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比较 VGG, resnet和inception的图像分类效果
来源:cnblogs  作者:yunfeng_net  时间:2019/2/25 9:11:03  对本文有异议

简介

VGG, resnet和inception是3种典型的卷积神经网络结构。

  • VGG采用了3*3的卷积核,逐步扩大通道数量
  • resnet中,每两层卷积增加一个旁路
  • inception实现了卷积核的并联,然后把各自通道拼接到一起

简单起见,直接使用了[1]的代码来测试 resnet,然后用[2],[4]的代码替换[1]中的model,改了改通道,测 VGG 和 inception。

GPU是gtx1050,主板开始是 x79,后来坏了,换成 x470,GPU占比提高很多。
CPU占比始终100%

实验结果

超参数:epochs=80,lr=0.001,optim=Adam
数据集:cifar10

_ 参数个数(k) 训练时间(m) 精度(%) GPU内存(M) GPU占比(%)
resnet 195 22 88 607 99
vgg_bn 207 17 84 535 60
inception 107 19 80 613 98

结论:条条道路通罗马。

附加实验

因为方便,注释掉 Batch Normalization,以及 Data Augmentation 又试了两次。

_ 参数个数(k) 训练时间(m) 精度(%) GPU内存(M) GPU占比(%)
resnet 195 22 88 607 99
resnet-BN 195 19 86 553 99
resnet-DA 195 22 64 607 99

结论:Data Augmentation很重要

代码改动

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=10):
        super(ResNet, self).__init__()
        self.in_channels = 16
        self.conv = conv3x3(3, 16)
        self.bn = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self.make_layer(block, 16, layers[0])
        self.layer2 = self.make_layer(block, 32, layers[1], 2)
        self.layer3 = self.make_layer(block, 64, layers[2], 2)
        self.avg_pool = nn.AvgPool2d(8)
        self.fc = nn.Linear(64, num_classes)

print('# generator parameters:', sum(param.numel() for param in model.parameters()))
class VGG(nn.Module):

    def __init__(self, features, num_classes=10, init_weights=True):
        super(VGG, self).__init__()
        self.features = features
        self.avgpool = nn.AdaptiveAvgPool2d((3, 3))
        self.classifier = nn.Sequential(
            nn.Linear(9 * 8 * 8, 64),
            nn.ReLU(True),
            #nn.Dropout(),
            nn.Linear(64, 64),
            nn.ReLU(True),
            #nn.Dropout(),
            nn.Linear(64, num_classes),
        )

def vgg_bn(**kwargs):

    cfg = [16, 16, 'M', 32, 32, 'M', 32, 32, 'M', 64, 64, 'M', 64, 64, 'M']
    model = VGG(make_layers(cfg, batch_norm=True), **kwargs)
class Inception_v1(nn.Module):
    def __init__(self, num_classes=10):
        super(Inception_v1, self).__init__()

        #conv2d0
        self.conv1 = conv3x3(3, 6)
        self.max_pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.lrn1 = nn.BatchNorm2d(6)

        self.inception_3a = Inception_base(1, 6, [[16], [16,32], [8, 16], [3, 16]]) #3a
        self.inception_3b = Inception_base(1, 80, [[40], [32,48], [12, 16], [3, 16]]) #3b
        self.max_pool_inc3= nn.MaxPool2d(kernel_size=3, stride=2, padding=0)

        self.inception_5a = Inception_base(1, 120, [[40], [32,48], [12, 16], [3, 16]]) #5a
        self.inception_5b = Inception_base(1, 120, [[40], [32,48], [12, 16], [3, 16]]) #5b
        self.avg_pool5 = nn.AvgPool2d(kernel_size=3, stride=2, padding=0)

        self.dropout_layer = nn.Dropout(0.4)
        self.fc = nn.Linear(120*9, num_classes)

引用

[1] https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/02-intermediate/deep_residual_network/main.py
[2] https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
[3] https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
[4] https://github.com/antspy/inception_v1.pytorch/blob/master/inception_v1.py

原文链接:http://www.cnblogs.com/liuyunfeng/p/10427860.html

 友情链接: NPS