Kaggle Dogs vs. Cats Redux: Kernels Edition （猫狗大战）by Pytorch

比赛简介

链接如下：https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition
比赛内容很简单，就是跟一些猫、狗图片，要求对其进行分类。

解法

在这里参考了这位兄台的解法https://ypw.io/dogs-vs-cats-2/，我使用了resnet50、vgg16、densenet161三个已经在imagenet数据集上提前训练好的模型，去掉最后的分类全连接层，产出产出图片的特征向量，进行拼接，最后再接入2个全连接层进行分类。算法使用pytorch实现，最终再取得了Public Score 0.18336的得分，还是有较大改进空间。
代码如下：

# encoding=utf-8import sysimport argparseimport numpy as npimport reimport mathimport osimport datetimeimport loggingimport logging.handlersimport redisimport tracebackimport operatorimport requestsimport bisectimport jsonimport hashlibimport randomimport inspectimport itertoolsimport numpyimport torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimimport torch.utils.dataimport timeimport csvimport torchvision.models as modelsfrom torch.autograd import Variablefrom PIL import Imagefrom conf import *from operator import itemgetterfrom tqdm import *from subprocess import Popenfrom subprocess import PIPEfrom threading import Lockfrom threading import Threadfrom urllib import urlencodefrom Queue import Queuefrom conf import *from gensim.models import KeyedVectorsfrom torch.autograd import Variablefrom torchvision import transforms, datasets'''./train    dogs/        1.jpg        2.jpg        .        .        .    cats/        1.jpg        2.jpg./val    dogs/        1.jpg        2.jpg        .        .        .    cats/        1.jpg        2.jpg'''def lp(content):    content = content.__str__()    file_path = os.path.abspath(__file__)    noline = str(inspect.currentframe().f_back.f_lineno)    print file_path + ':' + noline + ' ' + contentlogging.basicConfig(    format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)TRAIN_ROOT = './train'VAL_ROOT = './val'TEST_ROOT = './test'MODEL_PATH='./output/model_9'RESULT_PATH='result'EPOCH_NUM = 100# PREDICT = FalsePREDICT = TrueMODEL_SAVE_INTERVAL = 1class Model(nn.Module):    def __init__(self):        super(Model, self).__init__()        self.resnet50 = models.resnet50(pretrained=True)        self.resnet50.fc = nn.Linear(2048, 2048)        self.resnet50.fc.weight.data = torch.eye(2048)        for p in self.resnet50.parameters():            p.requires_grad = False        self.vgg16 = models.vgg16(pretrained=True)        mod = list(self.vgg16.classifier.children())        mod.pop()        self.vgg16.classifier = torch.nn.Sequential(*mod)        for p in self.vgg16.parameters():            p.requires_grad = False        self.densenet161 = models.densenet161(pretrained=True)        self.densenet161.classifier = nn.Linear(2208, 2208)        self.densenet161.classifier.weight.data = torch.eye(2208)        for p in self.densenet161.parameters():            p.requires_grad = False        self.fc1 = nn.Linear(8352, 1000)        self.dp1 = nn.Dropout()        self.fc2 = nn.Linear(1000, 2)    def forward(self, x):        x1 = self.resnet50(x)        x2 = self.vgg16(x)        x3 = self.densenet161(x)        x = torch.cat([x1, x3, x4], 1)        x = F.relu(self.fc1(x))        x = self.dp1(x)        x = self.fc2(x)        return xdef train():    data_transform = transforms.Compose([        transforms.Scale((224, 224), interpolation=2),        transforms.RandomHorizontalFlip(),        transforms.ToTensor(),        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[                             0.229, 0.224, 0.225])    ])    dataset = datasets.ImageFolder(root=TRAIN_ROOT,                                   transform=data_transform)    train_data_loader = torch.utils.data.DataLoader(        dataset, batch_size=25, shuffle=True, num_workers=10)    dataset = datasets.ImageFolder(root=VAL_ROOT,                                   transform=data_transform)    val_data_loader = torch.utils.data.DataLoader(        dataset, batch_size=50, shuffle=True, num_workers=10)    model = Model().cuda()    optimizer = optim.SGD(        filter(lambda p: p.requires_grad, model.parameters()), lr=1e-2)    model.train()    for i in range(EPOCH_NUM):        loss_list = []        for batch in tqdm(train_data_loader):            x = Variable(batch[0].cuda())            img = transforms.ToPILImage()(batch[0][0])            label = Variable(batch[1].cuda())            output = model(x)            output = F.log_softmax(output)            optimizer.zero_grad()            loss = F.nll_loss(output, label)            loss.backward()            optimizer.step()            loss_list.append(loss.data[0])        total_list = []        correct_list = []        for batch in (val_data_loader):            x = Variable(batch[0].cuda())            label = batch[1].cuda()            output = model(x)            _, predicted = torch.max(output.data, 1)            total = label.shape[0]            correct = (predicted == label).sum()            total_list.append(total)            correct_list.append(correct)        print 'epoch:%d avg_loss=%lf acc=%lf' % (i, sum(loss_list) / len(loss_list), sum(correct_list) * 1.0 / sum(total_list))        if i % MODEL_SAVE_INTERVAL == 0:            torch.save(model, '%s/model_%d' % ('./output', i))class TestImageFolder(torch.utils.data.Dataset):    def __init__(self, root, transform=None):        self.imgs = []        for filename in os.listdir('./test'):            if filename.endswith('jpg'):                self.imgs.append('{}'.format(filename))        self.root = root        self.transform = transform    def __getitem__(self, index):        filename = self.imgs[index]        img = Image.open(os.path.join(self.root, filename))        if self.transform is not None:            img = self.transform(img)            return img, filename    def __len__(self):        return len(self.imgs)def predict():    data_transform = transforms.Compose([        transforms.Scale((224, 224), interpolation=2),        transforms.RandomHorizontalFlip(),        transforms.ToTensor(),        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[                             0.229, 0.224, 0.225])    ])    dataset = TestImageFolder(TEST_ROOT, data_transform)    test_data_loader = torch.utils.data.DataLoader(        dataset, batch_size=50, shuffle=True, num_workers=10)    model = torch.load(MODEL_PATH)    model.eval()    f_test_predict = open(RESULT_PATH, 'w')    f_test_predict.write('id,label\n')    for batch in tqdm(test_data_loader):        x = Variable(batch[0].cuda())        output = model(x)        for i in range(output.data.shape[0]):            filepath = batch[1][i]            filepath = os.path.splitext(os.path.basename(filepath))[0]            filepath = int(filepath)            f_test_predict.write(str(filepath) + ',' +                                 str(math.exp(output.data[i][1])) + '\n')if __name__ == "__main__":    parser = argparse.ArgumentParser(description='')    args = parser.parse_args()    if not PREDICT:        train()    else:        predict()