Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth [Paper]

Last update: Jan 1, 2023

Related tags

Deep Learning GLPDepth

Overview

Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth [Paper]

Downloads

[Downloads] Trained ckpt files for NYU Depth V2 and KITTI
[Downloads] Predicted depth maps png files for NYU Depth V2 and KITTI Eigen split test set

Requirements

Tested on

python==3.7.7
torch==1.6.0
h5py==3.6.0
scipy==1.7.3
opencv-python==4.5.5
mmcv==1.4.3
timm=0.5.4
albumentations=1.1.0
tensorboardX==2.4.1

You can install above package with

$ pip install -r requirements.txt

Inference and Evaluate

Dataset

NYU Depth V2

$ cd ./datasets
$ wget http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_labeled.mat
$ python ../code/utils/extract_official_train_test_set_from_mat.py nyu_depth_v2_labeled.mat splits.mat ./nyu_depth_v2/official_splits/

KITTI

Download annotated depth maps data set (14GB) from [link] into ./datasets/kitti/data_depth_annotated

$ cd ./datasets/kitti/data_depth_annotated/
$ unzip data_depth_annotated.zip

With above two instrtuctions, you can perform eval_with_pngs.py/test.py for NYU Depth V2 and eval_with_pngs for KITTI.

To fully perform experiments, please follow [BTS] repository to obtain full dataset for NYU Depth V2 and KITTI datasets.

Your dataset directory should be

root
- nyu_depth_v2
  - bathroom_0001
  - bathroom_0002
  - ...
  - official_splits
- kitti
  - data_depth_annotated
  - raw_data
  - val_selection_cropped

Evaluation

Evaluate with png images

for NYU Depth V2

$ python ./code/eval_with_pngs.py --dataset nyudepthv2 --pred_path ./best_nyu_preds/ --gt_path ./datasets/nyu_depth_v2/ --max_depth_eval 10.0

for KITTI

$ python ./code/eval_with_pngs.py --dataset kitti --split eigen_benchmark --pred_path ./best_kitti_preds/ --gt_path ./datasets/kitti/ --max_depth_eval 80.0 --garg_crop

Evaluate with model (NYU Depth V2)

Result images will be saved in ./args.result_dir/args.exp_name (default: ./results/test)

To evaluate only

$ python ./code/test.py --dataset nyudepthv2 --data_path ./datasets/ --ckpt_dir 
       
         --do_evaluate  --max_depth 10.0 --max_depth_eval 10.0

To save pngs for eval_with_pngs

$ python ./code/test.py --dataset nyudepthv2 --data_path ./datasets/ --ckpt_dir 
       
         --save_eval_pngs  --max_depth 10.0 --max_depth_eval 10.0

To save visualized depth maps

$ python ./code/test.py --dataset nyudepthv2 --data_path ./datasets/ --ckpt_dir 
       
         --save_visualize  --max_depth 10.0 --max_depth_eval 10.0

In case of kitti, modify arguments to --dataset kitti --max_depth 80.0 --max_depth_eval 80.0 and add --kitti_crop [garg_crop or eigen_crop]

Inference

Inference with image directory

$ python ./code/test.py --dataset imagepath --data_path 
     
       --save_visualize

To-Do

Add inference
Add training codes
Add dockerHub link
Add colab

References

[1] From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation. [code]

[2] SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. [code]

Comments

Training not converge

 Firstly, thanks for the nice paper and open source project!

 I am trying to reproduce the training process. I am using NYUDepthV2 dataset from Kaggle, it should be identical, just filename is different. Nothing else changed, only dataloader was refactored as 'kaggle' and it loads csv files for training and test.

As you can see from the logs.txt (copied below) in the 25 epochs, the silog is reducing (as expected), but RMSE jumps up dramatically around epoch 15. Maybe it was caused by the poly LR schedule around half_epoch. Anyway, it seems with the default settings, I could not reproduce the best-trained model using NYU depth v2 dataset. Is there anything I should try/modify?

