1.理论

1.1 引出问题

标注一个数据集很贵，通常希望模型在较大数据集上进行训练
在这里插入图片描述

模型需要相同，才能使用pre-train的模型参数

训练：更小的学习率，更少的数据迭代

一些使用方法

2.代码

%matplotlib inline
import os
import torch
import torchvision
from torch import nn
from d2l import torch as d2l

#@save
d2l.DATA_HUB['hotdog'] = (d2l.DATA_URL + 'hotdog.zip',
                          'fba480ffa8aa7e0febbb511d181409f899b9baa5')

data_dir = d2l.download_extract('hotdog')

train_imgs = torchvision.datasets.ImageFolder(os.path.join(data_dir, 'train'))
test_imgs = torchvision.datasets.ImageFolder(os.path.join(data_dir, 'test'))

hotdogs = [train_imgs[i][0] for i in range(8)]
not_hotdogs = [train_imgs[-i - 1][0] for i in range(8)]
d2l.show_images(hotdogs + not_hotdogs, 2, 8, scale=1.4);

# 使用三个RGB通道的均值和标准偏差，以标准化每个通道
normalize = torchvision.transforms.Normalize([0.485, 0.456, 0.406],
                                             [0.229, 0.224, 0.225])

train_augs = torchvision.transforms.Compose([
    torchvision.transforms.RandomResizedCrop(224),
    torchvision.transforms.RandomHorizontalFlip(),
    torchvision.transforms.ToTensor(), normalize])

test_augs = torchvision.transforms.Compose([
    torchvision.transforms.Resize(256),
    torchvision.transforms.CenterCrop(224),
    torchvision.transforms.ToTensor(), normalize])

pretrained_net = torchvision.models.resnet18(pretrained=True)

pretrained_net.fc

finetune_net = torchvision.models.resnet18(pretrained=True)
finetune_net.fc = nn.Linear(finetune_net.fc.in_features, 2)
nn.init.xavier_uniform_(finetune_net.fc.weight);

# 如果 `param_group=True`，输出层中的模型参数将使用十倍的学习率
def train_fine_tuning(net, learning_rate, batch_size=128, num_epochs=5,
                      param_group=True):
    train_iter = torch.utils.data.DataLoader(
        torchvision.datasets.ImageFolder(os.path.join(data_dir, 'train'),
                                         transform=train_augs),
        batch_size=batch_size, shuffle=True)
    test_iter = torch.utils.data.DataLoader(
        torchvision.datasets.ImageFolder(os.path.join(data_dir, 'test'),
                                         transform=test_augs),
        batch_size=batch_size)
    devices = d2l.try_all_gpus()
    loss = nn.CrossEntropyLoss(reduction="none")
    if param_group:
        params_1x = [
            param for name, param in net.named_parameters()
            if name not in ["fc.weight", "fc.bias"]]
        trainer = torch.optim.SGD([{
            'params': params_1x}, {
                'params': net.fc.parameters(),
                'lr': learning_rate * 10}], lr=learning_rate,
                                  weight_decay=0.001)
    else:
        trainer = torch.optim.SGD(net.parameters(), lr=learning_rate,
                                  weight_decay=0.001)
    d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,
                   devices)

train_fine_tuning(finetune_net, 5e-5)

scratch_net = torchvision.models.resnet18()
scratch_net.fc = nn.Linear(scratch_net.fc.in_features, 2)
train_fine_tuning(scratch_net, 5e-4, param_group=False)

3.QA

当数据量很小的话，可以固定前边的参数，只train后边的。

for param in finetune_net.parameters():
    param.requires_grad = False

weight = pretrained_net.fc.weight
hotdog_w = torch.split(weight.data, 1, dim=0)[713]
hotdog_w.shape