引自官方:  Transfer Learning tutorial

Ng在Deeplearning.ai中讲过迁移学习适用于任务A、B有相同输入、任务B比任务A有更少的数据、A任务的低级特征有助于任务B。对于迁移学习,经验规则是如果任务B的数据很小,那可能只需训练最后一层的权重。若有足够多的数据则可以重新训练网络中的所有层。如果重新训练网络中的所有参数,这个在训练初期称为预训练(pre-training,因为事先利用任务A的权重初始化。在预训练的基础上更新权重,那么这个过程叫微调(fine tuning)。微调有两种方式:全局、局部。全局微调:在预训练的基础上重新更新所有权重。局部微调:例如冻结卷积层的权重,另其为特征提取器,而只更新最后的一两层全连接。这也是迁移学习的两种方式。

 

下面分别讨论这两种学习方式:

问题描述:蚂蚁和蜜蜂的二分类,利用resnet18预训练。

一. 全局微调

 1. Load Dada

利用 torchvision.datasets.ImageFolder 实现,即需要将每一类的所有图片单独放到每一个文件夹下,文件夹的命名即为类名。这里将数据设置为训练集与验证集,采用字典的形式。

Pytorch tutorial 之Transfer Learning

data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),     # 裁剪到224,224
        transforms.RandomHorizontalFlip(),     # 随机水平翻转给定的PIL.Image,概率为0.5。即:一半的概率翻转,一半的概率不翻转。
        transforms.ToTensor(),                 # 把一个取值范围是[0,255]的PIL.Image或者shape为(H,W,C)的numpy.ndarray,转换成形状为[C,H,W],取值范围是[0,1.0]的FloadTensor
        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: datasets.ImageFolder(os.path.join(data_dir, x),   # 同时进行transform
                                          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']} # 训练集与验证集数量
class_names
= image_datasets['train'].classes # 样本类别名(子文件夹名) use_gpu = torch.cuda.is_available() # 检验是否可用cuda

2. Visualize a few images

可视化一个批量数据,利用
torchvision.utils.make_grid 实现。
此时make_grid的输入仍为Tensor(C,W,H),而imshow的时候要转回(W,H,C)。而后要乘以方差并加上均值。注意之前的的预处理(减均值除方差)操作应该只是在一个batch上进行的,并非在全部样本上操作。

def imshow(inp, title=None):
    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated


# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))   # 取一个abtch的样本操作

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)   # 此时的输入为Tensor

imshow(out, title=[class_names[x] for x in classes])

 Pytorch tutorial 之Transfer Learning

 

3. Traning the model

这里的主要操作有:Scheduling the learning rate(规划学习率)、Saving the best model(保存最优模型)

先介绍 scheduler 的用法:

optim模块除了常规的用法外(一个参数组):

optim.SGD(model.parameters(), lr=1e-2, momentum=.9)

还可以制定任意一层的学习率(多个参数组):下面为两个参数组

optim.SGD([
            {'params': model.base.parameters()},
            {'params': model.classifier.parameters(), 'lr': 1e-3}
            ], lr=1e-2, momentum=0.9)

那么多个参数组如何进一步调整学习率呢?用到了 torch.optim.lr_scheduler ,它提供了几种方法来根据epoches的数量调整学习率。有些优化算法已经拥有了学习率衰减参数lr_decay ,例如:

class torch.optim.Adagrad(params, lr=0.01, lr_decay=0, weight_decay=0)

首先介绍: class torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1)

其中optimizer就是包装好的优化器, lr_lambda即为操作学习率的函数。将每个参数组的学习速率设置为初始的lr乘以一个给定的函数。当last_epoch=-1时,将初始lr设置为lr。

>>> # Assuming optimizer has two groups.  这里假定有两个参数组,固有两个函数
>>> lambda1 = lambda epoch: epoch // 30
>>> lambda2 = lambda epoch: 0.95 ** epoch
>>> scheduler = LambdaLR(optimizer, lr_lambda=[lambda1, lambda2])   # lambda的结果作为乘法因子与学习率相乘
>>> for epoch in range(100):
>>>     scheduler.step()    # 在训练的时候进行迭代
>>>     train(...)
>>>     validate(...)

