Swin Transformer实战: timm使用、Mixup、Cutout和评分一网打尽,图像分类任务

摘要

本例提取了植物幼苗数据集中的部分数据做数据集,数据集共有12种类别,演示如何使用timm版本的Swin Transformer图像分类模型实现分类任务已经对验证集得分的统计,本文实现了多个GPU并行训练。

通过本文你和学到:

1、如何从timm调用模型、loss和Mixup?

2、如何制作ImageNet数据集?

3、如何使用Cutout数据增强?

4、如何使用Mixup数据增强。

5、如何实现多个GPU训练和验证。

6、如何使用余弦退火调整学习率?

7、如何使用classification_report实现对模型的评价。

8、预测的两种写法。
 

Swin Transformer简介

目标检测刷到58.7 AP!

实例分割刷到51.1 Mask AP!

语义分割在ADE20K上刷到53.5 mIoU!

今年,微软亚洲研究院的Swin Transformer又开启了吊打CNN的模式,在速度和精度上都有很大的提高。这篇文章带你实现Swin Transformer图像分类。

资料汇总
论文: https://arxiv.org/abs/2103.14030

代码: https://github.com/microsoft/Swin-Transformer

论文翻译:https://wanghao.blog.csdn.net/article/details/120724040

一些大佬的B站视频:

1、霹雳吧啦Wz:https://www.bilibili.com/video/BV1yg411K7Yc?from=search&seid=18074716460851088132&spm_id_from=333.337.0.0

2、ClimbingVision社区:震惊!这个关于Swin Transformer的论文分享讲得太透彻了!_哔哩哔哩_bilibili

关于Swin Transformer的资料有很多,在这里就不一一列举了,我觉得理解这个模型的最好方式:源码+论文。

数据增强Cutout和Mixup

为了提高成绩我在代码中加入Cutout和Mixup这两种增强方式。实现这两种增强需要安装torchtoolbox。安装命令:

pip install torchtoolbox

Cutout实现,在transforms中。

from torchtoolbox.transform import Cutout

# 数据预处理

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    Cutout(),
    transforms.ToTensor(),
    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])

])

需要导入包:from timm.data.mixup import Mixup,

定义Mixup,和SoftTargetCrossEntropy

  mixup_fn = Mixup(
    mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None,
    prob=0.1, switch_prob=0.5, mode='batch',
    label_smoothing=0.1, num_classes=12)
    
 criterion_train = SoftTargetCrossEntropy()

项目结构

Swin_demo
├─data
│  ├─Black-grass
│  ├─Charlock
│  ├─Cleavers
│  ├─Common Chickweed
│  ├─Common wheat
│  ├─Fat Hen
│  ├─Loose Silky-bent
│  ├─Maize
│  ├─Scentless Mayweed
│  ├─Shepherds Purse
│  ├─Small-flowered Cranesbill
│  └─Sugar beet
├─mean_std.py
├─makedata.py
├─train.py
├─test1.py
└─test.py

mean_std.py:计算mean和std的值。

makedata.py:生成数据集。

计算mean和std

为了使模型更加快速的收敛,我们需要计算出mean和std的值,新建mean_std.py,插入代码:

from torchvision.datasets import ImageFolder
import torch
from torchvision import transforms

def get_mean_and_std(train_data):
    train_loader = torch.utils.data.DataLoader(
        train_data, batch_size=1, shuffle=False, num_workers=0,
        pin_memory=True)
    mean = torch.zeros(3)
    std = torch.zeros(3)
    for X, _ in train_loader:
        for d in range(3):
            mean[d] += X[:, d, :, :].mean()
            std[d] += X[:, d, :, :].std()
    mean.div_(len(train_data))
    std.div_(len(train_data))
    return list(mean.numpy()), list(std.numpy())


if __name__ == '__main__':
    train_dataset = ImageFolder(root=r'data1', transform=transforms.ToTensor())
    print(get_mean_and_std(train_dataset))

数据集结构:

21c4cc249821a490704e2f264dff09ec.png

运行结果:

([0.3281186, 0.28937867, 0.20702125], [0.09407319, 0.09732835, 0.106712654])

把这个结果记录下来,后面要用!

