PyTorch实现AlexNet示例

Python  /  管理员 发布于 5年前   328

PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks

import torchimport torch.nn as nnimport torchvisionclass AlexNet(nn.Module):  def __init__(self,num_classes=1000):    super(AlexNet,self).__init__()    self.feature_extraction = nn.Sequential(      nn.Conv2d(in_channels=3,out_channels=96,kernel_size=11,stride=4,padding=2,bias=False),      nn.ReLU(inplace=True),      nn.MaxPool2d(kernel_size=3,stride=2,padding=0),      nn.Conv2d(in_channels=96,out_channels=192,kernel_size=5,stride=1,padding=2,bias=False),      nn.ReLU(inplace=True),      nn.MaxPool2d(kernel_size=3,stride=2,padding=0),      nn.Conv2d(in_channels=192,out_channels=384,kernel_size=3,stride=1,padding=1,bias=False),      nn.ReLU(inplace=True),      nn.Conv2d(in_channels=384,out_channels=256,kernel_size=3,stride=1,padding=1,bias=False),      nn.ReLU(inplace=True),      nn.Conv2d(in_channels=256,out_channels=256,kernel_size=3,stride=1,padding=1,bias=False),      nn.ReLU(inplace=True),      nn.MaxPool2d(kernel_size=3, stride=2, padding=0),    )    self.classifier = nn.Sequential(      nn.Dropout(p=0.5),      nn.Linear(in_features=256*6*6,out_features=4096),      nn.ReLU(inplace=True),      nn.Dropout(p=0.5),      nn.Linear(in_features=4096, out_features=4096),      nn.ReLU(inplace=True),      nn.Linear(in_features=4096, out_features=num_classes),    )  def forward(self,x):    x = self.feature_extraction(x)    x = x.view(x.size(0),256*6*6)    x = self.classifier(x)    return xif __name__ =='__main__':  # model = torchvision.models.AlexNet()  model = AlexNet()  print(model)  input = torch.randn(8,3,224,224)  out = model(input)  print(out.shape)

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