然后介绍: torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1)

其中optimizer就是包装好的优化器,step_size (int) 为学习率衰减期,指几个epoch衰减一次。gamma为学习率衰减的乘积因子。 默认为0.1 。当last_epoch=-1时,将初始lr设置为lr。

>>> # Assuming optimizer uses lr = 0.5 for all groups  假定初始的所有参数组学习率都为0.5
>>> # lr = 0.05     if epoch < 30   因为衰减器为30个epoch,所以没够30个epoch学习率乘以0.1
>>> # lr = 0.005    if 30 <= epoch < 60
>>> # lr = 0.0005   if 60 <= epoch < 90
>>> # ...
>>> scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
>>> for epoch in range(100):
>>>     scheduler.step()
>>>     train(...)
>>>     validate(...)

好了,来看一下训练的代码吧:

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())  # 先深拷贝一份当前模型的参数,后面迭代过程中若遇到更优模型则替换
    best_acc = 0.0   # 初始准确率

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                scheduler.step()    # 训练的时候进行学习率规划,其定义在下面给出
                model.train(True)  # Set model to training mode  设置为训练模式
            else:
                model.train(False)  # Set model to evaluate mode  设置为测试模式

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for data in dataloaders[phase]:
                # get the inputs
                inputs, labels = data

                # wrap them in Variable
                if use_gpu:
                    inputs = Variable(inputs.cuda())
                    labels = Variable(labels.cuda())
                else:
                    inputs, labels = Variable(inputs), Variable(labels)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                outputs = model(inputs)
                _, preds = torch.max(outputs.data, 1)
                loss = criterion(outputs, labels)

                # backward + optimize only if in training phase
                if phase == 'train':
                    loss.backward()
                    optimizer.step()

                # statistics
                running_loss += loss.data[0] * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects / dataset_sizes[phase]

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

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:  # 当验证时遇到了更好的模型则予以保留
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())   # 深拷贝模型参数

        print()

    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))

    # load best model weights
    model.load_state_dict(best_model_wts)   # 载入最优模型参数
    return model

 

4.  Finetuning the convnet

model_ft = models.resnet18(pretrained=True)   # model_ft即为含训练好参数的残差网络
num_ftrs = model_ft.fc.in_features            # 最后一个全连接的输入维度,这里实为512
model_ft.fc = nn.Linear(num_ftrs, 2)          # 将最后一个全连接由(512, 1000)改为(512, 2)   因为原网络是在1000类的ImageNet数据集上训练的

if use_gpu:
    model_ft = model_ft.cuda()                # 将网络里的变量也调用cuda

criterion = nn.CrossEntropyLoss()             # 多累交叉熵

# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)    # 单参数组

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)     # 每7个epoch衰减0.1倍
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)

这里预训练好的model_ft 就是一个model,可以查看其参数与结构:

print (model_ft)     # 查看网络结构

for name, para in model_ft.named_parameters():    # 查看网络参数名字与尺寸
    print(name,':', para.size())

 

5.  Visualizing the model predictions

可视化预测结果:

def visualize_model(model, num_images=6):
    was_training = model.training             # 检验是否是训练模式
    model.eval()        # 模式设置为测试模式
    images_so_far = 0
    fig = plt.figure()

    for i, data in enumerate(dataloaders['val']):
        inputs, labels = data
        if use_gpu:
            inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
        else:
            inputs, labels = Variable(inputs), Variable(labels)

        outputs = model(inputs)
        _, preds = torch.max(outputs.data, 1)

        for j in range(inputs.size()[0]):
            images_so_far += 1
            ax = plt.subplot(num_images//2, 2, images_so_far)
            ax.axis('off')
            ax.set_title('predicted: {}'.format(class_names[preds[j]]))
            imshow(inputs.cpu().data[j])

            if images_so_far == num_images:
                model.train(mode=was_training)
                return
    model.train(mode=was_training)

 

二. 局部微调

ConvNet as fixed feature extractor

将conv的参数都固定,只调整全连接。

model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():
    param.requires_grad = False    # 将所有参数求导设为否

# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)   # 取代最后一个全连接

if use_gpu:
    model_conv = model_conv.cuda()

criterion = nn.CrossEntropyLoss()

# Observe that only parameters of final layer are being optimized as
# opoosed to before.
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)

需要注意的是:新构建的model的参数默认为 requires_grad=True

model_conv = train_model(model_conv, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=25)

训练结果比全局调优还好一些。