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[NN] GCNII model added <https://arxiv.org/pdf/2007.02133.pdf> (dmlc#2874
) * Auto stash before rebase of "origin/pytorch-nn-working" GCNII model added * linting * linting * lint * Frequency Adaptive gcn init comit * Revert "Frequency Adaptive gcn init comit" This reverts commit 86a8058. * Update python/dgl/nn/pytorch/conv/gcn2conv.py modified docstring Co-authored-by: Quan (Andy) Gan <[email protected]> * added beta formula and changed param name * fix docstring * lint * white space lint * update docstring Co-authored-by: Quan (Andy) Gan <[email protected]> * docstring formula update * added gcn2 * added GCN2Conv * Update nn.pytorch.rst Co-authored-by: Quan (Andy) Gan <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| """Torch Module for Graph Convolutional Network via Initial residual | ||
| and Identity mapping (GCNII) layer""" | ||
| # pylint: disable= no-member, arguments-differ, invalid-name | ||
| import math | ||
|
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| import torch as th | ||
| from torch import nn | ||
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| from .... import function as fn | ||
| from ....base import DGLError | ||
|
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| class GCN2Conv(nn.Module): | ||
|
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| r""" | ||
| Description | ||
| ----------- | ||
| The Graph Convolutional Network via Initial residual | ||
| and Identity mapping (GCNII) was introduced in `"Simple and Deep Graph Convolutional | ||
| Networks" <https://arxiv.org/abs/2007.02133>`_ paper. | ||
| It is mathematically is defined as follows: | ||
| .. math:: | ||
| \mathbf{h}^{(l+1)} =\left( (1 - \alpha)(\mathbf{D}^{-1/2} \mathbf{\hat{A}} | ||
| \mathbf{D}^{-1/2})\mathbf{h}^{(l)} + \alpha {\mathbf{h}^{(0)}} \right) | ||
| \left( (1 - \beta_l) \mathbf{I} + \beta_l \mathbf{W} \right) | ||
| where :math:`\mathbf{\hat{A}}` is the adjacency matrix with self-loops, | ||
| :math:`\mathbf{D}_{ii} = \sum_{j=0} \mathbf{A}_{ij}` is its diagonal degree matrix, | ||
| :math:`\mathbf{h}^{(0)}` is the initial node features, | ||
| :math:`\mathbf{h}^{(l)}` is the feature of layer :math:`l`, | ||
| :math:`\alpha` is the fraction of initial node features, and | ||
| :math:`\beta_l` is the hyperparameter to tune the strength of identity mapping. | ||
| It is defined by :math:`\beta_l = \log(\frac{\lambda}{l}+1)\approx\frac{\lambda}{l}`, | ||
| where :math:`\lambda` is a hyperparameter. :math: `\beta` ensures that the decay of | ||
| the weight matrix adaptively increases as we stack more layers. | ||
| Parameters | ||
| ---------- | ||
| in_feats : int | ||
| Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`. | ||
| layer : int | ||
| the index of current layer. | ||
| alpha : float | ||
| The fraction of the initial input features. Default: ``0.1`` | ||
| lambda_ : float | ||
| The hyperparameter to ensure the decay of the weight matrix | ||
| adaptively increases. Default: ``1`` | ||
| project_initial_features : bool | ||
| Whether to share a weight matrix between initial features and | ||
| smoothed features. Default: ``True`` | ||
| bias : bool, optional | ||
| If True, adds a learnable bias to the output. Default: ``True``. | ||
| activation : callable activation function/layer or None, optional | ||
| If not None, applies an activation function to the updated node features. | ||
| Default: ``None``. | ||
| allow_zero_in_degree : bool, optional | ||
| If there are 0-in-degree nodes in the graph, output for those nodes will be invalid | ||
| since no message will be passed to those nodes. This is harmful for some applications | ||
| causing silent performance regression. This module will raise a DGLError if it detects | ||
| 0-in-degree nodes in input graph. By setting ``True``, it will suppress the check | ||
| and let the users handle it by themselves. Default: ``False``. | ||
