flatten transformer插入
时间: 2023-11-17 13:02:24 浏览: 32
根据提供的引用内容,没有提到"flatten transformer插入"这个概念。但是,引用中提到了在输入Transformer Encoder之前需要加上[class]token以及Position Embedding,这是为了让Transformer能够处理图像数据。[class]token是一个可训练的参数,用于分类,而Position Embedding则是一个可训练的参数,用于表示每个token的位置信息。这些步骤都是为了将图像数据转换为Transformer可以处理的形式。如果您有更多的问题,请告诉我,我会尽力回答。
相关问题
FLatten Transformer
Flatten Transformer是一种基于Transformer结构的语义分割模型,其核心思想是通过将输入图像进行下采样并划分成小的patches,然后将这些patches进行flatten操作,以便作为Transformer的编码器输入。这样做的目的是为了减少计算量并将图像的全局信息编码到每个小的patch中。
在Flatten Transformer中,还会插入一个类别标记(Class token),用于表示整个图像的语义信息。此外,每个patch的位置信息也会被加入到输入中,这是通过对每个位置信息进行简单的加法运算实现的。这些位置信息也可以被视为可训练的参数。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [Dual-Flattening Transformers through Decomposed Row and Column Queries for Semantic Segmentation论文...](https://blog.csdn.net/qq_42476803/article/details/128570281)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *2* *3* [Transformer计算量和过程统计](https://blog.csdn.net/MicroE_Lv/article/details/122855331)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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flatten transformer代码
flatten transformer是一种用于自然语言处理任务的模型架构,它基于transformer模型,并在其基础上进行了改进。下面是flatten transformer的代码介绍:
首先,我们需要导入所需的库和模块:
```
import torch
import torch.nn as nn
import torch.nn.functional as F
```
接下来,定义flatten transformer的主要模块,包括Encoder、Decoder和Transformer模块。
1. Encoder模块:
```
class Encoder(nn.Module):
def __init__(self, input_dim, hid_dim, n_layers, n_heads, pf_dim, dropout):
super().__init__()
self.tok_embedding = nn.Embedding(input_dim, hid_dim)
self.pos_embedding = nn.Embedding(max_len, hid_dim)
self.layers = nn.ModuleList([EncoderLayer(hid_dim, n_heads, pf_dim, dropout) for _ in range(n_layers)])
self.dropout = nn.Dropout(dropout)
def forward(self, src, src_mask):
batch_size = src.shape
src_len = src.shape
pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(device)
src = self.dropout((self.tok_embedding(src) * math.sqrt(self.hid_dim)) + self.pos_embedding(pos))
for layer in self.layers:
src = layer(src, src_mask)
return src
```
2. Decoder模块:
```
class Decoder(nn.Module):
def __init__(self, output_dim, hid_dim, n_layers, n_heads, pf_dim, dropout):
super().__init__()
self.tok_embedding = nn.Embedding(output_dim, hid_dim)
self.pos_embedding = nn.Embedding(max_len, hid_dim)
self.layers = nn.ModuleList([DecoderLayer(hid_dim, n_heads, pf_dim, dropout) for _ in range(n_layers)])
self.fc_out = nn.Linear(hid_dim, output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, trg, enc_src, trg_mask, src_mask):
batch_size = trg.shape
trg_len = trg.shape
pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(device)
trg = self.dropout((self.tok_embedding(trg) * math.sqrt(self.hid_dim)) + self.pos_embedding(pos))
for layer in self.layers:
trg, attention = layer(trg, enc_src, trg_mask, src_mask)
output = self.fc_out(trg)
return output, attention
```
3. Transformer模块:
```
class Transformer(nn.Module):
def __init__(self, encoder, decoder, src_pad_idx, trg_pad_idx):
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.src_pad_idx = src_pad_idx
self.trg_pad_idx = trg_pad_idx
def make_src_mask(self, src):
src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)
return src_mask
def make_trg_mask(self, trg):
trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(3)
trg_len = trg.shape
trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device=device)).bool()
trg_mask = trg_pad_mask & trg_sub_mask
return trg_mask
def forward(self, src, trg):
src_mask = self.make_src_mask(src)
trg_mask = self.make_trg_mask(trg)
enc_src = self.encoder(src, src_mask)
output, attention = self.decoder(trg, enc_src, trg_mask, src_mask)
return output, attention
```
以上是flatten transformer的主要代码介绍,其中包括Encoder、Decoder和Transformer模块的定义和前向传播过程。这些模块通过嵌套调用实现了flatten transformer的功能。