Course Project of IEMS5726 Data Science in Practice (Fall 2023)

Here is the course project files of WANG XIANGZHI. The topic is Image Inpainting under Deep Image Processing. This specific project provides full pipeline of both non-deep and deep learning methods on image inpainting tasks.

All references are well-citated, and all codes are well documented and commented. This project is under MIT License, and doesn't disclose any sensitive and copyright-protected information about the course and lecturer.

Date of Creation: Dec-16-2023
Github URL: https://github.com/WPCJATH/Image_Inpainting

* The repository is made publicly available after the project DUE DATE: Dec-21-2023

Author Information

NAME: WANG Xiangzhi
STUDENT ID: 1155200446
E-MAIL: 1155200446@link.cuhk.edu.hk

Installation

This project is compeletly runnable on a windows machine with Nvidia GPU and conda environment, other platform or environment is not tested.

To install the conda environment with GPU support, run the commands below:

conda env create --file environment.yml
conda activate inpainting
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

or execute the batch file env_install_conda.bat

Web Demo

Run the command below:

conda activate inpainting
python app.py

Or execute the batch file start_service.bat

Train

The training config is at train-place.yaml with commented instructions

Pre-trained model URL: https://drive.google.com/u/0/uc?id=1L63oBNVgz7xSb_3hGbUdkYW1IuRgMkCa&export=download
Please put the pre-trained model under folder model/pretrained

Retrained model URL: https://drive.google.com/drive/folders/1d3PVcqljGTz_Z8Dc1J1aP-KdIsLvrtbR?usp=sharing
Please put the retrained model under folder model/retrained and specify the retrain model name in the config file (states_25000.pth is the default)

Data URL: The same as the retrained model Please put the unzipped data folder under folder data/, so the data can be accessed by path data/snowfield

Run the below command:

conda activate inpainting
python train_model.py

Visualization and evaluation

Run the below command:

conda activate inpainting
python data_visiualization.py

Training logging

Download the training log at the same url as the retrained model, and put the log file under path tb_logs/places/model_retrain
Run the following command:

conda activate inpainting
tensorboard --logdir=tb_logs\places2\model_retrain --host localhost --port 8088

Or execute the batch file tensorboard_log.bat

Then, the log can be viewed under http://localhost:8088

License

The MIT License (MIT)

Copyright (c) 2023 Eric Wang

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.