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# import the necessary packages
from tensorflow.keras import layers
import tensorflow as tf
import matplotlib.pyplot as plt
import math
class PlotAttention:
def __init__(self, model):
self.model = model
def __call__(self, 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 = self.model.stem(image)
# pass through the trunk
test_x = self.model.trunk(test_x)
# pass through the attention pooling block
_, test_viz_weights = self.model.attention_pooling(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]
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))
ax[0].imshow(selected_image)
ax[0].set_title(f"Original")
ax[0].axis("off")
img = ax[1].imshow(selected_image)
ax[1].imshow(selected_weight, cmap='inferno', alpha=0.6, extent=img.get_extent())
ax[1].set_title(f"Attended")
ax[1].axis("off")
plt.axis("off")
return plt |