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add code
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ablattmann
443
ldm/models/autoencoder.py
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443
ldm/models/autoencoder.py
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@ -0,0 +1,443 @@
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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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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(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
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last_layer=self.get_last_layer(), split="train")
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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(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
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last_layer=self.get_last_layer(), split="train")
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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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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(inputs, reconstructions, posterior, 0, self.global_step,
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last_layer=self.get_last_layer(), split="val")
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discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
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last_layer=self.get_last_layer(), split="val")
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self.log("val/rec_loss", log_dict_ae["val/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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opt_ae = torch.optim.Adam(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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lr=lr, betas=(0.5, 0.9))
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opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
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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, **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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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.*(x-x.min())/(x.max()-x.min()) - 1.
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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 # 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
|
||||
0
ldm/models/diffusion/__init__.py
Normal file
0
ldm/models/diffusion/__init__.py
Normal file
267
ldm/models/diffusion/classifier.py
Normal file
267
ldm/models/diffusion/classifier.py
Normal file
@ -0,0 +1,267 @@
|
||||
import os
|
||||
import torch
|
||||
import pytorch_lightning as pl
|
||||
from omegaconf import OmegaConf
|
||||
from torch.nn import functional as F
|
||||
from torch.optim import AdamW
|
||||
from torch.optim.lr_scheduler import LambdaLR
|
||||
from copy import deepcopy
|
||||
from einops import rearrange
|
||||
from glob import glob
|
||||
from natsort import natsorted
|
||||
|
||||
from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
|
||||
from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
|
||||
|
||||
__models__ = {
|
||||
'class_label': EncoderUNetModel,
|
||||
'segmentation': UNetModel
|
||||
}
|
||||
|
||||
|
||||
def disabled_train(self, mode=True):
|
||||
"""Overwrite model.train with this function to make sure train/eval mode
|
||||
does not change anymore."""
|
||||
return self
|
||||
|
||||
|
||||
class NoisyLatentImageClassifier(pl.LightningModule):
|
||||
|
||||
def __init__(self,
|
||||
diffusion_path,
|
||||
num_classes,
|
||||
ckpt_path=None,
|
||||
pool='attention',
|
||||
label_key=None,
|
||||
diffusion_ckpt_path=None,
|
||||
scheduler_config=None,
|
||||
weight_decay=1.e-2,
|
||||
log_steps=10,
|
||||
monitor='val/loss',
|
||||
*args,
|
||||
**kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.num_classes = num_classes
|
||||
# get latest config of diffusion model
|
||||
diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
|
||||
self.diffusion_config = OmegaConf.load(diffusion_config).model
|
||||
self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
|
||||
self.load_diffusion()
|
||||
|
||||
self.monitor = monitor
|
||||
self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
|
||||
self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
|
||||
self.log_steps = log_steps
|
||||
|
||||
self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
|
||||
else self.diffusion_model.cond_stage_key
|
||||
|
||||
assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
|
||||
|
||||
if self.label_key not in __models__:
|
||||
raise NotImplementedError()
|
||||
|
||||
self.load_classifier(ckpt_path, pool)
|
||||
|
||||
self.scheduler_config = scheduler_config
|
||||
self.use_scheduler = self.scheduler_config is not None
|
||||
self.weight_decay = weight_decay
|
||||
|
||||
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
|
||||
sd = torch.load(path, map_location="cpu")
|
||||
if "state_dict" in list(sd.keys()):
|
||||
sd = sd["state_dict"]
|
||||
keys = list(sd.keys())
|
||||
for k in keys:
|
||||
for ik in ignore_keys:
|
||||
if k.startswith(ik):
|
||||
print("Deleting key {} from state_dict.".format(k))
|
||||
del sd[k]
|
||||
