# RandomStructured

Source: https://www.tensorplay.cn/docs/generated/tensorplay.ao.pruning.RandomStructured.html

```python
class tensorplay.ao.pruning.RandomStructured(amount: int | float, dim: int = -1)
```

Zero out entire randomly selected channels of a tensor.

Parameters:

- amount – quantity of channels to prune. A float in [0, 1] denotes the fraction of channels to prune; an int denotes the absolute number of channels to prune.

- dim – axis along which channels are defined. Default: -1.

```python
classmethod apply(module: Module, name: str, amount: int | float, dim: int = -1) → BasePruningMethod
```

Install random structured pruning for module[name].

Parameters:

- module – module containing the tensor to prune.

- name – parameter name within module on which pruning acts.

- amount – quantity of channels to prune. A float in [0, 1] denotes the fraction of channels to prune; an int denotes the absolute number of channels to prune.

- dim – axis along which channels are defined. Default: -1.

```python
apply_mask(module: Module) → Tensor
```

Return the pruned version of the tensor held by module.

Fetches the mask and the original values from the module and returns
their elementwise product.

Parameters:

module – module holding the pruned tensor.

Returns:

The product of the mask and the original values.

```python
compute_mask(t: Tensor, default_mask: Tensor) → Tensor
```

Compute a channel mask for t by drawing channels at random.

Parameters:

- t – tensor whose channels are pruned.

- default_mask – mask accumulated from previous pruning iterations; must be respected by the new mask. Same shape as t.

Returns:

The mask to apply to t, with the same shape as t.

Raises:

[IndexError](https://docs.python.org/3/builtins/exceptions.html#IndexError) – if self.dim is not a valid axis of t.

```python
prune(t: Tensor, default_mask: Tensor | None = None, importance_scores: Tensor | None = None) → Tensor
```

Return a pruned copy of the input tensor t.

Applies the rule implemented by [compute_mask()](#tensorplay.ao.pruning.RandomStructured.compute_mask) without any
module-side reparameterization.

Parameters:

- t – tensor to prune (same shape as default_mask).

- importance_scores – tensor of importance scores with the same shape as t; each entry ranks the corresponding element of t. When unspecified, t itself is used.

- default_mask – mask from a previous pruning iteration, if any. Pruning must respect the entries it already zeroes. When unspecified, a mask of ones is used.

Returns:

The pruned version of t.

```python
remove(module: Module) → None
```

Make the current pruning of module permanent.

The pruned values remain pruned: the product of mask and original
values is written back into the parameter name, and the auxiliary
parameter name + '_orig' and buffer name + '_mask' are
dropped.

> **Note**
>
> Pruning itself is NOT undone or reversed!