swin transformer+FPN+PAN代码做目标检测

以下是使用Swin Transformer、FPN和PAN进行目标检测的代码示例：

首先，我们需要安装必要的库和工具：

pip install torch torchvision opencv-python tqdm

接下来，我们需要下载COCO数据集和预训练的Swin Transformer模型。我们可以使用以下命令来下载它们：

mkdir data
cd data
# Download COCO dataset
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
unzip train2017.zip
unzip val2017.zip
unzip annotations_trainval2017.zip
rm train2017.zip val2017.zip annotations_trainval2017.zip

# Download pre-trained Swin Transformer model
mkdir models
cd models
wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth

接下来，我们可以编写一个Python脚本来训练我们的模型。以下是一个简单的示例：

import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import CocoDetection
from swin_transformer import SwinTransformer
from fpn import FPN
from pan import PAN

# Define hyperparameters
batch_size = 16
num_epochs = 10
lr = 1e-4

# Define data transforms
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Load COCO dataset
train_dataset = CocoDetection(root='./data', annFile='./data/annotations/instances_train2017.json', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# Define Swin Transformer model
swin = SwinTransformer()
swin.load_state_dict(torch.load('./data/models/swin_tiny_patch4_window7_224.pth'))

# Define FPN and PAN models
fpn = FPN(in_channels=[96, 192, 384, 768], out_channels=256)
pan = PAN(in_channels=[256, 256, 256, 256], out_channels=256)

# Define detection head
detection_head = nn.Sequential(
    nn.Conv2d(256, 256, kernel_size=3, padding=1),
    nn.ReLU(inplace=True),
    nn.Conv2d(256, 4, kernel_size=1),
    nn.Sigmoid()
)

# Define optimizer and loss function
optimizer = optim.Adam(list(swin.parameters()) + list(fpn.parameters()) + list(pan.parameters()) + list(detection_head.parameters()), lr=lr)
criterion = nn.MSELoss()

# Train the model
for epoch in range(num_epochs):
    for images, targets in train_loader:
        # Forward pass
        features = swin(images)
        fpn_features = fpn(features)
        pan_features = pan(fpn_features)
        output = detection_head(pan_features[-1])

        # Compute loss
        loss = criterion(output, targets)

        # Backward pass and update weights
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # Print statistics
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

在上面的代码中，我们首先加载了预训练的Swin Transformer模型，并使用它提取特征。然后，我们将这些特征输入到FPN和PAN模型中，以生成具有不同分辨率的特征图。最后，我们使用一个简单的检测头来预测边界框。

在训练期间，我们使用均方误差（MSE）作为损失函数，并使用Adam优化器来更新模型的权重。

请注意，上面的代码仅提供了一个简单的示例，实际上，您可能需要进行一些其他的调整和修改，以便使其适用于您的具体任务和数据集。