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