生成数据集

我们整理还的图像分类的数据集结构是这样的

data
├─Black-grass
├─Charlock
├─Cleavers
├─Common Chickweed
├─Common wheat
├─Fat Hen
├─Loose Silky-bent
├─Maize
├─Scentless Mayweed
├─Shepherds Purse
├─Small-flowered Cranesbill
└─Sugar beet

pytorch和keras默认加载方式是ImageNet数据集格式,格式是

├─data
│  ├─val
│  │   ├─Black-grass
│  │   ├─Charlock
│  │   ├─Cleavers
│  │   ├─Common Chickweed
│  │   ├─Common wheat
│  │   ├─Fat Hen
│  │   ├─Loose Silky-bent
│  │   ├─Maize
│  │   ├─Scentless Mayweed
│  │   ├─Shepherds Purse
│  │   ├─Small-flowered Cranesbill
│  │   └─Sugar beet
│  └─train
│      ├─Black-grass
│      ├─Charlock
│      ├─Cleavers
│      ├─Common Chickweed
│      ├─Common wheat
│      ├─Fat Hen
│      ├─Loose Silky-bent
│      ├─Maize
│      ├─Scentless Mayweed
│      ├─Shepherds Purse
│      ├─Small-flowered Cranesbill
│      └─Sugar beet

新增格式转化脚本makedata.py,插入代码:

import glob
import os
import shutil

image_list=glob.glob('data1/*/*.png')
print(image_list)
file_dir='data'
if os.path.exists(file_dir):
    print('true')
    #os.rmdir(file_dir)
    shutil.rmtree(file_dir)#删除再建立
    os.makedirs(file_dir)
else:
    os.makedirs(file_dir)

from sklearn.model_selection import train_test_split

trainval_files, val_files = train_test_split(image_list, test_size=0.3, random_state=42)

train_dir='train'
val_dir='val'
train_root=os.path.join(file_dir,train_dir)
val_root=os.path.join(file_dir,val_dir)
for file in trainval_files:
    file_class=file.replace("\\","/").split('/')[-2]
    file_name=file.replace("\\","/").split('/')[-1]
    file_class=os.path.join(train_root,file_class)
    if not os.path.isdir(file_class):
        os.makedirs(file_class)
    shutil.copy(file, file_class + '/' + file_name)

for file in val_files:
    file_class=file.replace("\\","/").split('/')[-2]
    file_name=file.replace("\\","/").split('/')[-1]
    file_class=os.path.join(val_root,file_class)
    if not os.path.isdir(file_class):
        os.makedirs(file_class)
    shutil.copy(file, file_class + '/' + file_name)

训练

完成上面的步骤后,就开始train脚本的编写,新建train.py

导入项目使用的库

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from sklearn.metrics import classification_report
from timm.data.mixup import Mixup
from timm.loss import SoftTargetCrossEntropy
from timm.models import swin_small_patch4_window7_224
from torchtoolbox.transform import Cutout

设置全局参数

设置学习率、BatchSize、epoch等参数,判断环境中是否存在GPU,如果没有则使用CPU。建议使用GPU,CPU太慢了。

# 设置全局参数
model_lr = 1e-4
BATCH_SIZE = 4
EPOCHS = 1000
DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

图像预处理与增强

数据处理比较简单,加入了Cutout、做了Resize和归一化,定义Mixup函数。

# 数据预处理7
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    Cutout(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.51819474, 0.5250407, 0.4945761], std=[0.24228974, 0.24347611, 0.2530049])

])
transform_test = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.51819474, 0.5250407, 0.4945761], std=[0.24228974, 0.24347611, 0.2530049])
])
mixup_fn = Mixup(
    mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None,
    prob=0.1, switch_prob=0.5, mode='batch',
    label_smoothing=0.1, num_classes=12)

