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Monodepth in Pytorch

NOTE: Repo will be clean up and refactored soon. Roll back to initially stage ec97056, if necessary

Pytorch implementation of unsupervised single image depth prediction using a convolutional neural network. This is ported from the original tensorflow implementation by Clément Godard.

monodepth

Unsupervised Monocular Depth Estimation with Left-Right Consistency
Clément Godard, Oisin Mac Aodha and Gabriel J. Brostow
CVPR 2017

For more details:
project page
arXiv

Requirements

This code was tested with Pytorch 0.3, CUDA 8.0 and Ubuntu 16.04.
For now, you can train only on single GPU.

Data

This model requires rectified stereo pairs for training. There are two main datasets available:

KITTI

We used two different split of the data, kitti and eigen, amounting for respectively 29000 and 22600 training samples, you can find them in the filenames folder.
You can download the entire raw dataset by running:

wget -i utils/kitti_archives_to_download.txt -P ~/my/output/folder/

Warning: it weights about 175GB, make sure you have enough space to unzip too!
To save space you can convert the png images to jpeg.

find ~/my/output/folder/ -name '*.png' | parallel 'convert {.}.png {.}.jpg && rm {}'

Cityscapes

You will need to register in order to download the data, which already has a train/val/test set with 22973 training images.
We used leftImg8bit_trainvaltest.zip, rightImg8bit_trainvaltest.zip, leftImg8bit_trainextra.zip and rightImg8bit_trainextra.zip which weights 110GB.

Training

Set following flags

--isTrain to True
--name to any name of your choose eg. monodepth
--netG to name of the model eg. monodepth
--gpu_ids to GPU id eg. 0
--batchsize to batchsize eg. 8
--filename to location of the text of kitti image location eg. /dataset/filenames/kitti_train_files.txt

Command to train model:

python train.py --name monodepth --netG monodepth --isTrain --gpu_ids 0 --batchsize 8 
--filename ../dataset/filenames/kitti_train_files.txt

Check out other flags at main_options.py

Saved models and ouput disparities after each display_freq can be found at /checkpoints_dir/name/ and /checkpoints_dir/name/web/images/ respectively.

Testing

Set following flags

--ext_test_in flag to dir of test images eg. /monodepth/checkpoints/monodepth/test_img_in
--load to True
--ckpt_folder to path of your saved model eg. ckpt_backup
--which_model to model name eg. monodepth
--which_epoch to epoch number eg . 30

Command to test model:

python train.py --name monodepth --netG monodepth  --gpu_ids 0 
--ext_test_in /monodepth/ckpt_backup/monodepth/test_img_in --load --ckpt_folder ckpt_backup 
--which_model monodepth --which_epoch 30

Output of the test can be found at checkpoints_dir/name/test_img_out where checkpoints_dir and name are flags. Post-processing not yet implemented in the current version.

Bilinear sampler

Code for ported and in-built bilinear sampler can be found here.