Built on the work of utkuozbulak/pytorch-cnn-visualizations
vis_grad file contains model_compare function which is used to visualize guided_gradcam_back_prop and model_compare_cam perfroms grad_cam
from vis_grad import model_compare_cam , model_compare
from torchvision import models
md=models.alexnet(pretrained=True)
md2=models.densenet121(pretrained=True)
md3=models.resnet152(pretrained=True)
md4 = models.vgg16(pretrained=True)
size=[224,224]
create a list containing (model,'model name to print',[input image size,input image size]) for each model
list=[[md,'alexnet',size],[md2,'densenet121',size],[md3,'resnet152',size],[md4,'vgg',size]]
model_compare(list,56,6,'../input_images/snake.jpg')
Grad cam completed
Guided backpropagation completed
Guided grad cam completed
Grad cam completed
Guided backpropagation completed
Guided grad cam completed
Grad cam completed
Guided backpropagation completed
Guided grad cam completed
Grad cam completed
Guided backpropagation completed
Guided grad cam completed
model_compare_cam(list,56,10,'../input_images/snake.jpg')
Grad cam completed
Grad cam completed
Grad cam completed
Grad cam completed