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import numpy as np
import torch
import utils
from diffusion import diffusion_utils
class PredefinedNoiseSchedule(torch.nn.Module):
"""
Predefined noise schedule. Essentially creates a lookup array for predefined (non-learned) noise schedules.
"""
def __init__(self, noise_schedule, timesteps):
super(PredefinedNoiseSchedule, self).__init__()
self.timesteps = timesteps
if noise_schedule == 'cosine':
alphas2 = diffusion_utils.cosine_beta_schedule(timesteps)
elif noise_schedule == 'custom':
raise NotImplementedError()
else:
raise ValueError(noise_schedule)
# print('alphas2', alphas2)
sigmas2 = 1 - alphas2
log_alphas2 = np.log(alphas2)
log_sigmas2 = np.log(sigmas2)
log_alphas2_to_sigmas2 = log_alphas2 - log_sigmas2 # (timesteps + 1, )
# print('gamma', -log_alphas2_to_sigmas2)
self.gamma = torch.nn.Parameter(
torch.from_numpy(-log_alphas2_to_sigmas2).float(),
requires_grad=False)
def forward(self, t):
t_int = torch.round(t * self.timesteps).long()
return self.gamma[t_int]
class PredefinedNoiseScheduleDiscrete(torch.nn.Module):
"""
Predefined noise schedule. Essentially creates a lookup array for predefined (non-learned) noise schedules.
"""
def __init__(self, noise_schedule, timesteps):
super(PredefinedNoiseScheduleDiscrete, self).__init__()
self.timesteps = timesteps
if noise_schedule == 'cosine':
betas = diffusion_utils.cosine_beta_schedule_discrete(timesteps)
elif noise_schedule == 'custom':
betas = diffusion_utils.custom_beta_schedule_discrete(timesteps)
else:
raise NotImplementedError(noise_schedule)
self.register_buffer('betas', torch.from_numpy(betas).float())
self.alphas = 1 - torch.clamp(self.betas, min=0, max=0.9999)
log_alpha = torch.log(self.alphas)
log_alpha_bar = torch.cumsum(log_alpha, dim=0)
self.alphas_bar = torch.exp(log_alpha_bar)
# print(f"[Noise schedule: {noise_schedule}] alpha_bar:", self.alphas_bar)
def forward(self, t_normalized=None, t_int=None):
assert int(t_normalized is None) + int(t_int is None) == 1
if t_int is None:
t_int = torch.round(t_normalized * self.timesteps)
return self.betas[t_int.long()]
def get_alpha_bar(self, t_normalized=None, t_int=None):
assert int(t_normalized is None) + int(t_int is None) == 1
if t_int is None:
t_int = torch.round(t_normalized * self.timesteps)
return self.alphas_bar.to(t_int.device)[t_int.long()]
class DiscreteUniformTransition:
def __init__(self, x_classes: int, e_classes: int, y_classes: int):
self.X_classes = x_classes
self.E_classes = e_classes
self.y_classes = y_classes
self.u_x = torch.ones(1, self.X_classes, self.X_classes)
if self.X_classes > 0:
self.u_x = self.u_x / self.X_classes
self.u_e = torch.ones(1, self.E_classes, self.E_classes)
if self.E_classes > 0:
self.u_e = self.u_e / self.E_classes
self.u_y = torch.ones(1, self.y_classes, self.y_classes)
if self.y_classes > 0:
self.u_y = self.u_y / self.y_classes
def get_Qt(self, beta_t, device):
""" Returns one-step transition matrices for X and E, from step t - 1 to step t.
Qt = (1 - beta_t) * I + beta_t / K
beta_t: (bs) noise level between 0 and 1
returns: qx (bs, dx, dx), qe (bs, de, de), qy (bs, dy, dy).
"""
beta_t = beta_t.unsqueeze(1)
beta_t = beta_t.to(device)
self.u_x = self.u_x.to(device)
self.u_e = self.u_e.to(device)
self.u_y = self.u_y.to(device)
q_x = beta_t * self.u_x + (1 - beta_t) * torch.eye(self.X_classes, device=device).unsqueeze(0)
q_e = beta_t * self.u_e + (1 - beta_t) * torch.eye(self.E_classes, device=device).unsqueeze(0)
q_y = beta_t * self.u_y + (1 - beta_t) * torch.eye(self.y_classes, device=device).unsqueeze(0)
return utils.PlaceHolder(X=q_x, E=q_e, y=q_y)
def get_Qt_bar(self, alpha_bar_t, device):
""" Returns t-step transition matrices for X and E, from step 0 to step t.
Qt = prod(1 - beta_t) * I + (1 - prod(1 - beta_t)) / K
alpha_bar_t: (bs) Product of the (1 - beta_t) for each time step from 0 to t.
returns: qx (bs, dx, dx), qe (bs, de, de), qy (bs, dy, dy).
