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# torch.nn.utils.skip_init

torch.nn.utils.skip_init(module_cls, *args, **kwargs)

  Given a module class object and args / kwargs, instantiates the module without initializing parameters / buffers. This can be useful if initialization is slow or if custom initialization will be performed, making the default initialization unnecessary. There are some caveats to this, due to the way this function is implemented:

  1. The module must accept a device arg in its constructor that is passed to any parameters or buffers created during construction.

  2. The module must not perform any computation on parameters in its constructor except initialization (i.e. functions from `torch.nn.init`).

  If these conditions are satisfied, the module can be instantiated with parameter / buffer values uninitialized, as if having been created using `torch.empty()`.

  Parameters:

    - module_cls – Class object; should be a subclass of `torch.nn.Module`

    - args – args to pass to the module’s constructor

    - kwargs – kwargs to pass to the module’s constructor

  Returns:

    Instantiated module with uninitialized parameters / buffers

  Example:

      >>> import torch
      >>> m = torch.nn.utils.skip_init(torch.nn.Linear, 5, 1)
      >>> m.weight
      Parameter containing:
      tensor([[0.0000e+00, 1.5846e+29, 7.8307e+00, 2.5250e-29, 1.1210e-44]],
             requires_grad=True)
      >>> m2 = torch.nn.utils.skip_init(torch.nn.Linear, in_features=6, out_features=1)
      >>> m2.weight
      Parameter containing:
      tensor([[-1.4677e+24,  4.5915e-41,  1.4013e-45,  0.0000e+00, -1.4677e+24,
                4.5915e-41]], requires_grad=True)

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