Prerequisites

• Ubuntu 16.04
• Python 3.7
• CUDA 11.1 (lower versions may work but were not tested)
• NVIDIA GPU (>= 11G graphic memory) + CuDNN v7.3

This repository has been tested on RTX 3090. Configurations (e.g batch size, image patch size) may need to be changed on different platforms.

Installation

• Clone this repo:

cd CDARTS_segmentation

• Install dependencies:

bash install.sh

Usage

0. Prepare the dataset

• Download the leftImg8bit_trainvaltest.zip and gtFine_trainvaltest.zip from the Cityscapes.
• Prepare the annotations by using the createTrainIdLabelImgs.py.
• Put the file of image list into where you save the dataset.

1. Train from scratch

• cd HRTNet/train
• Set the dataset path via ln -s $YOUR_DATA_PATH ../DATASET
• Set the output path via mkdir ../OUTPUT
• Train from scratch

export DETECTRON2_DATASETS="$Your_DATA_PATH"
NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --world_size $NGPUS --seed 12367 --config ../configs/cityscapes/cydas.yaml

2. Evaluation

We provide training models and logs, which can be downloaded from Google Drive.

cd train

• Download the pretrained weights of the from Google Drive.
• Set config.model_path = $YOUR_MODEL_PATH in cydas.yaml.
• Set config.json_file = $CDARTS_MODEL in cydas.yaml.
• Start the evaluation process:

CUDA_VISIBLE_DEVICES=0 python test.py