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Zero
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import torch
import numpy as np
import sys
import os
import tqdm
from src.flair import degradations
import torchvision
def total_variation_loss(x):
"""
Compute the total variation loss for a batch of images.
Args:
x (torch.Tensor): Input tensor of shape (B, C, H, W)
Returns:
torch.Tensor: Total variation loss
"""
# Compute the differences between adjacent pixels
diff_x = torch.abs(x[:, :, :, :-1] - x[:, :, :, 1:])
diff_y = torch.abs(x[:, :, :-1, :] - x[:, :, 1:, :])
# Sum the differences
return torch.sum(diff_x) + torch.sum(diff_y)
class VariationalPosterior:
def __init__(self, model, config):
"""
Args:
model (torch.nn.Module): model to be used for inference, should have single_step, encode and decode methods
config (dict): configuration
"""
self.config = config
self.model = model
# Initialize degradation model
try:
degradation = getattr(degradations, config["degradation"]["name"])
except AttributeError:
print(f"Degradation {config['degradation']['name']} not defined.")
sys.exit(1)
self.forward_operator = degradation(**config["degradation"]["kwargs"])
if "optimized_reg_weight" in config and config["optimized_reg_weight"]:
reg_weight = np.load(config["optimized_reg_weight"])
if "reg_weight" in config["optimized_reg_weight"]:
self.regularizer_weight = reg_weight * config["regularizer_weight"]
else:
# reg_weight = reg_weight / np.nanmax(reg_weight)
reg_weight = 1 / (reg_weight + 1e-7)
reg_weight = reg_weight / np.nansum(reg_weight) * reg_weight.shape[0]
if "reg-shift" in config:
self.regularizer_weight = reg_weight + config["reg-shift"]
else:
self.regularizer_weight = reg_weight - reg_weight[-1]
self.regularizer_weight = np.clip(self.regularizer_weight, 0, None) * config["regularizer_weight"]
print("loaded opt reg weight.")
else:
self.regularizer_weight = config["regularizer_weight"]
def set_degradation(self):
try:
degradation = getattr(degradations, self.config["degradation"]["name"])
except AttributeError:
sys.exit(1)
self.forward_operator = degradation(**self.config["degradation"]["kwargs"])
def data_term(self, latent_mu, y, optimizer_dataterm, likelihood_weight, likelihood_steps, early_stopping):
"""
Performs data term optimization over several steps with early stopping.
"""
for k in range(likelihood_steps):
with torch.enable_grad():
data_loss = torch.nn.MSELoss(reduction='sum')(self.forward_operator(self.model.decode(latent_mu), noise=False), y)
loss = likelihood_weight * data_loss.sum()
if data_loss < early_stopping * y.numel():
if latent_mu.grad is not None:
latent_mu.grad = None
del loss
del data_loss
break
loss.backward()
optimizer_dataterm.step()
optimizer_dataterm.zero_grad()
del loss
del data_loss
return
@torch.no_grad()
def projection(self, latent_mu, y, alpha=1):
x_0 = self.model.decode(latent_mu)
y_hat = self.forward_operator(x_0, noise=False)
x_inv_hat = self.forward_operator.pseudo_inv(y_hat)
projection = x_0 - x_inv_hat + self.forward_operator.pseudo_inv(y)
latent_projection = self.model.encode(projection)
# soft projection in latent space
latent_projection = latent_mu - (latent_mu - latent_projection) * alpha
return projection, latent_projection
def find_closest_t(self, t):
ts = torch.linspace(1, 0.0, self.regularizer_weight.shape[0], device=t.device, dtype=t.dtype)
return torch.argmin(torch.abs(ts - t))
def forward(self, y, kwargs):
"""
Uses variational approach to infer the mode of the posterior distribution given a measurement y.
