MPRLNet

Official codes for

Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid Transformer

⚙️ Setup

Assuming a fresh Anaconda distribution, you can install the dependencies with:

conda install pytorch=0.4.1 torchvision=0.2.1 -c pytorch
pip install tensorboardX==1.4
conda install opencv=3.3.1   # just needed for evaluation

We ran our experiments with PyTorch 0.4.1, CUDA 9.1, Python 3.6.6 and Ubuntu 18.04. We have also successfully trained models with PyTorch 1.0, and our code is compatible with Python 2.7. You may have issues installing OpenCV version 3.3.1 if you use Python 3.7, we recommend to create a virtual environment with Python 3.6.6 conda create -n MPRLNet python=3.6.6 anaconda .

🖼️ Prediction for a single image

You can predict scaled disparity for a single image with:

python test_simple.py --image_path assets/test_image.jpg --model_name mono+stereo_640x192

or, if you are using a stereo-trained model, you can estimate metric depth with

python test_simple.py --image_path assets/test_image.jpg --model_name mono+stereo_640x192 --pred_metric_depth

💾 KITTI training data

You can download the entire raw KITTI dataset by running:

wget -i splits/kitti_archives_to_download.txt -P kitti_data/

Then unzip with

cd kitti_data
unzip "*.zip"
cd ..

Warning: it weighs about 175GB, so make sure you have enough space to unzip too!

Our default settings expect that you have converted the png images to jpeg with this command, which also deletes the raw KITTI .png files:

find kitti_data/ -name '*.png' | parallel 'convert -quality 92 -sampling-factor 2x2,1x1,1x1 {.}.png {.}.jpg && rm {}'

or you can skip this conversion step and train from raw png files by adding the flag --png when training, at the expense of slower load times.

The above conversion command creates images which match our experiments, where KITTI .png images were converted to .jpg on Ubuntu 16.04 with default chroma subsampling 2x2,1x1,1x1. We found that Ubuntu 18.04 defaults to 2x2,2x2,2x2, which gives different results, hence the explicit parameter in the conversion command.

You can also place the KITTI dataset wherever you like and point towards it with the --data_path flag during training and evaluation.

Splits

The train/test/validation splits are defined in the splits/ folder. By default, the code will train a depth model using Zhou's subset of the standard Eigen split of KITTI, which is designed for monocular training. You can also train a model using the new benchmark split or the odometry split by setting the --split flag.

Custom dataset

You can train on a custom monocular or stereo dataset by writing a new dataloader class which inherits from MonoDataset – see the KITTIDataset class in datasets/kitti_dataset.py for an example.

⏳ Training

By default models and tensorboard event files are saved to ~/tmp/<model_name>. This can be changed with the --log_dir flag.

Monocular training:

python train.py --model_name mono_model

GPUs

The code can only be run on a single GPU. You can specify which GPU to use with the CUDA_VISIBLE_DEVICES environment variable:

CUDA_VISIBLE_DEVICES=2 python train.py --model_name mono_model

💽 Finetuning a pretrained model

Add the following to the training command to load an existing model for finetuning:

python train.py --model_name finetuned_mono --load_weights_folder ~/tmp/mono_model/models/weights_19

🔧 Other training options

Run python train.py -h (or look at options.py) to see the range of other training options, such as learning rates and ablation settings.

📊 KITTI evaluation

To prepare the ground truth depth maps run:

python export_gt_depth.py --data_path kitti_data --split eigen
python export_gt_depth.py --data_path kitti_data --split eigen_benchmark

...assuming that you have placed the KITTI dataset in the default location of ./kitti_data/.

The following example command evaluates the epoch 19 weights of a model named mono_model:

python evaluate_depth.py --load_weights_folder ~/tmp/mono_model/models/weights_19/ --eval_mono

If you train your own model with our code you are likely to see slight differences to the publication results due to randomization in the weights initialization and data loading.

An additional parameter --eval_split can be set. The three different values possible for eval_split are explained here:

--eval_split	Test set size	For models trained with...	Description
eigen	697	--split eigen_zhou (default) or --split eigen_full	The standard Eigen test files
eigen_benchmark	652	--split eigen_zhou (default) or --split eigen_full	Evaluate with the improved ground truth from the new KITTI depth benchmark
benchmark	500	--split benchmark	The new KITTI depth benchmark test files.

Because no ground truth is available for the new KITTI depth benchmark, no scores will be reported when --eval_split benchmark is set. Instead, a set of .png images will be saved to disk ready for upload to the evaluation server.