Closed GabbySuwichaya closed 2 years ago
Hi! Thanks for your interest in our work! Here is a dummy run from my side:
python prepare_nn_distance_mat.py --scene=0001 --seq=0
---------------------- OPTIONS ----------------------
cells 10000
confirm True
crop_cam no_crop
dataset_name megadepth
info_level rgbd
num_cpus 6
out_dir /home/jiangwei/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0001/dense0/dist_mat
out_path /home/jiangwei/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0001/dense0/dist_mat/dist_mat.npy
scene 0001
scenes_name_list [{'scene_dir': '/home/jiangwei/dataset/megadepth/MegaDepth_v1_SfM/0001/sparse/manhattan/0_rectified/sparse', 'image_dir': '/home/jiangwei/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0001/dense0/imgs', 'depth_dir': '/home/jiangwei/dataset/megadepth/MegaDepth_v1/phoenix/S6/zl548/MegaDepth_v1/0001/dense0/depths'}]
seq 0
use_cc False
use_cuda True
use_ram False
----------------------------------------------------
OK to continue? [y/n] y
'NoneType' object cannot be interpreted as an integer
first time start working
reading cameras: 100%|█████████████████████████████████████████████████████████████████████████| 3409/3409 [00:00<00:00, 122429.19it/s]
reading images meta: 100%|███████████████████████████████████████████████████████████████████████| 3409/3409 [00:00<00:00, 3442.22it/s]
reading point cloud: 100%|█████████████████████████████████████████████████████████████████| 134678/134678 [00:01<00:00, 105808.42it/s]
calculating distance matrix: 4%|██▌ | 390/10000 [00:34<13:42, 11.68it/s]
One thing to notice is that this script is designed for ComputeCanada, therefore we need to cut a large job into multiple small jobs, each small job will compute cells
number of pairs for the distance matrix. You can set cells
to a huge value if you want to finish the distance matrix in one run.
Before you start the job, please modify dataset_config.json
, so the script can find the dataset.
Inside the megadepth dataset, we call each 4 digits folder a scene
, for example, 0007
, and inside each scene folder, there may be multiple reconstructions that COLMAP couldn't register into one single model, for example, the inside and outside of a church, we call these sub-models as seq
, like 0
, 1
.
Just fixed a dependency bug in the code
Hi! Firstly, thank you very much for releasing the code, and thank you very much for producing this research works. I enjoy reading your paper. It is awesome! Here, I would like to try to train COTR. So far I have tried to follow the steps in https://github.com/ubc-vision/COTR/blob/master/prepare_data.md... From those steps, I have successfully generated
rectify.sh
,megadepth_test.json
,megadepth_train.json
,megadepth_val.json
, andmegadepth_valid_list.json
...However, at the last step, that is, to generate the distance matrix...
I am stuck on how to provide the input for --scene, --seq which are the required input parameters...
The reason for my confusion is that I am not sure how many and which scenes and sequences are needed for training COTR ?
In other words, how should I run
python3 prepare_nn_distance_mat.py
such that it will prepare the data for training COTR?