class DenseBlock(nn.Module): def __init__(self, num_convs, input_channels, num_channels): super(DenseBlock, self).__init__() layer = [] for i in range(num_convs): layer.append(conv_block( num_channels * i + input_channels, num_channels)) self.net = nn.Sequential(*layer) def forward(self, X): for blk in self.net: Y = blk(X) # 连接通道维度上每个块的输入和输出 X = torch.cat((X, Y), dim=1) return X
时间: 2024-02-14 13:11:16 浏览: 28
这段代码实现了一个 DenseBlock,它是 DenseNet 网络中的一个重要模块。DenseBlock 的输入是一个张量 X,包含 input_channels 个通道,输出是一个张量 Y,包含 num_channels 个通道。DenseBlock 包含 num_convs 个卷积层,每个卷积层的输入是前面所有层的输出以及输入张量 X 的拼接,输出是 num_channels 个通道的特征图。每个卷积层的具体实现由 conv_block 函数定义。在前向计算过程中,每个卷积层的输出都与输入张量 X 进行拼接,作为下一层的输入。最终,DenseBlock 的输出张量包含 num_convs * num_channels 个通道,可以作为下一个 DenseBlock 或者全局平均池化层的输入。
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class GraphSAGE(nn.Module): def __init__(self, in_feats, hidden_feats, out_feats, num_layers, activation): super(GraphSAGE, self).__init__() self.num_layers = num_layers self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean') self.convs = nn.ModuleList() for i in range(num_layers - 2): self.convs.append(SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean')) self.conv_last = SAGEConv(hidden_feats, out_feats, aggregator_type='mean') self.activation = activation def forward(self, blocks, x): h = x for i, block in enumerate(blocks): h_dst = h[:block.number_of_dst_nodes()] h = self.convs[i](block, (h, h_dst)) if i != self.num_layers - 2: h = self.activation(h) h = self.conv_last(blocks[-1], (h, h_dst)) return h改写一下,让它适用于异质图
class GraphSAGE(nn.Module):
def __init__(self, in_feats, hidden_feats, out_feats, num_layers, activation):
super(GraphSAGE, self).__init__()
self.num_layers = num_layers
self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean')
self.convs = nn.ModuleList()
for i in range(num_layers - 2):
self.convs.append(SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean'))
self.conv_last = SAGEConv(hidden_feats, out_feats, aggregator_type='mean')
self.activation = activation
def forward(self, blocks, x_dict):
h = {k: v for k, v in x_dict.items()}
for i, block in enumerate(blocks):
edge_type = block.edata['type']
h_dst = h[str(edge_type)][block.dstdata[dgl.NID]]
h = self.convs[i](block, (h, h_dst))
if i != self.num_layers - 2:
h = self.activation(h)
h_dst = h[str(edge_type)][blocks[-1].dstdata[dgl.NID]]
h = self.conv_last(blocks[-1], (h, h_dst))
return h
class MapCNN(nn.Module): def __init__(self, cfg): super().__init__() self.convs = nn.ModuleList() map_channels = cfg.get('map_channels', 3) patch_size = cfg.get('patch_size', [100, 100]) hdim = cfg.get('hdim', [32, 32]) kernels = cfg.get('kernels', [3, 3]) strides = cfg.get('strides', [3, 3]) self.out_dim = out_dim = cfg.get('out_dim', 32) self.input_size = input_size = (map_channels, patch_size[0], patch_size[1]) x_dummy = torch.randn(input_size).unsqueeze(0) for i, _ in enumerate(hdim): self.convs.append(nn.Conv2d(map_channels if i == 0 else hdim[i-1], hdim[i], kernels[i], stride=strides[i])) x_dummy = self.convs[i](x_dummy) self.fc = nn.Linear(x_dummy.numel(), out_dim) def forward(self, x): for conv in self.convs: x = F.leaky_relu(conv(x), 0.2) x = torch.flatten(x, start_dim=1) x = self.fc(x) return x
这是一个PyTorch神经网络模型类,命名为MapCNN。在初始化函数__init__中,通过配置字典cfg读取网络的参数,如地图通道数、补丁大小、隐藏维度、卷积核大小和步长等。接着通过nn.ModuleList建立多个卷积层,并使用nn.Conv2d实现。最后通过nn.Linear实现全连接层。在前向传播函数forward中,依次通过每一个卷积层处理输入数据x,并使用F.leaky_relu实现激活函数。最后通过全连接层计算最终的输出。