比較 VGG, resnet和inception的圖像分類效果

簡介

VGG, resnet和inception是3種典型的卷積神經網絡結構。

• VGG採用了3*3的卷積核，逐步擴大通道數量
• resnet中，每兩層卷積增長一個旁路
• inception實現了卷積核的並聯，而後把各自通道拼接到一塊兒

簡單起見，直接使用了[1]的代碼來測試 resnet，而後用[2],[4]的代碼替換[1]中的model，改了改通道，測 VGG 和 inception。

GPU是gtx1050，主板開始是 x79，後來壞了，換成 x470，GPU佔比提升不少。
CPU佔比始終100%

實驗結果

超參數：epochs=80,lr=0.001,optim=Adam
數據集：cifar10

_	參數個數(k)	訓練時間(m)	精度(%)	GPU內存(M)	GPU佔比(%)
resnet	195	22	88	607	99
vgg_bn	207	17	84	535	60
inception	107	19	80	613	98

結論：條條道路通羅馬。

附加實驗

由於方便，註釋掉 Batch Normalization，以及 Data Augmentation 又試了兩次。

_	參數個數(k)	訓練時間(m)	精度(%)	GPU內存(M)	GPU佔比(%)
resnet	195	22	88	607	99
resnet-BN	195	19	86	553	99
resnet-DA	195	22	64	607	99

結論：Data Augmentation很重要

代碼改動

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