数据集下载

这里使用的数据集包含ants和bees训练图片各约120张，验证图片各75张。由于数据样本非常少，如果从0初始化一个网络进行训练很难有令人满意的结果，这时候迁移学习就派上了用场。数据集下载地址，下载后解压到项目目录

导入相关包

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
import torchvision.transforms as transforms
import time
import os
import copy

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

加载数据

PyTorch提供了 torchvision.datasets.ImageFolder 方法来私人数据集

# 训练数据集需要扩充和归一化
# 验证数据集仅需要归一化
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = 'hymenoptera_data'

image_datasets = {
    x: torchvision.datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) 
    for x in ['train', 'val']
    }

dataloaders = {
    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,  shuffle=True, num_workers=4)
    for x in ['train', 'val']
    }

dataset_sizes = {
    x: len(image_datasets[x])
    for x in ['train', 'val']
    }

class_names = image_datasets['train'].classes

定义一个通用的训练函数，每个epoch进行一次测试，最后返回具有最优参数的模型

# 训练模型函数，参数scheduler是一个 torch.optim.lr_scheduler 学习速率调整类对象
def train_model(model, criterion, optimizer, scheduler, num_epochs=2):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('-' * 20)
        print('Epoch {}/{}'.format(epoch+1, num_epochs))

        # 每个epoch都有一个训练和验证阶段
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()   # 训练模式
            else:
                model.eval()    # 验证模式

            running_loss = 0.0
            running_corrects = 0

            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # 训练阶段开启梯度跟踪
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # 仅在训练阶段进行后向+优化
                    if phase == 'train':
                        optimizer.zero_grad()
                        loss.backward()
                        optimizer.step()
                        scheduler.step()

                # 统计
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

            # 记录最好的状态
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

    print('-' * 20)
    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed//60, time_elapsed%60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # 返回最佳参数的模型
    model.load_state_dict(best_model_wts)
    return model

场景一：微调ConvNet

这里我们使用resnet18作为我们的初始网络，在自己的数据集上继续训练预训练好的模型，所不同的是，我们修改原网络最后的全连接层输出维度为2，因为我们只需要预测是蚂蚁还是蜜蜂，原网络输出维度是1000，预测了1000个类别

net = torchvision.models.resnet18(pretrained=True)     # 加载resnet网络结构和预训练参数
num_ftrs = net.fc.in_features      # 提取fc层的输入参数
net.fc = nn.Linear(num_ftrs, 2)    # 修改输出维度为2

net = net.to(device)

# 使用分类交叉熵 Cross-Entropy 作损失函数，动量SGD做优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 每5个epochs衰减一次学习率 new_lr = old_lr * gamma ^ (epoch/step_size)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)

# 训练模型
net = train_model(net, criterion, optimizer, lr_scheduler, num_epochs=10)

在这里插入图片描述

场景二：ConvNet作为固定特征提取器

这里我们通过设置 requires_grad == False 冻结除最后一层之外的所有网络，这样在反向传播的时候他们的梯度就不会被计算，参数也不会更新

net = torchvision.models.resnet18(pretrained=True)
# 通过设置requires_grad = False来冻结参数，这样在反向传播的时候他们的梯度就不会被计算
for param in net.parameters():
    param.requires_grad = False

# 新连接层参数默认requires_grad=True
num_ftrs = net.fc.in_features
net.fc = nn.Linear(num_ftrs, 2)

net = net.to(device)

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.fc.parameters(), lr=0.001, momentum=0.9)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)

net = train_model(net, criterion, optimizer, lr_scheduler, num_epochs=20)

在这里插入图片描述