mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-10-31 03:20:40 +00:00
380 lines
13 KiB
Python
380 lines
13 KiB
Python
# pylama:ignore=W0613
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import logging
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import math
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from contextlib import contextmanager
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import pytorch_lightning as pl
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import torch
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from torch.cuda import OutOfMemoryError
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from imaginairy.feather_tile import rebuild_image, tile_image
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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.modules.ema import LitEma
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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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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 = 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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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=None):
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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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ignore_keys = [] if ignore_keys is None else ignore_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(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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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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try:
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return self.decode_all_at_once(z)
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except OutOfMemoryError:
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# Out of memory, trying sliced decoding.
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try:
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return self.decode_sliced(z, chunk_size=128)
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except OutOfMemoryError:
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return self.decode_sliced(z, chunk_size=64)
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def decode_all_at_once(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 decode_sliced(self, z, chunk_size=128):
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"""
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decodes the tensor in slices.
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This results in images that don't exactly match, so we overlap, feather, and merge to reduce
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(but not completely elminate) impact.
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"""
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b, c, h, w = z.size()
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final_tensor = torch.zeros([1, 3, h * 8, w * 8], device=z.device)
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for z_latent in z.split(1):
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decoded_chunks = []
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overlap_pct = 0.5
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chunks = tile_image(
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z_latent, tile_size=chunk_size, overlap_percent=overlap_pct
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)
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for latent_chunk in chunks:
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latent_chunk = self.post_quant_conv(latent_chunk)
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dec = self.decoder(latent_chunk)
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decoded_chunks.append(dec)
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final_tensor = rebuild_image(
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decoded_chunks,
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base_img=final_tensor,
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tile_size=chunk_size * 8,
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overlap_percent=overlap_pct,
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)
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return final_tensor
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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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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(
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"aeloss",
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aeloss,
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prog_bar=True,
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logger=True,
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on_step=True,
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on_epoch=True,
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)
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self.log_dict(
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log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False
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)
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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(
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"discloss",
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discloss,
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prog_bar=True,
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logger=True,
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on_step=True,
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on_epoch=True,
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)
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self.log_dict(
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log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False
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)
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return discloss
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return None
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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( # noqa
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batch, batch_idx, postfix="_ema"
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)
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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(
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self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)
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)
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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 = {}
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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(
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torch.randn_like(posterior_ema.sample())
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)
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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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def chunk_latent(tensor, chunk_size=64, overlap_size=8):
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# Get the shape of the tensor
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batch_size, num_channels, height, width = tensor.shape
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# Calculate the number of chunks along each dimension
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num_rows = int(math.ceil(height / chunk_size))
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num_cols = int(math.ceil(width / chunk_size))
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# Initialize a list to store the chunks
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chunks = []
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# Loop over the rows and columns
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for row in range(num_rows):
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for col in range(num_cols):
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# Calculate the start and end indices for the chunk along each dimension
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row_start = max(row * chunk_size - overlap_size, 0)
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row_end = min(row_start + chunk_size + overlap_size, height)
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col_start = max(col * chunk_size - overlap_size, 0)
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col_end = min(col_start + chunk_size + overlap_size, width)
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# Extract the chunk from the tensor and append it to the list of chunks
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chunk = tensor[:, :, row_start:row_end, col_start:col_end]
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chunks.append((chunk, row_start, col_start))
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return chunks, num_rows, num_cols
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def merge_tensors(tensor_list, num_rows, num_cols):
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print(f"num_rows: {num_rows}")
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print(f"num_cols: {num_cols}")
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n, channel, h, w = tensor_list[0].size()
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assert n == 1
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final_width = 0
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final_height = 0
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for col_idx in range(num_cols):
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final_width += tensor_list[col_idx].size()[3]
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for row_idx in range(num_rows):
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final_height += tensor_list[row_idx * num_cols].size()[2]
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final_tensor = torch.zeros([1, channel, final_height, final_width])
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print(f"final size {final_tensor.size()}")
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for row_idx in range(num_rows):
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for col_idx in range(num_cols):
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list_idx = row_idx * num_cols + col_idx
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chunk = tensor_list[list_idx]
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print(f"chunk size: {chunk.size()}")
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_, _, chunk_h, chunk_w = chunk.size()
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final_tensor[
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:,
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:,
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row_idx * h : row_idx * h + chunk_h,
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col_idx * w : col_idx * w + chunk_w,
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] = chunk
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return final_tensor
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