app.py

from huggingface_hub import from_pretrained_keras
import tensorflow as tf
import gradio as gr

# download the model in the global context
vis_model = from_pretrained_keras("keras-io/involution")

def infer(test_image):
	# convert the image to a tensorflow tensor and resize the image
	# to a constant 32x32
	image = tf.constant(test_image)
	image = tf.image.resize(image, (32, 32))
	
	# Use the model and get the activation maps
	(inv1_out, inv2_out, inv3_out) = vis_model.predict(image[None, ...])
	_, inv1_kernel = inv1_out
	_, inv2_kernel = inv2_out
	_, inv3_kernel = inv3_out

	inv1_kernel = tf.reduce_sum(inv1_kernel, axis=[-1, -2, -3])
	inv2_kernel = tf.reduce_sum(inv2_kernel, axis=[-1, -2, -3])
	inv3_kernel = tf.reduce_sum(inv3_kernel, axis=[-1, -2, -3])

	return (
		tf.keras.utils.array_to_img(inv1_kernel[0, ..., None]),
		tf.keras.utils.array_to_img(inv2_kernel[0, ..., None]),
		tf.keras.utils.array_to_img(inv3_kernel[0, ..., None]),
	)

# define the article
article = """<center>
Authors: <a href='https://twitter.com/ariG23498' target='_blank'>Aritra Roy Gosthipaty</a> | 
<a href='https://twitter.com/ritwik_raha' target='_blank'>Ritwik Raha</a>
<br>
<a href='https://arxiv.org/abs/2103.06255' target='_blank'>Involution: Inverting the Inherence of Convolution for Visual Recognition</a>
<br>
Convolution Kernel
<img src='https://i.imgur.com/Y7xVrwb.png' alt='Convolution'>
<br>
Involution Kernel
<img src='https://i.imgur.com/jHIW26g.png' alt='Involution'>
</center>"""

# define the description
description="""
Visualize the activation maps from the Involution Kernel.🕵🏻‍♂️
"""
iface = gr.Interface(
	fn=infer,
	title="Involutional Neural Networks",
	article=article,
	description=description,
	inputs=gr.inputs.Image(label="Input Image"),
	outputs=[
		gr.outputs.Image(label="Activation from Kernel 1"),
		gr.outputs.Image(label="Activation from Kernel 2"),
		gr.outputs.Image(label="Activation from Kernel 3"),
	],
	examples=[["examples/lama.jpeg"], ["examples/dalai_lama.jpeg"]],
	layout="horizontal",
).launch(share=True)