本文主要是介绍详细讲一下PYG 里面的torch_geometric.nn.conv.transformer_conv函数，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
1.首先先讲一下代码
这是官方给的代码：torch_geometric.nn.conv.transformer_conv — pytorch_geometric documentation
import math
import typing
from typing import Optional, Tuple, Unionimport torch
import torch.nn.functional as F
from torch import Tensorfrom torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import (Adj,NoneType,OptTensor,PairTensor,SparseTensor,
)
from torch_geometric.utils import softmaxif typing.TYPE_CHECKING:from typing import overload
else:from torch.jit import _overload_method as overload[docs]class TransformerConv(MessagePassing):r"""The graph transformer operator from the `"Masked Label Prediction:Unified Message Passing Model for Semi-Supervised Classification"<https://arxiv.org/abs/2009.03509>`_ paper... math::\mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i +\sum_{j \in \mathcal{N}(i)} \alpha_{i,j} \mathbf{W}_2 \mathbf{x}_{j},where the attention coefficients :math:`\alpha_{i,j}` are computed viamulti-head dot product attention:.. math::\alpha_{i,j} = \textrm{softmax} \left(\frac{(\mathbf{W}_3\mathbf{x}_i)^{\top} (\mathbf{W}_4\mathbf{x}_j)}{\sqrt{d}} \right)Args:in_channels (int or tuple): Size of each input sample, or :obj:`-1` toderive the size from the first input(s) to the forward method.A tuple corresponds to the sizes of source and targetdimensionalities.out_channels (int): Size of each output sample.heads (int, optional): Number of multi-head-attentions.(default: :obj:`1`)concat (bool, optional): If set to :obj:`False`, the multi-headattentions are averaged instead of concatenated.(default: :obj:`True`)beta (bool, optional): If set, will combine aggregation andskip information via.. math::\mathbf{x}^{\prime}_i = \beta_i \mathbf{W}_1 \mathbf{x}_i +(1 - \beta_i) \underbrace{\left(\sum_{j \in \mathcal{N}(i)}\alpha_{i,j} \mathbf{W}_2 \vec{x}_j \right)}_{=\mathbf{m}_i}with :math:`\beta_i = \textrm{sigmoid}(\mathbf{w}_5^{\top}[ \mathbf{W}_1 \mathbf{x}_i, \mathbf{m}_i, \mathbf{W}_1\mathbf{x}_i - \mathbf{m}_i ])` (default: :obj:`False`)dropout (float, optional): Dropout probability of the normalizedattention coefficients which exposes each node to a stochasticallysampled neighborhood during training. (default: :obj:`0`)edge_dim (int, optional): Edge feature dimensionality (in casethere are any). Edge features are added to the keys afterlinear transformation, that is, prior to computing theattention dot product. They are also added to final valuesafter the same linear transformation. The model is:.. math::\mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i +\sum_{j \in \mathcal{N}(i)} \alpha_{i,j} \left(\mathbf{W}_2 \mathbf{x}_{j} + \mathbf{W}_6 \mathbf{e}_{ij}\right),where the attention coefficients :math:`\alpha_{i,j}` are nowcomputed via:.. math::\alpha_{i,j} = \textrm{softmax} \left(\frac{(\mathbf{W}_3\mathbf{x}_i)^{\top}(\mathbf{W}_4\mathbf{x}_j + \mathbf{W}_6 \mathbf{e}_{ij})}{\sqrt{d}} \right)(default :obj:`None`)bias (bool, optional): If set to :obj:`False`, the layer will not learnan additive bias. (default: :obj:`True`)root_weight (bool, optional): If set to :obj:`False`, the layer willnot add the transformed root node features to the output and theoption :attr:`beta` is set to :obj:`False`. (default: :obj:`True`)**kwargs (optional): Additional arguments of:class:`torch_geometric.nn.conv.MessagePassing`."""_alpha: OptTensordef __init__(self,in_channels: Union[int, Tuple[int, int]],out_channels: int,heads: int = 1,concat: bool = True,beta: bool = False,dropout: float = 0.,edge_dim: Optional[int] = None,bias: bool = True,root_weight: bool = True,**kwargs,):kwargs.setdefault('aggr', 'add')super().