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- import os
- # 选择使用0号卡
- os.environ['CUDA_VISIBLE_DEVICES'] = '0'
- import os.path as osp
- import paddlex as pdx
- # 下载和解压Imagenet果蔬分类数据集
- veg_dataset = 'https://bj.bcebos.com/paddlex/interpret/mini_imagenet_veg.tar.gz'
- pdx.utils.download_and_decompress(veg_dataset, path='./')
- # 下载和解压已训练好的MobileNetV2模型
- model_file = 'https://bj.bcebos.com/paddlex/interpret/mini_imagenet_veg_mobilenetv2.tar.gz'
- pdx.utils.download_and_decompress(model_file, path='./')
- # 加载模型
- model_file = 'mini_imagenet_veg_mobilenetv2'
- model = pdx.load_model(model_file)
- # 定义测试所用的数据集
- dataset = 'mini_imagenet_veg'
- test_dataset = pdx.datasets.ImageNet(
- data_dir=dataset,
- file_list=osp.join(dataset, 'test_list.txt'),
- label_list=osp.join(dataset, 'labels.txt'),
- transforms=model.test_transforms)
- import numpy as np
- np.random.seed(5)
- perm = np.random.permutation(len(test_dataset.file_list))
- # 可解释性可视化
- pdx.interpret.normlime(
- test_dataset.file_list[0][0],
- model,
- test_dataset,
- save_dir='./',
- normlime_weights_file='{}_{}.npy'.format(
- dataset.split('/')[-1], model.model_name))
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