model.to(memory_format=torch.channels_last)

This method converts the memory layout of the tensors in the model to the channels-last format. In this format, the channel dimension is the last dimension of the tensor, which can be more efficient for certain hardware accelerators. This method is only available for tensors that have at least three dimensions.

Here is an example usage:

import torch.nn as nn

model = nn.Sequential(
    nn.Conv2d(3, 16, kernel_size=3, padding=1),
    nn.BatchNorm2d(16),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2)
)

# Convert model to channels-last format
model.to(memory_format=torch.channels_last)

相关问题

-torch.empty_like(logits, memory_format=torch.legacy_contiguous_format).exponential_().log()

这段代码的作用是创建一个和logits tensor相同大小的空tensor，然后用指数函数生成随机数填充这个空tensor，并取其对数值作为新的tensor。其中，memory_format=torch.legacy_contiguous_format是为了确保新的tensor在内存中是连续存储的，以便后续计算的效率。

这段代码通常用于计算logits tensor的softmax分布，即将logits tensor中的每个元素作为指数函数的底数，然后对所有元素求和，并将每个元素除以总和，得到一个新的分布。在计算softmax分布时，使用指数函数可以将logits中的负值转化为正值，避免了分母为0的问题。

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