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https://github.com/zebrajr/pytorch.git
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1c01eabd3c
* Codemod to update our codebase to 0.4 standard * Update some of the test scri[ts * remove Variable in test_clip_grad_value * fix _symbolic_override_wrapper_maker
28 lines
1.2 KiB
Python
28 lines
1.2 KiB
Python
import torch
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class Parameter(torch.Tensor):
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r"""A kind of Tensor that is to be considered a module parameter.
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Parameters are :class:`~torch.Tensor` subclasses, that have a
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very special property when used with :class:`Module` s - when they're
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assigned as Module attributes they are automatically added to the list of
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its parameters, and will appear e.g. in :meth:`~Module.parameters` iterator.
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Assigning a Tensor doesn't have such effect. This is because one might
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want to cache some temporary state, like last hidden state of the RNN, in
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the model. If there was no such class as :class:`Parameter`, these
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temporaries would get registered too.
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Arguments:
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data (Tensor): parameter tensor.
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requires_grad (bool, optional): if the parameter requires gradient. See
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:ref:`excluding-subgraphs` for more details. Default: `True`
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"""
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def __new__(cls, data=None, requires_grad=True):
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if data is None:
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data = torch.Tensor()
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return torch.Tensor._make_subclass(cls, data, requires_grad)
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def __repr__(self):
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return 'Parameter containing:\n' + super(Parameter, self).__repr__()
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