实战Kaggle比赛：狗的品种识别（ImageNet Dogs）

在这场比赛中，我们将识别120类不同品种的狗。这个数据集实际上是著名的ImageNet的数据集子集。

这里我们选择小批量进行训练出于学习目的

代码实现

先导入

python 复制代码

import os
import torch
import torchvision
from torch import nn
from d2l import torch as d2l

下载小数据集

python 复制代码

d2l.DATA_HUB['dog_tiny'] = (d2l.DATA_URL + 'kaggle_dog_tiny.zip',
                            '0cb91d09b814ecdc07b50f31f8dcad3e81d6a86d')

# 如果使用Kaggle比赛的完整数据集，请将下面的变量更改为False
demo = True
if demo:
    data_dir = d2l.download_extract('dog_tiny')
else:
    data_dir = os.path.join('..', 'data', 'dog-breed-identification')

划分数据

python 复制代码

def reorg_dog_data(data_dir, valid_ratio):
    labels = d2l.read_csv_labels(os.path.join(data_dir, 'labels.csv'))
    d2l.reorg_train_valid(data_dir, labels, valid_ratio)
    d2l.reorg_test(data_dir)


batch_size = 32 if demo else 128
valid_ratio = 0.1
reorg_dog_data(data_dir, valid_ratio)

数据增强

python 复制代码

transform_train = torchvision.transforms.Compose([
    # 随机裁剪图像，所得图像为原始面积的0.08～1之间，高宽比在3/4和4/3之间。
    # 然后，缩放图像以创建224x224的新图像
    torchvision.transforms.RandomResizedCrop(224,scale=(0.8,1.0),ratio=(0.75,1.3)),
    # 随机水平翻转图像
    torchvision.transforms.RandomHorizontalFlip(),
    # 随机变化亮度、对比度和饱和度
    torchvision.transforms.ColorJitter(brightness=0.4,contrast=0.4,saturation=0.4),
    torchvision.transforms.ToTensor(),
    # 标准化图像
    torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
transform_test = torchvision.transforms.Compose([
    torchvision.transforms.Resize(256),
    torchvision.transforms.CenterCrop(224),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

dataset

python 复制代码

train_ds, train_valid_ds = [torchvision.datasets.ImageFolder(
    os.path.join(data_dir, 'train_valid_test', folder),
    transform=transform_train) for folder in ['train', 'train_valid']]

valid_ds, test_ds = [torchvision.datasets.ImageFolder(
    os.path.join(data_dir, 'train_valid_test', folder),
    transform=transform_test) for folder in ['valid', 'test']]

dataset iter

python 复制代码

train_iter,train_valid_iter=[torch.utils.data.DataLoader( dataset,batch_size=32,shuffle=True,num_workers=4,drop_last=True) for dataset in [train_ds,train_valid_ds]]
valid_iter = torch.utils.data.DataLoader(valid_ds, batch_size, shuffle=False,
                                         drop_last=True)

test_iter = torch.utils.data.DataLoader(test_ds, batch_size, shuffle=False,
                                        drop_last=False)

迁移学习

python 复制代码

# 迁移学习,不重新训练用于特征提取的预训练模型，训练的小型自定义输出网络替换原始输出层
# 微调是迁移学习的一种具体技术，指在预训练模型的基础上，通过继续训练（调整部分或全部参数）来适应新任务，FLOPS大
# 这里我们使用迁移学习，计算量小，只需要训练一个小型自定义输出网络
def get_net(devices):
    model=torchvision.models.vit_b_16(pretrained=True)
     # 获取原始分类头的输入特征维度
    num_classifier_feature = model.heads.head.in_features
    # 定义一个新的输出网络，共有120个输出类别
    model.heads.head = nn.Sequential(
        nn.Linear(num_classifier_feature, 120)
    )
    # 将模型移动到指定设备（如GPU）
    model = model.to(devices[0])
    
    # 冻结全部
    for param in model.parameters():
        param.requires_grad = False
    
    # 只训练新添加的分类头（解冻）
    for param in model.heads.head.parameters():
        param.requires_grad = True
    
    return model

loss评估函数

python 复制代码

loss = nn.CrossEntropyLoss(reduction='none')

def evaluate_loss(data_iter, net, devices):
    l_sum, n = 0.0, 0
    for features, labels in data_iter:
        features, labels = features.to(devices[0]), labels.to(devices[0])
        outputs = net(features)
        l = loss(outputs, labels)
        l_sum += l.sum()
        n += labels.numel()
    return (l_sum / n).to('cpu')

train code

python 复制代码

def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
          lr_decay):
    # 只训练小型自定义输出网络
    net = nn.DataParallel(net, device_ids=devices).to(devices[0])
    trainer = torch.optim.SGD((param for param in net.parameters()
                               if param.requires_grad), lr=lr,
                              momentum=0.9, weight_decay=wd)
    scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay)
    num_batches, timer = len(train_iter), d2l.Timer()
    legend = ['train loss']
    if valid_iter is not None:
        legend.append('valid loss')
    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],
                            legend=legend)
    for epoch in range(num_epochs):
        metric = d2l.Accumulator(2)
        for i, (features, labels) in enumerate(train_iter):
            timer.start()
            features, labels = features.to(devices[0]), labels.to(devices[0])
            trainer.zero_grad()
            output = net(features)
            l = loss(output, labels).sum()
            l.backward()
            trainer.step()
            metric.add(l, labels.shape[0])
            timer.stop()
            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
                animator.add(epoch + (i + 1) / num_batches,
                             (metric[0] / metric[1], None))
        measures = f'train loss {metric[0] / metric[1]:.3f}'
        if valid_iter is not None:
            valid_loss = evaluate_loss(valid_iter, net, devices)
            animator.add(epoch + 1, (None, valid_loss.detach().cpu()))
        scheduler.step()
    if valid_iter is not None:
        measures += f', valid loss {valid_loss:.3f}'
    print(measures + f'\n{metric[1] * num_epochs / timer.sum():.1f}'
          f' examples/sec on {str(devices)}')

python 复制代码

devices, num_epochs, lr, wd = d2l.try_all_gpus(), 30, 1e-4, 2e-4
lr_period, lr_decay, net = 2, 0.9, get_net(devices)
train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
      lr_decay)
# train loss 0.215, valid loss 0.399
# 150.5 examples/sec on [device(type='cuda', index=0)]

可以看到30轮后面差不多就收敛了，当然还有很多提升空间，比如数据增强or使用vit_l_16或者更大的预训练模型做迁移学习，这里不多加概述

一个低loss的代码实现：nb.pretrained.vit - Loss : 0.09921

完整代码实现：dive-into-deep-learning/d2l/dog-breed-identification/main.ipynb at main · hllqkb/dive-into-deep-learning

实战Kaggle比赛：狗的品种识别（ImageNet Dogs）

在这场比赛中，我们将识别120类不同品种的狗。 这个数据集实际上是著名的ImageNet的数据集子集。

代码实现

在这场比赛中，我们将识别120类不同品种的狗。这个数据集实际上是著名的ImageNet的数据集子集。