app.py

# import the necessary packages
from utilities import config
from tensorflow.keras import layers
from tensorflow import keras
import tensorflow as tf
import matplotlib.pyplot as plt
import math
import gradio as gr

# load the models from disk
conv_stem = keras.models.load_model(
	config.IMAGENETTE_STEM_PATH,
	compile=False
)
conv_trunk = keras.models.load_model(
	config.IMAGENETTE_TRUNK_PATH,
	compile=False
)
conv_attn = keras.models.load_model(
	config.IMAGENETTE_ATTN_PATH,
	compile=False
)

def plot_attention(image):
	# resize the image to a 224, 224 dim
	image = tf.image.convert_image_dtype(image, tf.float32)
	image = tf.image.resize(image, (224, 224))
	image = image[tf.newaxis, ...]

	# pass through the stem
	test_x = conv_stem(image)
	# pass through the trunk
	test_x = conv_trunk(test_x)
	# pass through the attention pooling block
	_, test_viz_weights = conv_attn(test_x)
	test_viz_weights = test_viz_weights[tf.newaxis, ...]
	
	# reshape the vizualization weights
	num_patches = tf.shape(test_viz_weights)[-1]
	height = width = int(math.sqrt(num_patches))
	test_viz_weights = layers.Reshape((height, width))(test_viz_weights)
	
	index = 0
	selected_image = image[index]
	selected_weight = test_viz_weights[index]
	
	img = plt.imshow(selected_image)
	plt.imshow(
		selected_weight,
		cmap="inferno",
		alpha=0.6,
		extent=img.get_extent()
	)
	plt.axis("off")
	return plt

iface = gr.Interface(
	fn=plot_attention,
	inputs=[gr.inputs.Image(label="Input Image")],
	outputs="Attention Map").launch()