liruihui / pointaugment

Code for PointAugment: an Auto-Augmentation Framework for Point Cloud Classification, CVPR 2020 (Oral)

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

This repository contains a PyTorch implementation of the paper:

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification.
Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu.
CVPR 2020 (Oral)

Dependencies

• Python 3.6
• CUDA 10.0.
• PyTorch. Codes are tested with version 1.2.0
• (Optional) TensorboardX for visualization of the training process.

Following is the suggested way to install these dependencies:

# Create a new conda environment
conda create -n PointAugment python=3.6
conda activate PointAugment

# Install pytorch (please refer to the commend in the official website)
conda install pytorch=1.2.0 torchvision cudatoolkit=10.0 -c pytorch -y

Usage

Download the ModelNet40 dataset from here.

To train a model to classify point clouds sampled from 3D shapes:

python train_PA.py --data_dir ModelNet40_Folder

Log files and network parameters will be saved to log folder in default.

Noted that the code may be not stable, if you can help please contact me.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{li2020pointaugment,
  title={{PointAugment}: An Auto-Augmentation Framework for Point Cloud Classification},
  author={Li, Ruihui and Li, Xianzhi and Heng, Pheng-Ann and Fu, Chi-Wing},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={6378--6387},
  year={2020}
}

pointaugment's Issues

PointAugment short explanation in the blog post

Thank you for your contribution!

I hope you would publish the code soon. I enjoyed the simplicity and effectiveness of your paper and wrote a short blog post with a summary of the main ideas. Maybe some people would be interested in it.

blog_post

How to augment custom data?

Hi! Could you please give some directions -
after training the model on ModelNet40 dataset,
how can I use Augmentor to produce augmented point clounds from my custom point cloud?

Where is the code to reduce model oscillation ?

looking forward to the code

modelnet10_ply_hdf5_2048

Hello, could you provide a data set modelnet10_ply_hdf5_2048, thank you very much！

NameError: name 'cls_pc_raw' is not defined

Hello, when I run this code I get the following error: "NameError: name 'cls_pc_raw' is not defined". I'm not sure if it helps but here is the entire debug message.

checkpoints: log/pointnet_cls/20210827-1421
PARAMETER ...
Namespace(augment=False, batch_size=24, data_dir='ModelNet40', decay_rate=0.0001, epoch=250, epoch_per_save=5, learning_rate=0.001, learning_rate_a=0.001, log_dir='log/pointnet_cls/20210827-1421', lr_decay=0.5, model_name='pointnet', no_decay=False, noise_dim=1024, num_points=1024, optimizer='Adam', pretrain=None, restore=False, use_normal=False, y_rotated=True)
Load dataset ...
The number of training data is: 9840
The number of test data is: 2468
No existing Augment, starting training from scratch...
Epoch 1 (1/250):
0% 0/410 [00:00<?, ?it/s]
Traceback (most recent call last):
File "train_PA.py", line 29, in
model.train()
File "/content/PointAugment/Augment/model.py", line 162, in train
aug_feat, ispn=ispn)
File "/content/PointAugment/Common/loss_utils.py", line 63, in cls_loss
parameters = torch.max(torch.tensor(NUM).cuda(), torch.exp(1.0-cls_pc_raw)**2).cuda()
NameError: name 'cls_pc_raw' is not defined

code for pointnet++, RS-CNN, and DGCNN

Dear Authors,

Thank you for sharing the code.
Can you please add the code for pointnet++, RS-CNN, and DGCNN?

How do you achieve end-to-end?

Hello,liruihui.Thank you for you contribution!
I find in your article, you split into two parts.One part is update the classifier, the other one is update augmentor.Is the reason why you implemented end-to-end because you used the loss function of Equation6 to update the classifier?

Question about Augmentor

It's a nice job which really inspired me a lot. But I have a question need to answer.

