export DATASET="isbi"

rm -rf data/
mkdir -p data

if [[ "$DATASET" == "isbi" ]]; then
    # binary segmentation
    # http://brainiac2.mit.edu/isbi_challenge/
    download-gdrive 1uyPb9WI0t2qMKIqOjFKMv1EtfQ5FAVEI isbi_cleared_191107.tar.gz
    tar -xf isbi_cleared_191107.tar.gz &>/dev/null
    mv isbi_cleared_191107 ./data/origin
elif [[ "$DATASET" == "voc2012" ]]; then
    # semantic segmentation
    # http://host.robots.ox.ac.uk/pascal/VOC/voc2012/
    wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
    tar -xf VOCtrainval_11-May-2012.tar &>/dev/null
    mkdir -p ./data/origin/images/; mv VOCdevkit/VOC2012/JPEGImages/* $_
    mkdir -p ./data/origin/raw_masks; mv VOCdevkit/VOC2012/SegmentationClass/* $_
fi

Data structure

Make sure, that final folder with data has the required structure:

/path/to/your_dataset/
        images/
            image_1
            image_2
            ...
            image_N
        raw_masks/
            mask_1
            mask_2
            ...
            mask_N

Data location

• The easiest way is to move your data:

  mv /path/to/your_dataset/* /catalyst.segmentation/data/origin
  

  In that way you can run pipeline with default settings.

• If you prefer leave data in /path/to/your_dataset/

  • In local environment:

    • Link directory

      ln -s /path/to/your_dataset $(pwd)/data/origin
      

    • Or just set path to your dataset DATADIR=/path/to/your_dataset when you start the pipeline.

  • Using docker

    You need to set:

       -v /path/to/your_dataset:/data \ #instead default  $(pwd)/data/origin:/data
    

    in the script below to start the pipeline.

Run in local environment:

CUDA_VISIBLE_DEVICES=0 \
CUDNN_BENCHMARK="True" \
CUDNN_DETERMINISTIC="True" \
WORKDIR=./logs \
DATADIR=./data/origin \
IMAGE_SIZE=256 \
CONFIG_TEMPLATE=./configs/templates/binary.yml \
NUM_WORKERS=4 \
BATCH_SIZE=256 \
bash ./bin/catalyst-binary-segmentation-pipeline.sh

Run in docker:

export LOGDIR=$(pwd)/logs
docker run -it --rm --shm-size 8G --runtime=nvidia \
   -v $(pwd):/workspace/ \
   -v $LOGDIR:/logdir/ \
   -v $(pwd)/data/origin:/data \
   -e "CUDA_VISIBLE_DEVICES=0" \
   -e "USE_WANDB=1" \
   -e "LOGDIR=/logdir" \
   -e "CUDNN_BENCHMARK='True'" \
   -e "CUDNN_DETERMINISTIC='True'" \
   -e "WORKDIR=/logdir" \
   -e "DATADIR=/data" \
   -e "IMAGE_SIZE=256" \
   -e "CONFIG_TEMPLATE=./configs/templates/binary.yml" \
   -e "NUM_WORKERS=4" \
   -e "BATCH_SIZE=256" \
   catalyst-segmentation ./bin/catalyst-binary-segmentation-pipeline.sh

Run in local environment:

CUDA_VISIBLE_DEVICES=0 \
CUDNN_BENCHMARK="True" \
CUDNN_DETERMINISTIC="True" \
WORKDIR=./logs \
DATADIR=./data/origin \
IMAGE_SIZE=256 \
CONFIG_TEMPLATE=./configs/templates/semantic.yml \
NUM_WORKERS=4 \
BATCH_SIZE=256 \
bash ./bin/catalyst-semantic-segmentation-pipeline.sh

Run in docker:

export LOGDIR=$(pwd)/logs
docker run -it --rm --shm-size 8G --runtime=nvidia \
   -v $(pwd):/workspace/ \
   -v $LOGDIR:/logdir/ \
   -v $(pwd)/data/origin:/data \
   -e "CUDA_VISIBLE_DEVICES=0" \
   -e "USE_WANDB=1" \
   -e "LOGDIR=/logdir" \
   -e "CUDNN_BENCHMARK='True'" \
   -e "CUDNN_DETERMINISTIC='True'" \
   -e "WORKDIR=/logdir" \
   -e "DATADIR=/data" \
   -e "IMAGE_SIZE=256" \
   -e "CONFIG_TEMPLATE=./configs/templates/semantic.yml" \
   -e "NUM_WORKERS=4" \
   -e "BATCH_SIZE=256" \
   catalyst-segmentation ./bin/catalyst-semantic-segmentation-pipeline.sh

• Common settings of stages of training and model parameters can be found in catalyst.segmentation/configs/_common.yml.

  • model_params: detailed configuration of models, including:
    • model, for instance ResnetUnet
    • detailed architecture description
    • using pretrained model
  • stages: you can configure training or inference in several stages with different hyperparameters. In our example:
    • optimizer params
    • first learn the head(s), then train the whole network

• The CONFIG_TEMPLATE with other experiment`s hyperparameters, such as data_params and is here: catalyst.segmentation/configs/templates/binary.yml. The config allows you to define:

  • data_params: path, batch size, num of workers and so on
  • callbacks_params: Callbacks are used to execute code during training, for example, to get metrics or save checkpoints. Catalyst provide wide variety of helpful callbacks also you can use custom.

You can find much more options for configuring experiments in catalyst documentation.