sgm/models/autoencoder.py

import re
from abc import abstractmethod
from contextlib import contextmanager
from typing import Any, Dict, Tuple, Union

import pytorch_lightning as pl
import torch
from omegaconf import ListConfig
from packaging import version
from safetensors.torch import load_file as load_safetensors

from ..modules.diffusionmodules.model import Decoder, Encoder
from ..modules.distributions.distributions import DiagonalGaussianDistribution
from ..modules.ema import LitEma
from ..util import default, get_obj_from_str, instantiate_from_config


class AbstractAutoencoder(pl.LightningModule):
    """
    This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators,
    unCLIP models, etc. Hence, it is fairly general, and specific features
    (e.g. discriminator training, encoding, decoding) must be implemented in subclasses.
    """

    def __init__(
        self,
        ema_decay: Union[None, float] = None,
        monitor: Union[None, str] = None,
        input_key: str = "jpg",
        ckpt_path: Union[None, str] = None,
        ignore_keys: Union[Tuple, list, ListConfig] = (),
    ):
        super().__init__()
        self.input_key = input_key
        self.use_ema = ema_decay is not None
        if monitor is not None:
            self.monitor = monitor

        if self.use_ema:
            self.model_ema = LitEma(self, decay=ema_decay)
            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")

        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)

        if version.parse(torch.__version__) >= version.parse("2.0.0"):
            self.automatic_optimization = False

    def init_from_ckpt(
        self, path: str, ignore_keys: Union[Tuple, list, ListConfig] = tuple()
    ) -> None:
        if path.endswith("ckpt"):
            sd = torch.load(path, map_location="cpu")["state_dict"]
        elif path.endswith("safetensors"):
            sd = load_safetensors(path)
        else:
            raise NotImplementedError

        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if re.match(ik, k):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        missing, unexpected = self.load_state_dict(sd, strict=False)
        print(
            f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
        )
        if len(missing) > 0:
            print(f"Missing Keys: {missing}")
        if len(unexpected) > 0:
            print(f"Unexpected Keys: {unexpected}")

    @abstractmethod
    def get_input(self, batch) -> Any:
        raise NotImplementedError()

    def on_train_batch_end(self, *args, **kwargs):
        # for EMA computation
        if self.use_ema:
            self.model_ema(self)

    @contextmanager
    def ema_scope(self, context=None):
        if self.use_ema:
            self.model_ema.store(self.parameters())
            self.model_ema.copy_to(self)
            if context is not None:
                print(f"{context}: Switched to EMA weights")
        try:
            yield None
        finally:
            if self.use_ema:
                self.model_ema.restore(self.parameters())
                if context is not None:
                    print(f"{context}: Restored training weights")

    @abstractmethod
    def encode(self, *args, **kwargs) -> torch.Tensor:
        raise NotImplementedError("encode()-method of abstract base class called")

    @abstractmethod
    def decode(self, *args, **kwargs) -> torch.Tensor:
        raise NotImplementedError("decode()-method of abstract base class called")

    def instantiate_optimizer_from_config(self, params, lr, cfg):
        print(f"loading >>> {cfg['target']} <<< optimizer from config")
        return get_obj_from_str(cfg["target"])(
            params, lr=lr, **cfg.get("params", dict())
        )

    def configure_optimizers(self) -> Any:
        raise NotImplementedError()


class AutoencodingEngine(AbstractAutoencoder):
    """
    Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL
    (we also restore them explicitly as special cases for legacy reasons).
    Regularizations such as KL or VQ are moved to the regularizer class.
    """

    def __init__(
        self,
        *args,
        encoder_config: Dict,
        decoder_config: Dict,
        loss_config: Dict,
        regularizer_config: Dict,
        optimizer_config: Union[Dict, None] = None,
        lr_g_factor: float = 1.0,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        # todo: add options to freeze encoder/decoder
        self.encoder = instantiate_from_config(encoder_config)
        self.decoder = instantiate_from_config(decoder_config)
        self.loss = instantiate_from_config(loss_config)
        self.regularization = instantiate_from_config(regularizer_config)
        self.optimizer_config = default(
            optimizer_config, {"target": "torch.optim.Adam"}
        )
        self.lr_g_factor = lr_g_factor

    def get_input(self, batch: Dict) -> torch.Tensor:
        # assuming unified data format, dataloader returns a dict.
        # image tensors should be scaled to -1 ... 1 and in channels-first format (e.g., bchw instead if bhwc)
        return batch[self.input_key]

    def get_autoencoder_params(self) -> list:
        params = (
            list(self.encoder.parameters())
            + list(self.decoder.parameters())
            + list(self.regularization.get_trainable_parameters())
            + list(self.loss.get_trainable_autoencoder_parameters())
        )
        return params

