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# 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(coordinates=coords, features=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(coordinates=coords0, features=feats0)
B = ME.SparseTensor(coordinates=coords1, features=feats1)
# The following fails
try:
C = A + B
except AssertionError:
pass
B = ME.SparseTensor(
coordinates=coords1,
features=feats1,
coordinate_manager=A.coordinate_manager # must share the same coordinate manager
)
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
features=feats0,
coordinate_manager=A.coordinate_manager, # must share the same coordinate manager
coordinate_map_key=A.coordinate_map_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 coordinate_manager by default
ME.set_sparse_tensor_operation_mode(
ME.SparseTensorOperationMode.SHARE_COORDINATE_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(coordinates=coords0, features=feats0)
B = ME.SparseTensor(
coordinates=coords1,
features=feats1,
# coords_manager=A.coordinate_manager, No need to feed the coordinate_manager
)
C = A + B
# When done using it for forward and backward, you must cleanup the coords man
ME.clear_global_coordinate_manager()
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(coordinates=coords, features=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()
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