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import torch
import numpy as np
from salad.models.base_model import BaseModel
from salad.utils import nputil, thutil
from salad.utils.spaghetti_util import clip_eigenvalues, project_eigenvectors
class Phase1Model(BaseModel):
def __init__(self, network, variance_schedule, **kwargs):
super().__init__(network, variance_schedule, **kwargs)
@torch.no_grad()
def sample(
self,
batch_size=0,
return_traj=False,
):
x_T = torch.randn([batch_size, 16, 16]).to(self.device)
traj = {self.var_sched.num_steps: x_T}
for t in range(self.var_sched.num_steps, 0, -1):
z = torch.randn_like(x_T) if t > 1 else torch.zeros_like(x_T)
alpha = self.var_sched.alphas[t]
alpha_bar = self.var_sched.alpha_bars[t]
sigma = self.var_sched.get_sigmas(t, flexibility=0)
c0 = 1.0 / torch.sqrt(alpha)
c1 = (1 - alpha) / torch.sqrt(1 - alpha_bar)
x_t = traj[t]
beta = self.var_sched.betas[[t] * batch_size]
e_theta = self.net(x_t, beta=beta)
# print(e_theta.norm(-1).mean())
x_next = c0 * (x_t - c1 * e_theta) + sigma * z
traj[t - 1] = x_next.detach()
traj[t] = traj[t].cpu()
if not return_traj:
del traj[t]
if return_traj:
return traj
else:
return traj[0]
def sampling_gaussians(self, num_shapes):
"""
Return:
ldm_gaus: np.ndarray
gt_gaus: np.ndarray
"""
ldm_gaus = self.sample(num_shapes)
if self.hparams.get("global_normalization"):
if not hasattr(self, "data_val"):
self._build_dataset("val")
if self.hparams.get("global_normalization") == "partial":
ldm_gaus = self.data_val.unnormalize_global_static(ldm_gaus, slice(12,None))
elif self.hparams.get("global_normalization") == "all":
ldm_gaus = self.data_val.unnormalize_global_static(ldm_gaus, slice(None))
ldm_gaus = clip_eigenvalues(ldm_gaus)
ldm_gaus = project_eigenvectors(ldm_gaus)
return ldm_gaus
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