SqueezeNetAruco1.py 文件源码

def __init__(self):
        super(SqueezeNet, self).__init__()

        self.lr = 0.01
        self.momentum = 0.01
        self.N_FRAMES = 2
        self.N_STEPS = 10
        self.pre_metadata_features = nn.Sequential(
            nn.Conv2d(12, 64, kernel_size=3, stride=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            Fire(64, 16, 64, 64),
        )
        self.post_metadata_features = nn.Sequential(
            Fire(256, 16, 64, 64),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            Fire(128, 32, 128, 128),
            Fire(256, 32, 128, 128),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            Fire(256, 48, 192, 192),
            Fire(384, 48, 192, 192),
            Fire(384, 64, 256, 256),
            Fire(512, 64, 256, 256),
        )
        final_conv = nn.Conv2d(512, 66, kernel_size=1)
        self.final_output_Aruco = nn.Sequential(
            nn.Dropout(p=0.5),
            final_conv,
            # nn.ReLU(inplace=True),
            nn.AvgPool2d(kernel_size=5, stride=6)
        )

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                if m is final_conv:
                    init.normal(m.weight.data, mean=0.0, std=0.01)
                else:
                    init.kaiming_uniform(m.weight.data)
                if m.bias is not None:
                    m.bias.data.zero_()