basic_run.py

"""
examples/basic_run.py — Minimal Inference Example
===================================================
Loads (or instantiates) a CSNS model and runs a single forward pass.
Demonstrates how to inspect the latent representation and stability score.
"""

import torch
import sys
import os

# Allow running from examples/ directly
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))

from csns import CSNS


# ── Instantiate model ─────────────────────────────────────────────────────────
model = CSNS(dim=32)
model.eval()

# ── Single forward pass ───────────────────────────────────────────────────────
x = torch.randn(8, 32)  # batch of 8

with torch.no_grad():
    output, latent = model(x)

# ── Diagnostics ───────────────────────────────────────────────────────────────
stability  = model.evaluator.score(latent).item()
resistance = model.resistance_loss(latent).item()
latent_norm = torch.norm(latent, dim=-1).mean().item()

print("── CSNS Inference ──────────────────────────────")
print(f"  Input shape    : {list(x.shape)}")
print(f"  Output shape   : {list(output.shape)}")
print(f"  Latent shape   : {list(latent.shape)}")
print(f"  Mean latent ‖·‖: {latent_norm:.4f}  (ceiling={model.constraint.max_norm})")
print(f"  Resistance     : {resistance:.4f}")
print(f"  Stability score: {stability:.4f}  (higher = more stable)")
print("────────────────────────────────────────────────")