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#!/usr/bin/env python3
"""Run the single frozen HFA-S2 full ResNet-20 validation baseline."""
import argparse
import json
import os
import subprocess
import sys
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--selection", default="results/oral_a_hfa_short_selection.json")
parser.add_argument("--device", default="cuda")
parser.add_argument("--dry_run", action="store_true")
args = parser.parse_args()
with open(args.selection) as handle:
selection = json.load(handle)
if selection.get("protocol") != "convolutional_hfa_S1_v1":
raise ValueError("unexpected HFA-S1 selection protocol")
if selection.get("status") != "selected_full_open":
raise ValueError("HFA-S1 full-validation gate did not pass")
rate = float(selection["selected"]["lr"])
if rate not in (0.01, 0.03, 0.1):
raise ValueError("selected HFA learning rate left the frozen grid")
command = [
sys.executable, "experiments/conv_run.py", "--mode", "hfa",
"--device", args.device, "--depth", "20", "--width", "16",
"--seed", "0", "--loader_seed", "0", "--batch_size", "128",
"--epochs", "200", "--train_limit", "0",
"--val_examples", "5000", "--split_seed", "2027",
"--eval_split", "validation", "--eval_every", "0",
"--augment_train", "1", "--lr_schedule", "step",
"--lr_milestones", "100,150", "--lr_gamma", "0.1",
"--warmup_epochs", "0", "--momentum", "0.9",
"--weight_decay", "1e-4", "--normalization", "batchnorm",
"--lr", str(rate), "--output_lr", "0.1", "--a_scale", "1",
"--alignment_probe", "32",
"--out", "results/oral_a_hfa_full/hfa.json",
]
os.makedirs("results/oral_a_hfa_full", exist_ok=True)
print(" ".join(command), flush=True)
if not args.dry_run:
subprocess.run(command, check=True)
if __name__ == "__main__":
main()
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