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"""
From https://github.com/CompVis/latent-diffusion/main/ldm/models/diffusion/ddpm.py
Pared down to simplify code.
The original file acknowledges:
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
"""
import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from contextlib import contextmanager
from functools import partial
from torchmetrics import MeanSquaredError
from .utils import make_beta_schedule, extract_into_tensor, noise_like, timestep_embedding
from .ema import LitEma
from ..blocks.afno import PatchEmbed3d, PatchExpand3d, AFNOBlock3d
class LatentDiffusion(pl.LightningModule):
def __init__(self,
model,
autoencoder,
context_encoder=None,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
use_ema=True,
lr=1e-4,
lr_warmup=0,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
parameterization="eps", # all assuming fixed variance schedules
):
super().__init__()
self.model = model
self.autoencoder = autoencoder.requires_grad_(False)
self.conditional = (context_encoder is not None)
self.context_encoder = context_encoder
self.lr = lr
self.lr_warmup = lr_warmup
self.val_loss = MeanSquaredError()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
self.register_schedule(
beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s
)
self.loss_type = loss_type
def register_schedule(self, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
betas = make_beta_schedule(
beta_schedule, timesteps,
linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s
)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(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)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
def apply_model(self, x_noisy, t, cond=None, return_ids=False):
if self.conditional:
cond = self.context_encoder(cond)
with self.ema_scope():
return self.model(x_noisy, t, context=cond)
def q_sample(self, x_start, t, noise=None):
if noise is None:
noise = torch.randn_like(x_start)
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
)
def get_loss(self, pred, target, mean=True):
if self.loss_type == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == 'l2':
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None, context=None):
if noise is None:
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t, context=context)
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
return self.get_loss(model_out, target, mean=False).mean()
def forward(self, x, *args, **kwargs):
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
return self.p_losses(x, t, *args, **kwargs)
def shared_step(self, batch):
(x,y) = batch
y = self.autoencoder.encode(y)[0]
context = self.context_encoder(x) if self.conditional else None
return self(y, context=context)
def training_step(self, batch, batch_idx):
loss = self.shared_step(batch)
self.log("train_loss", loss)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
#x, y = batch
#y_pred = self(x)
#loss2 = torch.nn.functional.mse_loss(y_pred, y)
loss = self.shared_step(batch)
with self.ema_scope():
loss_ema = self.shared_step(batch)
log_params = {"on_step": False, "on_epoch": True, "prog_bar": True}
self.log("val_loss", loss, **log_params)
self.log("val_loss_ema", loss, **log_params)
#self.log("mean_square_error", loss2, **log_params)
def test_step(self, batch, batch_idx):
return self.validation_step(batch, batch_idx)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def configure_optimizers(self):
optimizer = torch.optim.AdamW(self.parameters(), lr=self.lr,
betas=(0.5, 0.9), weight_decay=1e-3)
reduce_lr = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, patience=3, factor=0.25, verbose=True
)
return {
"optimizer": optimizer,
"lr_scheduler": {
"scheduler": reduce_lr,
"monitor": "val_loss_ema",
"frequency": 1,
},
}
def optimizer_step(
self,
epoch,
batch_idx,
optimizer,
optimizer_idx,
#optimizer_closure,
**kwargs
):
if self.trainer.global_step < self.lr_warmup:
lr_scale = (self.trainer.global_step+1) / self.lr_warmup
for pg in optimizer.param_groups:
pg['lr'] = lr_scale * self.lr
super().optimizer_step(
epoch, batch_idx, optimizer,
optimizer_idx,
#optimizer_closure,
**kwargs
)
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