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import os |
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import json |
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from typing import Union |
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import numpy as np |
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import pandas as pd |
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import torch |
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from torch.utils.data import Dataset |
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import utils3d |
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from .components import StandardDatasetBase |
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from ..representations.octree import DfsOctree as Octree |
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from ..renderers import OctreeRenderer |
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class SparseStructure(StandardDatasetBase): |
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""" |
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Sparse structure dataset |
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Args: |
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roots (str): path to the dataset |
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resolution (int): resolution of the voxel grid |
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min_aesthetic_score (float): minimum aesthetic score of the instances to be included in the dataset |
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""" |
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def __init__(self, |
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roots, |
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resolution: int = 64, |
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min_aesthetic_score: float = 5.0, |
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): |
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self.resolution = resolution |
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self.min_aesthetic_score = min_aesthetic_score |
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self.value_range = (0, 1) |
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super().__init__(roots) |
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def filter_metadata(self, metadata): |
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stats = {} |
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metadata = metadata[metadata[f'voxelized']] |
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stats['Voxelized'] = 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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position = utils3d.io.read_ply(os.path.join(root, 'voxels', f'{instance}.ply'))[0] |
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coords = ((torch.tensor(position) + 0.5) * self.resolution).int().contiguous() |
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ss = torch.zeros(1, self.resolution, self.resolution, self.resolution, dtype=torch.long) |
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ss[:, coords[:, 0], coords[:, 1], coords[:, 2]] = 1 |
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return {'ss': ss} |
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@torch.no_grad() |
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def visualize_sample(self, ss: Union[torch.Tensor, dict]): |
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ss = ss if isinstance(ss, torch.Tensor) else ss['ss'] |
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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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ss = ss.cuda() |
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for i in range(ss.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(ss[i, 0], as_tuple=False) |
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representation.position = coords.float() / self.resolution |
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representation.depth = torch.full((representation.position.shape[0], 1), int(np.log2(self.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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