如何从google colab的压缩文件夹中读取/导入用于多分类的训练和测试图像？gdrive已装载到gcolab

from google.colab import drive
drive.mount('/content/drive')

from google.colab import files
uploaded = files.upload()

import zipfile
with zipfile.ZipFile('images.zip', 'r') as zip_ref:
    zip_ref.extractall('images')

import os
folder_path = '/content/images'  # 解压缩后的文件夹路径
print(os.listdir(folder_path))

from keras.preprocessing.image import ImageDataGenerator

# 设置图像数据生成器
datagen = ImageDataGenerator(rescale=1./255)

# 导入训练集图像数据
train_generator = datagen.flow_from_directory(
        '/content/images/train',  # 训练集图像文件夹路径
        target_size=(150, 150),  # 图像大小
        batch_size=32,
        class_mode='categorical')  # 多分类

# 导入测试集图像数据
test_generator = datagen.flow_from_directory(
        '/content/images/test',  # 测试集图像文件夹路径
        target_size=(150, 150),  # 图像大小
        batch_size=32,
        class_mode='categorical')  # 多分类

# 构建模型并进行训练
model.fit_generator(
        train_generator,
        steps_per_epoch=2000 // 32,  # 每个epoch的步数
        epochs=50,
        validation_data=test_generator,
        validation_steps=800 // 32)  # 验证集的步数