基于pytorch的segformer

Segformer是一种基于Transformer的图像分割模型，其主要思想是将图像分成若干个块，然后使用Transformer进行特征提取和融合，最后通过一个头部网络进行像素级别的分类。Segformer相比于传统的卷积神经网络分割模型，具有更高的精度和更少的参数，同时也具有更好的可解释性。

在PyTorch中，可以使用以下代码实现Segformer模型：

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops.layers.torch import Rearrange

class Segformer(nn.Module):
    def __init__(self, num_classes, input_shape=(3, 224, 224), patch_size=16, hidden_dim=384, num_layers=12, num_heads=6):
        super(Segformer, self).__init__()
        self.input_shape = input_shape
        self.patch_size = patch_size
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.num_classes = num_classes
        
        # Input embedding
        self.input_embedding = nn.Sequential(
            nn.Conv2d(input_shape[0], hidden_dim, kernel_size=patch_size, stride=patch_size),
            Rearrange('b c h w -> b (h w) c')
        )
        
        # Transformer encoder
        encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=num_heads)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        
        # Output head
        self.output_head = nn.Sequential(
            Rearrange('b (h w) c -> b c h w', h=int(input_shape[1]/patch_size), w=int(input_shape[2]/patch_size)),
            nn.Conv2d(hidden_dim, num_classes, kernel_size=1),
            nn.Softmax(dim=1)
        )
        
    def forward(self, x):
        x = self.input_embedding(x)
        x = self.encoder(x)
        x = self.output_head(x)
        return x

其中，`num_classes`为分类数，`input_shape`为输入图像的大小，`patch_size`为块大小，`hidden_dim`为Transformer中每层的隐藏层大小，`num_layers`为Transformer中的层数，`num_heads`为Transformer中的头数。`input_embedding`用来将输入图像分成块并进行嵌入，`encoder`是Transformer编码器，`output_head`用来进行像素级别的分类。

使用以上代码可以创建一个Segformer模型，可以通过以下代码进行训练和评估：

model = Segformer(num_classes=2)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Train
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

# Evaluate
with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images)
        prediction = torch.argmax(outputs, dim=1).numpy()
        accuracy = np.mean(prediction == labels.numpy())
        print('Accuracy:', accuracy)