File size: 3,363 Bytes
c33f5fa | 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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | from __future__ import annotations
import json
from pathlib import Path
import gradio as gr
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import torch
from model import DynamicRoutingCapsuleNet, MatchedMLP
from PIL import Image
from safetensors.torch import load_file
PROJECT_DIR = Path(__file__).resolve().parent
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "capsule-pocket"
FRAME = pd.read_parquet(PROJECT_DIR / "data" / "test.parquet")
REPORT = json.loads((ARTIFACT_DIR / "evaluation.json").read_text(encoding="utf-8"))
CAPSULE = DynamicRoutingCapsuleNet()
CAPSULE.load_state_dict(load_file(ARTIFACT_DIR / "capsule.safetensors"))
CAPSULE.eval()
MLP = MatchedMLP()
MLP.load_state_dict(load_file(ARTIFACT_DIR / "matched_mlp.safetensors"))
MLP.eval()
@torch.inference_mode()
def inspect_capsules(
index: int,
vertical: int,
horizontal: int,
occlude: bool,
) -> tuple[Image.Image, go.Figure, dict]:
row = FRAME.iloc[int(index) % len(FRAME)]
image = torch.from_numpy(np.asarray(row["image"], dtype=np.float32) / 16).reshape(
8, 8
)
image = torch.roll(image, (int(vertical), int(horizontal)), (0, 1))
if vertical > 0:
image[: int(vertical)] = 0
elif vertical < 0:
image[int(vertical) :] = 0
if horizontal > 0:
image[:, : int(horizontal)] = 0
elif horizontal < 0:
image[:, int(horizontal) :] = 0
if occlude:
image[3:5, 3:5] = 0
pixels = image.reshape(1, 64)
_, lengths = CAPSULE(pixels)
mlp_logits = MLP(pixels)
figure = go.Figure(go.Bar(x=list(range(10)), y=lengths[0].numpy()))
figure.update_layout(
template="plotly_dark",
title="Digit-capsule vector lengths",
xaxis_title="Class",
yaxis_title="Length",
)
rendered = Image.fromarray(
image.mul(255).to(torch.uint8).numpy(), mode="L"
).resize((512, 512), Image.Resampling.NEAREST)
metrics = {
"true_label": int(row["label"]),
"capsule_prediction": int(lengths.argmax(1)),
"mlp_prediction": int(mlp_logits.argmax(1)),
"verified_capsule_translation_accuracy": REPORT["results"][
"dynamic_routing_capsule"
]["one_pixel_translation"]["accuracy"],
}
return rendered, figure, metrics
with gr.Blocks(title="Capsule Pocket") as demo:
gr.Markdown(
"# Capsule Pocket\n"
"Inspect dynamic-routing capsule lengths beside an exactly parameter-matched "
"MLP under translation and occlusion."
)
with gr.Row():
index = gr.Slider(0, len(FRAME) - 1, value=8, step=1, label="Test digit")
vertical = gr.Slider(-1, 1, value=0, step=1, label="Vertical shift")
horizontal = gr.Slider(-1, 1, value=0, step=1, label="Horizontal shift")
occlude = gr.Checkbox(False, label="Center occlusion")
initial = inspect_capsules(8, 0, 0, False)
with gr.Row():
image = gr.Image(value=initial[0], label="Input")
chart = gr.Plot(value=initial[1], label="Capsule lengths")
metrics = gr.JSON(value=initial[2], label="Matched prediction")
button = gr.Button("Route capsules", variant="primary")
button.click(
inspect_capsules,
inputs=[index, vertical, horizontal, occlude],
outputs=[image, chart, metrics],
)
if __name__ == "__main__":
demo.launch()
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