| Note | ||
| ---- | ||
| Zero in-degree nodes will lead to invalid output value. This is because no message | ||
| will be passed to those nodes, the aggregation function will be appied on empty input. | ||
| A common practice to avoid this is to add a self-loop for each node in the graph if | ||
| it is homogeneous, which can be achieved by: | ||
| >>> g = ... # a DGLGraph | ||
| >>> g = dgl.add_self_loop(g) | ||
| Calling ``add_self_loop`` will not work for some graphs, for example, heterogeneous graph | ||
| since the edge type can not be decided for self_loop edges. Set ``allow_zero_in_degree`` | ||
| to ``True`` for those cases to unblock the code and handle zero-in-degree nodes manually. | ||
| A common practise to handle this is to filter out the nodes with zero-in-degree when use | ||
| after conv. | ||
| Examples | ||
| -------- | ||
| >>> import dgl | ||
| >>> import numpy as np | ||
| >>> import torch as th | ||
| >>> from dgl.nn import GCN2Conv | ||
| >>> # Homogeneous graph | ||
| >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) | ||
| >>> feat = th.ones(6, 3) | ||
| >>> g = dgl.add_self_loop(g) | ||
| >>> conv1 = GCN2Conv(3, layer=1, alpha=0.5, \ | ||
| ... project_initial_features=True, allow_zero_in_degree=True) | ||
| >>> conv2 = GCN2Conv(3, layer=2, alpha=0.5, \ | ||
| ... project_initial_features=True, allow_zero_in_degree=True) | ||
| >>> res = feat | ||
| >>> res = conv1(g, res, feat) | ||
| >>> res = conv2(g, res, feat) | ||
| >>> print(res) | ||
| tensor([[1.3803, 3.3191, 2.9572], | ||
| [1.3803, 3.3191, 2.9572], | ||
| [1.3803, 3.3191, 2.9572], | ||
| [1.4770, 3.8326, 3.2451], | ||
| [1.3623, 3.2102, 2.8679], | ||
| [1.3803, 3.3191, 2.9572]], grad_fn=<AddBackward0>) | ||
| """ | ||
|
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||
| def __init__(self, | ||
| in_feats, | ||
| layer, | ||
| alpha=0.1, | ||
| lambda_=1, | ||
| project_initial_features=True, | ||
| allow_zero_in_degree=False, | ||
| bias=True, | ||
| activation=None): | ||
| super().__init__() | ||
|
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| self._in_feats = in_feats | ||
| self._project_initial_features = project_initial_features | ||
|
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| self.alpha = alpha | ||
| self.beta = math.log(lambda_ / layer + 1) | ||
|
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| self._bias = bias | ||
| self._activation = activation | ||
| self._allow_zero_in_degree = allow_zero_in_degree | ||
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| self.weight1 = nn.Parameter(th.Tensor(self._in_feats, self._in_feats)) | ||
|
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| if self._project_initial_features: | ||
| self.register_parameter("weight2", None) | ||
| else: | ||
| self.weight2 = nn.Parameter(th.Tensor(self._in_feats, self._in_feats)) | ||
|
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| if self._bias: | ||
| self.bias = nn.Parameter(th.Tensor(self._in_feats)) | ||
| else: | ||
| self.register_parameter("bias", None) | ||
|
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| self.reset_parameters() | ||
|
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| def reset_parameters(self): | ||
| r""" | ||
| Description | ||
| ----------- | ||
| Reinitialize learnable parameters. | ||
| """ | ||
| nn.init.normal_(self.weight1) | ||
| if not self._project_initial_features: | ||
| nn.init.normal_(self.weight2) | ||
| if self._bias is not None: | ||
| nn.init.zeros_(self.bias) | ||
|
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| def set_allow_zero_in_degree(self, set_value): | ||
| r""" | ||
| Description | ||
| ----------- | ||
| Set allow_zero_in_degree flag. | ||
| Parameters | ||
| ---------- | ||
| set_value : bool | ||
| The value to be set to the flag. | ||
| """ | ||
| self._allow_zero_in_degree = set_value | ||
|
