Method __init__

eval-lipsync/script/SyncNetModel.py:17–81

(self, num_layers_in_fc_layers=1024)

class S(nn.Module):
    def __init__(self, num_layers_in_fc_layers=1024):
        super(S, self).__init__()
        self.__nFeatures__ = 24
        self.__nChs__ = 32
        self.__midChs__ = 32
        self.netcnnaud = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=(1, 1), stride=(1, 1)),
            nn.Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
            nn.BatchNorm2d(192),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=(3, 3), stride=(1, 2)),
            nn.Conv2d(192, 384, kernel_size=(3, 3), padding=(1, 1)),
            nn.BatchNorm2d(384),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=(3, 3), padding=(1, 1)),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=(3, 3), padding=(1, 1)),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)),
            nn.Conv2d(256, 512, kernel_size=(5, 4), padding=(0, 0)),
            nn.BatchNorm2d(512),
            nn.ReLU(),
        )
        self.netfcaud = nn.Sequential(
            nn.Linear(512, 512),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Linear(512, num_layers_in_fc_layers),
        )
        self.netfclip = nn.Sequential(
            nn.Linear(512, 512),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Linear(512, num_layers_in_fc_layers),
        )
        self.netcnnlip = nn.Sequential(
            nn.Conv3d(3, 96, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=0),
            nn.BatchNorm3d(96),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2)),
            nn.Conv3d(
                96, 256, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 1, 1)
            ),
            nn.BatchNorm3d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)),
            nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
            nn.BatchNorm3d(256),
            nn.ReLU(inplace=True),
            nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
            nn.BatchNorm3d(256),
            nn.ReLU(inplace=True),
            nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)),