Pytorch 学习笔记：迁移学习使用VGG16进行kaggle 猫狗分类

kaggle上猫狗分类网址: https://www.kaggle.com/c/dogs-vs-cats

1.数据部分

分类工作的第一步是准确数据，从kaggle上面下载的dogs-vs-cats数据包含3个文件train.zip，test.zip和sample_submission.csv

train.zip里面共25000张图片，猫狗各12500张，名称格式为：XXX.YYYYY.jpg。其中XXX为cat或者dog，YYYYY为0到12499；示例如下：cat.0.jpg。

test.zip里面共12500张图片，名称格式为：YYYYY.jpg。里面既有猫又有狗，顺序是乱的。

sample_submission.csv是往kaggle网站上提交结果用的文件示例，文件包含两栏：id和label。

2.模型部分

把train.zip解压得到的图片分为两部分放到两个文件夹中，train分配20000张（猫狗各10000张），val分配5000张（猫狗各2500张），dog和cat图片也分别放到不同文件夹中方便会面使用Pytorch的API。

文件路径如下：

把数据分成两部分的代码

import os

# print(os.popen("dir").read())

for i in range(10000,12500,1):
    str1 = r"D:\datasets\dogs-vs-cats-redux-kernels-edition\202005\train\dog"
    str2 = r"D:\datasets\dogs-vs-cats-redux-kernels-edition\202005\valid\dog"
    command_tmp = "move {}\dog.{}.jpg {}\dog.{}.jpg".format(str1, i, str2, i)

    print(os.system(command_tmp))

for i in range(10000,12500,1):
    str1 = r"D:\datasets\dogs-vs-cats-redux-kernels-edition\202005\train\cat"
    str2 = r"D:\datasets\dogs-vs-cats-redux-kernels-edition\202005\valid\cat"
    command_tmp = "move {}\cat.{}.jpg {}\cat.{}.jpg".format(str1, i, str2, i)

    print(os.system(command_tmp))

训练代码

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from torch import optim
import matplotlib.pyplot as plt
import time
import os

train_transform = transforms.Compose([
    transforms.Resize(256),
    transforms.RandomResizedCrop(224),
    transforms.RandomAffine(degrees=15, scale=(0.8,1.5)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

val_transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

trainset = torchvision.datasets.ImageFolder(root='D:/datasets/dogs-vs-cats-redux-kernels-edition/202005/train/', transform=train_transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=8, shuffle=True, num_workers=6)

valset = torchvision.datasets.ImageFolder(root='D:/datasets/dogs-vs-cats-redux-kernels-edition/202005/valid/', transform=val_transform)
valloader = torch.utils.data.DataLoader(valset, batch_size=8, shuffle=True, num_workers=6)

print(len(trainloader))
print(len(valloader))

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

model = torchvision.models.vgg16(pretrained=True).cuda()
print(model)

for param in model.features.parameters(): param.requires_grad = False

model.classifier[6].out_features = 2
print(model)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.classifier.parameters(), lr=0.0001, momentum=0.5)

# train_losses, train_accuracy = [], []
# val_losses, val_accuracy = [], []

#保存每个epoch后的Accuracy Loss Val_Accuracy
Accuracy = []
Loss = []
Val_Accuracy = []
BEST_VAL_ACC = 0.
#训练
since = time.time()

for epoch in range(10):
    train_loss = 0.
    train_accuracy = 0.
    run_accuracy = 0.
    run_loss = 0.
    total = 0.
    model.train()
    for i, (images, labels) in enumerate(trainloader, 0):
        images = images.to(device)
        labels = labels.to(device)

#      经典4步
        optimizer.zero_grad()
        outs = model(images)
        loss = criterion(outs, labels)
        loss.backward()
        optimizer.step()

#      输出状态
        total += labels.size(0)
        run_loss += loss.item()
        _,prediction = torch.max(outs,1)
        run_accuracy += (prediction == labels).sum().item()

        if i % 20 == 19:
            print('epoch {}, iter{},train accuracy:{:4f}% loss: {:.4f}'.format(epoch, i+1, 100*run_accuracy/(labels.size(0)*20), run_loss/20))
            train_accuracy += run_accuracy
            train_loss += run_loss
            run_accuracy, run_loss = 0., 0.