Thanks a lot! Shawn

Copy of the logs.txt:

gpu_or_cpu:gpu, data_path:./datasets/, dataset:kaggle, exp_name:local_kaggle, batch_size:12, workers:8, max_depth:10.0, max_depth_eval:10.0, min_depth_eval:0.001, do_kb_crop:1, kitti_crop:None, epochs:25, lr:0.0001, crop_h:448, crop_w:576, log_dir:./logs, val_freq:1, save_freq:10, save_model:False, save_result:False,

Epoch: 001 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9725     2.6005     2.8542     3.6123     1.5674     2.5565

====================================================================================================

Epoch: 002 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9749     2.6100     2.8581     3.7024     1.6068     2.6198

====================================================================================================

Epoch: 003 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9734     2.6032     2.8548     3.6429     1.5810     2.5776

====================================================================================================

Epoch: 004 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9740     2.6057     2.8561     3.6663     1.5912     2.5941

====================================================================================================

Epoch: 005 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9737     2.6048     2.8557     3.6555     1.5865     2.5866

====================================================================================================

Epoch: 006 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9734     2.6035     2.8551     3.6427     1.5810     2.5775

====================================================================================================

Epoch: 007 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9734     2.6029     2.8547     3.6415     1.5805     2.5766

====================================================================================================

Epoch: 008 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9733     2.6028     2.8547     3.6377     1.5788     2.5739

====================================================================================================

Epoch: 009 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9734     2.6035     2.8551     3.6432     1.5812     2.5778

====================================================================================================

Epoch: 010 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9737     2.6048     2.8557     3.6541     1.5860     2.5854

====================================================================================================

Epoch: 011 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9734     2.6030     2.8547     3.6412     1.5804     2.5763

====================================================================================================

Epoch: 012 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9733     2.6027     2.8545     3.6391     1.5795     2.5748

====================================================================================================

Epoch: 013 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9735     2.6036     2.8551     3.6449     1.5820     2.5789

====================================================================================================

Epoch: 014 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9736     2.6043     2.8555     3.6496     1.5840     2.5822

====================================================================================================

Epoch: 015 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0606     3.5239    27.9788     7.3483     1.4549     0.6088     1.0641

====================================================================================================

Epoch: 016 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0606     3.5245    27.9877     7.3496     1.4550     0.6088     1.0642

====================================================================================================

Epoch: 017 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0605     3.5245    27.9878     7.3496     1.4550     0.6088     1.0642

====================================================================================================

Epoch: 018 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0605     3.5245    27.9878     7.3496     1.4550     0.6088     1.0642

====================================================================================================

Epoch: 019 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0605     3.5245    27.9878     7.3496     1.4550     0.6088     1.0642

====================================================================================================

Epoch: 020 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0605     3.5245    27.9878     7.3496     1.4550     0.6088     1.0642

====================================================================================================

Epoch: 021 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0605     3.5245    27.9878     7.3496     1.4550     0.6088     1.0642

====================================================================================================

Epoch: 022 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0605     3.5245    27.9878     7.3496     1.4550     0.6088     1.0642

====================================================================================================

Epoch: 023 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0605     3.5245    27.9878     7.3496     1.4550     0.6088     1.0642

====================================================================================================

Epoch: 024 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0605     3.5245    27.9878     7.3496     1.4550     0.6088     1.0642

====================================================================================================

Epoch: 025 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0071     0.0228     0.0605     3.5245    27.9878     7.3496     1.4550     0.6088     1.0642

====================================================================================================

opened by xiaoliangbai 6

Adding GLPN to HuggingFace Transformers
Hi,

Thanks for the impressive work! As the model uses SegFormer's Mix-b4 as encoder, and I already ported SegFormer to HuggingFace Transformers as seen here, it was relatively easy to port this model as well.

Here's a notebook for quick inference on an image: https://colab.research.google.com/drive/1v6fzr4XusKdXAaeGZ1gKe1kh9Ce_WQhl?usp=sharing

Both models are hosted on the hub: https://huggingface.co/models?other=glpn. If you're not familiar with HuggingFace's hub, basically each model has its own Github repo (it's based on git-LFS), so you can git add, git commit and git push to each repo separately. Each model has its own git history, like this one.

