• Contents
• Description
  • Citing
• Model architecture
• Dataset
• Environment Requirements
• Script description
  • Script and sample code
  • Training process
  • Eval process
  • Inference Process
    • Export MindIR
    • Infer on Ascend310
• ModelZoo Homepage

Hp-vae-gan uses a single image or video sample to generate different but similar new samples.

Paper Gur S , Benaim S , Wolf L . Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample[J]. 2020.

The BibTex citing format for this repository is as follows:

@article{hp-vae-gan,
  title={Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample},
  journal={Github repository},
  publisher={Github},
  year={2022},
  howpublished={\url{https://github.com/SakiRinn/mindspore-hp-vae-gan}}
}

The overall network architecture of hp-vae-gan is show below:

Link

Just a picture or a video. It can be specified by the user.

• Data format: RGB images.
• Note: We provide a sample dataset in ./data folder.

• Hardware(Ascend/GPU/CPU)
  • Prepare hardware environment with Ascend, GPU or CPU processor.
• Framework
  • MindSpore
• For more information, please check the resources below:
  • MindSpore Tutorials
  • MindSpore Python API

.
├── LICENSE
├── README.md
├── ascend310_infer
│   ├── CMakeLists.txt
│   ├── build.sh
│   ├── inc
│   │   └── utils.h
│   └── src
│       ├── main.cc
│       └── utils.cc
├── data                        # Sample dataset
│   ├── imgs
│   │   └── air_balloons.jpg
│   └── vids
│       └── air_balloons.mp4
├── eval_image.py
├── eval_video.py
├── export.py
├── postprocess.py
├── preprocess.py
├── requirements.txt
├── scripts
│   ├── run_eval_ascend.sh      # script for evaluation on Ascend 910
│   ├── run_infer_310.sh        # script for inference on Ascend 310
│   └── run_train_ascend.sh     # script for training on Ascend 910
├── src
│   ├── __init__.py
│   ├── datasets
│   │   ├── __init__.py
│   │   ├── generate_frames.py
│   │   ├── image.py
│   │   └── video.py
│   ├── modules
│   │   ├── __init__.py
│   │   ├── losses.py
│   │   ├── networks_2d.py
│   │   ├── networks_3d.py
│   │   └── optimizers.py
│   ├── sinFID
│   │   ├── __init__.py
│   │   ├── c3d.py
│   │   ├── fid_score.py
│   │   └── inception.py
│   ├── tools
│   │   ├── __init__.py
│   │   ├── pt2ms.py
│   │   ├── spectral_norm.py
│   │   └── trilinear.py
│   └── utils
│       ├── __init__.py
│       ├── extract.py
│       ├── images.py
│       ├── logger.py
│       ├── progress_bar.py
│       └── saver.py
├── train_image.py
├── train_video.py
└── train_video_baselines.py

You can start training using python or shell scripts. The usage of shell scripts as follows:

sh scripts/run_train_ascend.sh IMAGE_PATH [DEVICE_ID]

• IMAGE_PATH: The filename of the training image.
• DEVICE_ID: The number of the Ascend device.

You can start evaluation using python or shell scripts. The usage of shell scripts as follows:

sh scripts/run_eval_ascend.sh EXPERIMENT_DIR [DEVICE_ID]

• EXPERIMENT_DIR: The directory to the training output folder.
• DEVICE_ID: The number of the Ascend device.

Export MindIR on local.

python export.py --exp-dir [EXP_DIR] --device-id [DEVICE_ID]

• EXP_DIR: The directory to the training output folder.
• DEVICE_ID: The number of the Ascend device.

Before performing inference, the mindir file must bu exported by export.py script. We only provide an example of inference using MINDIR model.

sh scripts/run_infer_image_310.sh EXPERIMENT_DIR [DEVICE_ID]

• EXPERIMENT_DIR: The directory to the training output folder.
• DEVICE_ID: The number of the Ascend device.

Please check the official homepage.