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.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}")
|
||||
|
||||
def load_diffusion(self):
|
||||
model = instantiate_from_config(self.diffusion_config)
|
||||
self.diffusion_model = model.eval()
|
||||
self.diffusion_model.train = disabled_train
|
||||
for param in self.diffusion_model.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def load_classifier(self, ckpt_path, pool):
|
||||
model_config = deepcopy(self.diffusion_config.params.unet_config.params)
|
||||
model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
|
||||
model_config.out_channels = self.num_classes
|
||||
if self.label_key == 'class_label':
|
||||
model_config.pool = pool
|
||||
|
||||
self.model = __models__[self.label_key](**model_config)
|
||||
if ckpt_path is not None:
|
||||
print('#####################################################################')
|
||||
print(f'load from ckpt "{ckpt_path}"')
|
||||
print('#####################################################################')
|
||||
self.init_from_ckpt(ckpt_path)
|
||||
|
||||
@torch.no_grad()
|
||||
def get_x_noisy(self, x, t, noise=None):
|
||||
noise = default(noise, lambda: torch.randn_like(x))
|
||||
continuous_sqrt_alpha_cumprod = None
|
||||
if self.diffusion_model.use_continuous_noise:
|
||||
continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
|
||||
# todo: make sure t+1 is correct here
|
||||
|
||||
return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
|
||||
continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
|
||||
|
||||
def forward(self, x_noisy, t, *args, **kwargs):
|
||||
return self.model(x_noisy, t)
|
||||
|
||||
@torch.no_grad()
|
||||
def get_input(self, batch, k):
|
||||
x = batch[k]
|
||||
if len(x.shape) == 3:
|
||||
x = x[..., None]
|
||||
x = rearrange(x, 'b h w c -> b c h w')
|
||||
x = x.to(memory_format=torch.contiguous_format).float()
|
||||
return x
|
||||
|
||||
@torch.no_grad()
|
||||
def get_conditioning(self, batch, k=None):
|
||||
if k is None:
|
||||
k = self.label_key
|
||||
assert k is not None, 'Needs to provide label key'
|
||||
|
||||
targets = batch[k].to(self.device)
|
||||
|
||||
if self.label_key == 'segmentation':
|
||||
targets = rearrange(targets, 'b h w c -> b c h w')
|
||||
for down in range(self.numd):
|
||||
h, w = targets.shape[-2:]
|
||||
targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
|
||||
|
||||
# targets = rearrange(targets,'b c h w -> b h w c')
|
||||
|
||||
return targets
|
||||
|
||||
def compute_top_k(self, logits, labels, k, reduction="mean"):
|
||||
_, top_ks = torch.topk(logits, k, dim=1)
|
||||
if reduction == "mean":
|
||||
return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
|
||||
elif reduction == "none":
|
||||
return (top_ks == labels[:, None]).float().sum(dim=-1)
|
||||
|
||||
def on_train_epoch_start(self):
|
||||
# save some memory
|
||||
self.diffusion_model.model.to('cpu')
|
||||
|
||||
@torch.no_grad()
|
||||
def write_logs(self, loss, logits, targets):
|
||||
log_prefix = 'train' if self.training else 'val'
|
||||
log = {}
|
||||
log[f"{log_prefix}/loss"] = loss.mean()
|
||||
log[f"{log_prefix}/acc@1"] = self.compute_top_k(
|
||||
logits, targets, k=1, reduction="mean"
|
||||
)
|
||||
log[f"{log_prefix}/acc@5"] = self.compute_top_k(
|
||||
logits, targets, k=5, reduction="mean"
|
||||
)
|
||||
|
||||
self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
|
||||
self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
|
||||
self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
|
||||
lr = self.optimizers().param_groups[0]['lr']
|
||||
self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
|
||||
|
||||
def shared_step(self, batch, t=None):
|
||||
x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
|
||||
targets = self.get_conditioning(batch)
|
||||
if targets.dim() == 4:
|
||||
targets = targets.argmax(dim=1)
|
||||
if t is None:
|
||||
t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
|
||||
else:
|
||||
t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
|
||||
x_noisy = self.get_x_noisy(x, t)
|
||||
logits = self(x_noisy, t)
|
||||
|
||||
loss = F.cross_entropy(logits, targets, reduction='none')
|
||||
|
||||
self.write_logs(loss.detach(), logits.detach(), targets.detach())
|
||||
|
||||
loss = loss.mean()
|
||||
return loss, logits, x_noisy, targets
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
loss, *_ = self.shared_step(batch)
|
||||
return loss
|
||||
|
||||
def reset_noise_accs(self):
|
||||
self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
|
||||
range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
|
||||
|
||||
def on_validation_start(self):
|
||||
self.reset_noise_accs()
|
||||
|
||||
@torch.no_grad()
|
||||
def validation_step(self, batch, batch_idx):
|
||||
loss, *_ = self.shared_step(batch)
|
||||
|
||||
for t in self.noisy_acc:
|
||||
_, logits, _, targets = self.shared_step(batch, t)
|
||||
self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
|
||||
self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
|
||||
|
||||
return loss
|
||||
|
||||
def configure_optimizers(self):
|
||||
optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
|
||||
|
||||
if self.use_scheduler:
|
||||
scheduler = instantiate_from_config(self.scheduler_config)
|
||||
|
||||
print("Setting up LambdaLR scheduler...")