__init__(node_dim=0, **kwargs)self.in_channels = in_channelsself.out_channels = out_channelsself.heads = headsself.beta = beta and root_weightself.root_weight = root_weightself.concat = concatself.dropout = dropoutself.edge_dim = edge_dimself._alpha = Noneif isinstance(in_channels, int):in_channels = (in_channels, in_channels)self.lin_key = Linear(in_channels[0], heads * out_channels)self.lin_query = Linear(in_channels[1], heads * out_channels)self.lin_value = Linear(in_channels[0], heads * out_channels)if edge_dim is not None:self.lin_edge = Linear(edge_dim, heads * out_channels, bias=False)else:self.lin_edge = self.register_parameter('lin_edge', None)if concat:self.lin_skip = Linear(in_channels[1], heads * out_channels,bias=bias)if self.beta:self.lin_beta = Linear(3 * heads * out_channels, 1, bias=False)else:self.lin_beta = self.register_parameter('lin_beta', None)else:self.lin_skip = Linear(in_channels[1], out_channels, bias=bias)if self.beta:self.lin_beta = Linear(3 * out_channels, 1, bias=False)else:self.lin_beta = self.register_parameter('lin_beta', None)self.reset_parameters()[docs] def reset_parameters(self):super().reset_parameters()self.lin_key.reset_parameters()self.lin_query.reset_parameters()self.lin_value.reset_parameters()if self.edge_dim:self.lin_edge.reset_parameters()self.lin_skip.reset_parameters()if self.beta:self.lin_beta.reset_parameters()@overloaddef forward(self,x: Union[Tensor, PairTensor],edge_index: Adj,edge_attr: OptTensor = None,return_attention_weights: NoneType = None,) -> Tensor:pass@overloaddef forward( # noqa: F811self,x: Union[Tensor, PairTensor],edge_index: Tensor,edge_attr: OptTensor = None,return_attention_weights: bool = None,) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:pass@overloaddef forward( # noqa: F811self,x: Union[Tensor, PairTensor],edge_index: SparseTensor,edge_attr: OptTensor = None,return_attention_weights: bool = None,) -> Tuple[Tensor, SparseTensor]:pass[docs] def forward( # noqa: F811self,x: Union[Tensor, PairTensor],edge_index: Adj,edge_attr: OptTensor = None,return_attention_weights: Optional[bool] = None,) -> Union[Tensor,Tuple[Tensor, Tuple[Tensor, Tensor]],Tuple[Tensor, SparseTensor],]:r"""Runs the forward pass of the module.Args:x (torch.Tensor or (torch.Tensor, torch.Tensor)): The input nodefeatures.edge_index (torch.Tensor or SparseTensor): The edge indices.edge_attr (torch.Tensor, optional): The edge features.(default: :obj:`None`)return_attention_weights (bool, optional): If set to :obj:`True`,will additionally return the tuple:obj:`(edge_index, attention_weights)`, holding the computedattention weights for each edge. (default: :obj:`None`)"""H, C = self.heads, self.out_channelsif isinstance(x, Tensor):x = (x, x)query = self.lin_query(x[1]).view(-1, H, C)key = self.lin_key(x[0]).view(-1, H, C)value = self.lin_value(x[0]).view(-1, H, C)# propagate_type: (query: Tensor, key:Tensor, value: Tensor,# edge_attr: OptTensor)out = self.propagate(edge_index, query=query, key=key, value=value,edge_attr=edge_attr)alpha = self._alphaself._alpha = Noneif self.concat:out = out.view(-1, self.heads * self.out_channels)else:out = out.mean(dim=1)if self.root_weight:x_r = self.lin_skip(x[1])if self.lin_beta is not None:beta = self.lin_beta(torch.cat([out, x_r, out - x_r], dim=-1))beta = beta.sigmoid()out = beta * x_r + (1 - beta) * outelse:out = out + x_rif isinstance(return_attention_weights, bool):assert alpha is not Noneif isinstance(edge_index, Tensor):return out, (edge_index, alpha)elif isinstance(edge_index, SparseTensor):return out, edge_index.set_value(alpha, layout='coo')else:return outdef message(self, query_i: Tensor, key_j: Tensor, value_j: Tensor,edge_attr: OptTensor, index: Tensor, ptr: OptTensor,size_i: Optional[int]) -> Tensor:if self.lin_edge is not None:assert edge_attr is not Noneedge_attr = self.lin_edge(edge_attr).view(-1, self.heads,self.out_channels)key_j = key_j + edge_attralpha = (query_i * key_j).sum(dim=-1) / math.sqrt(self.out_channels)alpha = softmax(alpha, index, ptr, size_i)self._alpha = alphaalpha = F.dropout(alpha, p=self.dropout, training=self.training)out = value_jif edge_attr is not None:out = out + edge_attrout = out * alpha.view(-1, self.heads, 1)return outdef __repr__(self) -> str:return (f'{self.__class__.__name__}({self.in_channels}, 'f'{self.out_channels}, heads={self.heads})')2.详细解释一下
几个重要的参数
in_channels (int or tuple): Size of each input sample, or :obj:`-1` to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities.
