mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-11-19 03:25:41 +00:00
81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
import logging
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import pytorch_lightning as pl
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import torch
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from imaginairy.modules.diffusion.model import Decoder, Encoder
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from imaginairy.modules.distributions import DiagonalGaussianDistribution
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from imaginairy.utils import instantiate_from_config
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logger = logging.getLogger(__name__)
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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=None,
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image_key="image",
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colorize_nlabels=None,
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monitor=None,
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):
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super().__init__()
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ignore_keys = [] if ignore_keys is None else ignore_keys
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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 = instantiate_from_config(lossconfig)
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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 isinstance(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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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=None):
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ignore_keys = [] if ignore_keys is None else ignore_keys
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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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logger.info(f"Deleting key {k} from state_dict.")
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del sd[k]
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self.load_state_dict(sd, strict=False)
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logger.info(f"Restored from {path}")
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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): # noqa
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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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