jw9730 / setvae Goto Github PK

Initially, i received an error regarding "maximum recursion depth exceeded while calling a Python object". I then set recursion limit to the highest python allows:

import sys, threading sys.setrecursionlimit(10**7)

Following which the training exits without any informative error
[2022-08-26 06:40:38,321] [INFO] [launch.py:286:sigkill_handler] Killing subprocess 1122823 [2022-08-26 06:40:38,321] [ERROR] [launch.py:292:sigkill_handler] ['/nfshome/USERNAME/.conda/envs/setvae/bin/python', '-u', 'train.py', '--local_rank=0', '--cates', 'tooth', '--input_dim', '3', '--max_outputs', '2500', '--init_dim', '32', '--n_mixtures', '4', '--z_dim', '16', '--z_scales', '1', '1', '2', '4', '8', '16', '32', '--hidden_dim', '64', '--num_heads', '4', '--num_workers', '0', '--kl_warmup_epochs', '250', '--fixed_gmm', '--train_gmm', '--lr', '1e-3', '--beta', '1.0', '--epochs', '1000', '--dataset_type', 'shapenet15k', '--log_name', 'gen/shapenet15k/camera-ready', '--shapenet_data_dir', '/train/SetVae/ShapeNetCore.v2.PC15k', '--save_freq', '25', '--viz_freq', '1000', '--log_freq', '10', '--val_freq', '10000', '--scheduler', 'linear', '--slot_att', '--ln', '--eval', '--seed', '42', '--distributed', '--deepspeed_config', 'batch_size.json'] exits with return code = -11

Hi there,

Thank you for providing the codes.

I got this exception when running shapenet_airplane_test.sh in the docker image:

RuntimeError: CUDA kernel failed : 209

Any help would be appreciated.

tdn = self.top_down((~x_mask).sum(-1), list(reversed(bup['features'])))

AttributeError: 'SetVAE' object has no attribute 'optimizer'

Hello, thank you for releasing the code for your SetVAE paper.

I had a question about this line:

For the first decoder block, why is the residual from the hidden state ignored?

Thanks

Unexpected key(s) in state_dict: "encoder.0.att1.ln_x.weight", "encoder.0.att1.ln_x.bias", "encoder.0.att1.ln_y.weight", "encoder.0.att1.ln_y.bias", "encoder.0.att1.ln_o1.weight", "encoder.0.att1.ln_o1.bias", "encoder.0.att1.ln_o2.weight", "encoder.0.att1.ln_o2.bias", "encoder.0.att2.ln_x.weight", "encoder.0.att2.ln_x.bias", "encoder.0.att2.ln_y.weight", "encoder.0.att2.ln_y.bias", "encoder.0.att2.ln_o1.weight", "encoder.0.att2.ln_o1.bias", "encoder.0.att2.ln_o2.weight", "encoder.0.att2.ln_o2.bias", "encoder.1.att1.ln_x.weight", "encoder.1.att1.ln_x.bias", "encoder.1.att1.ln_y.weight", "encoder.1.att1.ln_y.bias", "encoder.1.att1.ln_o1.weight", "encoder.1.att1.ln_o1.bias", "encoder.1.att1.ln_o2.weight", "encoder.1.att1.ln_o2.bias", "encoder.1.att2.ln_x.weight", "encoder.1.att2.ln_x.bias", "encoder.1.att2.ln_y.weight", "encoder.1.att2.ln_y.bias", "encoder.1.att2.ln_o1.weight", "encoder.1.att2.ln_o1.bias", "encoder.1.att2.ln_o2.weight", "encoder.1.att2.ln_o2.bias", 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"encoder.6.att1.ln_y.weight", "encoder.6.att1.ln_y.bias", "encoder.6.att1.ln_o1.weight", "encoder.6.att1.ln_o1.bias", "encoder.6.att1.ln_o2.weight", "encoder.6.att1.ln_o2.bias", "encoder.6.att2.ln_x.weight", "encoder.6.att2.ln_x.bias", "encoder.6.att2.ln_y.weight", "encoder.6.att2.ln_y.bias", "encoder.6.att2.ln_o1.weight", "encoder.6.att2.ln_o1.bias", "encoder.6.att2.ln_o2.weight", "encoder.6.att2.ln_o2.bias", "decoder.0.att1.ln_x.weight", "decoder.0.att1.ln_x.bias", "decoder.0.att1.ln_y.weight", "decoder.0.att1.ln_y.bias", "decoder.0.att1.ln_o1.weight", "decoder.0.att1.ln_o1.bias", "decoder.0.att1.ln_o2.weight", "decoder.0.att1.ln_o2.bias", "decoder.0.att2.ln_x.weight", "decoder.0.att2.ln_x.bias", "decoder.0.att2.ln_y.weight", "decoder.0.att2.ln_y.bias", "decoder.0.att2.ln_o1.weight", "decoder.0.att2.ln_o1.bias", "decoder.0.att2.ln_o2.weight", "decoder.0.att2.ln_o2.bias", "decoder.1.att1.ln_x.weight", "decoder.1.att1.ln_x.bias", "decoder.1.att1.ln_y.weight", 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"decoder.5.att1.ln_o1.bias", "decoder.5.att1.ln_o2.weight", "decoder.5.att1.ln_o2.bias", "decoder.5.att2.ln_x.weight", "decoder.5.att2.ln_x.bias", "decoder.5.att2.ln_y.weight", "decoder.5.att2.ln_y.bias", "decoder.5.att2.ln_o1.weight", "decoder.5.att2.ln_o1.bias", "decoder.5.att2.ln_o2.weight", "decoder.5.att2.ln_o2.bias", "decoder.6.att1.ln_x.weight", "decoder.6.att1.ln_x.bias", "decoder.6.att1.ln_y.weight", "decoder.6.att1.ln_y.bias", "decoder.6.att1.ln_o1.weight", "decoder.6.att1.ln_o1.bias", "decoder.6.att1.ln_o2.weight", "decoder.6.att1.ln_o2.bias", "decoder.6.att2.ln_x.weight", "decoder.6.att2.ln_x.bias", "decoder.6.att2.ln_y.weight", "decoder.6.att2.ln_y.bias", "decoder.6.att2.ln_o1.weight", "decoder.6.att2.ln_o1.bias", "decoder.6.att2.ln_o2.weight", "decoder.6.att2.ln_o2.bias".

Hi,

Is the attention visualization created by training a model for one class?

can you please introduce how do you conduct the exp of generation of a scene layout?

Hi,
Thanks for the awesome work. I'm trying to run the code, via non-docker method (because I only have non-sudo access to my server), and I encounter this error when running bash install.sh. Here is the error message.

mv: cannot stat 'build/lib.linux-x86_64-3.6/StructuralLosses': No such file or directory
Makefile:75: recipe for target 'all' failed
make: *** [all] Error 1

What should I do?

I notice that without above installation, errors will be encountered in metrics/evaluation_metrics.py/metrics.StructuralLosses. For now, I only want to run the code with dummy data to see the data flow (value and each variable shape) to help me understand the details of the paper. Thus, running the non-optimal one (no cuda version) is also OK for me. Any suggestion?

Many thanks!