class Conv2d(nn.Module):
    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.conv_block = nn.Sequential(
                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
                            nn.BatchNorm2d(cout)
                            )
        self.act = nn.ReLU()
        self.residual = residual

    def forward(self, x):
        out = self.conv_block(x)
        if self.residual:
            out += x
        return self.act(out)

class Wav2Lip(nn.Module):
    def __init__(self):
        super(Wav2Lip, self).__init__()

        self.face_encoder_blocks = nn.ModuleList([
            nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)), # 96,96

            nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), # 48,48
            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)),

            nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1),    # 24,24
            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)),

            nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1),   # 12,12
            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)),

            nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1),       # 6,6
            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)),

            nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1),     # 3,3
            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),),
            
            nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=1, padding=0),     # 1, 1
            Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),
            # nn.AdaptiveAvgPool2d(output_size=1),
            ])

        self.audio_encoder = nn.Sequential(
            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),

            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),

            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),

            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),

            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)

        self.face_decoder_blocks = nn.ModuleList([
            nn.Sequential(Conv2d(512, 512, kernel_size=1, stride=1, padding=0),),

            nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=1, padding=0), # 3,3
            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),),

            nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1),
            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), # 6, 6

            nn.Sequential(Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1),
            Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),), # 12, 12

            nn.Sequential(Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1),
            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),), # 24, 24

            nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1), 
            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),), # 48, 48

            nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),),]) # 96,96

        self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0),
            nn.Sigmoid()) 

    def forward(self, audio_sequences, face_sequences):
        # audio_sequences = (B, T, 1, 80, 16)
        B = audio_sequences.size(0)

        input_dim_size = len(face_sequences.size())
        if input_dim_size > 4:
            audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
            face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)

        audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1

        feats = []
        x = face_sequences
        for f in self.face_encoder_blocks:
            x = f(x)
            feats.append(x)

        x = audio_embedding
        for f in self.face_decoder_blocks:
            x = f(x)
            try:
                x = torch.cat((x, feats[-1]), dim=1)
            except Exception as e:
                print(x.size())
                print(feats[-1].size())
                raise e
            
            feats.pop()

        x = self.output_block(x)

        if input_dim_size > 4:
            x = torch.split(x, B, dim=0) # [(B, C, H, W)]
            outputs = torch.stack(x, dim=2) # (B, C, T, H, W)

        else:
            outputs = x
            
        return outputs

当我修改face_encoder_blocks的输入为444,444时，需要怎么修改代码才能对齐网路