444 lines
17 KiB
Python
444 lines
17 KiB
Python
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import torch
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import pytorch_lightning as pl
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import torch.nn.functional as F
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from contextlib import contextmanager
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from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
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from ldm.modules.diffusionmodules.model import Encoder, Decoder
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from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
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from ldm.util import instantiate_from_config
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class VQModel(pl.LightningModule):
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def __init__(self,
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ddconfig,
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lossconfig,
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n_embed,
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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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batch_resize_range=None,
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scheduler_config=None,
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lr_g_factor=1.0,
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remap=None,
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sane_index_shape=False, # tell vector quantizer to return indices as bhw
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use_ema=False
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):
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super().__init__()
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self.embed_dim = embed_dim
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self.n_embed = n_embed
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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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self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
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remap=remap,
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sane_index_shape=sane_index_shape)
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self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
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self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
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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.batch_resize_range = batch_resize_range
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if self.batch_resize_range is not None:
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print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
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self.use_ema = use_ema
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if self.use_ema:
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self.model_ema = LitEma(self)
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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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self.scheduler_config = scheduler_config
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self.lr_g_factor = lr_g_factor
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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 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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missing, unexpected = self.load_state_dict(sd, strict=False)
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print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
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if len(missing) > 0:
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print(f"Missing Keys: {missing}")
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print(f"Unexpected Keys: {unexpected}")
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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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h = self.quant_conv(h)
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quant, emb_loss, info = self.quantize(h)
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return quant, emb_loss, info
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def encode_to_prequant(self, x):
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h = self.encoder(x)
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h = self.quant_conv(h)
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return h
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def decode(self, quant):
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quant = self.post_quant_conv(quant)
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dec = self.decoder(quant)
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return dec
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def decode_code(self, code_b):
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quant_b = self.quantize.embed_code(code_b)
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dec = self.decode(quant_b)
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return dec
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def forward(self, input, return_pred_indices=False):
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quant, diff, (_,_,ind) = self.encode(input)
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dec = self.decode(quant)
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if return_pred_indices:
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return dec, diff, ind
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return dec, diff
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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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if self.batch_resize_range is not None:
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lower_size = self.batch_resize_range[0]
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upper_size = self.batch_resize_range[1]
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if self.global_step <= 4:
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# do the first few batches with max size to avoid later oom
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new_resize = upper_size
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else:
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new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
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if new_resize != x.shape[2]:
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x = F.interpolate(x, size=new_resize, mode="bicubic")
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x = x.detach()
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return x
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def training_step(self, batch, batch_idx, optimizer_idx):
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# https://github.com/pytorch/pytorch/issues/37142
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# try not to fool the heuristics
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x = self.get_input(batch, self.image_key)
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xrec, qloss, ind = self(x, return_pred_indices=True)
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if optimizer_idx == 0:
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# autoencode
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aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
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last_layer=self.get_last_layer(), split="train",
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predicted_indices=ind)
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self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
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return aeloss
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if optimizer_idx == 1:
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# discriminator
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discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
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last_layer=self.get_last_layer(), split="train")
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self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
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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, suffix="_ema")
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return log_dict
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def _validation_step(self, batch, batch_idx, suffix=""):
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x = self.get_input(batch, self.image_key)
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xrec, qloss, ind = self(x, return_pred_indices=True)
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aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
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self.global_step,
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last_layer=self.get_last_layer(),
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split="val"+suffix,
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predicted_indices=ind
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)
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discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
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self.global_step,
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last_layer=self.get_last_layer(),
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split="val"+suffix,
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predicted_indices=ind
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)
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rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
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self.log(f"val{suffix}/rec_loss", rec_loss,
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prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
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self.log(f"val{suffix}/aeloss", aeloss,
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prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
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if version.parse(pl.__version__) >= version.parse('1.4.0'):
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del log_dict_ae[f"val{suffix}/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_d = self.learning_rate
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lr_g = self.lr_g_factor*self.learning_rate
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print("lr_d", lr_d)
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print("lr_g", lr_g)
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opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
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list(self.decoder.parameters())+
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list(self.quantize.parameters())+
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list(self.quant_conv.parameters())+
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list(self.post_quant_conv.parameters()),
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lr=lr_g, betas=(0.5, 0.9))
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opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
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lr=lr_d, betas=(0.5, 0.9))
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if self.scheduler_config is not None:
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scheduler = instantiate_from_config(self.scheduler_config)
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print("Setting up LambdaLR scheduler...")
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scheduler = [
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{
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'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
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'interval': 'step',
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'frequency': 1
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},
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{
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'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
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'interval': 'step',
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'frequency': 1
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},
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]
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return [opt_ae, opt_disc], scheduler
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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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def log_images(self, batch, only_inputs=False, plot_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 only_inputs:
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log["inputs"] = x
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return log
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xrec, _ = 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["inputs"] = x
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log["reconstructions"] = xrec
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if plot_ema:
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with self.ema_scope():
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xrec_ema, _ = self(x)
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if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
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log["reconstructions_ema"] = xrec_ema
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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.*(x-x.min())/(x.max()-x.min()) - 1.
