Captum is a model interpretability and understanding library for PyTorch. Captum means comprehension in latin and contains general purpose implementations of integrated gradients, saliency maps, smoothgrad, vargrad and others for PyTorch models. It has quick integration for models built with domain-specific libraries such as torchvision, torchtext, and others.
Captum is currently in beta and under active development!
With the increase in model complexity and the resulting lack of transparency, model interpretability methods have become increasingly important. Model understanding is both an active area of research as well as an area of focus for practical applications across industries using machine learning. Captum provides state-of-the-art algorithms, including Integrated Gradients, to provide researchers and developers with an easy way to understand which features are contributing to a model’s output.
For model developers, Captum can be used to improve and troubleshoot models by facilitating the identification of different features that contribute to a model’s output in order to design better models and troubleshoot unexpected model outputs.
Captum helps ML researchers more easily implement interpretability algorithms that can interact with PyTorch models. Captum also allows researchers to quickly benchmark their work against other existing algorithms available in the library.
The primary audiences for Captum are model developers who are looking to improve their models and understand which features are important and interpretability researchers focused on identifying algorithms that can better interpret many types of models.
Captum can also be used by application engineers who are using trained models in production. Captum provides easier troubleshooting through improved model interpretability, and the potential for delivering better explanations to end users on why they’re seeing a specific piece of content, such as a movie recommendation.
Installation Requirements
- Python >= 3.6
- PyTorch >= 1.2
The latest release of Captum is easily installed either via Anaconda (recommended):
conda install captum -c pytorchor via pip:
pip install captumIf you'd like to try our bleeding edge features (and don't mind potentially running into the occasional bug here or there), you can install the latest master directly from GitHub:
pip install git+https://github.com/pytorch/captum.gitManual / Dev install
Alternatively, you can do a manual install. For a basic install, run:
git clone https://github.com/pytorch/captum.git
cd captum
pip install -e .To customize the installation, you can also run the following variants of the above:
pip install -e .[dev]: Also installs all tools necessary for development (testing, linting, docs building; see Contributing below).pip install -e .[tutorials]: Also installs all packages necessary for running the tutorial notebooks.
To execute unit tests from a manual install, run:
# running a single unit test
python -m unittest -v tests.attr.test_saliency
# running all unit tests
pytest -raCaptum helps you interpret and understand predictions of PyTorch models by exploring features that contribute to a prediction the model makes. It also helps understand which neurons and layers are important for model predictions.
Currently, the library uses gradient-based interpretability algorithms and attributes contributions to each input of the model with respect to different neurons and layers, both intermediate and final.
Let's apply some of those algorithms to a toy model we have created for demonstration purposes. For simplicity, we will use the following architecture, but users are welcome to use any PyTorch model of their choice.
import numpy as np
import torch
import torch.nn as nn
from captum.attr import (
GradientShap,
IntegratedGradients,
LayerConductance,
NeuronConductance,
NoiseTunnel,
)
class ToyModel(nn.Module):
def __init__(self):
super().__init__()
self.lin1 = nn.Linear(3, 4)
self.lin1.weight = nn.Parameter(torch.ones(4, 3))
self.lin1.bias = nn.Parameter(torch.tensor([-10.0, 1.0, 1.0, 1.0]))
self.relu = nn.ReLU()
self.lin2 = nn.Linear(4, 1)
self.lin2.weight = nn.Parameter(torch.ones(1, 4))
self.lin2.bias = nn.Parameter(torch.tensor([-3.0]))
def forward(self, input):
lin1 = self.lin1(input)
relu = self.relu(lin1)
lin2 = self.lin2(relu)
return lin2
Let's create an instance of our model and set it to eval mode.
model = ToyModel()
model.eval()
Next, we need to define simple input and baseline tensors. Baselines belong to the input space and often carry no predictive signal. Zero tensor can serve as a baseline for many tasks. Some interpretability algorithms such as Integrated Gradients, Deeplift and GradientShap are designed to attribute the change between the input and baseline to a predictive class or a value that the neural network outputs.
We will apply model interpretability algorithms on the network mentioned above in order to understand the importance of individual neurons/layers and the parts of the input that play an important role in the final prediction.