|
||||
scheduler = [
|
||||
{
|
||||
'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
|
||||
'interval': 'step',
|
||||
'frequency': 1
|
||||
}]
|
||||
return [optimizer], scheduler
|
||||
|
||||
return optimizer
|
||||
|
||||
@torch.no_grad()
|
||||
def log_images(self, batch, N=8, *args, **kwargs):
|
||||
log = dict()
|
||||
x = self.get_input(batch, self.diffusion_model.first_stage_key)
|
||||
log['inputs'] = x
|
||||
|
||||
y = self.get_conditioning(batch)
|
||||
|
||||
if self.label_key == 'class_label':
|
||||
y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
|
||||
log['labels'] = y
|
||||
|
||||
if ismap(y):
|
||||
log['labels'] = self.diffusion_model.to_rgb(y)
|
||||
|
||||
for step in range(self.log_steps):
|
||||
current_time = step * self.log_time_interval
|
||||
|
||||
_, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
|
||||
|
||||
log[f'inputs@t{current_time}'] = x_noisy
|
||||
|
||||
pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
|
||||
pred = rearrange(pred, 'b h w c -> b c h w')
|
||||
|
||||
log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
|
||||
|
||||
for key in log:
|
||||
log[key] = log[key][:N]
|
||||
|
||||
return log
|
||||
186
ldm/models/diffusion/ddim.py
Normal file
186
ldm/models/diffusion/ddim.py
Normal file
@ -0,0 +1,186 @@
|
||||
"""SAMPLING ONLY."""
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
from functools import partial
|
||||
|
||||
from ldm.models.diffusion.ddpm import noise_like
|
||||
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps
|
||||
|
||||
|
||||
class DDIMSampler(object):
|
||||
def __init__(self, model, schedule="linear", **kwargs):
|
||||
super().__init__()
|
||||
self.model = model
|
||||
self.ddpm_num_timesteps = model.num_timesteps
|
||||
self.schedule = schedule
|
||||
|
||||
def register_buffer(self, name, attr):
|
||||
if type(attr) == torch.Tensor:
|
||||
if attr.device != torch.device("cuda"):
|
||||
attr = attr.to(torch.device("cuda"))
|
||||
setattr(self, name, attr)
|
||||
|
||||
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
|
||||
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
|
||||
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
|
||||
alphas_cumprod = self.model.alphas_cumprod
|
||||
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
|
||||
|
||||
to_torch = partial(torch.tensor, dtype=torch.float32, device=self.model.device)
|
||||
|
||||
self.register_buffer('betas', to_torch(self.model.betas))
|
||||
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
|
||||
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
|
||||
|
||||
# calculations for diffusion q(x_t | x_{t-1}) and others
|
||||
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
|
||||
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
|
||||
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
|
||||
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
|
||||
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
|
||||
|
||||
# ddim sampling parameters
|
||||
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
|
||||
ddim_timesteps=self.ddim_timesteps,
|
||||
eta=ddim_eta,verbose=verbose)
|
||||
self.register_buffer('ddim_sigmas', ddim_sigmas)
|
||||
self.register_buffer('ddim_alphas', ddim_alphas)
|
||||
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
|
||||
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
|
||||
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
|
||||
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
|
||||
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
|
||||
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
|
||||
|
||||
@torch.no_grad()
|
||||
def sample(self,
|
||||
S,
|
||||
batch_size,
|
||||
shape,
|
||||
conditioning=None,
|
||||
callback=None,
|
||||
normals_sequence=None,
|
||||
img_callback=None,
|
||||
quantize_x0=False,
|
||||
eta=0.,
|
||||
mask=None,
|
||||
x0=None,
|
||||
temperature=1.,
|
||||
noise_dropout=0.,
|
||||
score_corrector=None,
|
||||
corrector_kwargs=None,
|
||||
verbose=True,
|
||||
x_T=None,
|
||||
log_every_t=100
|
||||
):
|
||||
if conditioning is not None:
|
||||
if isinstance(conditioning, dict):
|
||||
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
|
||||
if cbs != batch_size:
|
||||
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
||||
