A Python visualization toolkit, built with PyTorch, for neural networks in PyTorch.
Neural networks are often described as "black box". The lack of understanding on how neural networks make predictions enables unpredictable/biased models, causing real harm to society and a loss of trust in AI-assisted systems.
Feature visualization is an area of research, which aims to understand how neural networks perceive images. However, implementing such techniques is often complicated.
FlashTorch was created to solve this problem!
You can apply feature visualization techniques (such as saliency maps and activation maximization) on your model, with as little as a few lines of code.
It is compatible with pre-trained models that come with torchvision, and seamlessly integrates with other custom models built in PyTorch.
Take a look at the quick 3min intro/demo to FlashTorch below!
Head over to example notebooks on Colab!
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Saliency maps:
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Activation maximization:
If you are installing FlashTorch for the first time:
$ pip install flashtorchOr if you are upgrading it:
$ pip install flashtorch -UThese are currently available modules.
flashtorch.utils: some useful utility functions for data handling & transformationflashtorch.utils.imagenet:ImageNetIndexclass for easy-ish retrieval of class indexflashtorch.saliency.backprop:Backpropclass for calculating gradientsflashtorch.activmax.gradient_ascent:GradientAscentclass for activation maximization
You can inspect each module with Python built-in function help. The output of that is available on Quick API Guide for your convenience.
Below, you can find simple demos to get you started, as well as links to some handy notebooks showing additional examples of using FlashTorch.
- Image handling notebook
- Saliency map with backpropagation notebook
- Google Colab version - best for trying it out
Saliency in human visual perception is a subjective quality that makes certain things within the field of view stand out from the rest and grabs our attention.
Saliency maps in computer vision provide indications of the most salient regions within images. By creating a saliency map for neural networks, we can gain some intuition on "where the network is paying the most attention to" in an input image.
Using flashtorch.saliency module, let's visualize image-specific class saliency maps of AlexNet pre-trained on ImageNet classification tasks.
The network is focusing on the sunken eyes and the round head for this owl.
- Activation maximization notebook
- Google Colab version - best for trying it out
Activation maximization is one form of feature visualization that allows us to visualize what CNN filters are "looking for", by applying each filter to an input image and updating the input image so as to maximize the activation of the filter of interest (i.e. treating it as a gradient ascent task with filter activation values as the loss).
Using flashtorch.activmax module, let's visualize images optimized with filters
from VGG16 pre-trained on ImageNet classification tasks.
Concepts such as 'eyes' (filter 45) and 'entrances (?)' (filter 271) seem to appear in the conv5_1 layer of VGG16.
Visit the notebook above to see what earlier layers do!
Thanks for your interest in contributing!
Please first head over to the Code of Conduct, which helps set the ground rules for participation in communities and helps build a culture of respect.
Still here? Great! There are many ways to contribute to this project. Get started here.
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Hopperx1 London, June 2019 - slide deck
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Introduction and overview of feature visualization: Feature Visualization
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The latest development in feature visualization: Exploring Neural Networks with Activation Atlases
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Using backpropagation for gradient visualization: Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
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Guided backprobagation: Striving for Simplicity: The All Convolutional Net
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Activation maximization: Visualizing Higher-Layer Features of a Deep Network
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pytorch-cnn-visualizations by utkuozbulak
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keras-vis by raghakot
Misa Ogura (2019, September 26).
MisaOgura/flashtorch: 0.1.1 (Version v0.1.1).
Zenodo. http://doi.org/10.5281/zenodo.3461737