thank for you work. I train the model with command:

python train.py --dataset rico --batch_size 64 --iteration 200000 --latent_size 4 --lr 1e-05 --G_d_model 256 --G_nhead 4 --G_num_layers 8 --D_d_model 256 --D_nhead 4 --D_num_layers 8

and it will show me that. does it look normal ? the generator loss become large and Discriminator loss become small :

Hey, I've been trying to train a model on Rico with more data In rico.py, I can see that number of elements are capped at 9

I've been going through your generator and discriminator code and I would greatly appreciate some help in where I need to make changes so the models can accept in data samples with bboxes and labels > 9.

Thanks so much! Sukriti

Hey, Thanks for releasing your code and environment. I was able to generate samples using pre-trained models. However, when I try to generate samples with constraints, I get the following key error:

  File "generate_const.py", line 199, in <module>
    main()
  File "generate_const.py", line 148, in main
    for z in optimizer.generator(z, data):
  File "/home/sukrverm/const_layout/clg/auglag.py", line 96, in generator
    assert data.attr[0]['has_canvas_element']
KeyError: 0

I suppose this is some attribute of publaynet data that is False here. Do I need to edit the get_dataset Publaynet class to remove such examples before hand?

Download rico_dataset_v0.1_semantic_annotations.zip from "UI Screenshots and Hierarchies with Semantic Annotations" and decompress it. but it is

Thank you for source code. I get an error when I run CLG-LO

If possible, please share the how to fix.

Traceback (most recent call last):
  File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/vscode/.vscode-server/extensions/ms-python.python-2022.7.11371008/pythonFiles/lib/python/debugpy/__main__.py", line 45, in <module>
    cli.main()
  File "/home/vscode/.vscode-server/extensions/ms-python.python-2022.7.11371008/pythonFiles/lib/python/debugpy/../debugpy/server/cli.py", line 444, in main
    run()
  File "/home/vscode/.vscode-server/extensions/ms-python.python-2022.7.11371008/pythonFiles/lib/python/debugpy/../debugpy/server/cli.py", line 285, in run_file
    runpy.run_path(target_as_str, run_name=compat.force_str("__main__"))
  File "/usr/lib/python3.8/runpy.py", line 265, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "/usr/lib/python3.8/runpy.py", line 97, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/vscode/const_layout/generate_const.py", line 199, in <module>
    main()
  File "/home/vscode/const_layout/generate_const.py", line 182, in main
    save_gif(out_path, j, netG,
  File "/home/vscode/const_layout/generate_const.py", line 52, in save_gif
    subprocess.run(['convert', '-delay', '50',
  File "/usr/lib/python3.8/subprocess.py", line 493, in run
    with Popen(*popenargs, **kwargs) as process:
  File "/usr/lib/python3.8/subprocess.py", line 858, in __init__
    self._execute_child(args, executable, preexec_fn, close_fds,
  File "/usr/lib/python3.8/subprocess.py", line 1704, in _execute_child
    raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'convert'

What does this mean, please? Why add "to_dense_batch" to process the location information x and category label y?

for i, data in enumerate(train_dataloader): data = data.to(device) label, mask = to_dense_batch(data.y, data.batch)#？？？？？？？？？？？？？？？ bbox_real, _ = to_dense_batch(data.x, data.batch)#？？？？？？？？？？？？？？？

Could you share a script to train LayoutNet for FID calculation? I see the model architecture file, could you maybe share the code to train it or the loss functions used?

I tried re-training LayoutGAN++ used magazine dataset. But, I can't reappearance Quantitative comparison, especially overlap. Results of multiple trials, overlap average 40 over.

ex.

Dataset: magazine
        FID: 13.88
        Max. IoU: 0.26
        Alignment: 0.82
        Overlap: 40.31

I think using the hyperparameters from the paper. By any chance, cause in the version of os or library?

NAME="Ubuntu"
VERSION="20.04.4 LTS (Focal Fossa)"

pytorch-fid==0.2.1
torch==1.11.0+cu113
torch-cluster==1.6.0
torch-geometric==2.0.4
torch-scatter==2.0.9
torch-sparse==0.6.13
torch-spline-conv==1.2.1
torchaudio==0.11.0+cu113
torchvision==0.12.0+cu113