下圖為VGG系列的結構表
VGG的結構其實是由AlexNet演變而來,
VGG原文參考:
https://arxiv.org/abs/1409.1556
AlexNet是由5層Convolution Layer + 3層Fully-connected Layer而組成,
AlexNet結構設計可參考如下:
https://jennaweng0621.pixnet.net/blog/post/403588460
下面為VGG系列結構的第一種寫法:
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import torch.nn as nn cfg = { #M表示為MaxPool2d, 其他數值表示為各Convolution輸出的深度 'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], 'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M',512, 512, 512, 512, 'M'] }
class VGG(nn.Module): def __init__(self, vgg_name): super(VGG, self).__init__() self.features = self._make_layers(cfg[vgg_name]) self.classifier = nn.Linear(512, 10)
def forward(self, x): out = self.features(x) out = out.view(out.size(0), -1) out = self.classifier(out) return out
def _make_layers(self, cfg): layers = [] in_channels = 3 for x in cfg: if x == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1), nn.BatchNorm2d(x), nn.ReLU(inplace=True)] #預設為False,表示新建一個對象對其修改, True則表示直接對這個對象進行修改 in_channels = x layers += [nn.AvgPool2d(kernel_size=1, stride=1)] return nn.Sequential(*layers)
#取得VGG系列的model vgg11 = VGG('VGG11') vgg13 = VGG('VGG13') vgg16 = VGG('VGG16') vgg19 = VGG('VGG19') |
下面為VGG11輸出結果:
下面為VGG系列結構的第二種寫法(官方預設結構):
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import torchvision.models as models vgg11 = models.vgg11() vgg13 = models.vgg13() vgg16 = models.vgg16() vgg19 = models.vgg19() |
可以導入預訓練的model權重, 只要在()內填入pretrained參數即可, 如下:
| vgg11 = models.vgg11(pretrained = True) |
下面為VGG11輸出結果:
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