-
Notifications
You must be signed in to change notification settings - Fork 8
/
attention_map.py
50 lines (42 loc) · 1.73 KB
/
attention_map.py
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
import numpy as np
import tensorflow as tf
from matplotlib import cm
from tensorflow import keras
from vit_keras import layers
def attmap(model, image, alpha=0.7):
grid_size = int(np.sqrt(model.layers[5].output_shape[0][-2] - 1))
X = image
outputs = [
l.output[1] for l in model.layers if isinstance(l, layers.TransformerBlock)
]
weights = np.array(
tf.keras.models.Model(inputs=model.inputs, outputs=outputs).predict(X)
)
num_layers = weights.shape[0]
num_heads = weights.shape[2]
reshaped = weights.reshape(
(num_layers, num_heads, grid_size ** 2 + 1, grid_size ** 2 + 1)
)
reshaped = reshaped.mean(axis=1)
reshaped = reshaped + np.eye(reshaped.shape[1])
reshaped = reshaped / reshaped.sum(axis=(1, 2))[:, np.newaxis, np.newaxis]
v = reshaped[-1]
for n in range(1, len(reshaped)):
v = np.matmul(v, reshaped[-1 - n])
# # Attention from the output token to the input space.
mask = v[0, 1:].reshape(grid_size, grid_size)
mask = (mask - np.min(mask)) / (np.max(mask) - np.min(mask))
img = image[0, ...] * 255
heatmap = np.uint8(255 * mask)
# Use jet colormap to colorize heatmap
jet = cm.get_cmap("jet")
# Use RGB values of the colormap
jet_colors = jet(np.arange(256))[:, :3]
jet_heatmap = jet_colors[heatmap]
jet_heatmap = keras.preprocessing.image.array_to_img(jet_heatmap)
jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
jet_heatmap = keras.preprocessing.image.img_to_array(jet_heatmap)
# Superimpose the heatmap on original image
superimposed_img = jet_heatmap * alpha + img
superimposed_img = keras.preprocessing.image.array_to_img(superimposed_img)
return heatmap, superimposed_img