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from typing import Union
import numpy as np
import torch
import torch.nn.functional as F
from salad.models.base_model import BaseModel
from salad.utils import imageutil, nputil, sysutil, thutil, visutil
from salad.utils.spaghetti_util import (clip_eigenvalues,
generate_zc_from_sj_gaus,
get_mesh_from_spaghetti, load_mesher,
load_spaghetti, project_eigenvectors)
class Phase2Model(BaseModel):
def __init__(self, network, variance_schedule, **kwargs):
super().__init__(network, variance_schedule, **kwargs)
def forward(self, x, cond):
return self.get_loss(x, cond)
def step(self, batch, stage: str):
x, cond = batch
loss = self(x, cond)
self.log(f"{stage}/loss", loss, on_step=stage == "train", prog_bar=True)
return loss
def get_loss(self, x0, cond, t=None, noisy_in=False, beta_in=None, e_rand_in=None):
B, G, D = x0.shape
if not noisy_in:
if t is None:
t = self.var_sched.uniform_sample_t(B)
x_noisy, beta, e_rand = self.add_noise(x0, t)
else:
x_noisy = x0
beta = beta_in
e_rand = e_rand_in
e_theta = self.net(x_noisy, beta, cond)
loss = F.mse_loss(e_theta.flatten(), e_rand.flatten(), reduction="mean")
return loss
@torch.no_grad()
def sample(
self,
num_samples_or_gaus: Union[torch.Tensor, np.ndarray, int],
return_traj=False,
classifier_free_guidance=None,
free_guidance_weight=-0.7,
augment_condition_in_test=False,
return_cond=False,
):
if isinstance(num_samples_or_gaus, int):
batch_size = num_samples_or_gaus
ds = self._build_dataset("val")
cond = torch.stack([ds[i][1] for i in range(batch_size)], 0)
elif isinstance(num_samples_or_gaus, np.ndarray) or isinstance(
num_samples_or_gaus, torch.Tensor
):
cond = nputil.np2th(num_samples_or_gaus)
if cond.dim() == 2:
cond = cond[None]
batch_size = len(cond)
else:
raise ValueError(
"'num_samples_or_gaus' should be int, torch.Tensor or np.ndarray."
)
x_T = torch.randn([batch_size, 16, 512]).to(self.device)
cond = cond.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, context=cond)
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:
if return_cond:
return traj, cond
return traj
else:
if return_cond:
return traj[0], cond
return traj[0]
def validation(self):
latent_ds = self._build_dataset("val")
vis_num_shapes = 3
num_variations = 3
sysutil.clean_gpu()
if not hasattr(self, "spaghetti"):
spaghetti = load_spaghetti(
self.device,
self.hparams.spaghetti_tag
if self.hparams.get("spaghetti_tag")
else "chairs_large",
)
self.spaghetti = spaghetti
else:
spaghetti = self.spaghetti
if not hasattr(self, "mesher"):
mesher = load_mesher(self.device)
self.mesher = mesher
else:
mesher = self.mesher
"""======== Sampling ========"""
gt_zs = []
gt_gaus = []
gt_zs, gt_gaus = zip(*[latent_ds[i + 3] for i in range(vis_num_shapes)])
gt_zs, gt_gaus = list(map(lambda x: torch.stack(x), [gt_zs, gt_gaus]))
if self.hparams.get("sj_global_normalization"):
gt_zs = thutil.th2np(gt_zs)
gt_zs = latent_ds.unnormalize_sj_global_static(gt_zs)
gt_zs = nputil.np2th(gt_zs).to(self.device)
gt_gaus_repeated = gt_gaus.repeat_interleave(num_variations, 0)
clean_ldm_zs, clean_gaus = self.sample(gt_gaus_repeated, return_cond=True)
clean_gaus = project_eigenvectors(clip_eigenvalues(clean_gaus))
clean_zcs = generate_zc_from_sj_gaus(spaghetti, clean_ldm_zs, clean_gaus)
gt_zcs = generate_zc_from_sj_gaus(spaghetti, gt_zs, gt_gaus)
sysutil.clean_gpu()
"""=========================="""
""" Spaghetti Decoding """
wandb_logger = self.get_wandb_logger()
resolution = (256, 256)
for i in range(vis_num_shapes):
img_per_shape = []
gaus_img = visutil.render_gaussians(gt_gaus[i], resolution=resolution)
vert, face = get_mesh_from_spaghetti(spaghetti, mesher, gt_zcs[i], res=128)
gt_mesh_img = visutil.render_mesh(vert, face, resolution=resolution)
gt_img = imageutil.merge_images([gaus_img, gt_mesh_img])
gt_img = imageutil.draw_text(gt_img, "GT", font_size=24)
img_per_shape.append(gt_img)
for j in range(num_variations):
try:
gaus_img = visutil.render_gaussians(
clean_gaus[i * num_variations + j], resolution=resolution
)
vert, face = get_mesh_from_spaghetti(
spaghetti, mesher, clean_zcs[i * num_variations + j], res=128
)
mesh_img = visutil.render_mesh(vert, face, resolution=resolution)
pred_img = imageutil.merge_images([gaus_img, mesh_img])
pred_img = imageutil.draw_text(
pred_img, f"{j}-th clean gaus", font_size=24
)
img_per_shape.append(pred_img)
except Exception as e:
print(e)
try:
image = imageutil.merge_images(img_per_shape)
wandb_logger.log_image("visualization", [image])
except Exception as e:
print(e)
""" ================== """
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