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# 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="image").launch() |