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import os
import os.path as osp
import shlex
import shutil
import subprocess
import lmdb
import msgpack_numpy
import numpy as np
import torch
import torch.utils.data as data
import tqdm
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
def pc_normalize(pc):
l = pc.shape[0]
centroid = np.mean(pc, axis=0)
pc = pc - centroid
m = np.max(np.sqrt(np.sum(pc ** 2, axis=1)))
pc = pc / m
return pc
class ModelNet40Cls(data.Dataset):
def __init__(self, num_points, transforms=None, train=True, download=True):
super().__init__()
self.transforms = transforms
self.set_num_points(num_points)
self._cache = os.path.join(BASE_DIR, "modelnet40_normal_resampled_cache")
if not osp.exists(self._cache):
self.folder = "modelnet40_normal_resampled"
self.data_dir = os.path.join(BASE_DIR, self.folder)
self.url = (
"https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip"
)
if download and not os.path.exists(self.data_dir):
zipfile = os.path.join(BASE_DIR, os.path.basename(self.url))
subprocess.check_call(
shlex.split("curl {} -o {}".format(self.url, zipfile))
)
subprocess.check_call(
shlex.split("unzip {} -d {}".format(zipfile, BASE_DIR))
)
subprocess.check_call(shlex.split("rm {}".format(zipfile)))
self.train = train
self.set_num_points(num_points)
self.catfile = os.path.join(self.data_dir, "modelnet40_shape_names.txt")
self.cat = [line.rstrip() for line in open(self.catfile)]
self.classes = dict(zip(self.cat, range(len(self.cat))))
os.makedirs(self._cache)
print("Converted to LMDB for faster dataloading while training")
for split in ["train", "test"]:
if split == "train":
shape_ids = [
line.rstrip()
for line in open(
os.path.join(self.data_dir, "modelnet40_train.txt")
)
]
else:
shape_ids = [
line.rstrip()
for line in open(
os.path.join(self.data_dir, "modelnet40_test.txt")
)
]
shape_names = ["_".join(x.split("_")[0:-1]) for x in shape_ids]
# list of (shape_name, shape_txt_file_path) tuple
self.datapath = [
(
shape_names[i],
os.path.join(self.data_dir, shape_names[i], shape_ids[i])
+ ".txt",
)
for i in range(len(shape_ids))
]
with lmdb.open(
osp.join(self._cache, split), map_size=1 << 36
) as lmdb_env, lmdb_env.begin(write=True) as txn:
for i in tqdm.trange(len(self.datapath)):
fn = self.datapath[i]
point_set = np.loadtxt(fn[1], delimiter=",").astype(np.float32)
cls = self.classes[self.datapath[i][0]]
cls = int(cls)
txn.put(
str(i).encode(),
msgpack_numpy.packb(
dict(pc=point_set, lbl=cls), use_bin_type=True
),
)
shutil.rmtree(self.data_dir)
self._lmdb_file = osp.join(self._cache, "train" if train else "test")
with lmdb.open(self._lmdb_file, map_size=1 << 36) as lmdb_env:
self._len = lmdb_env.stat()["entries"]
self._lmdb_env = None
def __getitem__(self, idx):
if self._lmdb_env is None:
self._lmdb_env = lmdb.open(
self._lmdb_file, map_size=1 << 36, readonly=True, lock=False
)
with self._lmdb_env.begin(buffers=True) as txn:
ele = msgpack_numpy.unpackb(txn.get(str(idx).encode()), raw=False)
point_set = ele["pc"]
pt_idxs = np.arange(0, self.num_points)
np.random.shuffle(pt_idxs)
point_set = point_set[pt_idxs, :]
point_set[:, 0:3] = pc_normalize(point_set[:, 0:3])
if self.transforms is not None:
point_set = self.transforms(point_set)
return point_set, ele["lbl"]
def __len__(self):
return self._len
def set_num_points(self, pts):
self.num_points = min(int(1e4), pts)
if __name__ == "__main__":
from torchvision import transforms
import data_utils as d_utils
transforms = transforms.Compose(
[
d_utils.PointcloudToTensor(),
d_utils.PointcloudRotate(axis=np.array([1, 0, 0])),
d_utils.PointcloudScale(),
d_utils.PointcloudTranslate(),
d_utils.PointcloudJitter(),
]
)
dset = ModelNet40Cls(16, train=True, transforms=transforms)
print(dset[0][0])
print(dset[0][1])
print(len(dset))
dloader = torch.utils.data.DataLoader(dset, batch_size=32, shuffle=True)
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