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from numpy import ndarray
from torch import Tensor, device
from tqdm import tqdm
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from TorchJaekwon.GetModule import GetModule
from TorchJaekwon.Util.UtilData import UtilData
from TorchJaekwon.Util.UtilTorch import UtilTorch
from TorchJaekwon.Model.Diffusion.DDPM.DiffusionUtil import DiffusionUtil
from TorchJaekwon.Model.Diffusion.DDPM.BetaSchedule import BetaSchedule
from TorchJaekwon.Model.Diffusion.DDPM.DDPM import DDPM
class DDPMLossVLB(DDPM):
def __init__(self,
use_vlb_loss:bool = True,
loss_simple_weight:float=1.0,
original_elbo_weight:float=0.0,
logvar_init:float=0.0,
learn_logvar:bool=False,
*args,
**kwargs):
self.use_vlb_loss = use_vlb_loss
super().__init__(*args, **kwargs)
self.loss_simple_weight:float = loss_simple_weight
self.original_elbo_weight:float = original_elbo_weight
self.logvar:float = torch.full(fill_value=logvar_init, size=(self.timesteps,))
self.learn_logvar:bool = learn_logvar
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
else:
self.logvar = nn.Parameter(self.logvar, requires_grad=False)
def set_noise_schedule(self,
betas: Optional[ndarray] = None,
beta_schedule_type:Literal['linear','cosine'] = 'linear',
beta_arg_dict:dict = dict(),
timesteps:int = 1000,
) -> None:
if betas is None:
beta_arg_dict.update({'timesteps':timesteps})
betas = getattr(BetaSchedule,beta_schedule_type)(**beta_arg_dict)
alphas:ndarray = 1. - betas
alphas_cumprod:ndarray = np.cumprod(alphas, axis=0)
alphas_cumprod_prev:ndarray = np.append(1., alphas_cumprod[:-1])
self.betas:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'betas', value = betas)
self.alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'alphas_cumprod', value = alphas_cumprod)
self.alphas_cumprod_prev:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'alphas_cumprod_prev', value = alphas_cumprod_prev)
# calculations for diffusion q(x_t | x_{t-1}) and others
self.sqrt_alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'sqrt_alphas_cumprod', value = np.sqrt(alphas_cumprod))
self.sqrt_one_minus_alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'sqrt_one_minus_alphas_cumprod', value = np.sqrt(1. - alphas_cumprod))
self.log_one_minus_alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'log_one_minus_alphas_cumprod', value = np.log(1. - alphas_cumprod))
self.sqrt_recip_alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'sqrt_recip_alphas_cumprod', value = np.sqrt(1. / alphas_cumprod))
self.sqrt_recipm1_alphas_cumprod:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'sqrt_recipm1_alphas_cumprod', value = np.sqrt(1. / alphas_cumprod - 1))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.posterior_variance:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'posterior_variance', value = posterior_variance)
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.posterior_log_variance_clipped:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'posterior_log_variance_clipped', value = np.log(np.maximum(posterior_variance, 1e-20)))
self.posterior_mean_coef1:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'posterior_mean_coef1', value = betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
self.posterior_mean_coef2:Tensor = UtilTorch.register_buffer(model = self, variable_name = 'posterior_mean_coef2', value = (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))
if self.use_vlb_loss:
if self.model_output_type == 'noise':
lvlb_weights = self.betas**2 / (
2
* self.posterior_variance
* torch.tensor(alphas, dtype=torch.float32)
* (1 - self.alphas_cumprod)
)
elif self.model_output_type == 'x_start':
lvlb_weights = (
0.5
* np.sqrt(torch.Tensor(alphas_cumprod))
/ (2.0 * 1 - torch.Tensor(alphas_cumprod))
)
elif self.model_output_type == 'v_prediction':
lvlb_weights = torch.ones_like(
self.betas**2
/ (
2
* self.posterior_variance
* torch.tensor(alphas, dtype=torch.float32)
* (1 - self.alphas_cumprod)
)
)
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
self.lvlb_weights = self.lvlb_weights
assert not torch.isnan(self.lvlb_weights).all()
def p_losses(self,
x_start:Tensor,
cond:Optional[Union[dict,Tensor]],
is_cond_unpack:bool,
t:Tensor,
noise:Optional[Tensor] = None):
if not self.use_vlb_loss:
return super().p_losses(x_start, cond, is_cond_unpack, t, noise)
noise:Tensor = UtilData.default(noise, lambda: torch.randn_like(x_start))
x_noisy:Tensor = self.q_sample(x_start=x_start, t=t, noise=noise)
model_output:Tensor = self.apply_model(x_noisy, t, cond, is_cond_unpack)
if self.model_output_type == 'x_start':
target:Tensor = x_start
elif self.model_output_type == 'noise':
target:Tensor = noise
elif self.model_output_type == 'v_prediction':
target:Tensor = self.get_v(x_start, noise, t)
else:
print(f'''model output type is {self.model_output_type}. It should be in [x_start, noise]''')
raise NotImplementedError()
if target.shape != model_output.shape: print(f'warning: target shape({target.shape}) and model shape({model_output.shape}) are different')
loss_dict = dict()
loss_simple:Tensor = self.get_loss(model_output, target, mean=False)
loss_simple = loss_simple.mean(dim = list(range(len(loss_simple.shape)))[1:])
loss_dict.update({f"loss_simple": loss_simple.mean()})
logvar_t = self.logvar[t]
loss = loss_simple / torch.exp(logvar_t) + logvar_t
if self.learn_logvar:
loss_dict.update({f"loss_gamma": loss.mean()})
loss_dict.update({"logvar": self.logvar.data.mean()})
loss = self.loss_simple_weight * loss.mean()
loss_vlb:Tensor = self.get_loss(model_output, target, mean=False)
loss_vlb = loss_vlb.mean(dim=list(range(len(loss_vlb.shape)))[1:])
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f"loss_vlb": loss_vlb})
loss += self.original_elbo_weight * loss_vlb
loss_dict.update({f"loss": loss})
return loss_dict
def get_loss(self, pred:Tensor, target:Tensor, mean=True) -> Tensor:
if self.loss_func == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_func == F.mse_loss:
if mean:
loss = self.loss_func(target, pred)
else:
loss = self.loss_func(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
if __name__ == '__main__':
ddpm = DDPMLossVLB(model = lambda x, t: x, model_output_type = 'v_prediction')
ddpm.p_losses(x_start = torch.randn(2,3,64,64), cond = None, is_cond_unpack = False, t = torch.tensor([30, 23]))
print('finish') |