mirror of https://github.com/hpcaitech/ColossalAI
249 lines
8.5 KiB
Python
249 lines
8.5 KiB
Python
from contextlib import contextmanager
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import lightning.pytorch as pl
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import torch
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from ldm.modules.diffusionmodules.model import Decoder, Encoder
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from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
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from ldm.modules.ema import LitEma
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from torch.nn import Identity
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from torch.nn import functional as F
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class AutoencoderKL(pl.LightningModule):
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def __init__(
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self,
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ddconfig,
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lossconfig,
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embed_dim,
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ckpt_path=None,
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ignore_keys=[],
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image_key="image",
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colorize_nlabels=None,
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monitor=None,
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ema_decay=None,
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learn_logvar=False,
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):
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super().__init__()
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self.learn_logvar = learn_logvar
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self.image_key = image_key
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self.encoder = Encoder(**ddconfig)
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self.decoder = Decoder(**ddconfig)
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self.loss = Identity()
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assert ddconfig["double_z"]
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self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1)
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self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
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self.embed_dim = embed_dim
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if colorize_nlabels is not None:
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assert type(colorize_nlabels) == int
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self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
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if monitor is not None:
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self.monitor = monitor
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self.use_ema = ema_decay is not None
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if self.use_ema:
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self.ema_decay = ema_decay
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assert 0.0 < ema_decay < 1.0
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self.model_ema = LitEma(self, decay=ema_decay)
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print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
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if ckpt_path is not None:
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self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
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def init_from_ckpt(self, path, ignore_keys=list()):
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sd = torch.load(path, map_location="cpu")["state_dict"]
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keys = list(sd.keys())
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for k in keys:
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for ik in ignore_keys:
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if k.startswith(ik):
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print("Deleting key {} from state_dict.".format(k))
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del sd[k]
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self.load_state_dict(sd, strict=False)
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print(f"Restored from {path}")
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@contextmanager
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def ema_scope(self, context=None):
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if self.use_ema:
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self.model_ema.store(self.parameters())
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self.model_ema.copy_to(self)
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if context is not None:
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print(f"{context}: Switched to EMA weights")
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try:
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yield None
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finally:
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if self.use_ema:
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self.model_ema.restore(self.parameters())
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if context is not None:
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print(f"{context}: Restored training weights")
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def on_train_batch_end(self, *args, **kwargs):
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if self.use_ema:
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self.model_ema(self)
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def encode(self, x):
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h = self.encoder(x)
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moments = self.quant_conv(h)
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posterior = DiagonalGaussianDistribution(moments)
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return posterior
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def decode(self, z):
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z = self.post_quant_conv(z)
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dec = self.decoder(z)
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return dec
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def forward(self, input, sample_posterior=True):
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posterior = self.encode(input)
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if sample_posterior:
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z = posterior.sample()
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else:
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z = posterior.mode()
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dec = self.decode(z)
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return dec, posterior
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def get_input(self, batch, k):
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x = batch[k]
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if len(x.shape) == 3:
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x = x[..., None]
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x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
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return x
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def training_step(self, batch, batch_idx, optimizer_idx):
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inputs = self.get_input(batch, self.image_key)
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reconstructions, posterior = self(inputs)
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if optimizer_idx == 0:
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# train encoder+decoder+logvar
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aeloss, log_dict_ae = self.loss(
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inputs,
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reconstructions,
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posterior,
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optimizer_idx,
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self.global_step,
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last_layer=self.get_last_layer(),
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split="train",
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)
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self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
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self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
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return aeloss
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if optimizer_idx == 1:
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# train the discriminator
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discloss, log_dict_disc = self.loss(
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inputs,
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reconstructions,
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posterior,
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optimizer_idx,
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self.global_step,
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last_layer=self.get_last_layer(),
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split="train",
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)
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self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
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self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
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return discloss
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def validation_step(self, batch, batch_idx):
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log_dict = self._validation_step(batch, batch_idx)
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with self.ema_scope():
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log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
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return log_dict
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def _validation_step(self, batch, batch_idx, postfix=""):
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inputs = self.get_input(batch, self.image_key)
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reconstructions, posterior = self(inputs)
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aeloss, log_dict_ae = self.loss(
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inputs,
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reconstructions,
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posterior,
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0,
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self.global_step,
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last_layer=self.get_last_layer(),
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split="val" + postfix,
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)
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discloss, log_dict_disc = self.loss(
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inputs,
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reconstructions,
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posterior,
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1,
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self.global_step,
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last_layer=self.get_last_layer(),
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split="val" + postfix,
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)
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self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
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self.log_dict(log_dict_ae)
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self.log_dict(log_dict_disc)
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return self.log_dict
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def configure_optimizers(self):
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lr = self.learning_rate
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ae_params_list = (
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list(self.encoder.parameters())
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+ list(self.decoder.parameters())
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+ list(self.quant_conv.parameters())
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+ list(self.post_quant_conv.parameters())
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)
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if self.learn_logvar:
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print(f"{self.__class__.__name__}: Learning logvar")
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ae_params_list.append(self.loss.logvar)
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opt_ae = torch.optim.Adam(ae_params_list, lr=lr, betas=(0.5, 0.9))
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opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9))
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return [opt_ae, opt_disc], []
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def get_last_layer(self):
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return self.decoder.conv_out.weight
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@torch.no_grad()
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def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
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log = dict()
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x = self.get_input(batch, self.image_key)
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x = x.to(self.device)
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if not only_inputs:
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xrec, posterior = self(x)
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if x.shape[1] > 3:
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# colorize with random projection
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assert xrec.shape[1] > 3
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x = self.to_rgb(x)
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xrec = self.to_rgb(xrec)
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log["samples"] = self.decode(torch.randn_like(posterior.sample()))
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log["reconstructions"] = xrec
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if log_ema or self.use_ema:
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with self.ema_scope():
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xrec_ema, posterior_ema = self(x)
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if x.shape[1] > 3:
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# colorize with random projection
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assert xrec_ema.shape[1] > 3
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xrec_ema = self.to_rgb(xrec_ema)
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log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample()))
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log["reconstructions_ema"] = xrec_ema
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log["inputs"] = x
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return log
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def to_rgb(self, x):
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assert self.image_key == "segmentation"
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if not hasattr(self, "colorize"):
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self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
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x = F.conv2d(x, weight=self.colorize)
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x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
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return x
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class IdentityFirstStage(torch.nn.Module):
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def __init__(self, *args, vq_interface=False, **kwargs):
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self.vq_interface = vq_interface
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super().__init__()
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def encode(self, x, *args, **kwargs):
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return x
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def decode(self, x, *args, **kwargs):
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return x
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def quantize(self, x, *args, **kwargs):
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if self.vq_interface:
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return x, None, [None, None, None]
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return x
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def forward(self, x, *args, **kwargs):
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return x
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