Let's fix random seeds to make computations deterministic
torch.manual_seed(123)
np.random.seed(124)
Let's define our input and baseline tensors. Baselines are used in some
interpretability algorithms such as IntegratedGradients, DeepLift, GradientShap, NeuronConductance, LayerConductance, InternalInfluence and NeuronIntegratedGradients.
input = torch.rand(2, 3)
baseline = torch.zeros(2, 3)
Next we will use IntegratedGradients algorithms to assign attribution
scores to each input feature with respect to final output.
ig = IntegratedGradients(model)
attributions, delta = ig.attribute(input, baseline)
print('IG Attributions: ', attributions, ' Approximation error: ', delta)
Output:
IG Attributions: tensor([[0.8883, 1.5497, 0.7550],
[2.0657, 0.2219, 2.5996]])
Approximation Error: 9.5367431640625e-07
The algorithm outputs an attribution score for each input element and an
approximation error that we would like to minimize. If the approximation error
is large, we can try larger number of integral approximation steps by setting
n_steps to a larger value. Not all algorithms return approximation error.
Those which do, they compute it based on the completeness property of the algorithms.
Positive attribution score means that the input in that particular position positively contributed to the final prediction and negative means the opposite. The magnitude of the attribution score signifies the strength of the contribution. Zero attribution score means no contribution from that particular feature.
Similarly, we can apply GradientShap, DeepLift and other attribution algorithms to the model.
gs = GradientShap(model)
# We define a distribution of baselines and draw `n_samples` from that
# distribution in order to estimate the expectations of gradients across all baselines
baseline_dist = torch.rand(100, 3)
attributions, delta = gs.attribute(input, baseline_dist, n_samples=50)
print('GradientShap Attributions: ', attributions, ' Approximation error: ', delta)
Output
GradientShap Attributions: tensor([[ 0.0159, -0.8478, 0.3028],
[ 0.1546, -1.0068, 0.2770]])
Approximation Error: tensor(0.0462)
In order to smooth and improve the quality of the attributions we can run
IntegratedGradients and other attribution methods through a NoiseTunnel.
NoiseTunnel allows to use SmoothGrad, SmoothGrad_Sq and VarGrad techniques
to smoothen the attributions by aggregating them for multiple noisy
samples that were generated by adding gaussian noise.
Here is an example how we can use NoiseTunnel with IntegratedGradients.
ig = IntegratedGradients(model)
nt = NoiseTunnel(ig)
attributions, delta = nt.attribute(input, nt_type='smoothgrad', baselines=baseline)
print('IG + SmoothGrad Attributions: ', attributions, ' Approximation error: ', delta)
Output
IG + SmoothGrad Attributions: tensor([[-1.2138, 0.6688, 0.7747],
[1.3862, 0.7529, 2.2907]])
Approximation Error: 0.07243824005126953
Let's look into the internals of our network and understand which layers and neurons are important for the predictions. We will start with the neuron conductance. Neuron conductance helps us to identify input features that are important for a particular neuron in a given layer. In this case, we choose to analyze the third neuron in the first layer.
nc = NeuronConductance(model, model.lin2)
attributions = nc.attribute(input, neuron_index=3, baselines=baseline)
print('Neuron Attributions: ', attributions)
Output
Neuron Attributions: tensor([[0.2902, 0.5062, 0.2466],
[0.6748, 0.0725, 0.8492]])
Layer conductance shows the importance of neurons for a layer and given input. It doesn't attribute the contribution scores to the input features but shows the importance of each neuron in selected layer.
lc = LayerConductance(model, model.lin1)
attributions, delta = lc.attribute(input, baselines=baseline)
print('Layer Attributions: ', attributions, ' Approximation Error: ', delta)
Outputs
Layer Attributions: tensor([[0.8883, 1.5497, 0.7550],
[2.0657, 0.2219, 2.5996]], grad_fn=<SumBackward1>)
Approximation error: 9.5367431640625e-07
More details on the list of supported algorithms and how to apply Captum on different types of models can be found in our tutorials.
See the CONTRIBUTING file for how to help out.
- Axiomatic Attribution for Deep Networks, Mukund Sundararajan et al. 2017
- Did the Model Understand the Question? Pramod K. Mudrakarta, et al. 2018
- Investigating the influence of noise and distractors on the interpretation of neural networks, Pieter-Jan Kindermans et al. 2016
- SmoothGrad: removing noise by adding noise, Daniel Smilkov et al. 2017
- Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values, Julius Adebayo et al. 2018
- Sanity Checks for Saliency Maps, Julius Adebayo et al. 2018
- How Important is a neuron?, Kedar Dhamdhere et al. 2018
- Learning Important Features Through Propagating Activation Differences, Avanti Shrikumar et al. 2017
- Computationally Efficient Measures of Internal Neuron Importance, Avanti Shrikumar et al. 2018
- A Unified Approach to Interpreting Model Predictions, Scott M. Lundberg et al. 2017
- Influence-Directed Explanations for Deep Convolutional Networks, Klas Leino et al. 2018
- Towards better understanding of gradient-based attribution methods for deep neural networks, Marco Ancona et al. 2018
Captum is BSD licensed, as found in the LICENSE file.