self.encoder1 = nn.Embedding(MAX_OCTREE_LEVEL+1, 6)
时间: 2024-05-17 20:16:57 浏览: 11
这段代码的作用是构建一个Embedding层,其中MAX_OCTREE_LEVEL表示八叉树的最大深度(层数),6表示嵌入向量的维度。Embedding层用于将输入的八叉树深度转换为嵌入向量序列,其中每个深度被转换为一个固定大小的向量表示,以便模型能够对输入序列进行处理。self.encoder1表示构建的Embedding层,可以用于将八叉树深度转换为嵌入向量序列。
相关问题
self.src_mask = None self.pos_encoder = PositionalEncodingTwo(feature_size) self.encoder_layer = nn.TransformerEncoderLayer(d_model=feature_size, nhead=8, dropout=dropout) self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers) self.decoder_layer = nn.TransformerDecoderLayer(d_model=feature_size, nhead=8, dropout=dropout) self.transformer_decoder = nn.TransformerDecoder(self.decoder_layer, num_layers=num_layers) self.decoder = nn.Linear(feature_size,1) self.init_weights()
这些代码是Transformer类的构造函数中的一部分,它们定义了Transformer模型的各个组件及其参数。
- `self.src_mask = None`:这是一个源端的掩码,初始值为None。在attention计算中,当某个位置的掩码为1时,表示该位置的信息不应该被考虑,因此可以在构造函数中初始化为None。
- `self.pos_encoder = PositionalEncodingTwo(feature_size)`:这是一个位置编码器,用于将输入序列中的每个位置映射到一个固定的向量表示。这里使用的是PositionalEncodingTwo类,它将位置编码的向量加到输入嵌入中,以便模型可以更好地处理序列数据。
- `self.encoder_layer = nn.TransformerEncoderLayer(d_model=feature_size, nhead=8, dropout=dropout)`:这是一个Transformer编码器层,它由多头注意力和前馈神经网络组成。这里使用的是nn.TransformerEncoderLayer类,它的d_model参数表示输入和输出的特征维度,nhead表示多头注意力的头数,dropout表示Dropout概率。
- `self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)`:这是一个Transformer编码器,它由多个编码器层堆叠而成。这里使用的是nn.TransformerEncoder类,它的第一个参数是编码器层,第二个参数是编码器层数。
- `self.decoder_layer = nn.TransformerDecoderLayer(d_model=feature_size, nhead=8, dropout=dropout)`:这是一个Transformer解码器层,它也由多头注意力和前馈神经网络组成。这里使用的是nn.TransformerDecoderLayer类,它的参数与编码器层相同。
- `self.transformer_decoder = nn.TransformerDecoder(self.decoder_layer, num_layers=num_layers)`:这是一个Transformer解码器,它也由多个解码器层堆叠而成。这里使用的是nn.TransformerDecoder类,它的第一个参数是解码器层,第二个参数是解码器层数。
- `self.decoder = nn.Linear(feature_size,1)`:这是一个线性层,用于将Transformer解码器的输出映射到一个标量值。这里将特征维度(feature_size)的向量映射到一个标量,以便进行回归任务。
- `self.init_weights()`:这是一个初始化权重的函数,用于初始化模型的参数。
请帮我编写下面网络的前向传播class UResNet_P(nn.Module):#ResNet_P编码器,参数量0.60M,595355 #def __init__(self): def __init__(self,in_channels=3, out_channels=3, init_features=32): super(UResNet_P,self).__init__() self.edge_detector=Edge_Detector() features = init_features self.pool = nn.MaxPool2d(kernel_size=2, stride=2) self.encoder1 = conv_block(in_channels, features) self.encoder2 = conv_block(features, features * 2) self.encoder3 = conv_block(features * 2, features * 4, dropout=True) self.residual_layer=self.stack_layer(Res_Block,16) self.conv=conv_block(features * 4, features * 8, dropout=True) self.upconv3 = nn.ConvTranspose2d(features * 8, features * 4, kernel_size=2, stride=2) self.decoder3 = conv_block(features * 8, features * 4) self.upconv2 = nn.ConvTranspose2d(features * 4, features * 2, kernel_size=2, stride=2) # 128 => 64 self.decoder2 = conv_block(features * 4, features * 2) self.upconv1 = nn.ConvTranspose2d(features * 2, features, kernel_size=2, stride=2) # 64 => 32 self.decoder1 = conv_block(features * 2, features) self.conv = nn.Conv2d(in_channels=features, out_channels=out_channels, kernel_size=1) self.input = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1, bias=False) self.output = nn.Conv2d(in_channels=32, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False) self.relu=nn.ReLU(inplace=True)
好的,这是 `UResNet_P` 网络的前向传播代码:
```python
import torch.nn as nn
class UResNet_P(nn.Module):
def __init__(self, in_channels=3, out_channels=3, init_features=32):
super(UResNet_P, self).__init__()
self.edge_detector = Edge_Detector()
features = init_features
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder1 = conv_block(in_channels, features)
self.encoder2 = conv_block(features, features * 2)
self.encoder3 = conv_block(features * 2, features * 4, dropout=True)
self.residual_layer = self.stack_layer(Res_Block, 16)
self.conv = conv_block(features * 4, features * 8, dropout=True)
self.upconv3 = nn.ConvTranspose2d(features * 8, features * 4, kernel_size=2, stride=2)
self.decoder3 = conv_block(features * 8, features * 4)
self.upconv2 = nn.ConvTranspose2d(features * 4, features * 2, kernel_size=2, stride=2)
self.decoder2 = conv_block(features * 4, features * 2)
self.upconv1 = nn.ConvTranspose2d(features * 2, features, kernel_size=2, stride=2)
self.decoder1 = conv_block(features * 2, features)
self.conv = nn.Conv2d(in_channels=features, out_channels=out_channels, kernel_size=1)
self.input = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1, bias=False)
self.output = nn.Conv2d(in_channels=32, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
edge = self.edge_detector(x)
x = self.input(x)
x = self.relu(x)
e1 = self.encoder1(x)
e2 = self.encoder2(self.pool(e1))
e3 = self.encoder3(self.pool(e2))
r = self.residual_layer(e3)
d = self.conv(torch.cat((e3, self.upconv3(r)), dim=1))
d = self.decoder3(d)
d = self.upconv2(d)
d = self.decoder2(torch.cat((e2, d), dim=1))
d = self.upconv1(d)
d = self.decoder1(torch.cat((e1, d), dim=1))
out = self.conv(d)
out = self.relu(out)
out = self.output(self.pool(out))
return out
```
这个网络包含了很多层,但是大部分都是相同的结构:卷积层 + ReLU 激活函数,或者是反卷积层。我这里只列出了 `conv_block` 和 `Res_Block` 的代码,因为这两个函数较为复杂,其他层都比较简单。你需要自己实现这两个函数,或者是从其他地方获取它们的代码。
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