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import os
import shutil
from pathlib import Path
from torch.utils.data import ConcatDataset
from compressai.datasets.cache import CacheDataset
from compressai.datasets.ndarray import NdArrayDataset
from compressai.datasets.stack import StackDataset
from compressai.datasets.utils import download_url, hash_file
from compressai.registry import register_dataset
[docs]
@register_dataset("S3disDataset")
class S3disDataset(CacheDataset):
"""S3DIS dataset.
The Stanford 3D Indoor Scene Dataset (S3DIS) dataset, introduced by
[Armeni2012]_, contains 3D point clouds of 6 large-scale indoor areas.
There are multiple rooms (e.g. office, lounge, hallway, etc) per area.
See the [ProjectPage_S3DIS]_ for a visualization.
The ``semantic_index`` is a number between 0 and 12 (inclusive),
which can be used as the semantic label for each point.
See also: [PapersWithCode_S3DIS]_.
References:
.. [Armeni2012] `"3D Semantic Parsing of Large-Scale Indoor Spaces,"
<https://openaccess.thecvf.com/content_cvpr_2016/html/Armeni_3D_Semantic_Parsing_CVPR_2016_paper.html>`_,
by Iro Armeni, Ozan Sener, Amir R. Zamir, Helen Jiang,
Ioannis Brilakis, Martin Fischer, and Silvio Savarese,
CVPR 2012.
.. [ProjectPage_S3DIS] `Project page
<http://buildingparser.stanford.edu/dataset.html>`_
.. [PapersWithCode_S3DIS] `PapersWithCode: S3DIS
<https://paperswithcode.com/dataset/s3dis>`_
"""
URLS = [
"https://shapenet.cs.stanford.edu/media/indoor3d_sem_seg_hdf5_data.zip",
]
HASHES = [
"587bb63b296d542c24910c384c41028f2caa1d749042ae891d0d64968c773185", # indoor3d_sem_seg_hdf5_data.zip
]
# Suggested splits:
AREAS = {
"train": (1, 2, 3, 4, 6),
"valid": (5,),
"test": (5,),
}
NUM_SAMPLES_PER_AREA = [0, 3687, 4440, 1650, 3662, 6852, 3294]
LABELS = [
"ceiling",
"floor",
"wall",
"beam",
"column",
"window",
"door",
"table",
"chair",
"sofa",
"bookcase",
"board",
"clutter",
]
ROOMS = [
"auditorium",
"conferenceRoom",
"copyRoom",
"hallway",
"lobby",
"lounge",
"office",
"openspace",
"pantry",
"storage",
"WC",
]
def __init__(
self,
root=None,
cache_root=None,
split="train",
split_name=None,
areas=AREAS["train"],
pre_transform=None,
transform=None,
download=True,
):
if cache_root is None:
assert root is not None
cache_root = f"{str(root).rstrip('/')}_cache"
self.root = Path(root) if root else None
self.cache_root = Path(cache_root)
self.split = split
self.split_name = split if split_name is None else split_name
self.areas = areas
if download and self.root:
self.download()
self._root_dataset = self._get_root_dataset()
super().__init__(
cache_root=self.cache_root / self.split_name,
pre_transform=pre_transform,
transform=transform,
)
self._ensure_cache()
def download(self, force=False):
if not force and self.root.exists():
return
tmpdir = self.root.parent / "tmp"
os.makedirs(tmpdir, exist_ok=True)
for expected_hash, url in zip(self.HASHES, self.URLS):
filepath = download_url(
url, tmpdir, check_certificate=False, overwrite=force
)
shutil.unpack_archive(filepath, tmpdir)
assert expected_hash == hash_file(filepath, method="sha256")
shutil.move(tmpdir / "indoor3d_sem_seg_hdf5_data", self.root)
def _get_root_dataset(self):
import h5py
h5_files = [h5py.File(path, "r") for path in sorted(self.root.glob("**/*.h5"))]
keys = ["data", "label"]
return ConcatDataset(
StackDataset(**{k: NdArrayDataset(h5_file[k], single=True) for k in keys})
for h5_file in h5_files
)
def _get_items(self):
with open(self.root / "room_filelist.txt") as f:
lines = f.read().splitlines()
return [
(i, line)
for i, line in enumerate(lines)
if int(line.split("_")[1]) in self.areas
]
def _load_item(self, item):
index, name = item
_, area_index_str, room_str, *_ = name.split("_")
data = self._root_dataset[index]
return {
"file_index": index,
"area_index": int(area_index_str),
"room_index": self.ROOMS.index(room_str),
"semantic_index": data["label"],
"pos": data["data"][:, 0:3], # xyz
"color": data["data"][:, 3:6], # rgb
"pos_normalized": data["data"][:, 6:9], # Normalized xyz
}
