如何纠正生成显著性图时的错误

Question

我正在尝试创建一个函数来生成显著性贴图。这是代码

def do_salience(image, model, label, prefix):
    img = cv2.imread(image)
    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    img= cv2.resize(img,(300,300))/255.0
    img= np.expand_dims(img,axis=0)

num_classes= 2

expected_output= tf.one_hot([label]* img.shape[0],num_classes)

with tf.GradientTape() as tape: 
    inputs = tf.cast(img,tf.float32)
    tape.watch(inputs)
    predictions = model(inputs)

loss= tf.keras.losses.categorical_crossentropy(
    expected_output,predictions
)

print(predictions)
gradients= tape.gradient(loss,inputs)
grayscale_tensor = tf.reduce_sum(tf.abs(gradients), axis=-1)

normalized_tensor = tf.cast(
  255
  * (grayscale_tensor-tf.reduce_min(grayscale_tensor))
  / (tf.reduce_max(grayscale_tensor)-tf.reduce_min(grayscale_tensor)),
  tf.uint8,
 )

normalized_tensor= tf.squeeze(normalized_tensor)

plt.figure(figsize=(8, 8))
plt.axis('off')
plt.imshow(normalized_tensor, cmap='gray')
plt.show()

这部分是将显著性贴图与原始图像叠加

gradient_color = cv2.applyColorMap(normalized_tensor.numpy(),
                                  cv2.COLORMAP_HOT)
gradient_color = gradient_color/255.0
super_imposed = cv2.addWeighted(img,0.5,gradient_color,0.5,0.0)

salient_image_name = prefix + image
normalized_tensor = tf.expand_dims(normalized_tensor, -1)
normalized_tensor = tf.io.encode_jpeg(normalized_tensor, 
                       quality=100, format='grayscale')
writer = tf.io.write_file(salient_image_name, normalized_tensor)

接下来，进入“使用未经训练的模型生成显著性图”阶段

model.load_weights('0_epochs.h5')
do_salience('cat1.jpg', model, 0, 'epoch0_salient')
do_salience('cat2.jpg', model, 0, 'epoch0_salient')
do_salience('catanddog.jpg', model, 0, 'epoch0_salient')
do_salience('dog1.jpg', model, 1, 'epoch0_salient')
do_salience('dog2.jpg', model, 1, 'epoch0_salient')

加载代码后，我被抛出一个错误

我相信代码中有一个错误，它叠加了显著性贴图。如何纠正