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INSTALL.md

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Installation

Requirements

All the codes are tested in the following environment:

  • Linux (tested on Ubuntu 14.04/16.04/18.04/20.04/21.04)
  • Python 3.6+
  • PyTorch 1.1 or higher (tested on PyTorch 1.1, 1,3, 1,5~1.10)
  • CUDA 9.0 or higher (PyTorch 1.3+ needs CUDA 9.2+)
  • spconv v1.x or spconv v2.x

Notice that all results in the paper is running in with Spconv 1.2.1, but the flops calculation for spconv-based network (i.e. CenterPoint-Voxel or SECOND) is running with Spconv 2.x.

Install pcdet v0.5.2

NOTE: Please re-install pcdet v0.5.2 by running python setup.py develop even if you have already installed previous version.

a. Clone this repository.

git clone https://github.com/jihanyang/SparseKD.git

b. Install the dependent libraries as follows:

  • Install the dependent python libraries:
pip install -r requirements.txt
  • Install the SparseConv library, we use the implementation from [spconv].
    • If you use PyTorch 1.1, then make sure you install the spconv v1.0 with (commit 8da6f96) instead of the latest one.
    • If you use PyTorch 1.3+, then you need to install the spconv v1.2.1. As mentioned by the author of spconv, you need to use their docker if you use PyTorch 1.4+.
    • You could also install latest spconv v2.x with pip, see the official documents of spconv.

c. Install this pcdet library and its dependent libraries by running the following command:

python setup.py develop

Install Thop

(If you don't need to calculate Flops, Parameters and Acts, just skip this part) To calculate Flops and Acts, we leverage the popular pytorch-OpCounter.

Note: that the Flops in our paper actually means Macs in pytorch-OpCounter. We use the term Flops in order to follow the same formulation as RegNetX, we name it as flops in the paper.

a. Clone our customized Thop.

git clone https://github.com/jihanyang/pytorch-OpCounter.git

b. Install.

cd pytorch-OpCounter && pip install .