dmlc--dgl
Lingfan Yu
274e1082f5
* builtin function docstring * init docstring * view docstring * add init to index and fix * node /edge feature * Example -> Examples * fix func hyperlink * traversal * fix * from/to_networkx docstring * minor * fix duplicate label * comment about tensor framework * from scipy sparse matrix * fix format errors
62 行
1.7 KiB
Python
62 行
1.7 KiB
Python
"""Module for common feature initializers."""
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from __future__ import absolute_import
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from . import backend as F
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__all__ = ['base_initializer', 'zero_initializer']
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def base_initializer(shape, dtype, ctx, range):
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"""The function signature for feature initializer.
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Any customized feature initializer should follow this signature (see
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example below).
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Parameters
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----------
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shape : tuple of int
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The shape of the result features. The first dimension
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is the batch dimension.
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dtype : data type object
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The data type of the returned features.
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ctx : context object
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The device context of the returned features.
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range : slice
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The start id and the end id of the features to be initialized.
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The id could be node or edge id depending on the scenario.
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Note that the step is always None.
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Examples
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--------
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If PyTorch is used as backend, the following code defines an feature
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initializer that initializes tensor value to 1
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>>> import torch
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>>> import dgl
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>>> def initializer(shape, dtype, ctx, range):
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>>> return torch.ones(shape, dtype=dtype, device=ctx)
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>>> g = dgl.DGLGraph()
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>>> g.set_n_initializer(initializer)
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See Also
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--------
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dgl.DGLGraph.set_n_initializer
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dgl.DGLGraph.set_e_initializer
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"""
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raise NotImplementedError
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def zero_initializer(shape, dtype, ctx, range):
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"""Zero feature initializer
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Examples
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--------
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>>> import dgl
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>>> g = dgl.DGLGraph()
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>>> g.set_n_initializer(dgl.init.zero_initializer)
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See Also
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--------
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dgl.DGLGraph.set_n_initializer
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dgl.DGLGraph.set_e_initializer
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"""
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return F.zeros(shape, dtype, ctx)
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