84 lines
2.2 KiB
Markdown
84 lines
2.2 KiB
Markdown
# pointMLP-pytorch
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Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
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## Install
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Please ensure that python3.7+ is installed. We suggest user use conda to create a new environment.
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Install dependencies
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```bash
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pip install -r requirement.txt
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```
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Install CUDA kernels
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```bash
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pip install pointnet2_ops_lib/.
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```
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## Classification ModelNet40
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The dataset will be automatically downloaded, run following command to train
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```bash
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# train pointMLP
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python main.py --model pointMLP
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# train pointMLP-elite
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python main.py --model pointMLPElite
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# please add other paramemters as you wish.
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```
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By default, it will create a fold named "checkpoints/{modelName}-{msg}-{randomseed}", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt.
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To conduct voting experiments, run
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```bash
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# please modify the msg accrodingly
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python voting.py --model pointMLP --msg demo
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```
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## Classification ScanObjectNN
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- Make data folder and download the dataset
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```bash
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cd pointMLP-pytorch/classification_ScanObjectNN
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mkdir data
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cd data
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wget http://103.24.77.34/scanobjectnn/h5_files.zip
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unzip h5_files.zip
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```
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- Train pointMLP/pointMLPElite by
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```bash
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# train pointMLP
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python main.py --model pointMLP
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# train pointMLP-elite
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python main.py --model pointMLPElite
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# please add other paramemters as you wish.
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```
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By default, it will create a fold named "checkpoints/{modelName}-{msg}-{randomseed}", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt.
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- To conduct voting experiments, run
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```bash
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# please modify the msg accrodingly
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python voting.py --model pointMLP --msg demo
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```
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## Part segmentation
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- Make data folder and download the dataset
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```bash
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cd pointMLP-pytorch/part_segmentation
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mkdir data
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cd data
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wget https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip --no-check-certificate
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unzip shapenetcore_partanno_segmentation_benchmark_v0_normal.zip
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```
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- Train pointMLP by
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```bash
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# train pointMLP
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python main.py --model pointMLP
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# please add other paramemters as you wish.
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```
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## Pre-trained models
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Please download the pre-trained models and log files here: [anonymous google drive](https://drive.google.com/drive/folders/1Jn9HNpPsrq-1XqSmOUtw4cwPMjsIiIpz?usp=sharing)
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