This repository contains code for the following saliency techniques:
- XRAI
- SmoothGrad
- Vanilla Gradients
- Guided Backpropogation
- Integrated Gradients
- (Guided) Grad-CAM
The methods should work with all models from the torchvision package. Tested models so far are:
- VGG variants
- ResNet variants
- DenseNet variants
- Inception/GoogLeNet*
*In order for Guided Backpropagation and Grad-CAM to work properly with the Inception and GoogLeNet models, they need to by modified slightly, such that all ReLUs are modules of the model rather than function calls.
# This class can be found at the very end of inception.py and googlenet.py respectively.
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
self.relu = nn.ReLU(inplace=True) # Add this line
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return self.relu(x) # Replaces F.relu(x, inplace=True)For a brief overview on how to use the package, please have a look at this short tutorial notebook. The bare minimum is summarized below.
# Standard imports
import torchvision
# Import desired utils and methods
from ml_utils import load_image, show_mask
from guided_backprop import GuidedBackprop
# Load model and image
model = torchvision.models.resnet50(pretrained=True)
doberman = load_image('images/doberman.png', size=224)
# Construct a saliency object and compute the saliency mask.
guided_backprop = GuidedBackprop(model)
rgb_mask = guided_backprop.get_mask(image_tensor=doberman)
# Visualize the result
show_mask(rgb_mask, title='Guided Backprop')The implementation follows closely that of the corresponding TensorFlow saliency repository, reusing its code were applicable (mostly for the XRAI method).
Further inspiration has been taken from this repository.