FM / utils

/usr/local/lib64/python3.6/site-packages/torch/nn/utils/

/usr/local/lib64/python3.6/site-packages/torch/nn/utils

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
clip_grad.py	4009	0644	edit dl rm
convert_parameters.py	3001	0644	edit dl rm
fusion.py	1651	0644	edit dl rm
init.py	2175	0644	edit dl rm
memory_format.py	3790	0644	edit dl rm
parametrizations.py	22399	0644	edit dl rm
parametrize.py	29176	0644	edit dl rm
prune.py	56700	0644	edit dl rm
rnn.py	17474	0644	edit dl rm
spectral_norm.py	14440	0644	edit dl rm
weight_norm.py	4577	0644	edit dl rm
__init__.py	468	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/nn/utils/init.py (2175B)

import inspect
import torch

def skip_init(module_cls, *args, **kwargs):
    r"""
    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 :mod:`torch.nn.init`).

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

Args:
        module_cls: Class object; should be a subclass of :class:`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)

"""
    if not issubclass(module_cls, torch.nn.Module):
        raise RuntimeError('Expected a Module; got {}'.format(module_cls))
    if 'device' not in inspect.signature(module_cls).parameters:
        raise RuntimeError('Module must support a \'device\' arg to skip initialization')

final_device = kwargs.pop('device', 'cpu')
    kwargs['device'] = 'meta'
    return module_cls(*args, **kwargs).to_empty(device=final_device)

import inspect import torch def skip_init(module_cls, *args, **kwargs): r""" 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 :mod:`torch.nn.init`). If these conditions are satisfied, the module can be instantiated with parameter / buffer values uninitialized, as if having been created using :func:`torch.empty`. Args: module_cls: Class object; should be a subclass of :class:`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) """ if not issubclass(module_cls, torch.nn.Module): raise RuntimeError('Expected a Module; got {}'.format(module_cls)) if 'device' not in inspect.signature(module_cls).parameters: raise RuntimeError('Module must support a \'device\' arg to skip initialization') final_device = kwargs.pop('device', 'cpu') kwargs['device'] = 'meta' return module_cls(*args, **kwargs).to_empty(device=final_device)