Range-GAN

This repository can be used to reproduce the results presented in the paper: Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis

Required packages

• tensorflow > 2.0.0
• numpy
• matplotlib
• tensorflow_addons
• tensorflow_probability
• tqdm
• Pymcubes

Project Page

For further information and latest news on the project visit Range-GAN

Usage

3D Shape Synthesis Background

In Range-GAN we take the approach presented in IM-NET to generate 3D shapes. In our project we use the Airplane models in ShapeNet to train an IMAE model to encode 3D shapes into 256 dimensional vectors. If you want to experiment with other 3D shapes please refer to the IM-NET Code. Here we provide the weights of the trained IMAE model we used for our paper (in the Weights directory).

Data

The data used in this paper is the airplane subset of the ShapeNET dataset. However, we only use the encodings produced by IMAE to train our GAN model. The pre calculated values are also provided in the data folder.

As discussed in the paper we also augment our data to produce better results. This augmentation is arduous and involves significant manual curation. Therefore, to allow for the replication of the results in the paper we have also included the augmented dataset we used in our work. To reproduce the results in the paper please use the augmented data instead. (Augmentation Code will be provided in the near future with automated curation)

If you require to use your own data, please create hdf5 files with encodings labeled as zs and paramteres that are to be conditioned for using Range-GAN(Further details provided later).

Training

To train using the data provied here and replicate the results of the paper one of the following can be done for each experiment:

1. Train For Aspect Ratio

   python run_experiment.py --param ratio --save_name ratio

   add --data ./augmented to use augmented data (paper results use this)

2. Train For Volume

   python run_experiment.py --param volume --save_name volume

   add --data ./augmented to use augmented data (paper results use this)

3. Multi-Objetive Training

   python run_experiment.py --param both --save_name MO

   add --data ./augmented to use augmented data (paper results use this)

4. Other Paramters Which Can be Adjusted

    optional arguments:
   
    -h, --help           show this help message and exit
    --data DATA           The path to the data. Default: ./data use ./augmented if you create an augmented dataset
    --save_name SAVE_NAME
                            The file name of the checkpoint saved in the Weights folder. Default: experiment
    --estimator_lr ESTIMATOR_LR
                            Initial estimator learning rate before decay. Default: 1e-4
    --estimator_train_steps ESTIMATOR_TRAIN_STEPS
                            Number of training steps for estimator. Default: 10000
    --estimator_batch_size ESTIMATOR_BATCH_SIZE
                            Batch size for estimator Default: 128
    --phi PHI             phi. Default: 50.0
    --lambda1 LAMBDA1     lambda1. Default: 4.0
    --lambda2 LAMBDA2     lambda2. Default: 0.1
    --disc_lr DISC_LR     Initial discriminator learning rate before decay. Default: 1e-4
    --gen_lr GEN_LR       Initial discriminator learning rate before decay. Default: 1e-4
    --train_steps TRAIN_STEPS
                            Number of training steps. Default: 50000
    --batch_size BATCH_SIZE
                            Batch size used for GAN training. Default: 32
    --custom_data CUSTOM_DATA
                            If custom data is being used then add this flag and indicate the names of parameters in
                            custom_param values.
    --param PARAM         The parameter to train for. Default: ratio. Either one of: ratio, volume, both
    --custom_dataset CUSTOM_DATASET
                            The name of the dataset in the data folder. Default: None. Depends on the custom dataset.
    --custom_param1 CUSTOM_PARAM1
                            The parameter to train for(Only if using custom dataset). Default: None. Depends on the custom
                            dataset.
    --custom_param2 CUSTOM_PARAM2
                            The parameter to train for(Only if using custom dataset). Default: None. Depends on the custom
                            dataset.

   The trained models will be saved under the specified folder under the subdirectory of Weights under the each models folder and the result plots will be saved under the results directory. Change save_name argument to control this.

   Note that we can set lambda2 to zero to train without uniformity loss.

5. Using A Custom Dataset

   python run_experiment.py --custom_data --custom_dataset dataset_name --custom_param1 param1 --custom_param2 param2

   Here you need to add the --custom_data flag and indicate the dataset_name. The dataset_name is the name of the hdf5 files in the data folder. Note that you must have a test set and a training set in the data folder. The custom_param1 argumnets are the name of the parameters in the hdf5 files. param2 is optional if multi-objective is being investigated.

6. Generating 3D samples To generate 3D samples using any saved model use:

   python generate_samples.py --save_name model_checkpoint

   Where you pass the save name of the model that was trained as described before. To set other parameters like range and number of samples, etc. see:

    optional arguments:
    -h, --help            show this help message and exit
    --data DATA           The path to the data. Default: ./data use ./augmented if you create an augmented dataset
    --random_seed RANDOM_SEED
                            Set the random seed used to generate samples.
    --save_name SAVE_NAME
                            The file name of the checkpoint saved in the Weights folder. Default: experiment
    --batch_size BATCH_SIZE
                            Batch size used for IMAE. Default: 200000. Change based on GPU performance.
    --resolution RESOLUTION
                            Resolution of the voxel model generated. Default: 256. Determines the quality of samples.
    --n_samples N_SAMPLES
                            Number of samples to generate.
    --param_type PARAM_TYPE
                            Which type of parameter. Default: ratio. Either of: ratio, volume, both, custom. is not custom
                            the actual label will be measured.
    --only_valid ONLY_VALID
                            If this flag is raised only samples that meet the input condition will be saved (less than
                            n_samples). Only for non-custom cases.
    --param1_start PARAM1_START
                            Start of range for parameter 1. Defaulr: 0.0
    --param1_end PARAM1_END
                            end of range for paramter 1. Default: 0.1
    --param2_start PARAM2_START
                            Start of range for parameter 2. Default: None. Only input if multi-objective model is used.
    --param2_end PARAM2_END
                            End of range for parameter 2. Default: None. Only input if multi-objective model is used.