本是应用于迁移学习的方法，使用预训练的模型，但是输出层自己定义
定义并加载模型
model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():
param.requires_grad = False
# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)
model_conv = model_conv.to(device)
剩下就是读取数据正常训练了
如果不冻结前面层的参数，建议前面层lr较小，输出层lr较大
optimizer = optim.SGD([{'params': feature_params},
{'params': pretrained_net.fc.parameters(), 'lr': lr * 10}],
lr=lr, weight_decay=0.001)
