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Demo for visualizing CNNs using Guided_Grad_Gam and Grad_cam

Sivateja Gollapudi

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
import pretrained models using torch vision models (custom models can be used)
from torchvision import models
using 3 models , alex net , dense net 121 and resnet 152
md=models.alexnet(pretrained=True)
md2=models.densenet121(pretrained=True)
md3=models.resnet152(pretrained=True)
md4 = models.vgg16(pretrained=True)
input image size used by the network
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]]
pass the list , class number , layer to visualize , input_image to visualize on
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

png

png

Images are automatically saved in result folder
For visualizing grad_cam
model_compare_cam(list,56,10,'../input_images/snake.jpg')
Grad cam completed
Grad cam completed
Grad cam completed
Grad cam completed

png

png

png

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Pytorch implementation of convolutional neural network visualization techniques

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