liruihui / pointaugment Goto Github PK

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

@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.

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?

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

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

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?

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.

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

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.

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)

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!"

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_

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

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 ?

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

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?

Dear Authors,

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

Hi, @liruihui ,

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

THX!