mirror of
https://github.com/wassname/Pointnet2_PyTorch.git
synced 2026-06-27 16:00:07 +08:00
erikwijmans
102 lines
3.2 KiB
Python
102 lines
3.2 KiB
Python
import torch
|
|
import torch.utils.data as data
|
|
import numpy as np
|
|
import os, sys, h5py, subprocess, shlex
|
|
|
|
|
|
def _get_data_files(list_filename):
|
|
return [line.rstrip() for line in open(list_filename)]
|
|
|
|
|
|
def _load_data_file(name):
|
|
f = h5py.File(name)
|
|
data = f['data'][:]
|
|
label = f['label'][:]
|
|
return data, label
|
|
|
|
|
|
class Indoor3DSemSeg(data.Dataset):
|
|
def __init__(self,
|
|
num_points,
|
|
root,
|
|
train=True,
|
|
download=True,
|
|
data_precent=1.0):
|
|
super().__init__()
|
|
self.data_precent = data_precent
|
|
root = os.path.abspath(root)
|
|
self.folder = "indoor3d_sem_seg_hdf5_data"
|
|
self.data_dir = os.path.join(root, self.folder)
|
|
self.url = "https://shapenet.cs.stanford.edu/media/indoor3d_sem_seg_hdf5_data.zip"
|
|
|
|
if download and not os.path.exists(self.data_dir):
|
|
zipfile = os.path.join(root, 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, root)))
|
|
|
|
subprocess.check_call(shlex.split("rm {}".format(zipfile)))
|
|
|
|
self.train, self.num_points = train, num_points
|
|
|
|
all_files = _get_data_files(
|
|
os.path.join(self.data_dir, "all_files.txt"))
|
|
room_filelist = _get_data_files(
|
|
os.path.join(self.data_dir, "room_filelist.txt"))
|
|
|
|
data_batchlist, label_batchlist = [], []
|
|
for f in all_files:
|
|
d, l = _load_data_file(os.path.join(root, f))
|
|
data_batchlist.append(d)
|
|
label_batchlist.append(l)
|
|
|
|
data_batches = np.concatenate(data_batchlist, 0)
|
|
labels_batches = np.concatenate(label_batchlist, 0)
|
|
|
|
test_area = 'Area_5'
|
|
train_idxs, test_idxs = [], []
|
|
for i, room_name in enumerate(room_filelist):
|
|
if test_area in room_name:
|
|
test_idxs.append(i)
|
|
else:
|
|
train_idxs.append(i)
|
|
|
|
if self.train:
|
|
self.points = data_batches[train_idxs, ...]
|
|
self.labels = labels_batches[train_idxs, ...]
|
|
else:
|
|
self.points = data_batches[test_idxs, ...]
|
|
self.labels = labels_batches[test_idxs, ...]
|
|
|
|
def __getitem__(self, idx):
|
|
pt_idxs = np.arange(0, self.num_points)
|
|
np.random.shuffle(pt_idxs)
|
|
|
|
current_points = torch.from_numpy(self.points[idx, pt_idxs, :]).type(
|
|
torch.FloatTensor)
|
|
current_labels = torch.from_numpy(self.labels[idx, pt_idxs]).type(
|
|
torch.LongTensor)
|
|
|
|
return current_points, current_labels
|
|
|
|
def __len__(self):
|
|
return int(self.points.shape[0] * self.data_precent)
|
|
|
|
def set_num_points(self, pts):
|
|
self.num_points = pts
|
|
|
|
def randomize(self):
|
|
pass
|
|
|
|
|
|
if __name__ == "__main__":
|
|
dset = Indoor3DSemSeg(16, "./", train=True)
|
|
print(dset[0])
|
|
print(len(dset))
|
|
dloader = torch.utils.data.DataLoader(dset, batch_size=32, shuffle=True)
|
|
for i, data in enumerate(dloader, 0):
|
|
inputs, labels = data
|
|
if i == len(dloader) - 1:
|
|
print(inputs.size())
|