Captum
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About
Captum is a PyTorch library for model interpretability. It provides tools for understanding and attributing the predictions of PyTorch models across various modalities like vision and text. It supports a wide range of PyTorch models and is designed to be extensible for interpretability research. Key features include multi-modal support, PyTorch integration, and an open-source design.
Platform
Features
• built on pytorch
• multi-modal support
• extensible
FAQs
How do I set the target parameter to an attribution method?
The purpose of target is to select a single (scalar) value for each example in the output of your model to compute attributions based on the given target parameter.
I am facing Out-Of-Memory (OOM) errors when using Captum. How do I resolve this?
To address this issue, you can either reduce n_steps, which may lead to lower-quality approximations, or use the internal_batch_size argument, which allows dividing the expanded input into batches which are processed sequentially.
I am using a perturbation based method, and attributions are taking too long to compute. How can I speed it up?
To enable this, simply set the perturbations_per_eval argument to the desired value.
Are SmoothGrad or VarGrad supported in Captum?
Yes! SmoothGrad and VarGrad are available through NoiseTunnel in Captum, which can be used with any attribution algorithm in Captum.
How do I use Captum with BERT models?
We have a tutorial demonstrating usage of Integrated Gradients on BERT here.
My model inputs or outputs token indices, and when using Captum I see errors relating to gradients, how do I resolve this?
To apply gradient-based attribution methods, it is necessary to replace the embedding layer with an InterpretableEmbedding layer or use LayerIntegratedGradients to compute attribution with respect to the embedding output.
Can my model use functional non-linearities (E.g. nn.functional.ReLU) or can reused modules be used with Captum?
Most methods will work fine with functional non-linearities and arbitrary operations.
Do JIT models, DataParallel models, or DistributedDataParallel models work with Captum?
Yes, we have support for all these model types. Note that JIT models do not yet support hooks, so any methods using hooks including layer and neuron attribution methods, DeepLift, Guided Backprop, and Deconvolution are not supported.
I am working on a new interpretability or attribution method and would like to add it to Captum. How do I proceed?
For interpretability methods created by the community, we have two methods of involvement: Awesome List and Inclusion in Captum.
How can I resolve cudnn RNN backward error for RNN or LSTM network?
To resolve the issue you can set`torch.backends.cudnn.enabled` flag to False - `torch.backends.cudnn.enabled=False`
Pricing Plans
Free
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