cosmos_predict1/tokenizer/networks/continuous_video.py

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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""The causal continuous video tokenizer with VAE or AE formulation for 3D data.."""
from collections import OrderedDict, namedtuple

from loguru import logger as logging
from torch import nn

from cosmos_predict1.tokenizer.modules import ContinuousFormulation, Decoder3DType, Encoder3DType
from cosmos_predict1.tokenizer.modules.layers3d import CausalConv3d

NetworkEval = namedtuple("NetworkEval", ["reconstructions", "posteriors", "latent"])


class CausalContinuousVideoTokenizer(nn.Module):
    def __init__(self, z_channels: int, z_factor: int, latent_channels: int, **kwargs) -> None:
        super().__init__()
        self.name = kwargs.get("name", "CausalContinuousVideoTokenizer")
        self.latent_channels = latent_channels

        encoder_name = kwargs.get("encoder", Encoder3DType.BASE.name)
        self.encoder = Encoder3DType[encoder_name].value(z_channels=z_factor * z_channels, **kwargs)
        decoder_name = kwargs.get("decoder", Decoder3DType.BASE.name)
        self.decoder = Decoder3DType[decoder_name].value(z_channels=z_channels, **kwargs)

        self.quant_conv = CausalConv3d(
            z_factor * z_channels,
            z_factor * latent_channels,
            kernel_size=1,
            padding=0,
        )
        self.post_quant_conv = CausalConv3d(latent_channels, z_channels, kernel_size=1, padding=0)

        formulation_name = kwargs.get("formulation", ContinuousFormulation.AE.name)
        self.distribution = ContinuousFormulation[formulation_name].value()
        logging.info(f"{self.name} based on {formulation_name} formulation, with {kwargs}.")

        num_parameters = sum(param.numel() for param in self.parameters())
        logging.info(f"model={self.name}, num_parameters={num_parameters:,}")
        logging.info(f"z_channels={z_channels}, latent_channels={self.latent_channels}.")

    def encoder_jit(self):
        return nn.Sequential(
            OrderedDict(
                [
                    ("encoder", self.encoder),
                    ("quant_conv", self.quant_conv),
                    ("distribution", self.distribution),
                ]
            )
        )

    def decoder_jit(self):
        return nn.Sequential(
            OrderedDict(
                [
                    ("post_quant_conv", self.post_quant_conv),
                    ("decoder", self.decoder),
                ]
            )
        )

    def last_decoder_layer(self):
        return self.decoder.conv_out

    def encode(self, x):
        h = self.encoder(x)
        moments = self.quant_conv(h)
        return self.distribution(moments)

    def decode(self, z):
        z = self.post_quant_conv(z)
        return self.decoder(z)

    def forward(self, input):
        latent, posteriors = self.encode(input)
        reconstructions = self.decode(latent)
        if self.training:
            return dict(
                reconstructions=reconstructions,
                posteriors=posteriors,
                latent=latent,
            )
        return NetworkEval(
            reconstructions=reconstructions,
            posteriors=posteriors,
            latent=latent,
        )