Table of Contents
Shortcuts

# CustomFromMask

class torch.nn.utils.prune.CustomFromMask(mask)
  classmethod apply(module, name, mask)

    Adds the forward pre-hook that enables pruning on the fly and the reparametrization of a tensor in terms of the original tensor and the pruning mask.

    Parameters:

      - module (nn.Module) – module containing the tensor to prune

      - name (str) – parameter name within `module` on which pruning will act.

  apply_mask(module)

    Simply handles the multiplication between the parameter being pruned and the generated mask. Fetches the mask and the original tensor from the module and returns the pruned version of the tensor.

    Parameters:

      module (nn.Module) – module containing the tensor to prune

    Returns:

      pruned version of the input tensor

    Return type:

      pruned_tensor (torch.Tensor)

  prune(t, default_mask=None, importance_scores=None)

    Computes and returns a pruned version of input tensor `t` according to the pruning rule specified in `compute_mask()`.

    Parameters:

      - t (torch.Tensor) – tensor to prune (of same dimensions as `default_mask`).

      - importance_scores (torch.Tensor) – tensor of importance scores (of same shape as `t`) used to compute mask for pruning `t`. The values in this tensor indicate the importance of the corresponding elements in the `t` that is being pruned. If unspecified or None, the tensor `t` will be used in its place.

      - default_mask (torch.Tensor, optional) – mask from previous pruning iteration, if any. To be considered when determining what portion of the tensor that pruning should act on. If None, default to a mask of ones.

    Returns:

      pruned version of tensor `t`.

  remove(module)

    Removes the pruning reparameterization from a module. The pruned parameter named `name` remains permanently pruned, and the parameter named `name+'_orig'` is removed from the parameter list. Similarly, the buffer named `name+'_mask'` is removed from the buffers.

    Note

    Pruning itself is NOT undone or reversed!

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.