MDNet中layers层的输出

àì夳堔傛蜴生んèń 2022-11-10 14:27 185阅读 0赞

layers = nn.Sequential(OrderedDict([
                    ('conv1', nn.Sequential(nn.Conv2d(3, 96, kernel_size=7, stride=2),
                                            nn.ReLU(inplace=True),#激活函数，同时inplace设置为True，原值将会被覆盖。
                                            nn.LocalResponseNorm(2),#归一化
                                            nn.MaxPool2d(kernel_size=3, stride=2))),
                    ('conv2', nn.Sequential(nn.Conv2d(96, 256, kernel_size=5, stride=2),
                                            nn.ReLU(inplace=True),
                                            nn.LocalResponseNorm(2),
                                            nn.MaxPool2d(kernel_size=3, stride=2))),
                    ('conv3', nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=1),
                                            nn.ReLU(inplace=True))),
                    ('fc4',   nn.Sequential(nn.Linear(512 * 3 * 3, 512),
                                            nn.ReLU(inplace=True))),
                    ('fc5',   nn.Sequential(nn.Dropout(0.5),
                                            nn.Linear(512, 512),
                                            nn.ReLU(inplace=True)))]))
    for l in layers:
        print(l)

输出结果为：

Sequential(
      (0): Conv2d(3, 96, kernel_size=(7, 7), stride=(2, 2))
      (1): ReLU(inplace=True)
      (2): LocalResponseNorm(2, alpha=0.0001, beta=0.75, k=1.0)
      (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    Sequential(
      (0): Conv2d(96, 256, kernel_size=(5, 5), stride=(2, 2))
      (1): ReLU(inplace=True)
      (2): LocalResponseNorm(2, alpha=0.0001, beta=0.75, k=1.0)
      (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    Sequential(
      (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1))
      (1): ReLU(inplace=True)
    )
    Sequential(
      (0): Linear(in_features=4608, out_features=512, bias=True)
      (1): ReLU(inplace=True)
    )
    Sequential(
      (0): Dropout(p=0.5, inplace=False)
      (1): Linear(in_features=512, out_features=512, bias=True)
      (2): ReLU(inplace=True)
    )