pytorch

BTS

From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
arXiv
Supplementary material

Note

This folder contains a PyTorch implementation of BTS.
We tested this code under python 3.6, PyTorch 1.2.0, CUDA 10.0 on Ubuntu 18.04.

Testing with NYU Depth V2

First make sure that you have prepared the test set using instructions in README.md at root of this repo.

$ cd ~/workspace/bts/pytorch
$ mkdir models
### Get BTS model trained with NYU Depth V2
$ cd models
$ wget https://cogaplex-bts.s3.ap-northeast-2.amazonaws.com/bts_nyu_v2_pytorch_densenet161.zip
$ unzip bts_nyu_v2_pytorch_densenet161.zip

Once the preparation steps completed, you can test BTS using following commands.

$ cd ~/workspace/bts/pytorch
$ python bts_test.py arguments_test_nyu.txt

This will save results to ./result_bts_nyu_v2_pytorch_densenet161. With a single RTX 2080 Ti it takes about 41 seconds for processing 654 testing images.

Evaluation

Following command will evaluate the prediction results for NYU Depvh V2.

$ cd ~/workspace/bts/pytorch
$ python ../utils/eval_with_pngs.py --pred_path result_bts_nyu_v2_pytorch_densenet161/raw/ --gt_path ../../dataset/nyu_depth_v2/official_splits/test/ --dataset nyu --min_depth_eval 1e-3 --max_depth_eval 10 --eigen_crop

You should see outputs like this:

Raw png files reading done
Evaluating 654 files
GT files reading done
0 GT files missing
Computing errors
     d1,      d2,      d3,  AbsRel,   SqRel,    RMSE, RMSElog,   SILog,   log10
  0.885,   0.978,   0.994,   0.110,   0.066,   0.392,   0.142,  11.533,   0.047
Done.

Preparation for Training

NYU Depvh V2

Download the dataset we used in this work.

$ cd ~/workspace/bts
$ python utils/download_from_gdrive.py 1AysroWpfISmm-yRFGBgFTrLy6FjQwvwP ../dataset/nyu_depth_v2/sync.zip
$ unzip sync.zip

Once the dataset is ready, you can train the network using following command.

$ cd ~/workspace/bts/pytorch
$ python bts_main.py arguments_train_nyu.txt

You can check the training using tensorboard:

$ tensorboard --logdir ./models/bts_nyu_test/ --port 6006

Open localhost:6006 with your favorite browser to see the progress of training.

KITTI

You can also train BTS with KITTI dataset by following procedures. First, make sure that you have prepared the ground truth depthmaps from KITTI. If you have not, please follow instructions on README.md at root of this repo. Then, download and unzip the raw dataset using following commands.

$ cd ~/workspace/dataset/kitti_dataset
$ aria2c -x 16 -i ../../bts/utils/kitti_archives_to_download.txt
$ parallel unzip ::: *.zip

Finally, we can train our network with

$ cd ~/workspace/bts/pytorch
$ python bts_main.py arguments_train_eigen.txt

Testing and Evaluation with KITTI

Once you have KITTI dataset and official ground truth depthmaps, you can test and evaluate our model with following commands.

### Get model trained with KITTI Eigen split
$ cd ~/workspace/bts/pytorch/models
$ wget https://cogaplex-bts.s3.ap-northeast-2.amazonaws.com/bts_eigen_v2_pytorch_densenet161.zip
$ cd unzip bts_eigen_v2_pytorch_densenet161.zip

Test and save results.

$ cd ~/workspace/bts/pytorch
$ python bts_test.py arguments_test_eigen.txt

This will save results to ./result_bts_eigen_v2_pytorch_densenet161. Finally, we can evaluate the prediction results with

$ cd ~/workspace/bts/pytorch
$ python ../utils/eval_with_pngs.py --pred_path result_bts_eigen_v2_pytorch_densenet161/raw/ --gt_path ../../dataset/kitti_dataset/data_depth_annotated/ --dataset kitti --min_depth_eval 1e-3 --max_depth_eval 80 --do_kb_crop --garg_crop

You should see outputs like this:

GT files reading done
45 GT files missing
Computing errors
     d1,      d2,      d3,  AbsRel,   SqRel,    RMSE, RMSElog,   SILog,   log10
  0.955,   0.993,   0.998,   0.060,   0.249,   2.798,   0.096,   8.933,   0.027
Done.

Also, in this pytorch implementation, you can use various base networks with pretrained weights as the encoder for bts.
Available options are: resnet50_bts, resnet101_bts, resnext50_bts, resnext101_bts, densenet121_bts and densenet161_bts
Simply change the argument '--encoder' in arguments_train_*.txt with your choice.

Model Zoo

KITTI Eigen Split

Base Network	d1	d2	d3	AbsRel	SqRel	RMSE	RMSElog	SILog	log10	#Params	Model Download
ResNet50	0.954	0.992	0.998	0.061	0.250	2.803	0.098	9.030	0.027	49.5M	bts_eigen_v2_pytorch_resnet50
ResNet101	0.954	0.992	0.998	0.061	0.261	2.834	0.099	9.075	0.027	68.5M	bts_eigen_v2_pytorch_resnet101
ResNext50	0.954	0.993	0.998	0.061	0.245	2.774	0.098	9.014	0.027	49.0M	bts_eigen_v2_pytorch_resnext50
ResNext101	0.956	0.993	0.998	0.059	0.241	2.756	0.096	8.781	0.026	112.8M	bts_eigen_v2_pytorch_resnext101
DenseNet121	0.951	0.993	0.998	0.063	0.256	2.850	0.100	9.221	0.028	21.2M	bts_eigen_v2_pytorch_densenet121
DenseNet161	0.955	0.993	0.998	0.060	0.249	2.798	0.096	8.933	0.027	47.0M	bts_eigen_v2_pytorch_densenet161

NYU Depth V2

Base Network	d1	d2	d3	AbsRel	SqRel	RMSE	RMSElog	SILog	log10	#Params	Model Download
ResNet50	0.865	0.975	0.993	0.119	0.075	0.419	0.152	12.368	0.051	49.5M	bts_nyu_v2_pytorch_resnet50
ResNet101	0.871	0.977	0.995	0.113	0.068	0.407	0.148	11.886	0.049	68.5M	bts_nyu_v2_pytorch_resnet101
ResNext50	0.867	0.977	0.995	0.116	0.070	0.414	0.150	12.186	0.050	49.0M	bts_nyu_v2_pytorch_resnext50
ResNext101	0.880	0.977	0.994	0.111	0.069	0.399	0.145	11.680	0.048	112.8M	bts_nyu_v2_pytorch_resnext101
DenseNet121	0.871	0.977	0.993	0.118	0.072	0.410	0.149	12.028	0.050	21.2M	bts_nyu_v2_pytorch_densenet121
DenseNet161	0.885	0.978	0.994	0.110	0.066	0.392	0.142	11.533	0.047	47.0M	bts_nyu_v2_pytorch_densenet161

Note: Modify arguments '--encoder', '--model_name', '--checkpoint_path' and '--pred_path' accordingly.

License

Copyright (C) 2019 Jin Han Lee, Myung-Kyu Han, Dong Wook Ko and Il Hong Suh
This Software is licensed under GPL-3.0-or-later.