"""
alpha_bar_t = alpha_bar_t.unsqueeze(1)
alpha_bar_t = alpha_bar_t.to(device)
self.u_x = self.u_x.to(device)
self.u_e = self.u_e.to(device)
self.u_y = self.u_y.to(device)
q_x = alpha_bar_t * torch.eye(self.X_classes, device=device).unsqueeze(0) + (1 - alpha_bar_t) * self.u_x
q_e = alpha_bar_t * torch.eye(self.E_classes, device=device).unsqueeze(0) + (1 - alpha_bar_t) * self.u_e
q_y = alpha_bar_t * torch.eye(self.y_classes, device=device).unsqueeze(0) + (1 - alpha_bar_t) * self.u_y
return utils.PlaceHolder(X=q_x, E=q_e, y=q_y)
class MarginalUniformTransition:
def __init__(self, x_marginals, e_marginals, y_classes):
self.X_classes = len(x_marginals)
self.E_classes = len(e_marginals)
self.y_classes = y_classes
self.x_marginals = x_marginals
self.e_marginals = e_marginals
self.u_x = x_marginals.unsqueeze(0).expand(self.X_classes, -1).unsqueeze(0)
self.u_e = e_marginals.unsqueeze(0).expand(self.E_classes, -1).unsqueeze(0)
self.u_y = torch.ones(1, self.y_classes, self.y_classes)
if self.y_classes > 0:
self.u_y = self.u_y / self.y_classes
def get_Qt(self, beta_t, device):
""" Returns one-step transition matrices for X and E, from step t - 1 to step t.
Qt = (1 - beta_t) * I + beta_t / K
beta_t: (bs) noise level between 0 and 1
returns: qx (bs, dx, dx), qe (bs, de, de), qy (bs, dy, dy). """
beta_t = beta_t.unsqueeze(1)
beta_t = beta_t.to(device)
self.u_x = self.u_x.to(device)
self.u_e = self.u_e.to(device)
self.u_y = self.u_y.to(device)
q_x = beta_t * self.u_x + (1 - beta_t) * torch.eye(self.X_classes, device=device).unsqueeze(0)
q_e = beta_t * self.u_e + (1 - beta_t) * torch.eye(self.E_classes, device=device).unsqueeze(0)
q_y = beta_t * self.u_y + (1 - beta_t) * torch.eye(self.y_classes, device=device).unsqueeze(0)
return utils.PlaceHolder(X=q_x, E=q_e, y=q_y)
def get_Qt_bar(self, alpha_bar_t, device):
""" Returns t-step transition matrices for X and E, from step 0 to step t.
Qt = prod(1 - beta_t) * I + (1 - prod(1 - beta_t)) * K
alpha_bar_t: (bs) Product of the (1 - beta_t) for each time step from 0 to t.
returns: qx (bs, dx, dx), qe (bs, de, de), qy (bs, dy, dy).
"""
alpha_bar_t = alpha_bar_t.unsqueeze(1)
alpha_bar_t = alpha_bar_t.to(device)
self.u_x = self.u_x.to(device)
self.u_e = self.u_e.to(device)
self.u_y = self.u_y.to(device)
q_x = alpha_bar_t * torch.eye(self.X_classes, device=device).unsqueeze(0) + (1 - alpha_bar_t) * self.u_x
q_e = alpha_bar_t * torch.eye(self.E_classes, device=device).unsqueeze(0) + (1 - alpha_bar_t) * self.u_e
q_y = alpha_bar_t * torch.eye(self.y_classes, device=device).unsqueeze(0) + (1 - alpha_bar_t) * self.u_y
return utils.PlaceHolder(X=q_x, E=q_e, y=q_y)
class AbsorbingStateTransition:
def __init__(self, abs_state: int, x_classes: int, e_classes: int, y_classes: int):
self.X_classes = x_classes
self.E_classes = e_classes
self.y_classes = y_classes
self.u_x = torch.zeros(1, self.X_classes, self.X_classes)
self.u_x[:, :, abs_state] = 1
self.u_e = torch.zeros(1, self.E_classes, self.E_classes)
self.u_e[:, :, abs_state] = 1
self.u_y = torch.zeros(1, self.y_classes, self.y_classes)
self.u_e[:, :, abs_state] = 1
def get_Qt(self, beta_t):
""" Returns two transition matrix for X and E"""
beta_t = beta_t.unsqueeze(1)
q_x = beta_t * self.u_x + (1 - beta_t) * torch.eye(self.X_classes).unsqueeze(0)
q_e = beta_t * self.u_e + (1 - beta_t) * torch.eye(self.E_classes).unsqueeze(0)
q_y = beta_t * self.u_y + (1 - beta_t) * torch.eye(self.y_classes).unsqueeze(0)
return q_x, q_e, q_y
def get_Qt_bar(self, alpha_bar_t):
""" beta_t: (bs)
Returns transition matrices for X and E"""
alpha_bar_t = alpha_bar_t.unsqueeze(1)
q_x = alpha_bar_t * torch.eye(self.X_classes).unsqueeze(0) + (1 - alpha_bar_t) * self.u_x
q_e = alpha_bar_t * torch.eye(self.E_classes).unsqueeze(0) + (1 - alpha_bar_t) * self.u_e
q_y = alpha_bar_t * torch.eye(self.y_classes).unsqueeze(0) + (1 - alpha_bar_t) * self.u_y
return q_x, q_e, q_y
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