Well, I can't understand the idea of generating a 3×3 rotation matrix from a quaternion vector in a augmentor. What is the meaning of the function of batch_quat_to_rotmat? The idea of the funtion batch_quat_to_rotmat does not embodies in the paper. Looking forward to your reply. Thank you very much.

def batch_quat_to_rotmat(q, out=None):
B = q.size(0)
if out is None:
out = q.new_empty(B, 3, 3)
# 2 / squared quaternion 2-norm
len = torch.sum(q.pow(2), 1)
s = 2 / len
s_ = torch.clamp(len, 2.0 / 3.0, 3.0 / 2.0)
# coefficients of the Hamilton product of the quaternion with itself
h = torch.bmm(q.unsqueeze(2), q.unsqueeze(1))
out[:, 0, 0] = (1 - (h[:, 2, 2] + h[:, 3, 3]).mul(s)) # .mul(s_)
out[:, 0, 1] = (h[:, 1, 2] - h[:, 3, 0]).mul(s)
out[:, 0, 2] = (h[:, 1, 3] + h[:, 2, 0]).mul(s)
out[:, 1, 0] = (h[:, 1, 2] + h[:, 3, 0]).mul(s)
out[:, 1, 1] = (1 - (h[:, 1, 1] + h[:, 3, 3]).mul(s)) # .mul(s_)
out[:, 1, 2] = (h[:, 2, 3] - h[:, 1, 0]).mul(s)
out[:, 2, 0] = (h[:, 1, 3] - h[:, 2, 0]).mul(s)
out[:, 2, 1] = (h[:, 2, 3] + h[:, 1, 0]).mul(s)
out[:, 2, 2] = (1 - (h[:, 1, 1] + h[:, 2, 2]).mul(s)) # .mul(s_)
return out, s_

Bugs

PointAugment/Common/loss_utils.py", line 63, in cls_loss
    parameters = torch.max(torch.tensor(NUM).cuda(), torch.exp(1.0-cls_pc_raw)**2).cuda()
NameError: name 'cls_pc_raw' is not defined

Intégration in pointcloud framework

Here is a cool pointcloud framework in which your work could be added: https://github.com/nicolas-chaulet/deeppointcloud-benchmarks

about the code

Hi, @liruihui ,

Congratulations on your paper accepted to coming CVPR conference. When will the code be released?

THX!

Question about the SR16 dataset.

In Table1, the SR16 has 36148 for training and 5165 for testing, but the SR16 has more than 50000 models, where are the missing models ?

Runtime Error

When i run code,
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 1. Got 6 and 4 in dimension 0 at /opt/conda/conda-bld/pytorch_1565272279342/work/aten/src/THC/generic/THCTensorMath.cu:71

The code for shape retrieval

Question about the normal feature

In your code

raw_pt = pt[:,:3,:].contiguous()
normal = pt[:,3:,:].transpose(1, 2).contiguous() if C > 3 else None

Therefore, I was wondering do you used the normal feature except xyz coordinate to further boost the performance?

Could you please share the code for shape retrieval? I will cite your work and compare with your results. Thank you!

A RuntimeError

Hello, when I run your code, I encountered the following problems when calculating the loss of classifier:
"RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [256, 4]], which is output 0 of TBackward, is at version 2; expected version 1 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!"

Generated samples

Where can I find the generated new samples after training?
I want to use both old and new samples to train pointnet to see how much the accuracy improves.
Thanks.

Classification Results

I downloaded the code and trained the pointnet model from scratch as per the instructions in the README file.

However, I could only see Best Accuracy: 0.890600

Please help us reproduce the results mentioned in the paper.

Thanks in Advance.

ValueError: need at least one array to concatenate

@liruihui Hello, I found this problem in the process of running the code. Do you know why? I hope you can give me some suggestions, thank you.

NameError: name 'cls_pc_raw' is not defined

To fix the issue please change line 56 and 57 in common/loss_utils.py from

cls_pc, _ = cal_loss_raw(pred, gold)
cls_aug, _ = cal_loss_raw(pred_aug, gold)

to
cls_pc, cls_pc_raw = cal_loss_raw(pred, gold)
cls_aug, cls_aug_raw = cal_loss_raw(pred_aug, gold)

How to get SHREC16 dataset ?

I've download shapenetcore-v2.zip, but how to separate it into train/test set ?