    def get_discriminator_params(self) -> list:
        params = list(self.loss.get_trainable_parameters())  # e.g., discriminator
        return params

    def get_last_layer(self):
        return self.decoder.get_last_layer()

    def encode(self, x: Any, return_reg_log: bool = False) -> Any:
        z = self.encoder(x)
        z, reg_log = self.regularization(z)
        if return_reg_log:
            return z, reg_log
        return z

    def decode(self, z: Any) -> torch.Tensor:
        x = self.decoder(z)
        return x

    def forward(self, x: Any) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        z, reg_log = self.encode(x, return_reg_log=True)
        dec = self.decode(z)
        return z, dec, reg_log

    def training_step(self, batch, batch_idx, optimizer_idx) -> Any:
        x = self.get_input(batch)
        z, xrec, regularization_log = self(x)

        if optimizer_idx == 0:
            # autoencode
            aeloss, log_dict_ae = self.loss(
                regularization_log,
                x,
                xrec,
                optimizer_idx,
                self.global_step,
                last_layer=self.get_last_layer(),
                split="train",
            )

            self.log_dict(
                log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True
            )
            return aeloss

        if optimizer_idx == 1:
            # discriminator
            discloss, log_dict_disc = self.loss(
                regularization_log,
                x,
                xrec,
                optimizer_idx,
                self.global_step,
                last_layer=self.get_last_layer(),
                split="train",
            )
            self.log_dict(
                log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True
            )
            return discloss

    def validation_step(self, batch, batch_idx) -> Dict:
        log_dict = self._validation_step(batch, batch_idx)
        with self.ema_scope():
            log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
            log_dict.update(log_dict_ema)
        return log_dict

    def _validation_step(self, batch, batch_idx, postfix="") -> Dict:
        x = self.get_input(batch)

        z, xrec, regularization_log = self(x)
        aeloss, log_dict_ae = self.loss(
            regularization_log,
            x,
            xrec,
            0,
            self.global_step,
            last_layer=self.get_last_layer(),
            split="val" + postfix,
        )

        discloss, log_dict_disc = self.loss(
            regularization_log,
            x,
            xrec,
            1,
            self.global_step,
            last_layer=self.get_last_layer(),
            split="val" + postfix,
        )
        self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
        log_dict_ae.update(log_dict_disc)
        self.log_dict(log_dict_ae)
        return log_dict_ae

    def configure_optimizers(self) -> Any:
        ae_params = self.get_autoencoder_params()
        disc_params = self.get_discriminator_params()

        opt_ae = self.instantiate_optimizer_from_config(
            ae_params,
            default(self.lr_g_factor, 1.0) * self.learning_rate,
            self.optimizer_config,
        )
        opt_disc = self.instantiate_optimizer_from_config(
            disc_params, self.learning_rate, self.optimizer_config
        )

        return [opt_ae, opt_disc], []

    @torch.no_grad()
    def log_images(self, batch: Dict, **kwargs) -> Dict:
        log = dict()
        x = self.get_input(batch)
        _, xrec, _ = self(x)
        log["inputs"] = x
        log["reconstructions"] = xrec
        with self.ema_scope():
            _, xrec_ema, _ = self(x)
            log["reconstructions_ema"] = xrec_ema
        return log


class AutoencoderKL(AutoencodingEngine):
    def __init__(self, embed_dim: int, **kwargs):
        ddconfig = kwargs.pop("ddconfig")
        ckpt_path = kwargs.pop("ckpt_path", None)
        ignore_keys = kwargs.pop("ignore_keys", ())
        super().__init__(
            encoder_config={"target": "torch.nn.Identity"},
            decoder_config={"target": "torch.nn.Identity"},
            regularizer_config={"target": "torch.nn.Identity"},
            loss_config=kwargs.pop("lossconfig"),
            **kwargs,
        )
        assert ddconfig["double_z"]
        self.encoder = Encoder(**ddconfig)
        self.decoder = Decoder(**ddconfig)
        self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1)
        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
        self.embed_dim = embed_dim

        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)

    def encode(self, x):
        assert (
            not self.training
        ), f"{self.__class__.__name__} only supports inference currently"
        h = self.encoder(x)
        moments = self.quant_conv(h)
        posterior = DiagonalGaussianDistribution(moments)
        return posterior

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


class AutoencoderKLInferenceWrapper(AutoencoderKL):
    def encode(self, x):
        return super().encode(x).sample()


class IdentityFirstStage(AbstractAutoencoder):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def get_input(self, x: Any) -> Any:
        return x

    def encode(self, x: Any, *args, **kwargs) -> Any:
        return x

    def decode(self, x: Any, *args, **kwargs) -> Any:
        return x