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shapenet_part_loader.py
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shapenet_part_loader.py
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#from __future__ import print_function
import torch.utils.data as data
import os
import os.path
import torch
import json
import numpy as np
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
dataset_path=os.path.abspath(os.path.join(BASE_DIR, '../dataset/shapenet_part/shapenetcore_partanno_segmentation_benchmark_v0/'))
class PartDataset(data.Dataset):
def __init__(self, root=dataset_path, npoints=2500, classification=False, class_choice=None, split='train', normalize=True):
self.npoints = npoints
self.root = root
self.catfile = os.path.join(self.root, 'synsetoffset2category.txt')
self.cat = {}
self.classification = classification
self.normalize = normalize
with open(self.catfile, 'r') as f:
for line in f:
ls = line.strip().split()
self.cat[ls[0]] = ls[1]
# print(self.cat)
if not class_choice is None:
self.cat = {k: v for k, v in self.cat.items() if k in class_choice}
print(self.cat)
self.meta = {}
with open(os.path.join(self.root, 'train_test_split', 'shuffled_train_file_list.json'), 'r') as f:
train_ids = set([str(d.split('/')[2]) for d in json.load(f)])
with open(os.path.join(self.root, 'train_test_split', 'shuffled_val_file_list.json'), 'r') as f:
val_ids = set([str(d.split('/')[2]) for d in json.load(f)])
with open(os.path.join(self.root, 'train_test_split', 'shuffled_test_file_list.json'), 'r') as f:
test_ids = set([str(d.split('/')[2]) for d in json.load(f)])
for item in self.cat:
# print('category', item)
self.meta[item] = []
dir_point = os.path.join(self.root, self.cat[item], 'points')
dir_seg = os.path.join(self.root, self.cat[item], 'points_label')
# print(dir_point, dir_seg)
fns = sorted(os.listdir(dir_point))
if split == 'trainval':
fns = [fn for fn in fns if ((fn[0:-4] in train_ids) or (fn[0:-4] in val_ids))]
elif split == 'train':
fns = [fn for fn in fns if fn[0:-4] in train_ids]
elif split == 'val':
fns = [fn for fn in fns if fn[0:-4] in val_ids]
elif split == 'test':
fns = [fn for fn in fns if fn[0:-4] in test_ids]
else:
print('Unknown split: %s. Exiting..' % (split))
sys.exit(-1)
for fn in fns:
token = (os.path.splitext(os.path.basename(fn))[0])
self.meta[item].append((os.path.join(dir_point, token + '.pts'), os.path.join(dir_seg, token + '.seg'),self.cat[item], token))
self.datapath = []
for item in self.cat:
for fn in self.meta[item]:
self.datapath.append((item, fn[0], fn[1], fn[2], fn[3]))
self.classes = dict(zip(sorted(self.cat), range(len(self.cat))))
print(self.classes)
self.num_seg_classes = 0
if not self.classification:
for i in range(len(self.datapath)//50):
l = len(np.unique(np.loadtxt(self.datapath[i][2]).astype(np.uint8)))
if l > self.num_seg_classes:
self.num_seg_classes = l
# print(self.num_seg_classes)
self.cache = {} # from index to (point_set, cls, seg) tuple
self.cache_size = 18000
def __getitem__(self, index):
if index in self.cache:
# point_set, seg, cls= self.cache[index]
point_set, seg, cls, foldername, filename = self.cache[index]
else:
fn = self.datapath[index]
cls = self.classes[self.datapath[index][0]]
# cls = np.array([cls]).astype(np.int32)
point_set = np.loadtxt(fn[1]).astype(np.float32)
if self.normalize:
point_set = self.pc_normalize(point_set)
seg = np.loadtxt(fn[2]).astype(np.int64) - 1
foldername = fn[3]
filename = fn[4]
if len(self.cache) < self.cache_size:
self.cache[index] = (point_set, seg, cls, foldername, filename)
#print(point_set.shape, seg.shape)
choice = np.random.choice(len(seg), self.npoints, replace=True)
# resample
point_set = point_set[choice, :]
seg = seg[choice]
# To Pytorch
point_set = torch.from_numpy(point_set)
seg = torch.from_numpy(seg)
cls = torch.from_numpy(np.array([cls]).astype(np.int64))
if self.classification:
return point_set, cls
else:
return point_set, seg , cls
def __len__(self):
return len(self.datapath)
def pc_normalize(self, pc):
""" pc: NxC, return NxC """
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
if __name__ == '__main__':
dset = PartDataset( root='./dataset/shapenetcore_partanno_segmentation_benchmark_v0/',classification=True, class_choice=None, npoints=4096, split='train')
# d = PartDataset( root='./dataset/shapenetcore_partanno_segmentation_benchmark_v0/',classification=False, class_choice=None, npoints=4096, split='test')
print(len(dset))
ps, cls = dset[10000]
print(cls)
# print(ps.size(), ps.type(), cls.size(), cls.type())
# print(ps)
# ps = ps.numpy()
# np.savetxt('ps'+'.txt', ps, fmt = "%f %f %f")