Are you interested in creating a KAIST organization on the hub, similar to other oganizations like facebook, microsoft, google? Such that we can host the models under that name? That way, you'll be able to do:

from transformers import GLPNForDepthEstimation model = GLPNForDepthEstimation.from_pretrained("kaist/glpn-nyu")

Or, if you prefer, we can host the weights under your name as well.

Let me know if you're interested!

Kind regards,

Niels
opened by NielsRogge 5

About train process and validate result

Thanks for your impressive work! I've been trying to replicate your work, but there were a strange validate result. Why the metrics suddenly became abnormal and constant from the 11th epoch? Logs are as follows: gpu_or_cpu:gpu, data_path:../dataset/, dataset:nyudepthv2, exp_name:test, batch_size:12, workers:1, max_depth:10.0, max_depth_eval:10.0, min_depth_eval:0.001, do_kb_crop:1, kitti_crop:None, epochs:25, lr:0.0001, crop_h:448, crop_w:576, log_dir:./logs, val_freq:1, save_freq:10, save_model:False, save_result:False,

Epoch: 001 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.8535     0.9817     0.9966     0.1276     0.0726     0.4282     0.1548     0.0534     0.1418

====================================================================================================

Epoch: 002 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.8559     0.9824     0.9962     0.1301     0.0729     0.4214     0.1528     0.0531     0.1376

====================================================================================================

Epoch: 003 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.8856     0.9853     0.9972     0.1135     0.0619     0.3891     0.1392     0.0474     0.1268

====================================================================================================

Epoch: 004 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.8847     0.9834     0.9970     0.1113     0.0608     0.3920     0.1403     0.0480     0.1282

====================================================================================================

Epoch: 005 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.8932     0.9869     0.9976     0.1072     0.0565     0.3765     0.1338     0.0456     0.1219

====================================================================================================

Epoch: 006 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.9033     0.9871     0.9969     0.1025     0.0534     0.3661     0.1297     0.0440     0.1187

====================================================================================================

Epoch: 007 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.9005     0.9865     0.9975     0.1050     0.0541     0.3617     0.1303     0.0444     0.1184

====================================================================================================

Epoch: 008 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.9038     0.9868     0.9974     0.1030     0.0535     0.3565     0.1285     0.0436     0.1169

====================================================================================================

Epoch: 009 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.9055     0.9879     0.9976     0.1032     0.0521     0.3543     0.1276     0.0435     0.1157

====================================================================================================

Epoch: 010 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.9060     0.9870     0.9975     0.1020     0.0559     0.3672     0.1299     0.0442     0.1180

====================================================================================================

Epoch: 011 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 012 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 013 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 014 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 015 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 016 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 017 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 018 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 019 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 020 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 021 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 022 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 023 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 024 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

Epoch: 025 - 025

    d1         d2         d3    abs_rel     sq_rel       rmse   rmse_log      log10      silog 
0.0000     0.0000     0.0000     0.9995     2.6884     2.8657     7.8043     3.3855     5.5246

====================================================================================================

opened by JHCheng111 4

dataset

Hello, I am a new student, I would like to ask you for the download link of the dataset about the file train_list. TXT data set, but I can't get it down.

opened by lighthappy 3
a problem of metrics

Hi，guys! Firstly, thanks for the great paper and open source project! I am trying to repeat your work. And I have downloaded your model from the url in model.py. However,after testing,we found that the metrics had a significant difference from your results. So I wonder if it is convenient for you to provide me with a well-trained model. Thanks a lot!

opened by WL678 3
About the license for this model

Thank you for sharing your great code. :smiley_cat:

What is the license for this model? I'd like to cite it to the repository I'm working on if possible, but I want to post the license correctly. https://github.com/PINTO0309/PINTO_model_zoo

Thank you.

opened by PINTO0309 3
I can't download pre_trained mit_b4 .

print("Download pre-trained encoder weights...") id = '1BUtU42moYrOFbsMCE-LTTkUE-mrWnfG2' url = 'https://drive.google.com/uc?id=' + id output = './code/models/weights/mit_b4.pth' gdown.download(url, output, quiet=False) I can't download mit_b4 。The probable reason is that this link has been downloaded by so many people that I can no longer download it。Can you provide a new download link?