Args:
y (torch.Tensor): measurement tensor
Returns:
torch.Tensor: estimated mu
"""
for key, value in kwargs.items():
try:
kwargs[key] = value.to(y.device)
except AttributeError:
pass
return_dict = {}
device = y.device
if "init" in self.config and self.config["init"] =="random":
# TODO: put this in model wrapper
shape = (
1,
16,
int(self.config["resolution"]) // self.model.vae_scale_factor,
int(self.config["resolution"]) // self.model.vae_scale_factor,
)
latent_mu = torch.randn(shape, device=device, dtype=y.dtype)
else:
x_inv = self.forward_operator.pseudo_inv(y)
latent_mu = self.model.encode(x_inv)
latent_mu = latent_mu.detach().clone()
latent_mu.requires_grad = True
start_noise = torch.randn_like(latent_mu)
optim_noise = start_noise.detach().clone()
timesteps = self._get_timesteps(device)
for epoch in range(self.config["epochs"]):
optimizer, optimizer_dataterm = self._initialize_optimizers(latent_mu)
for i, t in tqdm.tqdm(enumerate(timesteps), desc="Variational Optimization", total=len(timesteps)):
t = torch.tensor([t], device=device, dtype=latent_mu.dtype)
kwargs["noise"] = optim_noise.detach()
kwargs["inv_alpha"] = self.config["inv_alpha"]
eps_prediction, noise, a_t, sigma_t, v_pred = self.model.single_step(latent_mu, t, kwargs)
# predict x1 which is the start noise vector for DTA
optim_noise = a_t * latent_mu + sigma_t * noise + a_t * v_pred
reg_term = self._compute_regularization_term(eps_prediction, noise, a_t, sigma_t, t, latent_mu, v_pred)
if self.config["likelihood_weight_mode"] == "reg_weight":
reg_idx = self.find_closest_t(t)
likelihood_weight = self.regularizer_weight[reg_idx] * self.config["likelihood_weight"]
else:
likelihood_weight = self.config["likelihood_weight"]
with torch.enable_grad():
reg_term = (reg_term.detach() * latent_mu.view(reg_term.shape[0], -1)).sum()
reg_term.backward()
optimizer.step()
optimizer.zero_grad()
if self.config["projection"] and t>0.7:
with torch.enable_grad():
_, latent_mu_projection = self.projection(latent_mu, y)
proj_loss = (latent_mu - latent_mu_projection).detach() * latent_mu
proj_loss = proj_loss.sum()
optimizer_dataterm.zero_grad()
proj_loss.backward()
optimizer_dataterm.step()
optimizer_dataterm.zero_grad()
self.data_term(
latent_mu,
y.detach(),
optimizer_dataterm,
likelihood_weight,
self.config["likelihood_steps"],
self.config["early_stopping"]
)
# self.save_intermediate_results(latent_mu, i)
x_hat = self.model.decode(latent_mu)
return_dict.update({"x_hat": x_hat})
return return_dict
def _get_timesteps(self, device):
timesteps = self.model.get_timesteps(self.config["n_steps"], device=device, ts_min=self.config["ts_min"])
if self.config["t_sampling"] == "descending":
return timesteps
elif self.config["t_sampling"] == "ascending":
return timesteps.flip(0)
elif self.config["t_sampling"] == "random":
idx = torch.randperm(len(timesteps), device=device, dtype=timesteps.dtype)
return timesteps[idx]
else:
raise ValueError(f't_sampling {self.config["t_sampling"]} not supported.')
def _initialize_optimizers(self, latent_mu):
params = [latent_mu]
params2 = [latent_mu]
optimizer = self._get_optimizer(self.config["optimizer"], params)
optimizer_dataterm = self._get_optimizer(self.config["optimizer_dataterm"], params2)
if "scheduler" in self.config:
self.scheduler = self._get_scheduler(self.config["scheduler"], optimizer)
if "scheduler_dataterm" in self.config:
self.scheduler_dataterm = self._get_scheduler(self.config["scheduler_dataterm"], optimizer_dataterm)
return optimizer, optimizer_dataterm
def _get_optimizer(self, optimizer_config, params):
if optimizer_config["name"] == "Adam":
return torch.optim.Adam(params, **optimizer_config["kwargs"])
elif optimizer_config["name"] == "SGD":
return torch.optim.SGD(params, **optimizer_config["kwargs"])
else:
raise ValueError(f'optimizer {optimizer_config["name"]} not supported.')
def _get_scheduler(self, scheduler_config, optimizer):
if scheduler_config["name"] == "StepLR":
return torch.optim.lr_scheduler.StepLR(optimizer, **scheduler_config["kwargs"])
elif scheduler_config["name"] == "CosineAnnealingLR":
return torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, **scheduler_config["kwargs"])
elif scheduler_config["name"] == "LinearLR":
return torch.optim.lr_scheduler.LinearLR(optimizer, **scheduler_config["kwargs"])
# Add other schedulers as needed
else:
raise ValueError(f'scheduler {scheduler_config["name"]} not supported.')
def _compute_regularization_term(self, eps_prediction, noise, a_t, sigma_t, t, latent_mu, v):
reg_term = (eps_prediction - noise).reshape(eps_prediction.shape[0], -1)
# reg_term = (latent_mu-(a_t*latent_mu + sigma_t*noise - t *v)).reshape(eps_prediction.shape[0], -1)
#
# reg_term /= reg_term.norm() / 1000
if self.config["lambda_func"] == "sigma2":
reg_term *= sigma_t / a_t
elif self.config["lambda_func"] == "v":
x_t = a_t*latent_mu + sigma_t*noise
lambda_t_der = -2 * (1/(1-t) + 1/t)
reg_term = lambda_t_der * t * reg_term / 2
u_t = - 1 / (1-t) * x_t - t * lambda_t_der / 2 * noise
# u_t = noise - latent_mu
reg_term = -(u_t - v).reshape(eps_prediction.shape[0], -1)
elif self.config["lambda_func"] != "sigma":
raise ValueError(f'lambda_func {self.config["lambda_func"]} not supported.')
if isinstance(self.regularizer_weight, np.ndarray):
reg_idx = self.find_closest_t(t)
regularizer_weight = self.regularizer_weight[reg_idx]
else:
regularizer_weight = self.regularizer_weight
return reg_term * regularizer_weight
def save_intermediate_results(self, latent_mu, i):
"""
Saves intermediate results for debugging or visualization.
Args:
latent_mu (torch.Tensor): current latent representation
i (int): current iteration index
"""
x_hat = self.model.decode(latent_mu)
# create directory if it does not exist
os.makedirs("intermediate_results", exist_ok=True)
torchvision.utils.save_image(x_hat, f"intermediate_results/x_hat_{i}.png", normalize=True, value_range=(-1, 1))
print(f"Saved intermediate results for iteration {i}.") |