-
Notifications
You must be signed in to change notification settings - Fork 344
/
sparse_tensor_basic.py
164 lines (133 loc) · 5.32 KB
/
sparse_tensor_basic.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
# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
# Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
# of the code.
import torch
import MinkowskiEngine as ME
data_batch_0 = [
[0, 0, 2.1, 0, 0], #
[0, 1, 1.4, 3, 0], #
[0, 0, 4.0, 0, 0]
]
data_batch_1 = [
[1, 0, 0], #
[0, 2, 0], #
[0, 0, 3]
]
def to_sparse_coo(data):
# An intuitive way to extract coordinates and features
coords, feats = [], []
for i, row in enumerate(data):
for j, val in enumerate(row):
if val != 0:
coords.append([i, j])
feats.append([val])
return torch.IntTensor(coords), torch.FloatTensor(feats)
def sparse_tensor_initialization():
coords, feats = to_sparse_coo(data_batch_0)
# collate sparse tensor data to augment batch indices
# Note that it is wrapped inside a list!!
coords, feats = ME.utils.sparse_collate(coords=[coords], feats=[feats])
sparse_tensor = ME.SparseTensor(coords=coords, feats=feats)
def sparse_tensor_arithmetics():
coords0, feats0 = to_sparse_coo(data_batch_0)
coords0, feats0 = ME.utils.sparse_collate(coords=[coords0], feats=[feats0])
coords1, feats1 = to_sparse_coo(data_batch_1)
coords1, feats1 = ME.utils.sparse_collate(coords=[coords1], feats=[feats1])
# sparse tensors
A = ME.SparseTensor(coords=coords0, feats=feats0)
B = ME.SparseTensor(coords=coords1, feats=feats1)
# The following fails
try:
C = A + B
except AssertionError:
pass
B = ME.SparseTensor(
coords=coords1,
feats=feats1,
coords_manager=A.coords_man, # must share the same coordinate manager
force_creation=True # must force creation since tensor stride [1] exists
)
C = A + B
C = A - B
C = A * B
C = A / B
# in place operations
# Note that it requires the same coords_key (no need to feed coords)
D = ME.SparseTensor(
# coords=coords, not required
feats=feats0,
coords_manager=A.coords_man, # must share the same coordinate manager
coords_key=A.coords_key # For inplace, must share the same coords key
)
A += D
A -= D
A *= D
A /= D
# If you have two or more sparse tensors with the same coords_key, you can concatenate features
E = ME.cat(A, D)
def operation_mode():
# Set to share the coords_man by default
ME.set_sparse_tensor_operation_mode(
ME.SparseTensorOperationMode.SHARE_COORDS_MANAGER)
print(ME.sparse_tensor_operation_mode())
coords0, feats0 = to_sparse_coo(data_batch_0)
coords0, feats0 = ME.utils.sparse_collate(coords=[coords0], feats=[feats0])
coords1, feats1 = to_sparse_coo(data_batch_1)
coords1, feats1 = ME.utils.sparse_collate(coords=[coords1], feats=[feats1])
for _ in range(2):
# sparse tensors
A = ME.SparseTensor(coords=coords0, feats=feats0)
B = ME.SparseTensor(
coords=coords1,
feats=feats1,
# coords_manager=A.coords_man, No need to feed the coords_man
force_creation=True)
C = A + B
# When done using it for forward and backward, you must cleanup the coords man
ME.clear_global_coords_man()
def decomposition():
coords0, feats0 = to_sparse_coo(data_batch_0)
coords1, feats1 = to_sparse_coo(data_batch_1)
coords, feats = ME.utils.sparse_collate(
coords=[coords0, coords1], feats=[feats0, feats1])
# sparse tensors
A = ME.SparseTensor(coords=coords, feats=feats)
conv = ME.MinkowskiConvolution(
in_channels=1, out_channels=2, kernel_size=3, stride=2, dimension=2)
B = conv(A)
# Extract features and coordinates per batch index
list_of_coords = B.decomposed_coordinates
list_of_feats = B.decomposed_features
list_of_coords, list_of_feats = B.decomposed_coordinates_and_features
# To specify a batch index
batch_index = 1
coords = B.coordinates_at(batch_index)
feats = B.features_at(batch_index)
# Empty list if given an invalid batch index
batch_index = 3
print(B.coordinates_at(batch_index))
if __name__ == '__main__':
sparse_tensor_initialization()
sparse_tensor_arithmetics()
operation_mode()
decomposition()