A report and live demo are available here
To create environment using Conda and yaml
files:
conda env create -f environment.yaml
To activate created environment:
conda activate image-inpainting
To parametrize network or training:
Edit
yaml
adequate file in the/cfg
directory.
To integrate training with neptune:
For more details see: https://docs.neptune.ai/getting-started/hello-world
Create
/src/neptune.yaml
file containing project name and token using temaplate in the file/src/neptune_template.yaml
To run the training process:
python train.py
To run the training process in debugging mode:
Debugging stops logging to neptune and display intermediate results to standard output.
python train.py --debug
To automatically reproduce the entire training with a current selection of parameters in cfg
folder:
dvc repro
This will automatically run the following DVC stages:
generate_data
- generation of partial dataset with masked areastrain_model
- trains GAN-based architechture
To perform inference using generator run:
python infer.py --statedict path_to_statedict --images 10
Where--path_to_statedict
stands for file to a pickled generators state dict and--images
stands for number of images to use. Specifying number of images may be omitted.