| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
| import gradio as gr |
| import numpy as np |
| import plotly.graph_objects as go |
| import torch |
| from model import MatchedMLP, SplineKAN |
| from plotly.subplots import make_subplots |
| from safetensors.torch import load_file |
|
|
| ARTIFACT_DIR = Path(__file__).resolve().parent / "artifacts" / "spline-kan-pocket" |
| REPORT = json.loads((ARTIFACT_DIR / "evaluation.json").read_text(encoding="utf-8")) |
| MODELS = { |
| "Spline KAN": (SplineKAN(), "spline_kan"), |
| "Matched MLP": (MatchedMLP(), "matched_mlp"), |
| } |
| for model, key in MODELS.values(): |
| model.load_state_dict(load_file(ARTIFACT_DIR / f"{key}.safetensors")) |
| model.eval() |
|
|
|
|
| def target(x: np.ndarray, y: np.ndarray) -> np.ndarray: |
| return ( |
| np.sin(np.pi * x * y) |
| + 0.35 * (x**3 - y**2) |
| + 0.2 * np.cos(2 * np.pi * x) |
| ) |
|
|
|
|
| @torch.inference_mode() |
| def render_surface(model_name: str, domain: float) -> tuple[go.Figure, dict]: |
| axis = np.linspace(-float(domain), float(domain), 60, dtype=np.float32) |
| x, y = np.meshgrid(axis, axis) |
| inputs = torch.from_numpy(np.stack([x.ravel(), y.ravel()], axis=1)) |
| model, key = MODELS[model_name] |
| prediction = model(inputs)[:, 0].numpy().reshape(x.shape) |
| truth = target(x, y) |
| figure = make_subplots( |
| rows=1, |
| cols=2, |
| specs=[[{"type": "surface"}, {"type": "surface"}]], |
| subplot_titles=["True surface", model_name], |
| ) |
| figure.add_trace(go.Surface(x=x, y=y, z=truth, showscale=False), row=1, col=1) |
| figure.add_trace( |
| go.Surface(x=x, y=y, z=prediction, showscale=False), row=1, col=2 |
| ) |
| figure.update_layout(template="plotly_dark", height=560) |
| metrics = { |
| "parameters": REPORT["results"][key]["parameters"], |
| "interpolation_rmse": REPORT["results"][key]["interpolation"]["rmse"], |
| "extrapolation_rmse": REPORT["results"][key]["extrapolation"]["rmse"], |
| "live_surface_rmse": float(np.sqrt(np.mean((prediction - truth) ** 2))), |
| } |
| return figure, metrics |
|
|
|
|
| with gr.Blocks(title="Spline KAN Pocket") as demo: |
| gr.Markdown( |
| "# Spline KAN Pocket\n" |
| "Explore a learnable edge-spline network and its exactly parameter-matched " |
| "MLP control on a nonlinear symbolic surface." |
| ) |
| with gr.Row(): |
| model_name = gr.Dropdown(list(MODELS), value="Spline KAN", label="Model") |
| domain = gr.Slider(1.0, 1.5, value=1.0, step=0.1, label="Displayed domain") |
| initial = render_surface("Spline KAN", 1.0) |
| surface = gr.Plot(value=initial[0]) |
| metrics = gr.JSON(value=initial[1]) |
| button = gr.Button("Render learned surface", variant="primary") |
| button.click(render_surface, inputs=[model_name, domain], outputs=[surface, metrics]) |
|
|
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
