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Inference / demo script for Spatial Context Networks (SCN).
Designed for use as a HuggingFace Space or standalone demo.
Usage:
python inference.py
python inference.py --checkpoint path/to/model.pt --input_dim 10
"""
import argparse
import torch
import json
from model import SpatialContextNetwork
PATTERN_LABELS = ["Mathematics", "Language", "Vision", "Reasoning"]
def load_model(checkpoint_path: str | None, input_dim: int, n_neurons: int, output_dim: int):
model = SpatialContextNetwork(
input_dim=input_dim,
n_neurons=n_neurons,
output_dim=output_dim,
)
if checkpoint_path:
state = torch.load(checkpoint_path, map_location="cpu")
model.load_state_dict(state)
print(f"Loaded checkpoint: {checkpoint_path}")
else:
print("No checkpoint provided — using randomly initialized weights.")
model.eval()
return model
def run_inference(model: SpatialContextNetwork, x: torch.Tensor) -> dict:
"""
Run a single forward pass and return rich diagnostic output.
Returns:
dict with output logits, predicted pattern, network efficiency stats.
"""
with torch.no_grad():
output = model(x)
stats = model.get_network_stats(x)
probs = torch.softmax(output, dim=-1)
predicted_idx = probs.argmax(dim=-1)
results = {
"output_logits": output.tolist(),
"output_probabilities": probs.tolist(),
"predicted_pattern": [PATTERN_LABELS[i] for i in predicted_idx.tolist()],
"mean_active_neurons": round(stats["mean_active_neurons"], 2),
"network_efficiency": round(stats["network_efficiency"], 4),
"mean_context_score": round(stats["mean_context_score"], 4),
}
return results
def demo(args):
model = load_model(args.checkpoint, args.input_dim, args.n_neurons, args.output_dim)
total_params = sum(p.numel() for p in model.parameters())
print(f"\n{'='*60}")
print(" Spatial Context Network — Inference Demo")
print(f"{'='*60}")
print(f" Input dim : {args.input_dim}")
print(f" Hidden neurons: {args.n_neurons}")
print(f" Output dim : {args.output_dim}")
print(f" Parameters : {total_params}")
print(f"{'='*60}\n")
torch.manual_seed(42)
x = torch.randn(args.batch_size, args.input_dim)
print(f"Running inference on {args.batch_size} random samples...\n")
results = run_inference(model, x)
for i in range(args.batch_size):
probs = results["output_probabilities"][i]
predicted = results["predicted_pattern"][i]
prob_str = " | ".join(
f"{label}: {p:.3f}" for label, p in zip(PATTERN_LABELS, probs)
)
print(f" Sample {i}: [{prob_str}] → Predicted: {predicted}")
print(f"\n Network Stats:")
print(f" Active neurons : {results['mean_active_neurons']} / {args.n_neurons}")
print(f" Efficiency : {results['network_efficiency']:.1%}")
print(f" Context score : {results['mean_context_score']:.4f}")
if args.output_json:
with open(args.output_json, "w") as f:
json.dump(results, f, indent=2)
print(f"\n Results saved to {args.output_json}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="SCN Inference Demo")
parser.add_argument("--checkpoint", type=str, default=None)
parser.add_argument("--input_dim", type=int, default=10)
parser.add_argument("--n_neurons", type=int, default=32)
parser.add_argument("--output_dim", type=int, default=4)
parser.add_argument("--batch_size", type=int, default=8)
parser.add_argument("--output_json", type=str, default=None)
args = parser.parse_args()
demo(args) |