閔可夫斯基引擎Minkowski Engine

Minkowski引擎是一個用於稀疏張量的自動微分庫。它支援所有標準神經網路層，例如對稀疏張量的卷積，池化，解池和廣播操作。有關更多資訊，請訪問文件頁面。

pip install git+https://github.com/NVIDIA/MinkowskiEngine.git

稀疏張量網路：空間稀疏張量的神經網路

壓縮神經網路以加快推理速度並最小化記憶體佔用已被廣泛研究。用於模型壓縮的流行技術之一是修剪卷積網路中的權重，也被稱為稀疏卷積網路。用於模型壓縮的這種引數空間稀疏性壓縮在密集張量上執行的網路，並且這些網路的所有中間啟用也是密集張量。

但是，在這項工作中，專注於

在下面的稀疏張量卷積上可視化了一個稀疏張量網路操作。稀疏張量上的卷積層與密集張量上的卷積層相似。但是，在稀疏張量上，在一些指定點上計算卷積輸出，這些點可以在廣義卷積中進行控制。

特徵

• 無限的高維稀疏張量支援
• 所有標準神經網路層（卷積，池化，廣播等）
• 動態計算圖
• 自定義核心形狀
• 多GPU訓練
• 多執行緒核心對映
• 多執行緒編譯
• 高度優化的GPU核心

Requirements

• Ubuntu >= 14.04
• 11.1 > CUDA >= 10.1.243
• pytorch >= 1.5
• python >= 3.6
• GCC >= 7

Pip

MinkowskiEngine是通過PyPI MinkowskiEngine分發的，可以使用簡單安裝pip。按照說明安裝pytorch。接下來，安裝openblas。

sudo apt install libopenblas-dev

pip install torch

pip install -U MinkowskiEngine --install-option="--blas=openblas" -v
 

# For pip installation from the latest source

# pip install -U git+https://github.com/NVIDIA/MinkowskiEngine

If you want to specify arguments for the setup script, please refer to the following command.

# Uncomment some options if things don't work

pip install -U git+https://github.com/NVIDIA/MinkowskiEngine \

# \ # uncomment the following line if you want to force cuda installation

# --install-option="--force_cuda" \

# \ # uncomment the following line if you want to force no cuda installation. force_cuda supercedes cpu_only

# --install-option="--cpu_only" \

# \ # uncomment the following line when torch fails to find cuda_home.

# --install-option="--cuda_home=/usr/local/cuda" \

# \ # uncomment the following line to override to openblas, atlas, mkl, blas

# --install-option="--blas=openblas" \

快速啟動

要使用Minkowski引擎，首先需要匯入引擎。然後，將需要定義網路。如果沒有量化資料，則需要將（空間）資料體素化或量化為稀疏張量。幸運的是，Minkowski引擎提供了量化功能（MinkowskiEngine.utils.sparse_quantize）。

Anaconda

We recommend python>=3.6 for installation. First, follow the anaconda documentation to install anaconda on your computer.

sudo apt install libopenblas-dev

conda create -n py3-mink python=3.8

conda activate py3-mink

conda install numpy mkl-include pytorch cudatoolkit=11.0 -c pytorch

pip install -U git+https://github.com/NVIDIA/MinkowskiEngine

System Python

Like the anaconda installation, make sure that you install pytorch with the same CUDA version that nvcc uses.

# install system requirements

sudo apt install python3-dev libopenblas-dev

# Skip if you already have pip installed on your python3

curl https://bootstrap.pypa.io/get-pip.py | python3

# Get pip and install python requirements

python3 -m pip install torch numpy

git clone https://github.com/NVIDIA/MinkowskiEngine.git

cd MinkowskiEngine

python setup.py install

# To specify blas, CUDA_HOME and force CUDA installation, use the following command

# python setup.py install --blas=openblas --cuda_home=/usr/local/cuda --force_cuda

Creating a Network

import torch.nn as nn

import MinkowskiEngine as ME

class ExampleNetwork(ME.MinkowskiNetwork):

 def __init__(self, in_feat, out_feat, D):

 super(ExampleNetwork, self).__init__(D)

 self.conv1 = nn.Sequential(

 ME.MinkowskiConvolution(

 in_channels=in_feat,

 out_channels=64,

 kernel_size=3,

 stride=2,

 dilation=1,

 has_bias=False,

 dimension=D),

 ME.MinkowskiBatchNorm(64),

 ME.MinkowskiReLU())

 self.conv2 = nn.Sequential(

 ME.MinkowskiConvolution(

 in_channels=64,

 out_channels=128,

 kernel_size=3,

 stride=2,

 dimension=D),

 ME.MinkowskiBatchNorm(128),

 ME.MinkowskiReLU())

 self.pooling = ME.MinkowskiGlobalPooling()

 self.linear = ME.MinkowskiLinear(128, out_feat)

 def forward(self, x):

 out = self.conv1(x)

 out = self.conv2(out)

 out = self.pooling(out)

 return self.linear(out)

Forward and backward using the custom network

 # 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)