解码器网络有六个卷积块(“DCB11”、“DCB12”、“DCB13”;“DCB21”、“DCB22”;“DCB31”)和一个卷积层(“Conv”),代码
时间: 2023-10-03 17:06:31 浏览: 45
可以参考以下代码实现:
```python
import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self, vocab_size, max_len, emb_dim, hidden_dim, n_layers, dropout):
super(Decoder,self).__init__()
self.vocab_size = vocab_size
self.max_len = max_len
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.embedding = nn.Embedding(vocab_size, emb_dim)
self.dropout = nn.Dropout(dropout)
# Define the decoder layers
self.DCB11 = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True)
)
self.DCB12 = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True)
)
self.DCB13 = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True)
)
self.DCB21 = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True)
)
self.DCB22 = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True)
)
self.DCB31 = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True)
)
self.conv = nn.Conv2d(hidden_dim, vocab_size, kernel_size=1, stride=1)
def forward(self, encoder_out, captions):
embeddings = self.dropout(self.embedding(captions))
embeddings = embeddings.permute(0, 3, 1, 2)
h = encoder_out
# Decode the embeddings
for i in range(self.n_layers):
if i == 0:
h = self.DCB11(h + embeddings)
elif i == 1:
h = self.DCB12(h + embeddings)
elif i == 2:
h = self.DCB13(h + embeddings)
elif i == 3:
h = self.DCB21(h)
elif i == 4:
h = self.DCB22(h)
elif i == 5:
h = self.DCB31(h)
# Convert the output to the final vocab size
out = self.conv(h)
out = out.permute(0, 2, 3, 1)
return out
```
这个代码实现了一个 Decoder 类,其中有 6 个卷积块和一个卷积层。这些卷积块和卷积层用于将编码器输出和输入的文本嵌入向量解码为最终的文本序列。其中 `DCB11`、`DCB12`、`DCB13`、`DCB21`、`DCB22`、`DCB31` 分别代表了 6 个不同的卷积块,每个卷积块都包含两个卷积层和一个批量归一化层。在每个卷积块之间,我们使用 ResNet 的跳跃连接技术,将编码器输出和嵌入向量直接相加。最后,我们使用一个单独的卷积层将输出转换为最终的文本序列。
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