转自：https://www.cnblogs.com/miraclepbc/p/14360807.html
resnet预训练模型
resnet模型与之前笔记中的vgg模型不同，需要我们直接覆盖掉最后的全连接层
先看一下resnet模型的结构：
我们需要先将所有的参数都设置成requires_grad = False
然后再重新定义fc层，并覆盖掉原来的。
重新定义的fc层的requires_grad默认为True
for p in model.parameters():p.requries_grad = Falsein_f = model.fc.in_features
model.fc = nn.Linear(in_f, 4)
当定义optimizer的时候，需要注意，传进去的参数是fc层的参数，而不是所有层的参数
optimizer = torch.optim.Adam(model.fc.parameters(), lr = 0.001)
微调
微调的一般步骤是：
- 重新定义全连接层
- 训练重新定义的全连接层
- 解冻部分其他层
- 训练整个模型
注意：微调是在训练完新的全连接层后，才能进行的。也就相当于整个模型训练了两次。
optimizer这时的参数就是整个模型的参数了。
代码：
for param in model.parameters():param.requires_grad = Trueextend_epoch = 30
optimizer = torch.optim.Adam(model.parameters(), lr = 0.0001)
全部代码
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms, models
import os
import shutil
%matplotlib inlinetrain_transform = transforms.Compose([transforms.Resize(224),transforms.RandomCrop(192),transforms.RandomHorizontalFlip(),transforms.RandomRotation(0.2),transforms.ColorJitter(brightness = 0.5),transforms.ColorJitter(contrast = 0.5),transforms.ToTensor(),transforms.Normalize(mean = [0.5, 0.5, 0.5], std = [0.5, 0.5, 0.5])
])
test_transform = transforms.Compose([transforms.Resize((192, 192)),transforms.ToTensor(),transforms.Normalize(mean = [0.5, 0.5, 0.5], std = [0.5, 0.5, 0.5])
])
train_ds = datasets.ImageFolder("E:/datasets2/29-42/29-42/dataset2/4weather/train",transform = train_transform
)
test_ds = datasets.ImageFolder("E:/datasets2/29-42/29-42/dataset2/4weather/test",transform = test_transform
)
train_dl = torch.utils.data.DataLoader(train_ds, batch_size = 8, shuffle = True)
test_dl = torch.utils.data.DataLoader(test_ds, batch_size = 8)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")model = models.resnet101(pretrained = True)
for p in model.parameters():p.requries_grad = False
in_f = model.fc.in_features
model.fc = nn.Linear(in_f, 4)loss_func = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.fc.parameters(), lr = 0.001)
epochs = 30
exp_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 7, gamma = 0.1)def fit(epoch, model, trainloader, testloader):correct = 0total = 0running_loss = 0model.train()for x, y in trainloader:x, y = x.to(device), y.to(device)y_pred = model(x)loss = loss_func(y_pred, y)optimizer.zero_grad()loss.backward()optimizer.step()with torch.no_grad():y_pred = torch.argmax(y_pred, dim = 1)correct += (y_pred == y).sum().item()total += y.size(0)running_loss += loss.item()exp_lr_scheduler.step()epoch_acc = correct / totalepoch_loss = running_loss / len(trainloader.dataset)test_correct = 0test_total = 0test_running_loss = 0model.eval()with torch.no_grad():for x, y in testloader:x, y = x.to(device), y.to(device)y_pred = model(x)loss = loss_func(y_pred, y)y_pred = torch.argmax(y_pred, dim = 1)test_correct += (y_pred == y).sum().item()test_total += y.size(0)test_running_loss += loss.item()epoch_test_acc = test_correct / test_totalepoch_test_loss = test_running_loss / len(testloader.dataset)print('epoch: ', epoch, 'loss: ', round(epoch_loss, 3),'accuracy: ', round(epoch_acc, 3),'test_loss: ', round(epoch_test_loss, 3),'test_accuracy: ', round(epoch_test_acc, 3))return epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acctrain_loss = []
train_acc = []
test_loss = []
test_acc = []
for epoch in range(epochs):epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acc = fit(epoch, model, train_dl, test_dl)train_loss.append(epoch_loss)train_acc.append(epoch_acc)test_loss.append(epoch_test_loss)test_acc.append(epoch_test_acc)for param in model.parameters():param.requires_grad = True
extend_epoch = 30
optimizer = torch.optim.Adam(model.parameters(), lr = 0.0001)
train_loss = []
train_acc = []
test_loss = []
test_acc = []
for epoch in range(extend_epoch):epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acc = fit(epoch, model, train_dl, test_dl)train_loss.append(epoch_loss)train_acc.append(epoch_acc)test_loss.append(epoch_test_loss)test_acc.append(epoch_test_acc)