    Loss.append(train_loss/total)
    Accuracy.append(100*train_accuracy/total)
    #可视化训练过程
    fig1, ax1 = plt.subplots(figsize=(11, 8))
    ax1.plot(range(0, epoch+1, 1), Accuracy)
    ax1.set_title("Average trainset accuracy vs epochs")
    ax1.set_xlabel("Epoch")
    ax1.set_ylabel("Avg. train. accuracy")
    plt.savefig('Train_accuracy_vs_epochs.png')
    plt.clf()
    plt.close()

    fig2, ax2 = plt.subplots(figsize=(11,8))
    ax2.plot(range(epoch+1), Loss)
    ax2.set_title("Average trainset loss vs epochs")
    ax2.set_xlabel("Epoch")
    ax2.set_ylabel("Current loss")
    plt.savefig('loss_vs_epochs.png')

    plt.clf()
    plt.close()

#   验证
    acc = 0.
    model.eval()
    print('waitting for val...')
    with torch.no_grad():
        accuracy = 0.
        total = 0
        for (images,labels) in valloader:
            images = images.to(device)
            labels = labels.to(device)
            out = model(images)
            _, prediction = torch.max(out,1)
            total += labels.size(0)
            accuracy += (prediction == labels).sum().item()
            acc = 100.*accuracy/total
    print('epoch {} The ValSet accuracy is {:.4f}% \n'.format(epoch, acc))
    Val_Accuracy.append(acc)
    if acc > BEST_VAL_ACC:
        print('Find Better Model and Saving it...')
        if not os.path.isdir('checkpoint'):
            os.mkdir('checkpoint')
        torch.save(model.state_dict(), './checkpoint/VGG19_Cats_Dogs_hc.pth')
        BEST_VAL_ACC = acc
        print('Saved!')

    fig3, ax3 = plt.subplots(figsize=(11, 8))

    ax3.plot(range(epoch+1),Val_Accuracy )
    ax3.set_title("Average Val accuracy vs epochs")
    ax3.set_xlabel("Epoch")
    ax3.set_ylabel("Current Val accuracy")

    plt.savefig('val_accuracy_vs_epoch.png')
    plt.close()
    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed%60))
    print('Now the best val Acc is {:.4f}%'.format(BEST_VAL_ACC))

生成测试结果代码

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision
import numpy as np
from tqdm import tqdm_notebook as tqdm
from PIL import Image
import os
import pandas as pd


transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# 加载模型
model = torchvision.models.vgg16(pretrained=False)

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
if torch.cuda.device_count() > 1:
    model = nn.DataParallel(model)

model.to(device)
model.load_state_dict(torch.load('./checkpoint/VGG19_Cats_Dogs_hc.pth'))

# 测试
id_list = []
pred_list = []

test_path = 'D:/datasets/dogs-vs-cats-redux-kernels-edition/202005/test/'
test_files = os.listdir(test_path)
model.eval()
with torch.no_grad():
    for file in tqdm(test_files):
        img = Image.open(test_path+file)
        _id = int(file.split('.')[0])
        img = transform(img)
        img = img.unsqueeze(0)
        img = img.to(device)

        out = model(img)
#         print(out)
        prediction = F.softmax(out, dim=1)[:,1].tolist()
        _predict = np.array(prediction)
        # _predict = np.where(_predict>0.5, 1, 0)
        # print(_id, _predict[0])
        id_list.append(_id)
        pred_list.append(_predict.item())

res = pd.DataFrame({
    'id':id_list,
    'label':pred_list
})

res.sort_values(by='id', inplace=True)
res.reset_index(drop=True, inplace=True)
res.to_csv('submission.csv', index=False)

在kaggle上面提交了两次结果

测试结果提交地址：https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/submit

参考文献：

https://blog.csdn.net/qq_36560894/article/details/104923543