else:
|
||||
if conditioning.shape[0] != batch_size:
|
||||
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
|
||||
|
||||
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
|
||||
# sampling
|
||||
C, H, W = shape
|
||||
size = (batch_size, C, H, W)
|
||||
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
|
||||
|
||||
samples, intermediates = self.ddim_sampling(conditioning, size,
|
||||
callback=callback,
|
||||
img_callback=img_callback,
|
||||
quantize_denoised=quantize_x0,
|
||||
mask=mask, x0=x0,
|
||||
ddim_use_original_steps=False,
|
||||
noise_dropout=noise_dropout,
|
||||
temperature=temperature,
|
||||
score_corrector=score_corrector,
|
||||
corrector_kwargs=corrector_kwargs,
|
||||
x_T=x_T,
|
||||
log_every_t=log_every_t
|
||||
)
|
||||
return samples, intermediates
|
||||
|
||||
@torch.no_grad()
|
||||
def ddim_sampling(self, cond, shape,
|
||||
x_T=None, ddim_use_original_steps=False,
|
||||
callback=None, timesteps=None, quantize_denoised=False,
|
||||
mask=None, x0=None, img_callback=None, log_every_t=100,
|
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
|
||||
device = self.model.betas.device
|
||||
b = shape[0]
|
||||
if x_T is None:
|
||||
img = torch.randn(shape, device=device)
|
||||
else:
|
||||
img = x_T
|
||||
|
||||
if timesteps is None:
|
||||
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
|
||||
elif timesteps is not None and not ddim_use_original_steps:
|
||||
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
|
||||
timesteps = self.ddim_timesteps[:subset_end]
|
||||
|
||||
intermediates = {'x_inter': [img], 'pred_x0': [img]}
|
||||
time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
|
||||
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
|
||||
print(f"Running DDIM Sampling with {total_steps} timesteps")
|
||||
|
||||
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
|
||||
|
||||
for i, step in enumerate(iterator):
|
||||
index = total_steps - i - 1
|
||||
ts = torch.full((b,), step, device=device, dtype=torch.long)
|
||||
|
||||
if mask is not None:
|
||||
assert x0 is not None
|
||||
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
|
||||
img = img_orig * mask + (1. - mask) * img
|
||||
|
||||
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
|
||||
quantize_denoised=quantize_denoised, temperature=temperature,
|
||||
noise_dropout=noise_dropout, score_corrector=score_corrector,
|
||||
corrector_kwargs=corrector_kwargs)
|
||||
img, pred_x0 = outs
|
||||
if callback: callback(i)
|
||||
if img_callback: img_callback(pred_x0, i)
|
||||
|
||||
if index % log_every_t == 0 or index == total_steps - 1:
|
||||
intermediates['x_inter'].append(img)
|
||||
intermediates['pred_x0'].append(pred_x0)
|
||||
|
||||
return img, intermediates
|
||||
|
||||
@torch.no_grad()
|
||||
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
|
||||
b, *_, device = *x.shape, x.device
|
||||
e_t = self.model.apply_model(x, t, c)
|
||||
if score_corrector is not None:
|
||||
assert self.model.parameterization == "eps"
|
||||
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
|
||||
|
||||
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
|
||||
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
|
||||
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
|
||||
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
|
||||
# select parameters corresponding to the currently considered timestep
|
||||
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
||||
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
||||
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
||||
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
|
||||
|
||||
# current prediction for x_0
|
||||
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
||||
if quantize_denoised:
|
||||
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
|
||||
# direction pointing to x_t
|
||||
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
|
||||
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
|
||||
if noise_dropout > 0.:
|
||||
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
||||
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
||||
return x_prev, pred_x0
|
||||
1430
ldm/models/diffusion/ddpm.py
Normal file
1430
ldm/models/diffusion/ddpm.py
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user