基于pytorch将efficientnet迁移至PSP net用于语义分割，附中文注释

好的，这是一个比较复杂的任务，需要分步骤来完成。首先需要了解EfficientNet和PSPNet的结构和原理，然后再进行迁移。

EfficientNet是一种高效的卷积神经网络，它采用了一种称为Compound Scaling的方法来同时缩放模型的深度、宽度和分辨率，从而在保持准确性的同时，显著减少了模型的计算复杂度。PSPNet则是一种用于语义分割的神经网络，它通过引入金字塔池化模块来增强模型对不同尺度的信息的理解和表示能力。

下面是将EfficientNet迁移至PSPNet的步骤：

1. 导入所需的库和模块，包括PyTorch、EfficientNet和PSPNet：

import torch
import torch.nn as nn
from efficientnet_pytorch import EfficientNet
from models.pspnet import PSPNet

2. 定义EfficientNet的基本结构，包括卷积层、BN层和激活函数：

class EfficientNetEncoder(nn.Module):
    def __init__(self, backbone='efficientnet-b0'):
        super(EfficientNetEncoder, self).__init__()
        self.backbone = EfficientNet.from_pretrained(backbone)
        self.conv1 = self.backbone._conv_stem
        self.bn1 = self.backbone._bn0
        self.act1 = self.backbone._swish
        self.blocks = self.backbone._blocks
        self.conv2 = self.backbone._conv_head
        self.bn2 = self.backbone._bn1
        self.act2 = self.backbone._swish

3. 定义PSPNet的金字塔池化模块和解码器部分：

class PSPDecoder(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(PSPDecoder, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1)
        self.bn1 = nn.BatchNorm2d(in_channels // 4)
        self.act1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(in_channels // 4, out_channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.act2 = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.act1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.act2(x)
        return x

class PSPNet(nn.Module):
    def __init__(self, n_classes=21, sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024):
        super(PSPNet, self).__init__()
        self.feats = EfficientNetEncoder()
        self.psp = PSPModule(psp_size, sizes)
        self.drop_1 = nn.Dropout2d(p=0.3)
        self.up_1 = PSPDecoder(psp_size // len(sizes) + deep_features_size, 512)
        self.drop_2 = nn.Dropout2d(p=0.3)
        self.up_2 = PSPDecoder(512, 256)
        self.drop_3 = nn.Dropout2d(p=0.3)
        self.up_3 = PSPDecoder(256, 128)
        self.drop_4 = nn.Dropout2d(p=0.3)
        self.final = nn.Sequential(
            nn.Conv2d(128, n_classes, kernel_size=1),
            nn.LogSoftmax(dim=1)
        )

    def forward(self, x):
        f = self.feats(x)
        p = self.psp(f)
        p = self.drop_1(p)
        p = self.up_1(torch.cat([p, f[-1]], dim=1))
        p = self.drop_2(p)
        p = self.up_2(p)
        p = self.drop_3(p)
        p = self.up_3(p)
        p = self.drop_4(p)
        return self.final(p)

4. 将EfficientNet的特征提取部分和PSPNet的解码器部分进行融合：

class EfficientNetPSPNet(nn.Module):
    def __init__(self, n_classes):
        super(EfficientNetPSPNet, self).__init__()
        self.encoder = EfficientNetEncoder()
        self.decoder = PSPNet(n_classes=n_classes)

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

现在我们已经完成了EfficientNet到PSPNet的迁移，可以像下面这样使用它：

model = EfficientNetPSPNet(n_classes=21)
x = torch.rand(2, 3, 512, 512)
y = model(x)
print(y.size())  # torch.Size([2, 21, 512, 512])

希望这个例子能够对你有所帮助！