Pytorch reimplementation of PSM-Net: "Pyramid Stereo Matching Network"

Overview

This is a Pytorch Lightning version PSMNet which is based on JiaRenChang/PSMNet.

use python main.py to start training.

PSM-Net

Pytorch reimplementation of PSM-Net: "Pyramid Stereo Matching Network" paper (CVPR 2018) by Jia-Ren Chang and Yong-Sheng Chen.

Official repository: JiaRenChang/PSMNet

model

Usage

1) Requirements

  • Python3.5+
  • Pytorch0.4
  • Opencv-Python
  • Matplotlib
  • TensorboardX
  • Tensorboard

All dependencies are listed in requirements.txt, you execute below command to install the dependencies.

pip install -r requirements.txt

2) Train

usage: train.py [-h] [--maxdisp MAXDISP] [--logdir LOGDIR] [--datadir DATADIR]
                [--cuda CUDA] [--batch-size BATCH_SIZE]
                [--validate-batch-size VALIDATE_BATCH_SIZE]
                [--log-per-step LOG_PER_STEP]
                [--save-per-epoch SAVE_PER_EPOCH] [--model-dir MODEL_DIR]
                [--lr LR] [--num-epochs NUM_EPOCHS]
                [--num-workers NUM_WORKERS]

PSMNet

optional arguments:
  -h, --help            show this help message and exit
  --maxdisp MAXDISP     max diparity
  --logdir LOGDIR       log directory
  --datadir DATADIR     data directory
  --cuda CUDA           gpu number
  --batch-size BATCH_SIZE
                        batch size
  --validate-batch-size VALIDATE_BATCH_SIZE
                        batch size
  --log-per-step LOG_PER_STEP
                        log per step
  --save-per-epoch SAVE_PER_EPOCH
                        save model per epoch
  --model-dir MODEL_DIR
                        directory where save model checkpoint
  --lr LR               learning rate
  --num-epochs NUM_EPOCHS
                        number of training epochs
  --num-workers NUM_WORKERS
                        num workers in loading data

For example:

python train.py --batch-size 16 \
                --logdir log/exmaple \
                --num-epochs 500

3) Visualize result

This repository uses tensorboardX to visualize training result. Find your log directory and launch tensorboard to look over the result. The default log directory is /log.

tensorboard --logdir <your_log_dir>

Here are some of my training results (have been trained for 1000 epochs on KITTI2015):

disp

left

loss

error

4) Inference

usage: inference.py [-h] [--maxdisp MAXDISP] [--left LEFT] [--right RIGHT]
                    [--model-path MODEL_PATH] [--save-path SAVE_PATH]

PSMNet inference

optional arguments:
  -h, --help            show this help message and exit
  --maxdisp MAXDISP     max diparity
  --left LEFT           path to the left image
  --right RIGHT         path to the right image
  --model-path MODEL_PATH
                        path to the model
  --save-path SAVE_PATH
                        path to save the disp image

For example:

python inference.py --left test/left.png \
                    --right test/right.png \
                    --model-path checkpoint/08/best_model.ckpt \
                    --save-path test/disp.png

5) Pretrained model

A model trained for 1000 epochs on KITTI2015 dataset can be download here. (I choose the best model among the 1000 epochs)

state {
    'epoch': 857,
    '3px-error': 3.466
}

Task List

  • Train
  • Inference
  • KITTI2015 dataset
  • Scene Flow dataset
  • Visualize
  • Pretained model

Contact

Email: [email protected]

Welcome for any discussions!

Owner
XIAOTIAN LIU
XIAOTIAN LIU
This repo contains the pytorch implementation for Dynamic Concept Learner (accepted by ICLR 2021).

DCL-PyTorch Pytorch implementation for the Dynamic Concept Learner (DCL). More details can be found at the project page. Framework Grounding Physical

Zhenfang Chen 31 Jan 06, 2023
Official PyTorch implementation of Spatial Dependency Networks.

