pytorch识别CIFAR10：训练ResNet-34（自定义transform，动态调整学习率，准确率提升到94.33%）

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前面通过数据增强，ResNet-34残差网络识别CIFAR10，准确率达到了92.6。

这里对训练过程增加2个处理：

　　1、训练数据集做进一步处理：对图片随机加正方形马赛克。

　　2、每50个epoch，学习率降低0.1倍。

代码具体修改如下：

自定义transform：

class Cutout(object):
    def __init__(self, hole_size):
        # 正方形马赛克的边长，像素为单位
        self.hole_size = hole_size

    def __call__(self, img):
        return cutout(img, self.hole_size)


def cutout(img, hole_size):
    y = np.random.randint(32)
    x = np.random.randint(32)

    half_size = hole_size // 2

    x1 = np.clip(x - half_size, 0, 32)
    x2 = np.clip(x + half_size, 0, 32)
    y1 = np.clip(y - half_size, 0, 32)
    y2 = np.clip(y + half_size, 0, 32)

    imgnp = np.array(img)

    imgnp[y1:y2, x1:x2] = 0
    img = Image.fromarray(imgnp.astype('uint8')).convert('RGB')
    return img

数据集处理修改：

    transform_train = transforms.Compose([
        # 对原始32*32图像四周各填充4个0像素（40*40），然后随机裁剪成32*32
        transforms.RandomCrop(32, padding=4),
        
        # 随机马赛克，大小为6*6
        Cutout(6),

        # 按0.5的概率水平翻转图片
        transforms.RandomHorizontalFlip(),

        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])

    transform_test = tv.transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])

    # 定义数据集
    train_data = tv.datasets.CIFAR10(root=ROOT, train=True, download=True, transform=transform_train)
    test_data = tv.datasets.CIFAR10(root=ROOT, train=False, download=False, transform=transform_test)

训练过程中调整学习率：

    for epoch in range(1, args.epochs + 1):
        if epoch % 50 == 0:
            lr = args.lr * (0.1 ** (epoch // 50))

            for params in optimizer.param_groups:
                params['lr'] = lr

        net_train(net, train_load, optimizer, epoch, args.log_interval)

        # 每个epoch结束后用测试集检查识别准确度
        net_test(net, test_load, epoch)

运行结果如下：

Files already downloaded and verified

ResNet34(

(first): Sequential(

(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(2): ReLU(inplace)

(3): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)

)

(layer1): Sequential(

(0): ResBlock(

(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(1): ResBlock(

(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(2): ResBlock(

(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

)

(layer2): Sequential(

(0): ResBlock(

(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))

(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(downsample): Sequential(

(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))

(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

)

(1): ResBlock(

(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(2): ResBlock(

(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(3): ResBlock(

(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

)

(layer3): Sequential(

(0): ResBlock(

(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))

(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(downsample): Sequential(

(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2))

(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

)

(1): ResBlock(

(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(2): ResBlock(

(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(3): ResBlock(

(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(4): ResBlock(

(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(5): ResBlock(

(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

)

(layer4): Sequential(

(0): ResBlock(

(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))

(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(downsample): Sequential(

(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2))

(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

)

(1): ResBlock(

(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(2): ResBlock(

(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu): ReLU(inplace)

(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

)

(avg_pool): AvgPool2d(kernel_size=4, stride=4, padding=0)

(fc): Linear(in_features=512, out_features=10, bias=True)

)

one epoch spend: 0:01:11.775634

EPOCH:1, ACC:44.28

one epoch spend: 0:01:12.244757

EPOCH:2, ACC:54.46

one epoch spend: 0:01:12.360205

EPOCH:3, ACC:56.84

............

one epoch spend: 0:01:19.172188

EPOCH:198, ACC:94.2

one epoch spend: 0:01:19.213334

EPOCH:199, ACC:94.19

one epoch spend: 0:01:19.222612

EPOCH:200, ACC:94.21

CIFAR10 pytorch ResNet34 Train: EPOCH:200, BATCH_SZ:128, LR:0.1, ACC:94.33

train spend time: 4:21:32.548834

 

运行200个迭代，每个迭代耗时80秒，准确率提升了1.73%，达到94.33%。准确率变化曲线如下：