opened by hutingz 2
train_list.txt

I notice that train_list.txt does not include all the images from NYU depth V2 dataset from bts. For example, images in study_room_0005b scene: ... /study_room_0005b/rgb_00087.jpg /study_room_0005b/sync_depth_00087.png /study_room_0005b/rgb_00089.jpg /study_room_0005b/sync_depth_00089.png /study_room_0005b/rgb_00090.jpg /study_room_0005b/sync_depth_00090.png /study_room_0005b/rgb_00092.jpg /study_room_0005b/sync_depth_00092.png /study_room_0005b/rgb_00093.jpg /study_room_0005b/sync_depth_00093.png

Images such as rgb_00091.jpg, rgb_00094.jpg, etc. are missing from the list.

It looks like images were selected to generate the final list used in the training process. What were the criteria used to select images for training?

Thanks

opened by xiaoliangbai 2
test on SUN-RGB

Hello, I am a student and I would like to ask how to test on SUN RGB-D dataset and get the corresponding depth map? I hope the author can answer and give the code. Thanks, I hope to get your reply. Have a nice life.

opened by lighthappy 1
How is the pretrained model mit_b4 obtained?

Hi, I'm running your code and get a consistent result with your paper. And I wonder how is the pretrained encoder model mit_b4 obtained? Is there any reference paper?

opened by frspring 1
About SiLog Loss NaN

I replaced GLPDepth's backbone with MiT-B1, and after training 10 epochs, the loss of the model increased with each batch of data, and finally the loss became NaN. Has this happened to the author? I guarantee that the model has not changed anything other than backbone, the dataset is NYU V2, and the input size is 640x480.

opened by cheryllbl 0

TypeError: only integer scalar arrays can be converted to a scalar index while python ../code/utils/extract_official_train_test_set_from_mat.py nyu_depth_v2_labeled.mat splits.mat ./nyu_depth_v2/official_splits/

When I ran the following python ../code/utils/extract_official_train_test_set_from_mat.py nyu_depth_v2_labeled.mat splits.mat ./nyu_depth_v2/official_splits/

Got the follwoing:

795 training images
654 test images
reading nyu_depth_v2_labeled.mat
<HDF5 dataset "sceneTypes": shape (1, 1449), type "|O">
Traceback (most recent call last):
  File "../code/utils/extract_official_train_test_set_from_mat.py", line 88, in <module>
    scenes = [u''.join(chr(c) for c in h5_file[obj_ref]) for obj_ref in h5_file['sceneTypes'][0]]
  File "../code/utils/extract_official_train_test_set_from_mat.py", line 88, in <listcomp>
    scenes = [u''.join(chr(c) for c in h5_file[obj_ref]) for obj_ref in h5_file['sceneTypes'][0]]
  File "../code/utils/extract_official_train_test_set_from_mat.py", line 88, in <genexpr>
    scenes = [u''.join(chr(c) for c in h5_file[obj_ref]) for obj_ref in h5_file['sceneTypes'][0]]
TypeError: only integer scalar arrays can be converted to a scalar index

Any clue to how to resolve this error.

opened by premchedella 1

The input resolution seems to have a big impact on the model

Hi,I used the input size as 576x448,the model can achieve the result of the paper, but when I changed the input size to 224x224, the model rmse dropped from 0.34 to 0.50. I don't understand why this is, is this normal? Should I retrain the mit_b4 model again?Looking forward to your reply.

opened by puyiwen 5
The model and loaded state dict do not match exactly

Hi，I change the input size as 224x224,and I get a message 'the model and loaded state dict do not match exactly',the code still works.However,I'm not sure if the mit_b4 pre-trained model has already loaded.Can you help me?And I want to know,have you ever done an experiment which input size as 224x224.If you have done the experiment,can you tell me the corresponding evaluation index ?Thank you very much!!

opened by puyiwen 3

Owner

KAIST, EE, PhD student

GitHub

Light-weight network, depth estimation, knowledge distillation, real-time depth estimation, auxiliary data.

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