def concat_preds(preds): return torch.cat([flatten_pred(p) for p in preds], dim=1)
时间: 2024-04-09 13:31:04 浏览: 11
这段代码定义了一个函数 `concat_preds`,用于将多个预测结果进行连接。
函数的输入参数 `preds` 是一个列表,包含多个预测结果,每个预测结果都是一个张量。
函数中的操作包括:
- `[flatten_pred(p) for p in preds]`:对于 `preds` 中的每个预测结果 `p`,使用之前定义的 `flatten_pred` 函数将其展平为一维张量。
- `torch.cat(..., dim=1)`:使用 `cat` 函数将展平后的预测结果按照维度1进行连接,即将它们沿着水平方向拼接在一起。
这样,函数返回的张量将是连接起来的一维数组,其中包含了多个预测结果的信息。这个函数通常在目标检测或者分割任务中使用,用于将多个特征图的预测结果连接起来,得到最终的预测结果。
相关问题
把这段代码里的location_embedding_dim去掉class my_GAT(torch.nn.Module): def __init__(self, in_channels, out_channels, n_heads, location_embedding_dim, filters_1, filters_2, dropout): super(my_GAT, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.n_heads = n_heads location_embedding_dim = 0 self.filters_1 = filters_1 self.filters_2 = filters_2 self.dropout = dropout self.location_embedding_dim = location_embedding_dim self.setup_layers() def setup_layers(self): self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1) self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads + self.location_embedding_dim, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False) def forward(self, edge_indices, features, location_embedding): features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_1(features, edge_indices) features = torch.nn.functional.relu(features) features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training) features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_2(features, edge_indices) return features
这段代码里的location_embedding_dim是一个参数,它表示位置嵌入的维度。如果你想去掉它,可以把它的值设为0,然后在构造函数中把self.location_embedding_dim也设为0即可。具体来说,修改后的代码如下:
```
class my_GAT(torch.nn.Module):
def __init__(self, in_channels, out_channels, n_heads, filters_1, filters_2, dropout):
super(my_GAT, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.n_heads = n_heads
self.filters_1 = filters_1
self.filters_2 = filters_2
self.dropout = dropout
self.location_embedding_dim = 0
self.setup_layers()
def setup_layers(self):
self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1)
self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False)
def forward(self, edge_indices, features, location_embedding):
features = self.GAT_1(features, edge_indices)
features = torch.nn.functional.relu(features)
features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training)
features = self.GAT_2(features, edge_indices)
return features
```
class Positional_GAT(torch.nn.Module): def __init__(self, in_channels, out_channels, n_heads, location_embedding_dim, filters_1, filters_2, dropout): super(Positional_GAT, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.n_heads = n_heads self.filters_1 = filters_1 self.filters_2 = filters_2 self.dropout = dropout self.location_embedding_dim = location_embedding_dim self.setup_layers() def setup_layers(self): self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1) self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads + self.location_embedding_dim, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False) def forward(self, edge_indices, features, location_embedding): features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_1(features, edge_indices) features = torch.nn.functional.relu(features) features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training) features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_2(features, edge_indices) return features
这段代码实现了一个名为Positional_GAT的模型,它基于GAT(Graph Attention Network)模型,并添加了位置嵌入(location embedding)来考虑节点在图中的位置信息。具体来说,该模型包含一个GATConv层(表示第一层GAT),它将输入的特征向量(features)和边的索引(edge_indices)作为输入,并输出一个新的特征向量。第二层GATConv层将第一层的输出、位置嵌入和边的索引作为输入,并输出最终的特征向量。在模型的前向传播过程中,将输入的特征向量和位置嵌入在最开始的时候拼接在一起,然后经过第一层GATConv层进行处理,接着经过ReLU激活函数和dropout层。最后再次将特征向量和位置嵌入拼接在一起,经过第二层GATConv层得到输出结果。整个模型可以用于图分类、节点分类等任务。