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SPOCO: Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings

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This repository provides a PyTorch implementation of our method from the paper:

@InProceedings{Wolny_2022_CVPR,
    author    = {Wolny, Adrian and Yu, Qin and Pape, Constantin and Kreshuk, Anna},
    title     = {Sparse Object-Level Supervision for Instance Segmentation With Pixel Embeddings},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {4402-4411}
}

Installation

Checkout the repo and set up conda environment:

conda env create -f environment.yaml

Activate the new environment:

conda activate spoco

Training

This implementation uses DistributedDataParallel training. In order to restrict the number of GPUs used for training use CUDA_VISIBLE_DEVICES, e.g. CUDA_VISIBLE_DEVICES=0 python spoco_train.py ... will execute training on GPU:0.

CVPPP dataset

We used A1 subset of the CVPPP2017_LSC challenge for training. In order to train with 10% of randomly selected objects, run:

python spoco_train.py \
    --spoco \
    --ds-name cvppp --ds-path CVPPP_ROOT_DIR \
    --instance-ratio 0.1 \
    --batch-size 4  \
    --model-name UNet2D \
    --model-feature-maps 16 32 64 128 256 512 \ 
    --learning-rate 0.0002 \
    --weight-decay 0.00001 \
    --cos \
    --loss-delta-var 0.5 \
    --loss-delta-dist 2.0 \
    --loss-unlabeled-push 1.0 \ 
    --loss-instance-weight 1.0 \
    --loss-consistency-weight 1.0 \
    --kernel-threshold 0.5 \
    --checkpoint-dir CHECKPOINT_DIR \ 
    --log-after-iters 256  --max-num-iterations 80000

CVPPP_ROOT_DIR is assumed to have the following subdirectories:

- train:
    - A1:
        - plantXXX_rgb.png
        - plantXXX_label.png
        ...
    - ...
    - A4:
        - ...
- val:
    - A1:
        - plantXXX_rgb.png
        - plantXXX_label.png
        ...
    - ...
    - A4:
        - ...

Since the CVPPP dataset consist of only training and testing subdirectories, one has to create the train/val split manually using the training subdir.

Cityscapes Dataset

Download the images leftImg8bit_trainvaltest.zip and the labels gtFine_trainvaltest.zip from the Cityscapes website and extract them into the CITYSCAPES_ROOT_DIR of your choice, so it has the following structure:

    - gtFine:
        - train
        - val
        - test

    - leftImg8bit:
        - train
        - val
        - test

Create random samplings of each class using the cityscapesampler.py script:

python spoco/datasets/cityscapesampler.py --base_dir CITYSCAPES_ROOT_DIR --class_names person rider car truck bus train motorcycle bicycle

this will randomly sample 10%, 20%, ..., 90% of objects from the specified class(es) and save the results in dedicated directories, e.g. CITYSCAPES_ROOT_DIR/gtFine/train/darmstadt/car/0.4 will contain random 40% of objects of class car.

One can also sample from all of the objects (people, riders, cars, trucks, buses, trains, motorcycles, bicycles) collectively by simply:

python spoco/datasets/cityscapesampler.py --base_dir CITYSCAPES_ROOT_DIR

this will randomly sample 10%, 20%, ..., 90% of all objects and save the results in dedicated directories, e.g. CITYSCAPES_ROOT_DIR/gtFine/train/darmstadt/all/0.4 will contain random 40% of all objects.

In order to train with 40% of randomly selected objects of class car, run:

python spoco_train.py \
    --spoco \
    --ds-name cityscapes 
    --ds-path CITYSCAPES_ROOT_DIR \
    --things-class car \
    --instance-ratio 0.4 \
    --batch-size 16  \
    --model-name UNet2D \
    --model-feature-maps 16 32 64 128 256 512 \
    --model-out-channels 8 \
    --learning-rate 0.001 \
    --weight-decay 0.00001 \
    --cos \
    --loss-delta-var 0.5 \
    --loss-delta-dist 2.0 \
    --loss-unlabeled-push 1.0 \ 
    --loss-instance-weight 1.0 \
    --loss-consistency-weight 1.0 \
    --kernel-threshold 0.5 \
    --checkpoint-dir CHECKPOINT_DIR \ 
    --log-after-iters 500  --max-num-iterations 90000

In order to train with a random 40% of all ground truth objects, just remove the --things-class argument from the command above.

Prediction

Give a model trained on the CVPPP dataset, run the prediction using the following command:

python spoco_predict.py \
    --spoco \
    --ds-name cvppp --ds-path CVPPP_ROOT_DIR \
    --batch-size 4 \ 
    --model-path MODEL_DIR/best_checkpoint.pytorch \
    --model-name UNet2D \
    --model-feature-maps 16 32 64 128 256 512 \
    --output-dir OUTPUT_DIR

Results will be saved in the given OUTPUT_DIR directory. For each test input image plantXXX_rgb.png the following 3 output files will be saved in the OUTPUT_DIR:

plantXXX_rgb_predictions.h5 - HDF5 file with datasets /raw (input image), /embeddings1 (output from the f embedding network), /embeddings2 (output from the g momentum contrast network)
plantXXX_rgb_predictions_1.png - output from the f embedding network PCA-projected into the RGB-space
plantXXX_rgb_predictions_2.png - output from the g momentum contrast network PCA-projected into the RGB-space

And similarly for the Cityscapes dataset

python spoco_predict.py \
    --spoco \
    --ds-name cityscapes --ds-path CITYSCAPES_ROOT_DIR \ 
    --batch-size 16 \ 
    --model-path MODEL_DIR/best_checkpoint.pytorch \
    --model-name UNet2D \
    --model-feature-maps 16 32 64 128 256 512 \
    --model-out-channels 8 \
    --output-dir OUTPUT_DIR

Clustering

To produce the final segmentation one needs to cluster the embeddings with and algorithm of choice. Supported algoritms: mean-shift, HDBSCAN and Consistency Clustering (as described in the paper). E.g. to cluster CVPPP with HDBSCAN, run:

python cluster_predictions.py \ 
    --ds-name cvppp \
    --emb-dir PREDICTION_DIR \
    --clustering hdbscan --delta-var 0.5 --min-size 200 --remove-largest

Where PREDICTION_DIR is the directory where h5 files containing network predictions are stored. Resulting segmentation will be saved as a separate dataset (named segmentation) inside each of the H5 prediction files.

In order to cluster the Cityscapes predictions and extract the instances of class car and compute the segmentation scores on the validation set:

python cluster_predictions.py \ 
    --ds-name cityscapes \
    --gt-dir CITYSCAPES_ROOT_DIR/gtFine/val \
    --emb-dir PREDICTION_DIR \
    --sem-dir SEM_PREDICTION_DIR \
    --things-class car \
    --clustering msplus --delta-var 0.5 --delta-dist 2.0

Where SEM_PREDICTION_DIR is the directory containing the semantic segmentation predictions for your validation images. We used pre-trained DeepLabv3 model from here.