OpenNIG (Open Neural Image Generator) is a toolkit that generates new images from a given distribution. Its role is to accelerate research in that direction by offering a flexible and easy to use ecosystem for such models.
Simply clone the repository and install the requirements.
git clone https://github.com/avramandrei/OpenNIG.git
cd OpenNIG/
pip3 install -r requirements.txt
| Model | Dataset | Iterations | Samples |
|---|---|---|---|
| DCVAESmall | mnist | 1k |           |
| DCGANSmall | mnist | 2.5k |           |
| DCVAESmall | fashion-mnist | 5k |           |
| DCGANSmall | fashion-mnist | 10k |           |
| DCVAEMedium | cifar10 | 100k |           |
| DCGANMedium | cifar10 | 150k |           |
Note: The above samples are just some examples of the generated images during training. The final results can be improved by tuning the hyperparameters of the models.
OpenNIG offers three demos databases that can be downloaded with the download.py script: mnist, fashion-mnist and cifar10. The images will be saved in two directories, train and valid, in data/raw/<database>.
python3 download.py [database]
| Argument | Type | Description |
|---|---|---|
| dataset | str | Dataset to be downloaded: mnist, fashion-mnist or cifar10. |
This step is optional, if you already have your own preprocessing pipeline, please proceed to the next section, but remember that OpenNIG uses NumPy's .npy data format.
If you want to use other datasets, you have to manually create two directories, train and valid, that contain the training and the validation images, respectevly. Run the process.py script to process the images in these directories. The script will create two files, train.npy and valid.npy.
python3 process.py [train_dir_path] [valid_dir_path]
[output_path]
[--normalize]
[--reshape]
[--flip_left_right]
[--flip_top_bottom]
| Argument | Type | Description |
|---|---|---|
| train_dir_path | str | Path to the train directory containing the training images. |
| valid_dir_path | str | Path to the valid directory containing the validation images. |
| output_path | str | Path where processed data will be saved |
| --normalize | str | Normalize data to [-1,1] or [0,1]. Default: [-1,1]. |
| --reshape_y | str | Reshape x data to specified shape. Shape must be specified as (width,height). Default: None. |
| --reshape_x | str | Reshape y data to specified shape. Shape must be specified as (width,height). Default: None. |
| --flip_left_right | bool | Adds 50% horizontally flipped images to the dataset. Default: False. |
| --flip_top_bottom | bool | Adds 50% vertically flipped images to the dataset. Default: False. |
To train, run the train.py script. This script automatically generates 10 GIF images in <save_checkpoint_path>/samples, that show how the training process evolves at every checkpoint.
python3 train.py [model]
[train_path] [valid_path]
[--optimizer] [--learning_rate] [--iterations] [--batch_size] [--label_smooth]
[--save_checkpoint_steps] [--save_checkpoint_path]
[--valid_batch_size] [--valid_steps]
[--generate_train_samples] [--num_train_samples]
| Argument | Type | Description |
|---|---|---|
| model | str | Type of the model: DCVAESmall, DCVAEMedium, DCVAEBig, DCGANSmall, DCGANMedium, DCGANBig. |
| --model_path | str | Load the model weights from this path. |
| train_path | str | Path to the train data, saved as a .npy file. |
| valid_path | str | Path to the validation data, saved as a .npy file. |
| --optimizer | str | Name of the optimizer, as described in https://keras.io/optimizers/. Default value: "Adam" |
| --learning_rate | float | Learning rate of the optimizer. Default: 0.001. |
| --iterations | int | Number of training steps. Default: 100000. |
| --batch_size | int | Batch size for training. Defaul: 32. |
| --save_checkpoint_steps | int | Save a checkpoint every X steps. Default: 1000 |
| --save_checkpoint_path | str | Save the model at this path every --save_checkpoint_steps. Default: trained_model/model |
| --valid_batch_size | int | Batch size for validation. Defaul: 32. |
| --valid_steps | int | Perfom validation every X steps. Default: 250. |
| --generate_train_samples | bool | Whether to generate samples during training. Default: True. |
| --num_train_samples | int | Number of generated training samples. Default: 10. |
| --label_smooth | float | Number used for label smoothing. Default: 0 |
To generate new images, run generate.py.
python3 generate.py [model] [model_path] [--num_sample] [--sample_save_path][--normalize]
| Argument | Type | Description |
|---|---|---|
| model | str | Type of the model. Here is a list of all the available models. |
| model_path | str | Load the model from this path. |
| --num_sample | int | Number of samples to generate.Default: 10. |
| --sample_save_path | str | Save the samples at this path. Default: samples. |
If you found this work useful, please consider citing the following paper:
@inproceedings{avram2020opennig,
title={OpenNIG-Open Neural Image Generator},
author={Avram, Andrei-Marius and Morogan, Luciana and Toma, Stefan-Adrian},
booktitle={2020 13th International Conference on Communications (COMM)},
pages={177--181},
year={2020},
organization={IEEE}
}
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