torch 一文迁移学习　蜜蜂蚂蚁　BeeAndAnt

本文实际上译自 Transfer Learning Tutorial,严重参考了斯坦福有关Transfer Learning的介绍

这篇博客将详细介绍:

1. 介绍使用预训练模型的两种方法（fine-tuning及fixed feature extractor）

2. pytorch官方给出的对应的样例程序

１．介绍使用预训练模型的两种方法（fine-tuning及fixed feature extractor）

简单来说，fixed feature extractor只训练最后的隐藏层与输出层（可以去掉原模型的输出层，加入自己的输出层）之间的参数，或者干脆不进行训练，把最后一层隐藏层作为特征向量输出，从而成为一个特征提取器。而fine-tuning可以训练之前隐藏层的参数。

使用情况：上述斯坦福的文章中讨论了四种情况下（数据集大小，数据集与预训练模型使用的数据集之间的差别大小），预训练模型应该怎么使用。fixed feature extractor适合于数据集较小，数据集与预训练模型使用的数据集之间的差别不大的情况，而fine-tuning适合于数据集较大，数据集与预训练模型使用的数据集之间的差别不大的情况。

详细内容，推荐阅读斯坦福的文章。

２．pytorch官方给出的对应的样例程序

1. 首先，导入具体的库

２．构造数据集

首先下载数据集，点击这里，解压后把里面的train及valid文件夹放在当前工作目录。包括对数据进行数据增强（Data augmentation），包括进行随机固定尺寸裁剪（预训练模型对深入图片的大小有要求），进行随机旋转镜像，以及规范化，这些操作多有明确的好处（可理解成固定的优秀的套路，祖传操作，，，），具体介绍请参考cs231n课程。

# Data augmentation and normalization for training
# Just normalization for validation
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 = ''
image_datasets = {x: 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

３．先看一波图片样子

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 )  # 如果感觉停留时间短可以调整0.001单位秒


# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)
imshow(out, title=[class_names[x] for x in classes])
plt.show()

　　　　　　　　　　　　　 　

４．模型训练及可视化模板

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

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

    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()
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

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

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

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

                # statistics
                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))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        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)
    return model
def visualize_model(model, num_images=6):
    was_training = model.training
    model.eval()
    images_so_far = 0
    fig = plt.figure()

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(dataloaders['val']):
            inputs = inputs.to(device)
            labels = labels.to(device)

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

５．如果你有ＧＰＵ可以使用，那将极大的提高你的训练速度，设置使用ＧＰＵ的方法如下:

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

６．１构建模型 fine-tuning，在最后隐藏层接输出层处断开，然后姐两类输出。

注意，一般模型最后的一层为全连接层（fully connected layer），简称　fc　。model_ft.fc.in_features是该层输入的神经元（特征）数。这里并没有冻结前面层的参数。

model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)

model_ft = model_ft.to(device)

７．１指定评价函数，优化器，及训练计划开始训练 

criterion = nn.CrossEntropyLoss()

# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.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_ft, step_size=7, gamma=0.1)
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,num_epochs=25)
# visualize_model(model_ft)

６．２构建模型 fine-tuning，在最后隐藏层接输出层处断开，然后姐两类输出。

注意，一般模型最后的一层为全连接层（fully connected layer），简称　fc　。model_ft.fc.in_features是该层输入的神经元（特征）数。并通过  param.requires_grad = False　冻结前面层的参数（即训练时，不会更新这些参数）。

model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():
    param.requires_grad = False

# 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)

model_conv = model_conv.to(device)

７．１指定评价函数，优化器，及训练计划开始训练  

criterion = nn.CrossEntropyLoss()

# Observe that only parameters of final layer are being optimized as
# opposed 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)
model_conv = train_model(model_conv, criterion, optimizer_conv,
                         exp_lr_scheduler, num_epochs=25)