pncnn

KO | EN

gitlite — search

#javascript #python #ai #deep-learning #macos #nodejs #java #windows #cli #kubernetes #vue #security #mcp #open-source

(★ 97)

The official PyTorch implementation for "Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End"

pncnn 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Probabilistic Normalized Convolutional Neural Networks (pNCNN)

This is the official PyTorch implementation for "Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End" presented at CVPR 2020, Seattle, USA.

[[PDF]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Eldesokey_Uncertainty-Aware_CNNs_for_Depth_Completion_Uncertainty_from_Beginning_to_End_CVPR_2020_paper.pdf) [[ArXiv (With Supplementary)]](https://arxiv.org/abs/2006.03349) [[1min Video]](https://www.youtube.com/watch?v=Iw_yk-UoKEo&feature=youtu.be) [[Slides]]

```
@InProceedings{Eldesokey_2020_CVPR,
author = {Eldesokey, Abdelrahman and Felsberg, Michael and Holmquist, Karl and Persson, Michael},
title = {Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
```
---

## Dependecies
The code was developed using Python 3.7.4 and PyTorch 1.4, but it should work on any PyTorch version > 1.1

* pytorch>1.1
* torchvision>0.5.0
* json
* matplotlib
* opencv
* h5py
 
---

## Datasets

### Kitti-Depth
To download the Kitti-Depth dataset, use the provided Python script [`dataloaders/download_kitti_depth_rgb.py`](dataloaders/download_kitti_depth_rgb.py).

*Remeber to edit the script first to set download directories.*

### NYU-Depth-v2
Download and extract the dataset in h5 format provided from [sparse-to-dense](https://github.com/fangchangma/sparse-to-dense.pytorch#requirements).

```
wget http://datasets.lids.mit.edu/sparse-to-dense/data/nyudepthv2.tar.gz
tar -xvf nyudepthv2.tar.gz && rm -f nyudepthv2.tar.gz
```

---

## Training

Experiments are stored in `workspace` directory, where you can have different workspaces in sub-directories.

To train a new experiment, you should create a new directory with the name of the experiment inside your workspace directory which has the following files:
* `network.py` which has the desired architecture.
* `args.json` which has the experiment arguments.

You can copy these files from any of the pretrained models and modify it.

To run the training, you need to run the following command:
```bash
python main.py --ws <WORKSPACE> --exp <EXP> --args <ARGS>
```

`--ws` is the name of the sub-direcotry inside `workspace` that has your experiment.

`--exp` is the name of the experiment.

`--args` You have two options: either to set this argument to `json` which will load all arguments from `args.json` described above, 
*OR* discard it and set all the arguments in the terminal manually.

### Example 
To create an experiemnt called `my_experiment` inside a workspace called `my_workspace`, then you should create a directory for the experiments at `workspace/my_workspace/my_experiment`. This direcotry should have two main files `network.py` and `args.json` as described above.

You need to modify the following arguments in the json file to match your new experiments: [`exp`, `workspace`, `dataset`, `dataset_path`]. Other arguments, you can change as needed.

Now you are ready to start training by calling:
```bash
python main.py --ws my_workspace --exp my_experiment --args json
```

---

## Logging 
Tensorboard is supported by default and you can initiate it as usual by calling:
```bash
tensorboard --logdir=workspace/my_workspace
```
By default, tensorboard log files are save to the directory `tb_log` inside the experiment directory.

Also, the evaluation metrics are saved after each epoch both for the training and the test set as CSV files inside the experiment directory.

---

## Pretrained Models
We provide the pretrained models for the `KITTI-Depth` dataset and the `NYU-Depth-v2` dataset inside `workspace/kitti` and `workspace/nyu` respectively.

---

## Resuming Training
To resume training, you can call:
```bash
python main.py --resume <path-to-checkpoint>
```
By default, the argument will be loaded from the checkpoint. If you want to change some arguments, you can edit `args.json` for the experiment and it will override the arguments in the checkpoints.

---

## Testing
To test a pretrained model, you can call:
```bash
python main.py --evaluate <path-to-checkpoint>
```

---

## Remarks
If you use our code or our paper, please consider citing us. The bibtex is provided above.

If you have questions, please create an issue.

pncnn

pncnn 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

pncnn

pncnn 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README