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datasets.py
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datasets.py
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import torch.utils.data as data
from PIL import Image
import os
import os.path
import errno
import torch
import json
import codecs
import numpy as np
import sys
import torchvision.transforms as transforms
import argparse
import json
import sys
num_seg_classes = {'Earphone': 3, 'Motorbike': 6, 'Rocket': 3, 'Car': 4, 'Laptop': 2, 'Cap': 2, 'Skateboard': 3, 'Mug': 2, 'Guitar': 3, 'Bag': 2, 'Lamp': 3, 'Table': 3, 'Airplane': 4, 'Pistol': 3, 'Chair': 4, 'Knife': 2}
class PartDataset(data.Dataset):
def __init__(self, root, npoints = 2500, classification = False, class_choice = None, train = True,reference = [[0,0,0],[1,1,1],[1,1,-1],[-1,1,1],[-1,-1,1],[1,-1,1],[1,-1,-1],[-1,-1,1],[-1,-1,-1],[1,1,0],[-1,1,0],[-1,-1,0],[1,-1,0]]):
self.npoints = npoints
self.root = root
self.catfile = os.path.join(self.root, 'synsetoffset2category.txt')
self.cat = {}
self.ref = reference
self.classification = classification
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}
self.meta = {}
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 train:
fns = fns[:int(len(fns) * 0.9)]
else:
fns = fns[int(len(fns) * 0.9):]
#print(os.path.basename(fns))
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.datapath = []
self.num_seg_classes = 0
for item in self.cat:
for fn in self.meta[item]:
self.datapath.append((item, fn[0], fn[1]))
self.num_seg_classes+= num_seg_classes[item]
self.classes = dict(zip(sorted(self.cat), range(len(self.cat))))
# if not self.classification:
# for i in range(len(self.datapath)//50):
# l = len(np.unique(np.loadtxt(self.datapath[i][-1]).astype(np.uint8)))
# # if l > self.num_seg_classes:
# self.num_seg_classes += l
#print(self.num_seg_classes)
def __getitem__(self, index):
fn = self.datapath[index]
cls = self.classes[self.datapath[index][0]]
point_set = np.loadtxt(fn[1]).astype(np.float32)
seg = np.loadtxt(fn[2]).astype(np.int64)
choice = np.random.choice(len(seg), self.npoints, replace=True)
#resample
point_set = point_set[choice, :]
seg = seg[choice]
ref = np.array(self.ref)
point_set = self.expand_ref(point_set,ref)
#add the reference
# print point_set.shape
point_set = torch.from_numpy(point_set)
seg = torch.from_numpy(seg)
cls = torch.from_numpy(np.array([cls]).astype(np.int64))
point_set=point_set.float()
if self.classification:
return point_set, cls
else:
return point_set, seg
def __len__(self):
return len(self.datapath)
"""
params:
pointset_dim:N*3
ref_dim:K*3
return:
expanded_pointset:N*K*6
"""
def expand_ref(self,pointset,ref):
K,_ = ref.shape
N,_ = pointset.shape
pointset = np.expand_dims(pointset,1)
pointset = np.repeat(pointset,K,1)
ref = np.expand_dims(ref,0)
ref = np.repeat(ref,N,0)
expanded_pointset = np.concatenate((pointset,ref),2)
return expanded_pointset
if __name__ == '__main__':
print('test')
d = PartDataset(root = 'shapenetcore_partanno_segmentation_benchmark_v0', class_choice = ['Chair'])
print(len(d))
ps, seg = d[0]
print(ps.size(), ps.type(), seg.size(),seg.type())
d = PartDataset(root = 'shapenetcore_partanno_segmentation_benchmark_v0', classification = True)
print(len(d))
ps, cls = d[0]
print(ps.size(), ps.type(), cls.size(),cls.type())