File size: 2,811 Bytes
bd894ac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | 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()
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