Pointnet autoencoder pytorch

Download_data

mkdir data && cd data
mkdir ModelNet && cd ModelNet
curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=1zE1d_eYD_QEnmS01LlZlEOMSZTIXRwIA" > /dev/null
curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=1zE1d_eYD_QEnmS01LlZlEOMSZTIXRwIA" -o modelnet_classification.h5
md5sum modelnet_classification.h5

The output should be exactly the same as this:

87e763a66819066da670053a360889ed  modelnet_classification.h5

Models

There are model.py and model1.py in model directory.
Both have 128 dimensional latent space, and they have slightly different architecture. Model1 have better performance.

I used the average Chamfer distance as Test Loss

How to train

At train.ipynb,

# Set hyperparameters.
args = easydict.EasyDict({
    'train': True,
    'batch_size': 32,       # input batch size
    'n_epochs': 50,         # number of epochs
    'n_workers': 4,         # number of data loading workers
    'learning_rate': 0.001, # learning rate
    'beta1': 0.9,           # beta 1
    'beta2': 0.999,         # beta 2
    'step_size': 20,        # step size
    'gamma': 0.5,           # gamma
    'in_data_file': 'data/ModelNet/modelnet_classification.h5', # data directory
    'model': '',  # model path
    'model_type': 'hankyu1'             # hankyu = model, hankyu1 = model1
})

make sure train = true, and model = ''.

How to eval

At train.ipynb,

# Set hyperparameters.
args = easydict.EasyDict({
    'train': False,
    'batch_size': 32,       # input batch size
    'n_epochs': 50,         # number of epochs
    'n_workers': 4,         # number of data loading workers
    'learning_rate': 0.001, # learning rate
    'beta1': 0.9,           # beta 1
    'beta2': 0.999,         # beta 2
    'step_size': 20,        # step size
    'gamma': 0.5,           # gamma
    'in_data_file': 'data/ModelNet/modelnet_classification.h5', # data directory
    'model': 'saved_models/autoencoder_50.pth',  # model path
    'model_type': 'hankyu1'             # hankyu = model, hankyu1 = model1
})

make sure train = False and model = 'model path'.