def __init__(
        self,
        input_nc=1,
        ndf=64,
        n_layers=5,
        norm_layer=nn.BatchNorm3d,
        conv_cls="conv3d",
        dropout=0.30,
    ):
        """
        Construct a 3D PatchGAN discriminator

        Parameters:
            input_nc (int)  -- the number of channels in input volumes
            ndf (int)       -- the number of filters in the last conv layer
            n_layers (int)  -- the number of conv layers in the discriminator
            use_actnorm (bool) -- flag to use actnorm instead of batchnorm
        """
        super(NLayerDiscriminator3D, self).__init__()
        assert conv_cls == "conv3d"
        use_bias = False

        kw = 3
        padw = 1
        sequence = [nn.Conv3d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
        nf_mult = 1
        nf_mult_prev = 1
        for n in range(1, n_layers):  # gradually increase the number of filters
            nf_mult_prev = nf_mult
            nf_mult = min(2**n, 8)

            sequence += [
                nn.Conv3d(
                    ndf * nf_mult_prev,
                    ndf * nf_mult,
                    kernel_size=(kw, kw, kw),
                    stride=2 if n == 1 else (1, 2, 2),
                    padding=padw,
                    bias=use_bias,
                ),
                norm_layer(ndf * nf_mult),
                nn.LeakyReLU(0.2, True),
                nn.Dropout(dropout),
            ]

        nf_mult_prev = nf_mult
        nf_mult = min(2**n_layers, 8)
        sequence += [
            nn.Conv3d(
                ndf * nf_mult_prev,
                ndf * nf_mult,
                kernel_size=(kw, kw, kw),
                stride=1,
                padding=padw,
                bias=use_bias,
            ),
            norm_layer(ndf * nf_mult),
            nn.LeakyReLU(0.2, True),
            nn.Dropout(dropout),
            nn.Conv3d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw),
        ]
        self.main = nn.Sequential(*sequence)

        Construct a 3D PatchGAN discriminator

        Parameters:
            input_nc (int)  -- the number of channels in input volumes
            ndf (int)       -- the number of filters in the last conv layer
            n_layers (int)  -- the number of conv layers in the discriminator
            use_actnorm (bool) -- flag to use actnorm instead of batchnorm
        
opensora/models/vae/discriminator.py