读取数据

使用pytorch默认读取数据的方式,然后将dataset_train.class_to_idx打印出来,预测的时候要用到。

# 读取数据
dataset_train = datasets.ImageFolder('data/train', transform=transform)
dataset_test = datasets.ImageFolder("data/val", transform=transform_test)
print(dataset_train.class_to_idx)
# 导入数据
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False)

class_to_idx的结果:

{‘Black-grass’: 0, ‘Charlock’: 1, ‘Cleavers’: 2, ‘Common Chickweed’: 3, ‘Common wheat’: 4, ‘Fat Hen’: 5, ‘Loose Silky-bent’: 6, ‘Maize’: 7, ‘Scentless Mayweed’: 8, ‘Shepherds Purse’: 9, ‘Small-flowered Cranesbill’: 10, ‘Sugar beet’: 11}

设置模型

  • 设置loss函数,train的loss为:SoftTargetCrossEntropy,val的loss:nn.CrossEntropyLoss()。
  • 设置模型为swin_small_patch4_window7_224,预训练设置为true,num_classes设置为12。
  • 检测可用显卡的数量,如果大于1,则要用torch.nn.DataParallel加载模型,开启多卡训练。
  • 优化器设置为adam。
  • 学习率调整策略选择为余弦退火。
# 实例化模型并且移动到GPU
criterion_train = SoftTargetCrossEntropy()
criterion_val = torch.nn.CrossEntropyLoss()
model_ft = swin_small_patch4_window7_224(pretrained=True)
print(model_ft)
num_ftrs = model_ft.head.in_features
model_ft.head = nn.Linear(num_ftrs, 12)
model_ft.to(DEVICE)
print(model_ft)

if torch.cuda.device_count() > 1:
    print("Let's use", torch.cuda.device_count(), "GPUs!")
    model_ft = torch.nn.DataParallel(model_ft)
print(model_ft)
# 选择简单暴力的Adam优化器,学习率调低
optimizer = optim.Adam(model_ft.parameters(), lr=model_lr)
cosine_schedule = optim.lr_scheduler.CosineAnnealingLR(optimizer=optimizer, T_max=20, eta_min=1e-9)

定义训练和验证函数

定义训练函数和验证函数,在一个epoch完成后,使用classification_report计算详细的得分情况。

# 定义训练过程
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    sum_loss = 0
    total_num = len(train_loader.dataset)
    print(total_num, len(train_loader))
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device, non_blocking=True), target.to(device, non_blocking=True)
        samples, targets = mixup_fn(data, target)
        optimizer.zero_grad()
        output = model(samples)
        loss = criterion_train(output, targets)
        loss.backward()
        optimizer.step()
        lr = optimizer.state_dict()['param_groups'][0]['lr']
        print_loss = loss.data.item()
        sum_loss += print_loss
        if (batch_idx + 1) % 10 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tLR:{:.9f}'.format(
                epoch, (batch_idx + 1) * len(data), len(train_loader.dataset),
                       100. * (batch_idx + 1) / len(train_loader), loss.item(), lr))
    ave_loss = sum_loss / len(train_loader)
    print('epoch:{},loss:{}'.format(epoch, ave_loss))


ACC = 0


# 验证过程
def val(model, device, test_loader):
    global ACC
    model.eval()
    test_loss = 0
    correct = 0
    total_num = len(test_loader.dataset)
    print(total_num, len(test_loader))
    val_list = []
    pred_list = []
    with torch.no_grad():
        for data, target in test_loader:
            for t in target:
                val_list.append(t.data.item())
            data, target = data.to(device), target.to(device)
            output = model(data)
            loss = criterion_val(output, target)
            _, pred = torch.max(output.data, 1)
            for p in pred:
                pred_list.append(p.data.item())
            correct += torch.sum(pred == target)
            print_loss = loss.data.item()
            test_loss += print_loss
        correct = correct.data.item()
        acc = correct / total_num
        avgloss = test_loss / len(test_loader)
        print('\nVal set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            avgloss, correct, len(test_loader.dataset), 100 * acc))
        if acc > ACC:
            torch.save(model_ft, 'model_' + str(epoch) + '_' + str(round(acc, 3)) + '.pth')
            ACC = acc
    return val_list, pred_list


