Facade Project

Semester Project at Swiss Data Science Center @EPFL

Description

This project is part of collaboration between civil engineers and the Swiss Data Science Center (SDSC). Its goal is to help civil engineers automating the process of evaluating the damage on a building after an earthquake occurred. This report focuses on a sub-part of this task, which is identifying the building structure, where are the walls, windows, doors, etc.

Find more details about the project in the report.pdf.

Install

To install the facade_project library and use it within your python environment:

pip install -e .

where . is the root directory of this git repository, containing the installer (setup.py file).

Note that the Docker container already includes the library.

Dependencies

Please use this docker image which contains every single dependency in order to be sure that each line of code run smoothly.

docker pull gregunz/jupyterlab:facade_project

It requires nvidia-docker and CUDA 10.0.

Find in directory docker, scripts to start your docker environment easily.

Source code for the docker image can be found here.

Data

All the data are stored at /data inside the docker. Then the structure is the following:

/data/heatmaps/{json, tensor}/ stores respectively heatmaps as json and tensors
/data/images/{coco, labelme, png, tensor}/ stores images respectively as coco like dataset, labelme tool json, png and tensors
/data/models/<model_name> stores the parameters and weights of each model trained using the scripts/run.py script

Note that for example, /data/*/tensor does not contain all tensors directly, they are divided into directories in case one want to generate data differently. At the moment, rotated_rescaled folder contains latest generated version of the dataset.

How-to-use

Find use examples in notebooks and scripts.

Training

To train, the scripts/run.py script is ready to use:

usage: run.py [-h] [--model MODEL] [--epochs EPOCHS] [--split-seed SPLIT_SEED]
              [--batch-train BATCH_TRAIN] [--batch-val BATCH_VAL] [--wf WF]
              [--lr LR] [--not-pretrained]
              [--predictions PREDICTIONS [PREDICTIONS ...]]
              [--pred-weights PRED_WEIGHTS [PRED_WEIGHTS ...]]
              [--path-for-weights PATH_FOR_WEIGHTS] [--device DEVICE]
              [--use-ce] [--use-scheduler] [--crop-size CROP_SIZE]
              [--load-trained-name LOAD_TRAINED_NAME]
              [--load-trained-epoch LOAD_TRAINED_EPOCH]

Script to perform facade parsing

optional arguments:
  -h, --help            show this help message and exit
  --model MODEL
  --epochs EPOCHS
  --split-seed SPLIT_SEED
  --batch-train BATCH_TRAIN
  --batch-val BATCH_VAL
  --wf WF
  --lr LR
  --not-pretrained
  --predictions PREDICTIONS [PREDICTIONS ...]
  --pred-weights PRED_WEIGHTS [PRED_WEIGHTS ...]
  --path-for-weights PATH_FOR_WEIGHTS
  --device DEVICE
  --use-ce
  --use-scheduler
  --crop-size CROP_SIZE
  --load-trained-name LOAD_TRAINED_NAME
  --load-trained-epoch LOAD_TRAINED_EPOCH

For example:

python3 run.py --batch-train=4 --epochs=25 --use-dice=true --device=cuda:0 --model=albunet --pretrained=true --pred-weights 1 0.005

One can also use the scipts/run.sh script to launch sequentially many times the scripts.run.py script with different parameters:

./run.sh 0 albunet 4

The arguments being respectively the cuda device index, name of the model and training batch size are optional.

Predicting

For predictions, one should look into notebooks/nb_predictions.ipynb

Datasets

notebooks/nb_demo_datasets.ipynb showcase how to use the datasets implemented in this project.

Generating

notebooks/nb01_generate_image_rotated_rescaled_tensors.ipynb shows the current way to generate the dataset (tensors).

notebooks/nb02_generate_image_rotated_rescaled_tensors.ipynb shows another way to generate the dataset.

notebooks/nb_generate_heatmaps_tensors.ipynb shows how the current heatmaps tensors are generated.

notebooks/labelme_to_coco.ipynb shows how to from labelme files to a COCO style dataset.

Else

notebooks/labelme_to_heatmaps_info.ipynb shows how heatmaps info are extracted from a labelme file.

notebooks/nb_tensorboard_to_csv.ipynb show how to export into csv the tensorboard logs generated by the scripts/run.py script.

Plots

Plots showing performance, statistics and script to generate them can be found in the plots directory.

gregunz/FacadeParsing