(self, z_channels: int, z_factor: int, embedding_dim: int, **kwargs)
| 31 | |
| 32 | class CausalContinuousVideoTokenizer(nn.Module): |
| 33 | def __init__(self, z_channels: int, z_factor: int, embedding_dim: int, **kwargs) -> None: |
| 34 | super().__init__() |
| 35 | self.name = kwargs.get("name", "CausalContinuousVideoTokenizer") |
| 36 | self.embedding_dim = embedding_dim |
| 37 | self.sigma_data = 0.5 |
| 38 | self.encoder = EncoderFactorized(z_channels=z_factor * z_channels, **kwargs) |
| 39 | self.decoder = DecoderFactorized(z_channels=z_channels, **kwargs) |
| 40 | |
| 41 | self.quant_conv = CausalConv3d(z_factor * z_channels, embedding_dim, kernel_size=1, padding=0) |
| 42 | self.post_quant_conv = CausalConv3d(embedding_dim, z_channels, kernel_size=1, padding=0) |
| 43 | |
| 44 | latent_temporal_chunk = 16 |
| 45 | self.latent_mean = nn.Parameter(torch.zeros([self.embedding_dim * latent_temporal_chunk], dtype=torch.float32)) |
| 46 | self.latent_std = nn.Parameter(torch.ones([self.embedding_dim * latent_temporal_chunk], dtype=torch.float32)) |
| 47 | |
| 48 | |
| 49 | def encode(self, x): |
nothing calls this directly
no test coverage detected