saliency-maps-metrics 0.0.8

This repository implements the faithfulness metrics mentionned in the paper --Computing and evaluating saliency maps for image classification: a tutorial-- in Pytorch.

Faithfulness metrics for saliency maps

TODO: explain the role of each script

This repository implements the faithfulness metrics mentionned in the paper "Computing and evaluating saliency maps for image classification: a tutorial" in Pytorch. This can be used to compute the following metrics:

• Deletion Area Under Curve (DAUC)/Insertion Area Under Curve (IAUC) (Petsiuk et al. 2019)
• Deletion Correlation (DC)/Insertion Correlation (IC) (Gomez et al. 2022)
• Increase In Confidence (IIC)/Average Drop (AD) (Chattopadhyay et al. 2017)
• Avereage Drop in Deletion (ADD) (Jung et al.)

Single step metrics

This section covers the use of the Increase In Confidence (IIC), Average Drop (AD) and Average Drop in Deletion (ADD) metrics. First, generate the saliency map of the image however you want. The only constraint is that the map should be a tensor of size (Nx1xH'xW'):

saliency_map = gradcam.attribute(img,class_ind)

Then, compute the metric. In this example, we use the class IIC_AD of this library to compute both the AD and IIC metric, as they require similar computations:

iic_ad = IIC_AD()
metric_dict = iic_ad(model,data,explanations,class_to_explain)
mean_iic = metric_dict["iic"]
mean_ad = metric_dict["ad"]

The resulting dictionary has two entries ic and ad which correspond to the mean value of the two metrics.

The __call__() method for all the metrics requires the following arguments :

• model: a torch.nn.Module that outputs a score tensor of shape (NxC), on which a softmax activation has been applied.
• data: the input image tensor of shape (Nx3xHxW).
• explanations: the saliency maps tensor of shape (Nx1xH'xW')
• class_to_explain: The index of the class to explain for each input image. The shape shoud be (N).

The ADD class is used similarly:

add = ADD()
metric_dict = add(model,data,explanations,class_to_explain)
mean = metric_dict["add"]

This resulting dictionary has only one entry add which correspond to the mean value of the ADD metric.

Multi-step metrics

This section covers the use of the DAUC, IAUC, DC and IC metrics. These metrics work similarly but some argument have to be passed to the constructor:

deletion = Deletion(data_shape,explanation_shape,bound_max_step=True)

Where data_shape and explanation_shape are the shape of the image and saliency map tensor. A high resolution saliency map of size 56x56 would require approximately 3k inferences. To prevent too much computation, you can set the bound_max_step argument to True to limits the amount of computation that can be computed. More precisly, this argument forces to mask/reveal several pixels before computing a new inference if the resolution of the saliency map is superior to 14x14. The call method returns a dictionary with two entries: dauc (which correspond to the original Deletion metric by Petsiuk et al.) and dc (the variant proposed by Gomez et al.).

result_dic = deletion(model,data,explanations,class_to_explain)
dauc = result_dic["dauc"]
dc = result_dic["dc"]

The Insertion metric is computed similarly:

insertion = Insertion(data_shape,explanation_shape,bound_max_step=True)
result_dic = insertion(model,data,explanations,class_to_explain)
iauc = result_dic["iauc"]
ic = result_dic["ic"]

Changing the filling method

These metrics work by removing parts of the image and replacing it with something else, for e.g. black pixels (Deletion, AD, IIC, ADD) or a blurred version of the input image (Insertion). The default replacing method can be changed with the data_replace_method argument passed to the constructor:

insertion = Insertion(data_shape,explanation_shape,bound_max_step=True,data_replace_method="black")
add = ADD(data_replace_method="blur")

Currently, the autorized values are:

• "black": replaces with black pixels
• "blur": replaces with a blurred version of the input image

Demonstration

Look at the demo.ipynb script for a demonstration. If you want to re-run the demo, you should download the model's weights pretrained on the CUB dataset and put it on the project's root. You should also download the CUB test dataset and put it in a "data" folder located at the project's root. The dataset should be formated as expected by the torchvision.datasets.ImageFolder class from torchvision.