File size: 2,514 Bytes
477b01d | 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 | 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()
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