Competitive Collaboration

This is an official repository of Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation. The project was formerly referred by Adversarial Collaboration. We recently ported the entire code to pytorch-1.0, so if you discover bugs, please file an issue.

[Project Page] [Arxiv]

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• Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
• Mixed Domain Learning using MNIST+SVHN
• Downloads

Prerequisites

Python3 and pytorch are required. Third party libraries can be installed (in a python3 virtualenv) using:

pip3 install -r requirements.txt

Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

Preparing training data

KITTI

For KITTI, first download the dataset using this script provided on the official website, and then run the following command.

python3 data/prepare_train_data.py /path/to/raw/kitti/dataset/ --dataset-format 'kitti' --dump-root /path/to/resulting/formatted/data/ --width 832 --height 256 --num-threads 1 --static-frames data/static_frames.txt --with-gt

For testing optical flow ground truths on KITTI, download KITTI2015 dataset. You need to download 1) stereo 2015/flow 2015/scene flow 2015 data set (2 GB), 2) multi-view extension (14 GB), and 3) calibration files (1 MB) . In addition, download semantic labels from here. You should have the following directory structure:

kitti2015
  | data_scene_flow  
  | data_scene_flow_calib
  | data_scene_flow_multiview  
  | data_stereo_flow
  | semantic_labels

Cityscapes

For Cityscapes, download the following packages: 1) leftImg8bit_sequence_trainvaltest.zip, 2) camera_trainvaltest.zip. You will probably need to contact the administrators to be able to get it.

python3 data/prepare_train_data.py /path/to/cityscapes/dataset/ --dataset-format 'cityscapes' --dump-root /path/to/resulting/formatted/data/ --width 832 --height 342 --num-threads 1

Notice that for Cityscapes the img_height is set to 342 because we crop out the bottom part of the image that contains the car logo, and the resulting image will have height 256.

Training an experiment

Once the data are formatted following the above instructions, you should be able to run a training experiment. Every experiment you run gets logged in experiment_recorder.md.

python3 train.py /path/to/formatted/data --dispnet DispResNet6 --posenet PoseNetB6 \
  --masknet MaskNet6 --flownet Back2Future --pretrained-disp /path/to/pretrained/dispnet \
  --pretrained-pose /path/to/pretrained/posenet --pretrained-flow /path/to/pretrained/flownet \
  --pretrained-mask /path/to/pretrained/masknet -b4 -m0.1 -pf 0.5 -pc 1.0 -s0.1 -c0.3 \
  --epoch-size 1000 --log-output -f 0 --nlevels 6 --lr 1e-4 -wssim 0.997 --with-flow-gt \
  --with-depth-gt --epochs 100 --smoothness-type edgeaware  --fix-masknet --fix-flownet \
  --log-terminal --name EXPERIMENT_NAME

You can then start a tensorboard session in this folder by

tensorboard --logdir=checkpoints/

and visualize the training progress by opening https://localhost:6006 on your browser.

Evaluation

Disparity evaluation

python3 test_disp.py --dispnet DispResNet6 --pretrained-dispnet /path/to/dispnet --pretrained-posent /path/to/posenet --dataset-dir /path/to/KITTI_raw --dataset-list /path/to/test_files_list

Test file list is available in kitti eval folder. To get fair comparison with Original paper evaluation code, don't specify a posenet. However, if you do, it will be used to solve the scale factor ambiguity, the only ground truth used to get it will be vehicle speed which is far more acceptable for real conditions quality measurement, but you will obviously get worse results.

For pose evaluation, you need to download KITTI Odometry dataset.

python test_pose.py pretrained/pose_model_best.pth.tar --img-width 832 --img-height 256 --dataset-dir /path/to/kitti/odometry/ --sequences 09 --posenet PoseNetB6

Optical Flow evaluation

python test_flow.py --pretrained-disp /path/to/dispnet --pretrained-pose /path/to/posenet --pretrained-mask /path/to/masknet --pretrained-flow /path/to/flownet --kitti-dir /path/to/kitti2015/dataset

Mask evaluation

python test_mask.py --pretrained-disp /path/to/dispnet --pretrained-pose /path/to/posenet --pretrained-mask /path/to/masknet --pretrained-flow /path/to/flownet --kitti-dir /path/to/kitti2015/dataset

Mixed Domain Learning using MNIST+SVHN

Training

For learning classification using Competitive Collaboration with two agents, Alice and Bob, run,

python3 mnist.py path/to/download/mnist/svhn/datasets/ --name EXP_NAME --log-output --log-terminal --epoch-size 1000 --epochs 400 --wr 1000

Evaluation

To evaluate the performance of Alice, Bob and Moderator trained using CC, run,

python3 mnist_eval.py path/to/mnist/svhn/datasets --pretrained-alice pretrained/mnist_svhn/alice.pth.tar --pretrained-bob pretrained/mnist_svhn/bob.pth.tar --pretrained-mod pretrained/mnist_svhn/mod.pth.tar

Downloads

Pretrained Models

• DispNet, PoseNet, MaskNet and FlowNet in joint unsupervised learning of depth, camera motion, optical flow and motion segmentation.
• Alice, Bob and Moderator in Mixed Domain Classification

Evaluation Data

• Semantic Labels for KITTI

Acknowlegements

We thank Frederik Kunstner for verifying the convergence proofs. We are grateful to Clement Pinard for his github repository. We use it as our initial code base. We thank Georgios Pavlakos for helping us with several revisions of the paper. We thank Joel Janai for preparing optical flow visualizations, and Clement Gorard for his Make3d evaluation code.

References

Anurag Ranjan, Varun Jampani, Lukas Balles, Deqing Sun, Kihwan Kim, Jonas Wulff and Michael J. Black. Competitive Collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation. CVPR 2019.