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
|
import glob
import os
import os.path as osp
from typing import Callable, List, Optional
import torch
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_zip,
)
from torch_geometric.io import fs
class PascalPF(InMemoryDataset):
r"""The Pascal-PF dataset from the `"Proposal Flow"
<https://arxiv.org/abs/1511.05065>`_ paper, containing 4 to 16 keypoints
per example over 20 categories.
Args:
root (str): Root directory where the dataset should be saved.
category (str): The category of the images (one of
:obj:`"Aeroplane"`, :obj:`"Bicycle"`, :obj:`"Bird"`,
:obj:`"Boat"`, :obj:`"Bottle"`, :obj:`"Bus"`, :obj:`"Car"`,
:obj:`"Cat"`, :obj:`"Chair"`, :obj:`"Diningtable"`, :obj:`"Dog"`,
:obj:`"Horse"`, :obj:`"Motorbike"`, :obj:`"Person"`,
:obj:`"Pottedplant"`, :obj:`"Sheep"`, :obj:`"Sofa"`,
:obj:`"Train"`, :obj:`"TVMonitor"`)
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` object and returns a transformed
version. The data object will be transformed before every access.
(default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
pre_filter (callable, optional): A function that takes in an
:obj:`torch_geometric.data.Data` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
"""
url = ('https://www.di.ens.fr/willow/research/proposalflow/dataset/'
'PF-dataset-PASCAL.zip')
categories = [
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
]
def __init__(
self,
root: str,
category: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.category = category.lower()
assert self.category in self.categories
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
self.load(self.processed_paths[0])
self.pairs = fs.torch_load(self.processed_paths[1])
@property
def raw_file_names(self) -> List[str]:
return ['Annotations', 'parsePascalVOC.mat']
@property
def processed_file_names(self) -> List[str]:
return [f'{self.category}.pt', f'{self.category}_pairs.pt']
def download(self) -> None:
path = download_url(self.url, self.root)
extract_zip(path, self.root)
fs.rm(self.raw_dir)
os.rename(osp.join(self.root, 'PF-dataset-PASCAL'), self.raw_dir)
def process(self) -> None:
from scipy.io import loadmat
path = osp.join(self.raw_dir, 'Annotations', self.category, '*.mat')
filenames = glob.glob(path)
names = []
data_list = []
for filename in filenames:
name = osp.basename(filename).split('.')[0]
pos = torch.from_numpy(loadmat(filename)['kps']).to(torch.float)
mask = ~torch.isnan(pos[:, 0])
pos = pos[mask]
# Normalize points to unit sphere.
pos = pos - pos.mean(dim=0, keepdim=True)
pos = pos / pos.norm(dim=1).max()
y = mask.nonzero(as_tuple=False).flatten()
data = Data(pos=pos, y=y, name=name)
if self.pre_filter is not None and not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
names.append(name)
data_list.append(data)
pairs = loadmat(osp.join(self.raw_dir, 'parsePascalVOC.mat'))
pairs = pairs['PascalVOC']['pair'][0, 0][
0, self.categories.index(self.category)]
pairs = [(names.index(x[0][0]), names.index(x[1][0])) for x in pairs]
self.save(data_list, self.processed_paths[0])
torch.save(pairs, self.processed_paths[1])
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({len(self)}, '
f'category={self.category})')
|