out_channels (int): Size of each output sample.
heads (int, optional): Number of multi-head-attentions. (default: :obj:`1`)
怎么理解这几个参数？
in_channels表示每个输入样本的大小。如果设置为整数，则表示所有输入样本的大小相同；如果设置为-1，则表示输入样本的大小将从forward方法的第一个输入中推导出来；如果设置为元组，则表示输入样本的大小对应于源维度和目标维度的大小。
out_channels表示每个输出样本的大小，即经过卷积操作后产生的特征向量的维度大小。
当使用
tg.nn.TransformerConv时，可以通过以下方式理解in_channels和out_channels：假设我们有一个图数据集，每个节点都有一个 10 维的特征向量表示。那么在这种情况下：
如果我们想将每个节点的特征向量作为输入，然后使用
tg.nn.TransformerConv进行卷积操作，那么in_channels应该设置为 10，表示每个输入样本的大小为 10。假设我们想将节点的特征向量转换为一个 16 维的特征向量，那么
out_channels应该设置为 16，表示每个输出样本的大小为 16，即经过卷积操作后每个节点的特征向量将变为 16 维。在
tg.nn.TransformerConv中，heads参数表示多头注意力的数量。举个例子，如果heads参数设置为 4，那么模型将学习 4 组注意力权重，每组权重都用于计算输入的不同子空间的注意力，然后将这些头的输出进行合并以产生最终的输出。
举个整体的例子：
我们有一个输入张量
x，它的形状是(batch_size, seq_length, input_dim)，其中：
batch_size表示批量大小；seq_length表示序列长度；input_dim表示输入特征的维度。现在假设我们使用了
tg.nn.TransformerConv，并设置heads=2，那么模型将学习两组注意力权重，每组用于计算不同的注意力。输出张量的形状将取决于out_channels参数，我们假设out_channels=64。
import torch
import torch_geometric.nn as tg# 假设输入张量的形状是 (batch_size, seq_length, input_dim)
x = torch.randn(32, 10, 128) # 32 个样本，每个样本有 10 个时间步，每个时间步有 128 个特征# 创建 TransformerConv 模型，设置 heads=2，out_channels=64
conv_layer = tg.nn.TransformerConv(in_channels=128, out_channels=64, heads=2)# 使用模型进行前向传播
output = conv_layer(x)print("输出张量的形状:", output.shape)
2.1将特征映射到键值对中
在这里，通过线性变换层 Linear，输入特征被转换成了键（key）、查询（query）和数值（value）的表示形式，以便用于多头自注意力机制。
具体来说：
self.lin_key用于将输入特征（in_channels[0]）映射到键的表示形式。self.lin_query用于将输入特征（in_channels[1]）映射到查询的表示形式。self.lin_value用于将输入特征（in_channels[0]）映射到数值的表示形式。
具体地，假设输入特征的维度是 (batch_size, num_nodes, in_channels)，其中 batch_size 是批量大小，num_nodes 是节点数，in_channels 是输入特征的通道数。在映射到键的过程中，线性变换层的权重矩阵将是一个维度为 (in_channels, heads * out_channels) 的矩阵，其中 heads 是注意力头的数量，out_channels 是输出特征的通道数。因此，通过矩阵乘法运算，输入特征将被映射到一个新的特征空间，其维度为 (batch_size, num_nodes, heads, out_channels)。在这个新的特征空间中，每个节点的每个头都有一个键表示。
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