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return x
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class VQModelInterface(VQModel):
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def __init__(self, embed_dim, *args, **kwargs):
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super().__init__(embed_dim=embed_dim, *args, **kwargs)
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self.embed_dim = embed_dim
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def encode(self, x):
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h = self.encoder(x)
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h = self.quant_conv(h)
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return h
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def decode(self, h, force_not_quantize=False):
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# also go through quantization layer
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if not force_not_quantize:
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quant, emb_loss, info = self.quantize(h)
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else:
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quant = h
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quant = self.post_quant_conv(quant)
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dec = self.decoder(quant)
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return dec
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class AutoencoderKL(pl.LightningModule):
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def __init__(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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):
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super().__init__()
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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 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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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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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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||
|
|
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||
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|
def training_step(self, batch, batch_idx, optimizer_idx):
|
||
|
|
inputs = self.get_input(batch, self.image_key)
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||
|
|
reconstructions, posterior = self(inputs)
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||
|
|
|
||
|
|
if optimizer_idx == 0:
|
||
|
|
# train encoder+decoder+logvar
|
||
|
|
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
|
||
|
|
last_layer=self.get_last_layer(), split="train")
|
||
|
|
self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
|
||
|
|
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
|
||
|
|
return aeloss
|
||
|
|
|
||
|
|
if optimizer_idx == 1:
|
||
|
|
# train the discriminator
|
||
|
|
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
|
||
|
|
last_layer=self.get_last_layer(), split="train")
|
||
|
|
|
||
|
|
self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
|
||
|
|
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
|
||
|
|
return discloss
|
||
|
|
|
||
|
|
def validation_step(self, batch, batch_idx):
|
||
|
|
inputs = self.get_input(batch, self.image_key)
|
||
|
|
reconstructions, posterior = self(inputs)
|
||
|
|
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
|
||
|
|
last_layer=self.get_last_layer(), split="val")
|
||
|
|
|
||
|
|
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
|
||
|
|
last_layer=self.get_last_layer(), split="val")
|
||
|
|
|
||
|
|
self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
|
||
|
|
self.log_dict(log_dict_ae)
|
||
|
|
self.log_dict(log_dict_disc)
|
||
|
|
return self.log_dict
|
||
|
|
|
||
|
|
def configure_optimizers(self):
|
||
|
|
lr = self.learning_rate
|
||
|
|
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
|
||
|
|
list(self.decoder.parameters())+
|
||
|
|
list(self.quant_conv.parameters())+
|
||
|
|
list(self.post_quant_conv.parameters()),
|
||
|
|
lr=lr, betas=(0.5, 0.9))
|
||
|
|
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
|
||
|
|
lr=lr, betas=(0.5, 0.9))
|
||
|
|
return [opt_ae, opt_disc], []
|
||
|
|
|
||
|
|
def get_last_layer(self):
|
||
|
|
return self.decoder.conv_out.weight
|
||
|
|
|
||
|
|
@torch.no_grad()
|
||
|
|
def log_images(self, batch, only_inputs=False, **kwargs):
|
||
|
|
log = dict()
|
||
|
|
x = self.get_input(batch, self.image_key)
|
||
|
|
x = x.to(self.device)
|
||
|
|
if not only_inputs:
|
||
|
|
xrec, posterior = self(x)
|
||
|
|
if x.shape[1] > 3:
|
||
|
|
# colorize with random projection
|
||
|
|
assert xrec.shape[1] > 3
|
||
|
|
x = self.to_rgb(x)
|
||
|
|
xrec = self.to_rgb(xrec)
|
||
|
|
log["samples"] = self.decode(torch.randn_like(posterior.sample()))
|
||
|
|
log["reconstructions"] = xrec
|
||
|
|
log["inputs"] = x
|
||
|
|
return log
|
||
|
|
|
||
|
|
def to_rgb(self, x):
|
||
|
|
assert self.image_key == "segmentation"
|
||
|
|
if not hasattr(self, "colorize"):
|
||
|
|
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
|
||
|
|
x = F.conv2d(x, weight=self.colorize)
|
||
|
|
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class IdentityFirstStage(torch.nn.Module):
|
||
|
|
def __init__(self, *args, vq_interface=False, **kwargs):
|
||
|
|
self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
def encode(self, x, *args, **kwargs):
|
||
|
|
return x
|
||
|
|
|
||
|
|
def decode(self, x, *args, **kwargs):
|
||
|
|
return x
|
||
|
|
|
||
|
|
def quantize(self, x, *args, **kwargs):
|
||
|
|
if self.vq_interface:
|
||
|
|
return x, None, [None, None, None]
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward(self, x, *args, **kwargs):
|
||
|
|
return x
|