<p>2015顶峰 何凯明</p> 讯享网
讯享网from streamlit.testing.v1.element_tree import SpecialBlock from torch import nn from torch.testing._internal.common_nn import output_sizedef CommonBlock(param, param1, param2):passdef SpecialBlockBlock(param, param1, param2):讯享网passclass ResNet18(nn.Module):def __init__(self,classes_num): super(ResNet18,self).__init__() self.prepare = nn.Sequential( nn.Conv2d(3,64,7,2,3), nn.BatchNorm2d(64), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.MaxPool2d(3,2,1) ) self.layer1 = nn.Sequential( CommonBlock(64,64,1), CommonBlock(64,64,1))讯享网self.layer2 = nn.Sequential( SpecialBlockBlock(64,128,[2,1]), CommonBlock(64,128,1))self.layer3 = nn.Sequential( SpecialBlock(128,256,[2,1]), CommonBlock(256,256,1))讯享网self.layer4 = nn.Sequential( SpecialBlock(256,512,[2,1]), CommonBlock(512,512,1))self.pool = nn.AdaptiveAvgPool2d(output_size) self.fc = nn.Sequential( # nn.Dropout(p=0.5), # nn.Linear(512,256), # nn.ReLU(inplace=True), # nn.Dropout(p=0.5), nn.Linear(512,classes_num)) def forward(self,x):x = self.prepare(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.pool(x) x = x.reshape(x.shape[0],-1) x = self.fc(x) return x</pre>
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