# 训练

for epoch in range(1, EPOCHS + 1):
    train(model_ft, DEVICE, train_loader, optimizer, epoch)
    cosine_schedule.step()
    val_list, pred_list = val(model_ft, DEVICE, test_loader)
    print(classification_report(val_list, pred_list, target_names=dataset_train.class_to_idx))

运行结果:

62ca730f6e5b11c00025109b9ae4446e.png

测试

我介绍两种常用的测试方式,第一种是通用的,通过自己手动加载数据集然后做预测,具体操作如下:

测试集存放的目录如下图:
8731150f4923fff64380f0fe1c4b64f4.png

第一步 定义类别,这个类别的顺序和训练时的类别顺序对应,一定不要改变顺序!!!!

第二步 定义transforms,transforms和验证集的transforms一样即可,别做数据增强。

第三步 加载model,并将模型放在DEVICE里,

第四步 读取图片并预测图片的类别,在这里注意,读取图片用PIL库的Image。不要用cv2,transforms不支持。

import torch.utils.data.distributed
import torchvision.transforms as transforms
from PIL import Image
from torch.autograd import Variable
import os
classes = ('Black-grass', 'Charlock', 'Cleavers', 'Common Chickweed',
           'Common wheat','Fat Hen', 'Loose Silky-bent',
           'Maize','Scentless Mayweed','Shepherds Purse','Small-flowered Cranesbill','Sugar beet')
transform_test = transforms.Compose([
         transforms.Resize((224, 224)),
        transforms.ToTensor(),
       transforms.Normalize(mean=[0.51819474, 0.5250407, 0.4945761], std=[0.24228974, 0.24347611, 0.2530049])
])
 
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = torch.load("model.pth")
model.eval()
model.to(DEVICE)
 
path='data/test/'
testList=os.listdir(path)
for file in testList:
        img=Image.open(path+file)
        img=transform_test(img)
        img.unsqueeze_(0)
        img = Variable(img).to(DEVICE)
        out=model(img)
        # Predict
        _, pred = torch.max(out.data, 1)
        print('Image Name:{},predict:{}'.format(file,classes[pred.data.item()]))

运行结果:

fd3ee210314d528b3170d58514013c18.png

第二种 使用自定义的Dataset读取图片

import torch.utils.data.distributed
import torchvision.transforms as transforms
from dataset.dataset import SeedlingData
from torch.autograd import Variable
 
classes = ('Black-grass', 'Charlock', 'Cleavers', 'Common Chickweed',
           'Common wheat','Fat Hen', 'Loose Silky-bent',
           'Maize','Scentless Mayweed','Shepherds Purse','Small-flowered Cranesbill','Sugar beet')
transform_test = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.51819474, 0.5250407, 0.4945761], std=[0.24228974, 0.24347611, 0.2530049])
])
 
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = torch.load("model.pth")
model.eval()
model.to(DEVICE)
 
dataset_test =SeedlingData('data/test/', transform_test,test=True)
print(len(dataset_test))
# 对应文件夹的label
 
for index in range(len(dataset_test)):
    item = dataset_test[index]
    img, label = item
    img.unsqueeze_(0)
    data = Variable(img).to(DEVICE)
    output = model(data)
    _, pred = torch.max(output.data, 1)
    print('Image Name:{},predict:{}'.format(dataset_test.imgs[index], classes[pred.data.item()]))
    index += 1
 

运行结果:

9a4cc01056c7b13d4161cc075c1eebb1.png
代码:
https://download.csdn.net/download/hhhhhhhhhhwwwwwwwwww/81895764

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