SqueezeNetLSTM.py 文件源码

def __init__(self, n_frames=2, n_steps=10):
        """Sets up layers"""
        super(SqueezeNetLSTM, self).__init__()

        self.n_frames = n_frames
        self.n_steps = n_steps
        self.pre_metadata_features = nn.Sequential(
            nn.Conv2d(3 * 2 * self.n_frames, 16, kernel_size=3, stride=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            Fire(16, 6, 12, 12)
        )
        self.post_metadata_features = nn.Sequential(
            Fire(36, 8, 16, 16),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            Fire(32, 12, 24, 24),
            Fire(48, 12, 24, 24),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            Fire(48, 16, 32, 32),
            Fire(64, 16, 32, 32),
            Fire(64, 24, 48, 48),
            Fire(96, 24, 48, 48),
        )
        final_conv = nn.Conv2d(96, self.n_steps * 2, kernel_size=1)
        self.pre_lstm_output = nn.Sequential(
            nn.Dropout(p=0.5),
            final_conv,
            nn.AvgPool2d(kernel_size=3, stride=2),
        )
        self.lstms = nn.ModuleList([
            nn.LSTM(16, 32, 2, batch_first=True),
            nn.LSTM(32, 4, 1, batch_first=True)
        ])

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