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling Đorđe Miladinović   Aleksandar Stanić   Stefan Bauer   Jürgen Schmid

Djordje Miladinovic 34 Jan 19, 2022
Generative Handwriting using LSTM Mixture Density Network with TensorFlow

Generative Handwriting Demo using TensorFlow An attempt to implement the random handwriting generation portion of Alex Graves' paper. See my blog post

hardmaru 686 Nov 24, 2022
Improving Calibration for Long-Tailed Recognition (CVPR2021)

MiSLAS Improving Calibration for Long-Tailed Recognition Authors: Zhisheng Zhong, Jiequan Cui, Shu Liu, Jiaya Jia [arXiv] [slide] [BibTeX] Introductio

DV Lab 116 Dec 20, 2022
A scikit-learn-compatible module for estimating prediction intervals.

|Anaconda|_ MAPIE - Model Agnostic Prediction Interval Estimator MAPIE allows you to easily estimate prediction intervals using your favourite sklearn

SimAI 584 Dec 27, 2022
PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos. By adopting a unified pipeline-ba

PyKale 370 Dec 27, 2022
Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore

[AI6122] Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6122 of Semester 1, AY2021-2022, starting from 08/2021. The instructor of this course

HT. Li 5 Sep 12, 2022
Compositional Sketch Search

Compositional Sketch Search Official repository for ICIP 2021 Paper: Compositional Sketch Search Requirements Install and activate conda environment c

Alexander Black 8 Sep 06, 2021
Tools for investing in Python

InvestOps Original repository on GitHub Original author is Magnus Erik Hvass Pedersen Introduction This is a Python package with simple and effective

24 Nov 26, 2022
Official implementation for the paper: Generating Smooth Pose Sequences for Diverse Human Motion Prediction

Generating Smooth Pose Sequences for Diverse Human Motion Prediction This is official implementation for the paper Generating Smooth Pose Sequences fo

Wei Mao 28 Dec 10, 2022
Method for facial emotion recognition compitition of Xunfei and Datawhale .

人脸情绪识别挑战赛-第3名-W03KFgNOc-源代码、模型以及说明文档 队名:W03KFgNOc 排名:3 正确率: 0.75564 队员:yyMoming,xkwang,RichardoMu。 比赛链接:人脸情绪识别挑战赛 文章地址:link emotion 该项目分别训练八个模型并生成csv文

6 Oct 17, 2022
STEM: An approach to Multi-source Domain Adaptation with Guarantees

STEM: An approach to Multi-source Domain Adaptation with Guarantees Introduction This is the official implementation of ``STEM: An approach to Multi-s

5 Dec 19, 2022
HeartRate detector with ArduinoandPython - Use Arduino and Python create a heartrate detector.

Syllabus of Contents Syllabus of Contents Introduction Of Project Features Develop With Python code introduction Installation License Developer Contac

1 Jan 05, 2022
Natural Intelligence is still a pretty good idea.

Human Learn Machine Learning models should play by the rules, literally. Project Goal Back in the old days, it was common to write rule-based systems.

vincent d warmerdam 641 Dec 26, 2022
Code for intrusion detection system (IDS) development using CNN models and transfer learning

Intrusion-Detection-System-Using-CNN-and-Transfer-Learning This is the code for the paper entitled "A Transfer Learning and Optimized CNN Based Intrus

Western OC2 Lab 38 Dec 12, 2022
PyTorch implementation of Tacotron speech synthesis model.

tacotron_pytorch PyTorch implementation of Tacotron speech synthesis model. Inspired from keithito/tacotron. Currently not as much good speech quality

Ryuichi Yamamoto 279 Dec 09, 2022
Auto-Lama combines object detection and image inpainting to automate object removals

Auto-Lama Auto-Lama combines object detection and image inpainting to automate object removals. It is build on top of DE:TR from Facebook Research and

44 Dec 09, 2022
MohammadReza Sharifi 27 Dec 13, 2022
Clockwork Variational Autoencoder

Clockwork Variational Autoencoders (CW-VAE) Vaibhav Saxena, Jimmy Ba, Danijar Hafner If you find this code useful, please reference in your paper: @ar

Vaibhav Saxena 35 Nov 06, 2022
A knowledge base construction engine for richly formatted data

Fonduer is a Python package and framework for building knowledge base construction (KBC) applications from richly formatted data. Note that Fonduer is

HazyResearch 386 Dec 05, 2022