add interpret tutorials

tutorials/interpret/interpret.py

+import os
+# 选择使用0号卡
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+import paddlex as pdx
+from paddlex.cla import transforms
+
+# 下载和解压Imagenet果蔬分类数据集
+veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/mini_imagenet_veg.tar.gz'
+pdx.utils.download_and_decompress(veg_dataset, path='./')
+
+# 定义测试集的transform
+test_transforms = transforms.Compose([
+    transforms.ResizeByShort(short_size=256),
+    transforms.CenterCrop(crop_size=224),
+    transforms.Normalize()
+])
+
+# 定义测试所用的数据集
+test_dataset = pdx.datasets.ImageNet(
+    data_dir='mini_imagenet_veg',
+    file_list=osp.join('mini_imagenet_veg', 'test_list.txt'),
+    label_list=osp.join('mini_imagenet_veg', 'labels.txt'),
+    transforms=test_transforms)
+
+# 下载和解压已训练好的MobileNetV2模型
+model_file = 'https://bj.bcebos.com/paddlex/models/mini_imagenet_veg_mobilenetv2.tar.gz'
+pdx.utils.download_and_decompress(model_file, path='./')
+
+# 导入模型
+model = pdx.load_model('mini_imagenet_veg_mobilenetv2')
+
+# 可解释性可视化
+save_dir = 'interpret_results'
+if not osp.exists(save_dir):
+    os.makedirs(save_dir)
+visualize('mini_imagenet_veg/mushroom/n07734744_1106.JPEG', 
+          model,
+          test_dataset, 
+          algo='lime',
+          save_dir=save_dir)
+visualize('mini_imagenet_veg/mushroom/n07734744_1106.JPEG', 
+          model, 
+          test_dataset, 
+          algo='normlime',
+          save_dir=save_dir)
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