188 lines
7.0 KiB
Python
Executable File
188 lines
7.0 KiB
Python
Executable File
import os
|
|
import json
|
|
from typing import *
|
|
import numpy as np
|
|
import torch
|
|
import utils3d
|
|
from ..representations.octree import DfsOctree as Octree
|
|
from ..renderers import OctreeRenderer
|
|
from .components import StandardDatasetBase, TextConditionedMixin, ImageConditionedMixin
|
|
from .. import models
|
|
|
|
|
|
class SparseStructureLatentVisMixin:
|
|
def __init__(
|
|
self,
|
|
*args,
|
|
pretrained_ss_dec: str = 'microsoft/TRELLIS-image-large/ckpts/ss_dec_conv3d_16l8_fp16',
|
|
ss_dec_path: Optional[str] = None,
|
|
ss_dec_ckpt: Optional[str] = None,
|
|
**kwargs
|
|
):
|
|
super().__init__(*args, **kwargs)
|
|
self.ss_dec = None
|
|
self.pretrained_ss_dec = pretrained_ss_dec
|
|
self.ss_dec_path = ss_dec_path
|
|
self.ss_dec_ckpt = ss_dec_ckpt
|
|
|
|
def _loading_ss_dec(self):
|
|
if self.ss_dec is not None:
|
|
return
|
|
if self.ss_dec_path is not None:
|
|
cfg = json.load(open(os.path.join(self.ss_dec_path, 'config.json'), 'r'))
|
|
decoder = getattr(models, cfg['models']['decoder']['name'])(**cfg['models']['decoder']['args'])
|
|
ckpt_path = os.path.join(self.ss_dec_path, 'ckpts', f'decoder_{self.ss_dec_ckpt}.pt')
|
|
decoder.load_state_dict(torch.load(ckpt_path, map_location='cpu', weights_only=True))
|
|
else:
|
|
decoder = models.from_pretrained(self.pretrained_ss_dec)
|
|
self.ss_dec = decoder.cuda().eval()
|
|
|
|
def _delete_ss_dec(self):
|
|
del self.ss_dec
|
|
self.ss_dec = None
|
|
|
|
@torch.no_grad()
|
|
def decode_latent(self, z, batch_size=4):
|
|
self._loading_ss_dec()
|
|
ss = []
|
|
if self.normalization is not None:
|
|
z = z * self.std.to(z.device) + self.mean.to(z.device)
|
|
for i in range(0, z.shape[0], batch_size):
|
|
ss.append(self.ss_dec(z[i:i+batch_size]))
|
|
ss = torch.cat(ss, dim=0)
|
|
self._delete_ss_dec()
|
|
return ss
|
|
|
|
@torch.no_grad()
|
|
def visualize_sample(self, x_0: Union[torch.Tensor, dict]):
|
|
x_0 = x_0 if isinstance(x_0, torch.Tensor) else x_0['x_0']
|
|
x_0 = self.decode_latent(x_0.cuda())
|
|
|
|
renderer = OctreeRenderer()
|
|
renderer.rendering_options.resolution = 512
|
|
renderer.rendering_options.near = 0.8
|
|
renderer.rendering_options.far = 1.6
|
|
renderer.rendering_options.bg_color = (0, 0, 0)
|
|
renderer.rendering_options.ssaa = 4
|
|
renderer.pipe.primitive = 'voxel'
|
|
|
|
# Build camera
|
|
yaws = [0, np.pi / 2, np.pi, 3 * np.pi / 2]
|
|
yaws_offset = np.random.uniform(-np.pi / 4, np.pi / 4)
|
|
yaws = [y + yaws_offset for y in yaws]
|
|
pitch = [np.random.uniform(-np.pi / 4, np.pi / 4) for _ in range(4)]
|
|
|
|
exts = []
|
|
ints = []
|
|
for yaw, pitch in zip(yaws, pitch):
|
|
orig = torch.tensor([
|
|
np.sin(yaw) * np.cos(pitch),
|
|
np.cos(yaw) * np.cos(pitch),
|
|
np.sin(pitch),
|
|
]).float().cuda() * 2
|
|
fov = torch.deg2rad(torch.tensor(30)).cuda()
|
|
extrinsics = utils3d.torch.extrinsics_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda())
|
|
intrinsics = utils3d.torch.intrinsics_from_fov_xy(fov, fov)
|
|
exts.append(extrinsics)
|
|
ints.append(intrinsics)
|
|
|
|
images = []
|
|
|
|
# Build each representation
|
|
x_0 = x_0.cuda()
|
|
for i in range(x_0.shape[0]):
|
|
representation = Octree(
|
|
depth=10,
|
|
