This repository was archived by the owner on Feb 9, 2025. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 95
/
Copy pathdataset.py
145 lines (121 loc) · 5.9 KB
/
dataset.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
import os
import random
from copy import copy
import torch
from torch.utils.data import Dataset
import numpy as np
import h5py
from tqdm.auto import tqdm
synsetid_to_cate = {
'02691156': 'airplane', '02773838': 'bag', '02801938': 'basket',
'02808440': 'bathtub', '02818832': 'bed', '02828884': 'bench',
'02876657': 'bottle', '02880940': 'bowl', '02924116': 'bus',
'02933112': 'cabinet', '02747177': 'can', '02942699': 'camera',
'02954340': 'cap', '02958343': 'car', '03001627': 'chair',
'03046257': 'clock', '03207941': 'dishwasher', '03211117': 'monitor',
'04379243': 'table', '04401088': 'telephone', '02946921': 'tin_can',
'04460130': 'tower', '04468005': 'train', '03085013': 'keyboard',
'03261776': 'earphone', '03325088': 'faucet', '03337140': 'file',
'03467517': 'guitar', '03513137': 'helmet', '03593526': 'jar',
'03624134': 'knife', '03636649': 'lamp', '03642806': 'laptop',
'03691459': 'speaker', '03710193': 'mailbox', '03759954': 'microphone',
'03761084': 'microwave', '03790512': 'motorcycle', '03797390': 'mug',
'03928116': 'piano', '03938244': 'pillow', '03948459': 'pistol',
'03991062': 'pot', '04004475': 'printer', '04074963': 'remote_control',
'04090263': 'rifle', '04099429': 'rocket', '04225987': 'skateboard',
'04256520': 'sofa', '04330267': 'stove', '04530566': 'vessel',
'04554684': 'washer', '02992529': 'cellphone',
'02843684': 'birdhouse', '02871439': 'bookshelf',
# '02858304': 'boat', no boat in our dataset, merged into vessels
# '02834778': 'bicycle', not in our taxonomy
}
cate_to_synsetid = {v: k for k, v in synsetid_to_cate.items()}
class ShapeNetCore(Dataset):
GRAVITATIONAL_AXIS = 1
def __init__(self, path, cates, split, scale_mode, transform=None):
super().__init__()
assert isinstance(cates, list), '`cates` must be a list of cate names.'
assert split in ('train', 'val', 'test')
assert scale_mode is None or scale_mode in ('global_unit', 'shape_unit', 'shape_bbox', 'shape_half', 'shape_34')
self.path = path
if 'all' in cates:
cates = cate_to_synsetid.keys()
self.cate_synsetids = [cate_to_synsetid[s] for s in cates]
self.cate_synsetids.sort()
self.split = split
self.scale_mode = scale_mode
self.transform = transform
self.pointclouds = []
self.stats = None
self.get_statistics()
self.load()
def get_statistics(self):
basename = os.path.basename(self.path)
dsetname = basename[:basename.rfind('.')]
stats_dir = os.path.join(os.path.dirname(self.path), dsetname + '_stats')
os.makedirs(stats_dir, exist_ok=True)
if len(self.cate_synsetids) == len(cate_to_synsetid):
stats_save_path = os.path.join(stats_dir, 'stats_all.pt')
else:
stats_save_path = os.path.join(stats_dir, 'stats_' + '_'.join(self.cate_synsetids) + '.pt')
if os.path.exists(stats_save_path):
self.stats = torch.load(stats_save_path)
return self.stats
with h5py.File(self.path, 'r') as f:
pointclouds = []
for synsetid in self.cate_synsetids:
for split in ('train', 'val', 'test'):
pointclouds.append(torch.from_numpy(f[synsetid][split][...]))
all_points = torch.cat(pointclouds, dim=0) # (B, N, 3)
B, N, _ = all_points.size()
mean = all_points.view(B*N, -1).mean(dim=0) # (1, 3)
std = all_points.view(-1).std(dim=0) # (1, )
self.stats = {'mean': mean, 'std': std}
torch.save(self.stats, stats_save_path)
return self.stats
def load(self):
def _enumerate_pointclouds(f):
for synsetid in self.cate_synsetids:
cate_name = synsetid_to_cate[synsetid]
for j, pc in enumerate(f[synsetid][self.split]):
yield torch.from_numpy(pc), j, cate_name
with h5py.File(self.path, mode='r') as f:
for pc, pc_id, cate_name in _enumerate_pointclouds(f):
if self.scale_mode == 'global_unit':
shift = pc.mean(dim=0).reshape(1, 3)
scale = self.stats['std'].reshape(1, 1)
elif self.scale_mode == 'shape_unit':
shift = pc.mean(dim=0).reshape(1, 3)
scale = pc.flatten().std().reshape(1, 1)
elif self.scale_mode == 'shape_half':
shift = pc.mean(dim=0).reshape(1, 3)
scale = pc.flatten().std().reshape(1, 1) / (0.5)
elif self.scale_mode == 'shape_34':
shift = pc.mean(dim=0).reshape(1, 3)
scale = pc.flatten().std().reshape(1, 1) / (0.75)
elif self.scale_mode == 'shape_bbox':
pc_max, _ = pc.max(dim=0, keepdim=True) # (1, 3)
pc_min, _ = pc.min(dim=0, keepdim=True) # (1, 3)
shift = ((pc_min + pc_max) / 2).view(1, 3)
scale = (pc_max - pc_min).max().reshape(1, 1) / 2
else:
shift = torch.zeros([1, 3])
scale = torch.ones([1, 1])
pc = (pc - shift) / scale
self.pointclouds.append({
'pointcloud': pc,
'cate': cate_name,
'id': pc_id,
'shift': shift,
'scale': scale
})
# Deterministically shuffle the dataset
self.pointclouds.sort(key=lambda data: data['id'], reverse=False)
random.Random(2020).shuffle(self.pointclouds)
def __len__(self):
return len(self.pointclouds)
def __getitem__(self, idx):
data = {k:v.clone() if isinstance(v, torch.Tensor) else copy(v) for k, v in self.pointclouds[idx].items()}
if self.transform is not None:
data = self.transform(data)
return data