Note: For the newer PointConv layers in tensorflow 2.x visit the repostiory here.
The repository contains implementations of the pointnet++ set abstraction and feature propagation layers as tf.keras.layers classes. The intention is not to be a full pointnet++ tensorflow 2.0 implementation, but provide an easy way to build a pointnet++ style network architecture using the tensorflow 2.0 keras api. For reference here is the original paper and code. Where possible I have tried to directly copy and paste original code to avoid discrepancies.
Requirements:
- python >= 3.0+
- tensorflow-gpu >= 2.2+
- cuda == 10.1
Note: This repository uses the
train_stepmodel override which is new fortensorflow 2.2.0, as such if you wish to use the provided training scripts it is important your tensorflow is not an older version. The layers will work for tensorflow 2.0+.
To compile the tensorflow Ops first ensure the CUDA_ROOT path in tf_ops/compile_ops.sh points correctly to you cuda folder then compile the ops with:
chmod u+x tf_ops/compile_ops.sh
tf_ops/compile_ops.sh
The layers should work as direct replacements for standard tf.keras.layers layers, most similarly Conv2D. To import just run from pnet2_layers.layers import <LAYER_NAME>. To use in your own project just copy the pnet2_layers folder into your project structure and locate with either relative or absolute imports.
For example, to mimic the pointnet2_cls_ssg model in the original repository as a custom model, it would look like:
import tensorflow
from pnet2_layers.layers import Pointnet_SA
class CLS_SSG_Model(tf.keras.Model)
def __init__(self, batch_size, activation=tf.nn.relu):
super(Pointnet2Encoder, self).__init__()
self.batch_size = batch_size
self.layer1 = Pointnet_SA(npoint=512, radius=0.2, nsample=32, mlp=[64, 64, 128], group_all=False, activation=self.activation)
self.layer1 = Pointnet_SA(npoint=128, radius=0.4, nsample=32, mlp=[128, 128, 256], group_all=False, activation=self.activation)
self.layer1 = Pointnet_SA(npoint=None, radius=None, nsample=None, mlp=[256, 512, 1024], group_all=False, activation=self.activation)
# The rest of the model can be implemented using standard tf.keras.layers (Dense and dropout).
def call():
xyz, points = self.layer1(input, None)
xyz, points = self.layer2(xyz, points)
xyz, points = self.layer3(xyz, points)
points = tf.reshape(points, (self.batch_size, -1))
# run points through dense / dropout layers.
return points
Examples of a few of the models from the original repository can be found in the models folder.
To run the ModelNet or ScanNet example first download the tfrecords containing the training data from here and place in a folder called data. To start the training script run either:
python train_modelnet.py
or:
python train_scannet.py
You can view training logs with:
tensorboard --logdir=logs --port=6006
and navigate to localhost:6006 in a web browser.
By default this runs the multi-scale grouping modules. To run the standard set abstraction layer without multi-scale grouping, set msg to False in the params dictionary.
I have implemented batch normalization for all pointnet++ layers in the model files. I personally find I get better results on real world datasets when this option is set to False. In the original implementation this batch normalization is set to True.
If you use this repository and find any bugs please submit an issue so I can fix them for anyone else using the code.
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