The MinkowskiEngine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors. For more information, please visit the documentation page.
- Dynamic computation graph
- Custom kernel shapes
- Generalized sparse convolution
- Dilated convolution
- Multi-GPU training
- Multi-threaded kernel map
- Multi-threaded compilation
- Highly-optimized GPU kernels
You can install the MinkowskiEngine without sudo using anaconda. Using anaconda is highly recommended.
We recommend python>=3.6 for installation. If you have compilation issues, please checkout the common compilation issues page first.
First, follow the anaconda documentation to install anaconda on your computer.
conda create -n py3-mink python=3.7 anaconda
conda activate py3-mink
conda install openblas numpy
conda install -c bioconda google-sparsehash
conda install pytorch torchvision -c pytorch
conda activate py3-mink
git clone https://github.com/StanfordVL/MinkowskiEngine.git
cd MinkowskiEngine
python setup.py install
Like the anaconda installation, make sure that you install pytorch with the the same CUDA version that nvcc uses.
sudo apt install libsparsehash-dev libopenblas-dev
# within a python3 environment
pip install torch
git clone https://github.com/StanfordVL/MinkowskiEngine.git
cd MinkowskiEngine
pip install -r requirements.txt
python setup.py install
To use the Minkowski Engine, you first would need to import the engine.
Then, you would need to define the network. If the data you have is not
quantized, you would need to voxelize or quantize the (spatial) data into a
sparse tensor. Fortunately, the Minkowski Engine provides the quantization
function (MinkowskiEngine.utils.sparse_quantize).
import MinkowskiEngine as ME
class ExampleNetwork(ME.MinkowskiNetwork):
def __init__(self, in_feat, out_feat, D):
super(ExampleNetwork, self).__init__(D)
self.conv1 = ME.MinkowskiConvolution(
in_channels=in_feat,
out_channels=64,
kernel_size=3,
stride=2,
dilation=1,
has_bias=False,
dimension=D)
self.bn1 = ME.MinkowskiBatchNorm(64)
self.conv2 = ME.MinkowskiConvolution(
in_channels=64,
out_channels=128,
kernel_size=3,
stride=2,
dimension=D)
self.bn2 = ME.MinkowskiBatchNorm(128)
self.pooling = ME.MinkowskiGlobalPooling(dimension=D)
self.linear = ME.MinkowskiLinear(128, out_feat)
self.relu = ME.MinkowskiReLU(inplace=True)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.pooling(out)
return self.linear(out) # loss and network
criterion = nn.CrossEntropyLoss()
net = ExampleNetwork(in_feat=3, out_feat=5, D=2)
print(net)
# a data loader must return a tuple of coords, features, and labels.
coords, feat, label = data_loader()
input = ME.SparseTensor(feat, coords=coords)
# Forward
output = net(input)
# Loss
loss = criterion(output.F, label)After installing the package, run python examples/example.py in the package root directory.
For indoor semantic segmentation. run python examples/indoor.py in the package root directory.
For discussion and questions, please use minkowskiengine@googlegroups.com.
For API and general usage, please refer to the MinkowskiEngine documentation
page for more detail.
For issues not listed on the API and feature requests, feel free to submit create an issue on the github issue page.