-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathdataset.py
183 lines (136 loc) · 6.64 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
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
import os
import numpy as np
import torch
from torch.utils.data import Dataset
from glob import glob
from tqdm import tqdm
class SDF_dataset(Dataset):
def __init__(self,args):
super().__init__()
data_path=args.data_path
num_frames=args.num_frames
pin_memory=args.pin_memory
self.mask_threshold=args.mask_threshold
self.data_list=[]
self.sdf_data_path_list=[]
self.offset_data_path_list=[]
self.pin_memory=pin_memory
if not num_frames:
num_frames=len(glob(os.path.join(data_path,'*','sdf_grid.npy')))
for i in tqdm(range(num_frames)):
self.sdf_data_path_list.append(os.path.join(data_path,'%04d'%i,'sdf_grid.npy'))
self.offset_data_path_list.append(os.path.join(data_path,'%04d'%i,'offset_grid.npy'))
if self.pin_memory:
sdf_grids=np.load(os.path.join(data_path,'%04d'%i,'sdf_grid.npy'))
#sdf_grids=sdf_grids
mask=(np.abs(sdf_grids)<self.mask_threshold).astype(np.float32)
offset_grids=np.load(os.path.join(data_path,'%04d'%i,'offset_grid.npy'))
sdf_offset=np.concatenate([sdf_grids,offset_grids],axis=-1).astype(np.float32)
self.data_list.append((sdf_offset,mask))
def __len__(self):
return len(self.sdf_data_path_list)
def __getitem__(self, index):
if self.pin_memory:
sdf_offset,mask=self.data_list[index]
else:
sdf_grids=np.load(self.sdf_data_path_list[index])
mask=(np.abs(sdf_grids)<self.mask_threshold).astype(np.float32)
offset_grids=np.load(self.offset_data_path_list[index])
sdf_offset=np.concatenate([sdf_grids,offset_grids],axis=-1).astype(np.float32)
return {'t':torch.tensor(float(index)/len(self.data_list)),
'index': torch.tensor(index).long(),
'sdf_offset':torch.from_numpy(sdf_offset).float(),
'mask':torch.from_numpy(mask).float()}
class SDF_dataset_npz(Dataset):
def __init__(self,args):
super().__init__()
data_path=args.data_path
num_frames=args.num_frames
pin_memory=args.pin_memory
self.mask_threshold=args.mask_threshold
self.data_list=[]
#self.sdf_data_path_list=[]
#self.offset_data_path_list=[]
self.npz_path_list=[]
self.pin_memory=pin_memory
if num_frames<1:
num_frames=len(glob(os.path.join(data_path,'data','*.npz')))
#print(os.path.join(data_path,'data','*.npz'))
for i in tqdm(range(num_frames)):
#self.sdf_data_path_list.append(os.path.join(data_path,'%04d'%i,'sdf_grid.npy'))
#self.offset_data_path_list.append(os.path.join(data_path,'%04d'%i,'offset_grid.npy'))
self.npz_path_list.append(os.path.join(data_path,'data','%04d.npz'%i))
if self.pin_memory:
npz_data=np.load(os.path.join(data_path,'data','%04d.npz'%i))
#sdf_grids=np.load(os.path.join(data_path,'%04d'%i,'sdf_grid.npy'))
#sdf_grids=sdf_grids
sdf_grids=npz_data['sdf']
offset_grids=npz_data['offset']
mask=(np.abs(sdf_grids)<self.mask_threshold).astype(np.float32)
#offset_grids=np.load(os.path.join(data_path,'%04d'%i,'offset_grid.npy'))
sdf_offset=np.concatenate([sdf_grids,offset_grids],axis=-1).astype(np.float32)
self.data_list.append((sdf_offset,mask))
def __len__(self):
return len(self.npz_path_list)
def __getitem__(self, index):
if self.pin_memory:
sdf_offset,mask=self.data_list[index]
else:
npz_data=np.load(self.npz_path_list[index])
#sdf_grids=np.load(os.path.join(data_path,'%04d'%i,'sdf_grid.npy'))
#sdf_grids=sdf_grids
sdf_grids=npz_data['sdf']
offset_grids=npz_data['offset']
#sdf_grids=np.load(self.sdf_data_path_list[index])
mask=(np.abs(sdf_grids)<self.mask_threshold).astype(np.float32)
#offset_grids=np.load(self.offset_data_path_list[index])
sdf_offset=np.concatenate([sdf_grids,offset_grids],axis=-1).astype(np.float32)
return {'t':torch.tensor(float(index)/len(self.data_list)),
'index':torch.tensor(index).long(),
'sdf_offset':torch.from_numpy(sdf_offset).float(),
'mask':torch.from_numpy(mask).float()}
class SDF_dataset6(Dataset):
def __init__(self,args):
super().__init__()
data_path=args.data_path
num_frames=args.num_frames
pin_memory=args.pin_memory
self.mask_threshold=args.mask_threshold
self.data_list=[]
self.sdf_data_path_list=[]
self.offset_data_path_list=[]
self.pin_memory=pin_memory
if not num_frames:
num_frames=len(glob(os.path.join(data_path,'*','sdf_grid.npy')))
for i in tqdm(range(num_frames)):
self.sdf_data_path_list.append(os.path.join(data_path,'%06d'%i,'sdf_grid.npy'))
self.offset_data_path_list.append(os.path.join(data_path,'%06d'%i,'offset_grid.npy'))
if self.pin_memory:
sdf_grids=np.load(os.path.join(data_path,'%06d'%i,'sdf_grid.npy'))
#sdf_grids=sdf_grids
mask=(np.abs(sdf_grids)<self.mask_threshold).astype(np.float32)
offset_grids=np.load(os.path.join(data_path,'%06d'%i,'offset_grid.npy'))
sdf_offset=np.concatenate([sdf_grids,offset_grids],axis=-1).astype(np.float32)
self.data_list.append((sdf_offset,mask))
def __len__(self):
return len(self.sdf_data_path_list)
def __getitem__(self, index):
if self.pin_memory:
sdf_offset,mask=self.data_list[index]
else:
sdf_grids=np.load(self.sdf_data_path_list[index])
mask=(np.abs(sdf_grids)<self.mask_threshold).astype(np.float32)
offset_grids=np.load(self.offset_data_path_list[index])
sdf_offset=np.concatenate([sdf_grids,offset_grids],axis=-1).astype(np.float32)
return {'t':torch.tensor(float(index)/len(self.data_list)),
'sdf_offset':torch.from_numpy(sdf_offset).float(),
'mask':torch.from_numpy(mask).float()}
def get_dataset(name,args):
if name == 'SDF_dataset':
return SDF_dataset(args)
elif name == 'SDF_dataset6':
return SDF_dataset6(args)
elif name=='SDF_dataset_npz':
return SDF_dataset_npz(args)
else:
assert False