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Installation

Preparation

Requirements

  • Ubuntu 16.04
  • Anaconda with python=3.6
  • pytorch>=1.3
  • torchvision with pillow<7
  • cuda=10.1
  • others: pip install termcolor opencv-python tensorboard h5py easydict
  • note

Datasets

Shape Classification on ModelNet40

You can download ModelNet40 for here (1.6 GB). Unzip and move (or link) it to data/ModelNet40/modelnet40_normal_resampled.

Part Segmentation on PartNet

You can download PartNet dataset from the ShapeNet official webpage (8.0 GB). Unzip and move (or link) it to data/PartNet/sem_seg_h5.

Scene Segmentation on S3DIS

You can download the S3DIS dataset from here (4.8 GB). You only need to download the file named Stanford3dDataset_v1.2.zip, unzip and move (or link) it to data/S3DIS/Stanford3dDataset_v1.2.

The file structure should look like:

<pt-code-root>
├── cfgs
│   ├── modelnet
│   ├── partnet
│   └── s3dis
├── data
│   ├── ModelNet40
│   │   └── modelnet40_normal_resampled
│   │       ├── modelnet10_shape_names.txt
│   │       ├── modelnet10_test.txt
│   │       ├── modelnet10_train.txt
│   │       ├── modelnet40_shape_names.txt
│   │       ├── modelnet40_test.txt
│   │       ├── modelnet40_train.txt
│   │       ├── airplane
│   │       ├── bathtub
│   │       └── ...
│   ├── PartNet
│   │   └── sem_seg_h5
│   │       ├── Bag-1
│   │       ├── Bed-1
│   │       ├── Bed-2
│   │       ├── Bed-3
│   │       ├── Bottle-1
│   │       ├── Bottle-3
│   │       └── ...
│   └── S3DIS
│       └── Stanford3dDataset_v1.2
│           ├── Area_1
│           ├── Area_2
│           ├── Area_3
│           ├── Area_4
│           ├── Area_5
│           └── Area_6
├── init.sh
├── datasets
├── function
├── models
├── ops
└── utils

Compile custom operators and pre-processing data

sh init.sh

Usage

Training

ModelNet

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> \
    function/train_modelnet_dist.py --cfg <config file> [--log_dir <log directory>]
  • <port_num> is the port number used for distributed training, you can choose like 12347.
  • <config file> is the yaml file that determines most experiment settings. Most config file are in the cfgs directory.
  • <log directory> is the directory that the log file, checkpoints will be saved, default is log.

PartNet

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> \
    function/train_partnet_dist.py --cfg <config file> [--log_dir <log directory>]

S3DIS

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> \
    function/train_s3dis_dist.py --cfg <config file> [--log_dir <log directory>]

Evaluating

For evaluation, we recommend using 1 gpu for more precise result.

ModelNet40

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node 1 \
    function/evaluate_modelnet_dist.py --cfg <config file> --load_path <checkpoint> [--log_dir <log directory>]
  • <port_num> is the port number used for distributed evaluation, you can choose like 12347.
  • <config file> is the yaml file that determines most experiment settings. Most config file are in the cfgs directory.
  • <checkpoint> is the model checkpoint used for evaluating.
  • <log directory> is the directory that the log file, checkpoints will be saved, default is log_eval.

PartNet

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node 1 \
    function/evaluate_partnet_dist.py --cfg <config file> --load_path <checkpoint> [--log_dir <log directory>]

S3DIS

python -m torch.distributed.launch --master_port <port_num> --nproc_per_node 1 \
    function/evaluate_s3dis_dist.py --cfg <config file> --load_path <checkpoint> [--log_dir <log directory>]

Models

Method ModelNet40
Point-wise MLP 93.2
Pseudo Grid 93.0
Adapt Weights 93.1
PosPool 92.7
PosPool* 93.2

We will release other models soon.