def __init__(self, n, dim, num_classes, temperature): self.n = n self.dim = dim self.features = torch.FloatTensor(self.n, self.dim) #self.features = torch.FloatTensor(self.n, 512) self.targets = torch.LongTensor(self.n) self.ptr = 0 self.device = 'cpu' self.K = 100 self.temperature = temperature self.C = num_classes
时间: 2024-04-19 11:30:39 浏览: 42
这段代码是一个类的构造函数。它接受四个参数:n,dim,num_classes和temperature。在构造函数中,它初始化了一些变量和张量,包括self.n,self.dim,self.features,self.targets,self.ptr,self.device,self.K和self.C。其中,self.features是一个大小为self.n x self.dim的浮点型张量,self.targets是一个大小为self.n的长整型张量。这些张量用于存储特征和目标数据。构造函数还设置了一些默认值,例如将self.device设置为'cpu',self.K设置为100。最后,构造函数将传入的num_classes参数赋值给self.C。
相关问题
class MemoryBank(object): def __init__(self, n, dim, num_classes, temperature): self.n = n self.dim = dim self.features = torch.FloatTensor(self.n, self.dim) self.targets = torch.LongTensor(self.n) self.ptr = 0 self.device = 'cpu' self.K = 100 self.temperature = temperature self.C = num_classes,这段代码是什么意思
这段代码是一个Python类 `MemoryBank` 的定义,其中包含一个构造函数 `__init__` 和一些成员变量和方法。
成员变量:
- `n` 表示存储在内存库中的样本数量
- `dim` 表示每个样本的特征维度
- `num_classes` 表示分类数
- `temperature` 表示温度参数
成员变量还包括:
- `features` 是一个大小为 `(n, dim)` 的浮点型张量,用于存储所有样本的特征向量
- `targets` 是一个大小为 `(n,)` 的长整型张量,用于存储所有样本的类别标签
- `ptr` 是一个指针,指向内存库中下一个可用的位置
- `device` 是一个字符串,表示内存库所在的设备
- `K` 是一个常数,表示最近邻算法中的邻居数
- `C` 是一个常数,表示分类数
其中,`self` 是Python中对象的引用,表示正在创建的MemoryBank对象本身。
这个类的作用是实现一个内存库,用于存储模型训练中的样本特征向量和类别标签,并支持最近邻分类算法。该类的实例可以在训练过程中被用来保存样本和计算样本与库中其他样本之间的相似度。
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
```