aabb=[-0.5, -0.5, -0.5, 1, 1, 1],
|
|
device='cuda',
|
|
primitive='voxel',
|
|
sh_degree=0,
|
|
primitive_config={'solid': True},
|
|
)
|
|
coords = torch.nonzero(x_0[i, 0] > 0, as_tuple=False)
|
|
resolution = x_0.shape[-1]
|
|
representation.position = coords.float() / resolution
|
|
representation.depth = torch.full((representation.position.shape[0], 1), int(np.log2(resolution)), dtype=torch.uint8, device='cuda')
|
|
|
|
image = torch.zeros(3, 1024, 1024).cuda()
|
|
tile = [2, 2]
|
|
for j, (ext, intr) in enumerate(zip(exts, ints)):
|
|
res = renderer.render(representation, ext, intr, colors_overwrite=representation.position)
|
|
image[:, 512 * (j // tile[1]):512 * (j // tile[1] + 1), 512 * (j % tile[1]):512 * (j % tile[1] + 1)] = res['color']
|
|
images.append(image)
|
|
|
|
return torch.stack(images)
|
|
|
|
|
|
class SparseStructureLatent(SparseStructureLatentVisMixin, StandardDatasetBase):
|
|
"""
|
|
Sparse structure latent dataset
|
|
|
|
Args:
|
|
roots (str): path to the dataset
|
|
latent_model (str): name of the latent model
|
|
min_aesthetic_score (float): minimum aesthetic score
|
|
normalization (dict): normalization stats
|
|
pretrained_ss_dec (str): name of the pretrained sparse structure decoder
|
|
ss_dec_path (str): path to the sparse structure decoder, if given, will override the pretrained_ss_dec
|
|
ss_dec_ckpt (str): name of the sparse structure decoder checkpoint
|
|
"""
|
|
def __init__(self,
|
|
roots: str,
|
|
*,
|
|
latent_model: str,
|
|
min_aesthetic_score: float = 5.0,
|
|
normalization: Optional[dict] = None,
|
|
pretrained_ss_dec: str = 'microsoft/TRELLIS-image-large/ckpts/ss_dec_conv3d_16l8_fp16',
|
|
ss_dec_path: Optional[str] = None,
|
|
ss_dec_ckpt: Optional[str] = None,
|
|
):
|
|
self.latent_model = latent_model
|
|
self.min_aesthetic_score = min_aesthetic_score
|
|
self.normalization = normalization
|
|
self.value_range = (0, 1)
|
|
|
|
super().__init__(
|
|
roots,
|
|
pretrained_ss_dec=pretrained_ss_dec,
|
|
ss_dec_path=ss_dec_path,
|
|
ss_dec_ckpt=ss_dec_ckpt,
|
|
)
|
|
|
|
if self.normalization is not None:
|
|
self.mean = torch.tensor(self.normalization['mean']).reshape(-1, 1, 1, 1)
|
|
self.std = torch.tensor(self.normalization['std']).reshape(-1, 1, 1, 1)
|
|
|
|
def filter_metadata(self, metadata):
|
|
stats = {}
|
|
metadata = metadata[metadata[f'ss_latent_{self.latent_model}']]
|
|
stats['With sparse structure latents'] = len(metadata)
|
|
metadata = metadata[metadata['aesthetic_score'] >= self.min_aesthetic_score]
|
|
stats[f'Aesthetic score >= {self.min_aesthetic_score}'] = len(metadata)
|
|
return metadata, stats
|
|
|
|
def get_instance(self, root, instance):
|
|
latent = np.load(os.path.join(root, 'ss_latents', self.latent_model, f'{instance}.npz'))
|
|
z = torch.tensor(latent['mean']).float()
|
|
if self.normalization is not None:
|
|
z = (z - self.mean) / self.std
|
|
|
|
pack = {
|
|
'x_0': z,
|
|
}
|
|
return pack
|
|
|
|
|
|
class TextConditionedSparseStructureLatent(TextConditionedMixin, SparseStructureLatent):
|
|
"""
|
|
Text-conditioned sparse structure dataset
|
|
"""
|
|
pass
|
|
|
|
|
|
class ImageConditionedSparseStructureLatent(ImageConditionedMixin, SparseStructureLatent):
|
|
"""
|
|
Image-conditioned sparse structure dataset
|
|
"""
|
|
pass
|
|
|