pytorch 预训练模型修改

最新推荐文章于 2025-06-07 11:09:10 发布

原创

最新推荐文章于 2025-06-07 11:09:10 发布 · 1.9k 阅读

3 ·

CC 4.0 BY-SA版权

本文详细介绍了如何在PyTorch中对预训练模型进行修改，包括加载模型、调整网络结构、微调参数等步骤，帮助开发者实现模型的个性化需求。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >


# coding=UTF-8
import torchvision.models as models
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
 
class CNN(nn.Module):
 
    def __init__(self, block, layers, num_classes=9):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        #新增一个反卷积层
        self.convtranspose1 = nn.ConvTranspose2d(2048