EyeNetPlus

Preparation

• Clone this repository.

• There are two ways to prepare the environment. (However, it is recommended to use the docker container)

a. Using docker container.

You can install my docker container:

docker pull ausmlab/mrnet:cuda10.2-cudnn7-py3.7_pytorch1.5_TF2.9.1_automl

b. Setting up the environment on your own.

The code has been tested with Python 3.7, Tensorflow 11.1, Cuda 10.2, cuDNN 7.4.1 on Ubuntu 16.04.

• Create Conda Environment:

conda create -n eyenet python=3.5 source activate eyenet

• You need to update pip:

curl https://bootstrap.pypa.io/pip/3.5/get-pip.py -o get-pip.py python get-pip.py

• Install Required Libraries and compile custom libraries:

pip install -r helper_requirements.txt conda install -c conda-forge zip sh compile_op.sh conda install cudatoolkit=9.0

Sensat Urban

• Download the SensatUrban Dataset from the official website (https://github.com/QingyongHu/SensatUrban).

• cambridgeblock0.ply and cambridgeblock1.ply contain less than 4mb of data, so they have to be removed before processing.

• Pre-processing dataset (Grid Sampling) by running: python data_processing/input_preparation_Sensat.py --dataset_path "YOUR_DATA_PATH" --output_path "YOUR_OUTPUT_PATH" Note: Grid size can be also adjusted for further details, please refer to the code.

• Start Training

python main_Sensat.py

Note: Before Training, please modify datasetdir (line 21) to your sampled data directory in tool.py.

• Start Evaluation on validation set (for visualization):

python main_Sensat.py --mode val --model_path "YOUR_SAVED_MODEL"

Note: saved models are located in the "trained_weights/Sensat" folder.

Note: The "YOURSAVEDMODEL" path has to include snap-NumberofSteps e.g. trainedweights/Sensat/FirstTrain/snapshots/snap-17001.

• Start Evaluation on test set:

python main_Sensat.py --mode test --model_path "YOUR_SAVED_MODEL"

• The folder that contains the submission file will be saved in the "test" folder.
• This code will generate the submission file for submitting your result on the Online Server (https://codalab.lisn.upsaclay.fr/competitions/7113).

DALES

• Downloading the LAS version of the DALES data set from the website https://udayton.edu/engineering/research/centers/visionlab/research/wasdataanalysisand_processing/dale.php. Ply version does not include the number of return feature.

• Pre-processing dataset (Grid Sampling) by running:

python data_processing/input_preparation_DALES.py --dataset_path "YOUR_DATA_PATH" --output_path "YOUR_OUTPUT_PATH"

• Start Training:

python main_DALES.py Note: Before Training, please modify datasetdir (line 73) to your sampled data directory in tool.py.

• Start Evaluation: python main_DALES.py --mode test --model_path "YOUR_SAVED_MODEL" Note: saved models are located in the "trainedweights/DALES" folder. Note: The "YOURSAVEDMODEL" path has to include snap-NumberofSteps e.g. trainedweights/DALES/First_Train/snapshots/snap-17001.

• The evaluation results will be saved in the "test" folder.

Toronto3D

• If you have access to our Nas2 server, you can just download all dataset from NAS2/VM/jacob/data/Toronto3D

• Pre-processing dataset (Grid Sampling) by running: python data_processing/input_preparation_toronto3D.py --dataset_path "YOUR_DATA_PATH" --output_path "YOUR_OUTPUT_PATH"

• Start Training:

python main_Toronto3D.py Note: Before Training, please modify datasetdir (line 125) to your sampled data directory in tool.py.

• Start Evaluation: python main_Toronto3D.py --mode test --model_path "YOUR_SAVED_MODEL" Note: saved models are located in the "trainedweights/Toronto3D" folder. Note: The "YOURSAVEDMODEL" path has to include snap-NumberofSteps e.g. trainedweights/Toronto3D/First_Train/snapshots/snap-17001.

• The evaluation results will be saved in the "test" folder.