marian42 / shapegan Goto Github PK

Hello, I want to ask some questions about the paper , he said that training will experience four stages in the process, I want to ask how long is the training in each stage is over

QObject::moveToThread: Current thread (0x563d74d14da0) is not the object's thread (0x563e00957b30).
Cannot move to target thread (0x563d74d14da0)

qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "/home/alberto/anaconda3/lib/python3.8/site-packages/cv2/qt/plugins" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.

Available platform plugins are: xcb, eglfs, linuxfb, minimal, minimalegl, offscreen, vnc, wayland-egl, wayland, wayland-xcomposite-egl, wayland-xcomposite-glx, webgl.

Aborted (core dumped)

Did you by chance run into this error during the visualization of the latent walk?

Hi,
after reading your paper, I am trying to reproduce your works.
Firstly, I trained VAE with the chair dataset.
Before testing tsne space, I am testing plotting process.
When I run "python create_plot.py color-test", it occurs ModuleNotFoundError.

In the code (create_plot.py),

from dataset import dataset as dataset
dataset.load_voxels('cpu')
dataset.load_labels()
voxels = dataset.voxels

but I could not find 'dataset'.
is it a typo of datasets?
Also, I could not find where load_voxels() and load_labels() are defined.
could you clarify which dataset it is pointing to?

Thank you,

Hi, thanks for your work and this repository! I was taking a look at the original DeepSDF training code, and your own training code for the autodecoder, and I noticed some differences. I was hoping I could perhaps pick your brain on your own experiences with DeepSDF-style training?

For instance, I notice that your training completely shuffles the data samples across all points and across all shapes. So a batch will contain a random assortment of shapes. Whereas the DeepSDF code will instead randomly pick a set of shapes, and from those randomly select the points. So DeepSDF batches are only composed of a handful of shapes. Was there a reason you departed from the DeepSDF batch composition? I'm curious if you found it more stable?

I also noticed that your autodecoder model includes no normalization layers. Was there a reason you decided not to include those?

Because of these differences, I'm just wondering if there are any quick tips/strategies you can pass on - thanks in advance if you have the time to respond!

I have facing error as show in image below. Please guide me.

Hi Marian,
Is there any chance you could upload the visualization scripts for the point-based GAN? I am precisely training the point_sdf_net model and would like to see what comes out from it.
Cheers!

According to the prompt of the file, I run the prepare_shapenet_dataset.py file to generate the voxel of the chair. After running, the following file appeared: 1a6f615e8b1b5ae4dbbc9440457e303e.npy
When I tried to draw this voxel with the matplotlib tool, it showed a 323232 cube without the shape of a chair.

Hi,

Is there a installation guide or codelab option?
I like the project and would use it in an art project for 3D printing. Showing what's possible with GAN's etc..
And a 3D output would be amazing?

Kindest regards,

fake_loss = torch.mean(-torch.log(critic_output_fake)) => fake_loss = -torch.mean(critic_output_fake)

Could you please add a license to this repo? Thanks!

Thank you for a great work.
Is it possible to share sdf_net_latent_codes.to to run demo_latent_space.py?
Also I need sdf_points.to and sdf_values.to to run train_sdf_autodecoder.py, how can I get it?

For demo_latent_space there is the assertion regarding our labels.to
how can we generate that file?

Hi,

First of all, thanks for sharing this excellent work!

However, I have 2 questions regarding to code release:

1. Will you open-source the training script of "Point-based discriminator" ?
2. Would you provide the evaluation script ?

Thanks!

May you have any suggestions for solve the out of memory issue in the last iteration of the hybrid progressive GAN? what GPU did you use.
I am trying with GCP 1-2 tier GPU and my RTX 2080 Ti, I also tried with Batch Size = 2 but still same issue.
Any suggestions would be much appreciated

Hello, the dataset you provided is too large, can you provide a smaller data set, about 1g

I'm running into issues trying to get the right conda environment set up for this project -- pygame requires 2.7 or 3.5, but scikit-spatial requires >=3.7. Is there possibly a list of package versions to set up the correct environment?

Hi, I'm trying to re-implement your work and am running into some confusion.
It seems like the file 'data/chairs/train.txt' is being called in train_hybrid_progressive_gan.py, but I don't see where this file is generated (or any writeup about how to generate it yourself). I followed the data preprocessing steps as was outlined - any pointers would be helpful!

Hi @marian42 ,

Thanks for contributing an amazing amount of code.
I am trying to use your repo to retrain DeepSDF on the Sofa category and have two queries :

1. I tried downloading the precomputed dataset but it seems that the cloud folder is missing from the downloaded data. Do you confirm that I need to rerun prepare_shapenet_data.py to get the sdf.to file for training?
2. Is your code (prepare_shapenet_data) designed for ShapeNetv1 or ShapeNetv2 or both?

Thanks a lot!
Best regards,
Thibault

I ran demo_gan.py using the pre-trained gan_generator_voxels_sofas.to file. I made the following changes:

0. Copied gan_generator_voxels_sofas.to from examples folder to models folder.

1. Did pip install scipy==1.5.2

2. In rendering/__init__.py
vertices, faces, normals, _ = skimage.measure.marching_cubes_lewiner(voxels, level=level, spacing=(2.0 / voxel_resolution, 2.0 / voxel_resolution, 2.0 / voxel_resolution))
   to
   vertices, faces, normals, _ = skimage.measure.marching_cubes(voxels, level=level, spacing=(2.0 / voxel_resolution, 2.0 / voxel_resolution, 2.0 / voxel_resolution),method='lewiner')

3. In model/gan.py
super(Generator, self).__init__(filename="generator.to")
   to
   super(Generator, self).__init__(filename="gan_generator_voxels_sofas.to")

I got this visualization as a result:

How do I resolve this?

I would like to ask, is the data set of the pre-training model trained with point clouds or voxels?

Hi,

Could you share your evaluation scripts to reproduce the table1 in paper?

Thanks!

I am running the demo and I am getting an empty view.

Exception in thread Thread-1:
Traceback (most recent call last):
  File "/home/alberto/anaconda3/lib/python3.8/threading.py", line 932, in _bootstrap_inner
    self.run()
  File "/home/alberto/anaconda3/lib/python3.8/threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "/home/alberto/Documents/shapegan/rendering/__init__.py", line 299, in _run
    self._render()
  File "/home/alberto/Documents/shapegan/rendering/__init__.py", line 230, in _render
    light_vp_matrix = get_camera_transform(6, self.rotation[0], 50, project=True)
  File "/home/alberto/Documents/shapegan/rendering/math.py", line 20, in get_camera_transform
    camera_transform = np.matmul(camera_transform, get_rotation_matrix(rotation_x, axis='x'))
  File "/home/alberto/Documents/shapegan/rendering/math.py", line 14, in get_rotation_matrix
    matrix[:3, :3] = rotation.as_dcm()
AttributeError: 'scipy.spatial.transform.rotation.Rotation' object has no attribute 'as_dcm'

could you please provide some point_sdf dataset of 3D warehouse ?****

Were the demo models provided in examples directory trained in progressive fashion or without it? Also what are the other hyper-parameters for the provided models. Thanks!

Hey,
I'd like to create the latent space animation with the model I have trained.
However, I'm not sure how to create the following files required by your code:
sdf_net_latent_codes.to
labels.to
Could you elaborate on how to initialize them?

Also, I assumed that sdf_net.to is basically the generative model I trained hybrid_progressive_gan_generator_3.to - Is that correct?

Thanks!