如何在tensorflow attention_ocr上运行预训练模型？

pip install tensorflow
pip install attention_ocr

import tensorflow as tf

# 指定预训练模型的路径
model_path = 'path/to/pretrained/model'

# 加载预训练模型
model = tf.saved_model.loader.load(sess=tf.Session(), tags=['serve'], export_dir=model_path)

import numpy as np
from PIL import Image

# 加载图像并进行预处理
image = Image.open('path/to/image.jpg')
image = image.resize((width, height))  # 根据模型要求调整大小
image = np.array(image) / 255.0  # 归一化
image = np.expand_dims(image, axis=0)  # 增加批次维度

# 获取模型的输入和输出张量
input_tensor = model.signature_def['serving_default'].inputs['input_image']
output_tensor = model.signature_def['serving_default'].outputs['output']

# 运行模型进行预测
predictions = sess.run(output_tensor, feed_dict={input_tensor: image})

# 假设输出是一个包含文本和边界框的张量
text = predictions['text']
boxes = predictions['boxes']

print("识别的文本:", text)
print("边界框:", boxes)

import tensorflow as tf
import numpy as np
from PIL import Image

# 加载预训练模型
model_path = 'path/to/pretrained/model'
sess = tf.Session()
model = tf.saved_model.loader.load(sess=sess, tags=['serve'], export_dir=model_path)

# 准备输入数据
image = Image.open('path/to/image.jpg')
image = image.resize((width, height))  # 根据模型要求调整大小
image = np.array(image) / 255.0  # 归一化
image = np.expand_dims(image, axis=0)  # 增加批次维度

# 获取模型的输入和输出张量
input_tensor = model.signature_def['serving_default'].inputs['input_image']
output_tensor = model.signature_def['serving_default'].outputs['output']

# 运行模型进行预测
predictions = sess.run(output_tensor, feed_dict={input_tensor: image})

# 处理预测结果
text = predictions['text']
boxes = predictions['boxes']

print("识别的文本:", text)
print("边界框:", boxes)