28 lines
1.4 KiB
Python
28 lines
1.4 KiB
Python
|
from task.image_interpretability import ImageInterpretability
|
|||
|
|
|
|||
|
|
|
|||
|
|
# # TODO: 使用gradcam算法，针对resnet模型，利用本地图片both.png进行可解释性分析，参数为默认参数
|
|||
|
|
# files_path = ImageInterpretability().perform(image_path='sample/both.png',method='gradcam', model_name='resnet',output_path='D:\桌面\image_interprebility\image_interprebility\image_interprebility/test')
|
|||
|
|
# print(files_path)
|
|||
|
|
#
|
|||
|
|
kwargs={
|
|||
|
|
"aug_smooth":True,
|
|||
|
|
"eigen_smooth":True
|
|||
|
|
}
|
|||
|
|
model_info ={"model_name":'resnet50',
|
|||
|
|
"source": "torchvision"
|
|||
|
|
}
|
|||
|
|
# TODO: 使用fullgrad算法，针对resnet模型，利用本地图片both.png进行可解释性分析,参数为默认参数
|
|||
|
|
files_path = ImageInterpretability().perform(image_path='sample/img_2.png',method='fullgrad',model_info=model_info,output_path="D:/test_image/",**kwargs)
|
|||
|
|
print(files_path)
|
|||
|
|
|
|||
|
|
|
|||
|
|
# kwargs={
|
|||
|
|
# "target_layer":'',
|
|||
|
|
# "aug_smooth":True,
|
|||
|
|
# "eigen_smooth":True
|
|||
|
|
# }
|
|||
|
|
# # TODO: 使用fullgrad算法，针对resnet模型，利用本地图片both.png进行可解释性分析,采用数据增强技术来改善cam质量，并使用提取主成分的方式减少噪声,通过改变target_layer得到不同层的结果
|
|||
|
|
# files_path = ImageInterpretability().perform(image_path='sample/ILSVRC2012_val_00000002.JPEG',method='gradcam', model_name='resnet',**kwargs)
|
|||
|
|
# print(files_path)
|