels_cache: continue
for bb in ("vjepa2", "dinov2", "clip"):
if (src, bb) not in feats or (tgt, bb) not in feats: continue
for seed in range(N_SEEDS):
if (prop, src, bb, seed) not in bases: continue
base = bases[(prop, src, bb, seed)]
tgt_labels = labels_cache[(tgt, prop)]
train_ids_tgt, holdout_ids_tgt = make_splits(tgt_labels, seed)
# If src == tgt, report within-acc (no transfer)
if src == tgt:
acc_zs = base["task_acc"]
acc_16 = base["task_acc"]
else:
try:
acc_zs = eval_zero_shot(base, feats[(tgt, bb)],
tgt_labels, holdout_ids_tgt)
except Exception as e:
log(f" ERROR zero-shot {prop}/{src}→{tgt}/{bb}/seed{seed}: {e}")
acc_zs = float("nan")
try:
acc_16 = train_receiver_frozen_sender(
base, feats[(tgt, bb)], tgt_labels,
train_ids_tgt, holdout_ids_tgt, seed,
max_examples=16, n_epochs=80)
except Exception as e:
log(f" ERROR 16-shot {prop}/{src}→{tgt}/{bb}/seed{seed}: {e}")
acc_16 = float("nan")
results.append({"property": prop, "src": src, "tgt": tgt,
"backbone": bb, "seed": seed,
"zero_shot_acc": float(acc_zs),
"sixteen_shot_acc": float(acc_16)})
# Aggregate matrices
def matrix(prop, bb, mode_key):
scens = PROPERTY_SCENARIOS[prop]
M = np.full((len(scens), len(scens)), np.nan)
for r in results:
if r["property"] != prop or r["backbone"] != bb: continue
i = scens.index(r["src"]); j = scens.index(r["tgt"])
# Average across seeds
# (gather all matching, avg)
# Do it by seed-aggregation properly
for i, src in enumerate(scens):
for j, tgt in enumerate(scens):
vals = [r[mode_key] for r in results
if r["property"] == prop and r["backbone"] == bb
and r["src"] == src and r["tgt"] == tgt]
if vals:
M[i, j] = np.nanmean(vals)
return M, scens
lines = []
lines.append(f"PRIORITY 3: PROPERTY TRANSFER MATRIX (2 seeds, 16-shot mode)")
lines.append(f"Within-scenario cells (diagonal) show within-dataset training accuracy.")
lines.append("")
for prop in PROPERTY_SCENARIOS:
lines.append(f"\n=== {prop.upper()} ({len(PROPERTY_SCENARIOS[prop])} scenarios) ===")
for bb in ("vjepa2", "dinov2", "clip"):
M, scens = matrix(prop, bb, "sixteen_shot_acc")
if np.all(np.isnan(M)): continue
lines.append(f"\n {bb}:")
head = " " + "Train\\Test | " + " | ".join(f"{s[:11]:>11s}" for s in scens)
lines.append(head)
lines.append(" " + "-" * (len(head) - 2))
for i, src in enumerate(scens):
row = f" {src[:15]:<15s} | " + " | ".join(
f"{M[i,j]*100:>10.1f}%" if not np.isnan(M[i,j]) else f"{'—':>11s}"
for j in range(len(scens)))
lines.append(row)
# Also zero-shot matrix
lines.append("\n\nZERO-SHOT MODE (no receiver retraining)")
for prop in PROPERTY_SCENARIOS:
lines.append(f"\n=== {prop.upper()} ===")
for bb in ("vjepa2", "dinov2", "clip"):
M, scens = matrix(prop, bb, "zero_shot_acc")
if np.all(np.isnan(M)): continue
lines.append(f"\n {bb}:")
head = " " + "Train\\Test | " + " | ".join(f"{s[:11]:>11s}" for s in scens)
lines.append(head)
lines.append(" " + "-" * (len(head) - 2))
for i, src in enumerate(scens):
row = f" {src[:15]:<15s} | " + " | ".join(
f"{M[i,j]*100:>10.1f}%" if not np.isnan(M[i,j]) else f"{'—':>11s}"
for j in range(len(scens)))
lines.append(row)
total_s = time.time() - t0_all
lines.append(f"\n\nTotal P3 runtime: {total_s/60:.1f} min ({total_s:.0f}s)")
lines.append(f"N transfer evals: {len(results)}")
lines.append(f"N base senders trained: {len(bases)}")
summary = "\n".join(lines)
(OUT / "p3_matrix_summary.txt").write_text(summary + "\n")
with open(OUT / "p3_matrix_raw.json", "w") as f:
json.dump({"runs": results, "total_runtime_s": total_s}, f, indent=2, default=str)
log(f"\n{summary}")
log(f"\nSaved: {OUT / 'p3_matrix_summary.txt'}")
if __name__ == "__main__":
main()
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