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| def forward(self, graph, feat, feat_0): | ||
| r""" | ||
| Description | ||
| ----------- | ||
| Compute graph convolution. | ||
| Parameters | ||
| ---------- | ||
| graph : DGLGraph | ||
| The graph. | ||
| feat : torch.Tensor | ||
| The input feature of shape | ||
| :math:`(N, D_{in})` | ||
| where :math:`D_{in}` is the size of input feature and :math:`N` is the number of nodes. | ||
| feat_0 : torch.Tensor | ||
| The initial feature of shape :math:`(N, D_{in})` | ||
| Returns | ||
| ------- | ||
| torch.Tensor | ||
| The output feature | ||
| Raises | ||
| ------ | ||
| DGLError | ||
| If there are 0-in-degree nodes in the input graph, it will raise DGLError | ||
| since no message will be passed to those nodes. This will cause invalid output. | ||
| The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``. | ||
| Note | ||
| ---- | ||
| * Input shape: :math:`(N, *, \text{in_feats})` where * means any number of additional | ||
| dimensions, :math:`N` is the number of nodes. | ||
| * Output shape: :math:`(N, *, \text{out_feats})` where all but the last dimension are | ||
| the same shape as the input. | ||
| * Weight shape: :math:`(\text{in_feats}, \text{out_feats})`. | ||
| """ | ||
| with graph.local_scope(): | ||
| if not self._allow_zero_in_degree: | ||
| if (graph.in_degrees() == 0).any(): | ||
| raise DGLError( | ||
| "There are 0-in-degree nodes in the graph, " | ||
| "output for those nodes will be invalid. " | ||
| "This is harmful for some applications, " | ||
| "causing silent performance regression. " | ||
| "Adding self-loop on the input graph by " | ||
| "calling `g = dgl.add_self_loop(g)` will resolve " | ||
| "the issue. Setting ``allow_zero_in_degree`` " | ||
| "to be `True` when constructing this module will " | ||
| "suppress the check and let the code run." | ||
| ) | ||
|
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| # normalize to get smoothed representation | ||
| degs = graph.in_degrees().float().clamp(min=1) | ||
| norm = th.pow(degs, -0.5) | ||
| norm = norm.to(feat.device).unsqueeze(1) | ||
|
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| feat = feat * norm | ||
| graph.ndata["h"] = feat | ||
| graph.update_all(fn.copy_u("h", "m"), fn.sum("m", "h")) | ||
| feat = graph.ndata.pop("h") | ||
| feat = feat * norm | ||
| # scale | ||
| feat = feat * (1 - self.alpha) | ||
|
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| # initial residual connection to the first layer | ||
| feat_0 = feat_0[: feat.size(0)] * self.alpha | ||
|
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| if self._project_initial_features: | ||
| rst = feat.add_(feat_0) | ||
| rst = th.addmm( | ||
| feat, feat, self.weight1, beta=(1 - self.beta), alpha=self.beta | ||
| ) | ||
| else: | ||
| rst = th.addmm( | ||
| feat, feat, self.weight1, beta=(1 - self.beta), alpha=self.beta | ||
| ) | ||
| rst += th.addmm( | ||
| feat_0, feat_0, self.weight2, beta=(1 - self.beta), alpha=self.beta | ||
| ) | ||
|
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| if self._bias is not None: | ||
| rst = rst + self._bias | ||
|
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| if self._activation is not None: | ||
| rst = self._activation(rst) | ||
|
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| return rst | ||
|
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| def extra_repr(self): | ||
| """Set the extra representation of the module, | ||
| which will come into effect when printing the model. | ||
| """ | ||
| summary = "in={_in_feats}" | ||
| summary += ", share_weight={_share_weights}, alpha={alpha}, beta={beta}" | ||
| if "self._bias" in self.__dict__: | ||
| summary += ", bias={bias}" | ||
| if "self._activation" in self.__dict__: | ||
| summary += ", activation={_activation}" | ||
|
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| return summary.format(**self.__dict__) |