| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
| import gradio as gr |
| import plotly.graph_objects as go |
| import torch |
| from data import irregular_batch |
| from model import LiquidTimeConstantRNN, MatchedGRU, MatchedRNN |
| from safetensors.torch import load_file |
|
|
| ARTIFACT_DIR = Path(__file__).resolve().parent / "artifacts" / "liquid-time-pocket" |
| REPORT = json.loads((ARTIFACT_DIR / "evaluation.json").read_text(encoding="utf-8")) |
| MODELS = { |
| "Liquid time constant": (LiquidTimeConstantRNN(), "liquid_time_constant"), |
| "Matched GRU": (MatchedGRU(), "matched_gru"), |
| "Matched RNN": (MatchedRNN(), "matched_rnn"), |
| } |
| for model, key in MODELS.values(): |
| model.load_state_dict(load_file(ARTIFACT_DIR / f"{key}.safetensors")) |
| model.eval() |
|
|
|
|
| @torch.inference_mode() |
| def forecast(seed: int, large_gaps: bool) -> tuple[go.Figure, dict]: |
| inputs, targets, time = irregular_batch( |
| 1, 128, int(seed), large_gaps=bool(large_gaps) |
| ) |
| figure = go.Figure() |
| figure.add_trace( |
| go.Scatter(x=time[0], y=targets[0, :, 0], name="Target", line={"width": 4}) |
| ) |
| metrics = {} |
| for label, (model, key) in MODELS.items(): |
| prediction = model(inputs)[0, :, 0] |
| figure.add_trace(go.Scatter(x=time[0], y=prediction, name=label)) |
| metrics[label] = { |
| "live_rmse": float( |
| (prediction - targets[0, :, 0]).square().mean().sqrt() |
| ), |
| "verified_large_gap_rmse": REPORT["results"][key][ |
| "unseen_large_gaps" |
| ]["rmse"], |
| } |
| figure.update_layout( |
| template="plotly_dark", |
| title="Irregularly sampled multiscale forecast", |
| xaxis_title="Continuous time", |
| yaxis_title="Signal", |
| ) |
| return figure, metrics |
|
|
|
|
| with gr.Blocks(title="Liquid Time Pocket") as demo: |
| gr.Markdown( |
| "# Liquid Time Pocket\n" |
| "Compare a learned continuous decay cell with exactly parameter-matched " |
| "GRU and RNN controls under irregular time gaps." |
| ) |
| with gr.Row(): |
| seed = gr.Slider(0, 100_000, value=2203, step=1, label="Task seed") |
| large_gaps = gr.Checkbox(True, label="Use unseen large time gaps") |
| initial = forecast(2203, True) |
| chart = gr.Plot(value=initial[0]) |
| metrics = gr.JSON(value=initial[1]) |
| button = gr.Button("Forecast irregular telemetry", variant="primary") |
| button.click(forecast, inputs=[seed, large_gaps], outputs=[chart, metrics]) |
|
|
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
|
|
