Code for an university project based on PInet_PG

First one has to create an environment with all depencies. We used venv (How to install venv) but conda or pipenv should work too. However the scripts and examples are based on venv and that the environment is named venv.

python3 -m venv venv
source ./venv/bin/activate
pip install -r requirements.txt
## if there are problems with the requirements try and install missing depencies manually
pip install -r requirements.txt --no-deps

Install Pytorch according to the instructions on pytorch.org.

For generating the training data and for running inference two additional pretrained models are required:

• Grapy-ML for human parsing can be found on their Google Drive
• A pose estimator based on HRNet can be found here.

Both required models can be found in assets.tar.gz on Google Drive

Download the images from the In-shop Clothes Retrieval Benchmark from the DeepFashion dataset and split them according to the list provided in fashion_data into the subfolders fashion_data/train/, fashion_data/test/ and fashion_data/val/. Generate additional training data by running scripts/generate_train_data.sh.

See scripts/train_star.sh for examples of configuring a training run.

After installing the requirements and auxiliary models as seen above, the next step is to install the node modules (Javascript dependencies). We are using npm for this which is shipped with node.js (Install node.js). After installing node and npm one can install the node modules:

## switch to the webapp/static folder
cd webapp/static
## install the dependencies from the package-lock.json
npm install

Additional we need to install redis a database for the task queue:

sudo apt install redis

To run the app we need to download some additional files (i.e. the models and video files): (https://drive.google.com/drive/folders/1oOC3G_CMR8hQPDY9ob65meFww77LIdqZ)

## change to top-level directory
cd PINet_PG
## create the folders for the transfer model
mkdir -p checkpoints/fashion_PInet_cycle
## download `latest_net_netG.pth` to `PINet_PG/checkpoints/fashion_PInet_cycle
## download and extract `assets.tar.gz` at top-level
cd PINet_PG
tar -xf assets.tar.gz
## download and extract `videos.tar.gz` in the PINet_PG/webapp/static folder
cd PINet_PG/webapp/static
tar -xf videos.tar.gz

The last step is to run the start_webapp.sh script:

## switch to the top level folder
cd PINet_PG
## run the script
bash start_webapp.sh

## to stop the webapp
ctrl+C

Our model and code is heavily based on PINet_PG, their code is based on Pose-Transfer.
We use some additional code from the Grapy-ML for running the human parsing model.