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
Demo for the paper "Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation"

Streaming speaker diarization Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation by Juan Manuel Coria, Hervé

Juanma Coria 187 Jan 06, 2023
Official implementation of Pixel-Level Bijective Matching for Video Object Segmentation

BMVOS This is the official implementation of Pixel-Level Bijective Matching for Video Object Segmentation, to appear in WACV 2022. @article{cho2021pix

Suhwan Cho 13 Dec 14, 2022
Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)

VIN: Value Iteration Networks A quick thank you A few others have released amazing related work which helped inspire and improve my own implementation

Kent Sommer 297 Dec 26, 2022
PyTorch implementation of Deformable Convolution

PyTorch implementation of Deformable Convolution !!!Warning: There is some issues in this implementation and this repo is not maintained any more, ple

Wei Ouyang 893 Dec 18, 2022
Single-stage Keypoint-based Category-level Object Pose Estimation from an RGB Image

CenterPose Overview This repository is the official implementation of the paper "Single-stage Keypoint-based Category-level Object Pose Estimation fro

NVIDIA Research Projects 188 Dec 27, 2022
Workshop Materials Delivered on 28/02/2022

intro-to-cnn-p1 Repo for hosting workshop materials delivered on 28/02/2022 Questions you will answer in this workshop Learning Objectives What are co

Beginners Machine Learning 5 Feb 28, 2022
A module that used for encrypt code which includes RSA and AES

软件加密模块 requirement: Crypto,pycryptodome,pyqt5 本地加密信息为随机字符串 使用说明 命令行参数 -h 帮助 -checkWorking 检查是否能正常工作,后接1确认指令 -checkEndDate 检查截至日期,后接1确认指令 -activateCode

2 Sep 27, 2022
Using python and scikit-learn to make stock predictions

MachineLearningStocks in python: a starter project and guide EDIT as of Feb 2021: MachineLearningStocks is no longer actively maintained MachineLearni

Robert Martin 1.3k Dec 29, 2022
Contextual Attention Network: Transformer Meets U-Net

Contextual Attention Network: Transformer Meets U-Net Contexual attention network for medical image segmentation with state of the art results on skin

Reza Azad 67 Nov 28, 2022
Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation"

SharinGAN Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation" The official project we

Koutilya PNVR 23 Oct 19, 2022
Poisson Surface Reconstruction for LiDAR Odometry and Mapping

Poisson Surface Reconstruction for LiDAR Odometry and Mapping Surfels TSDF Our Approach Table: Qualitative comparison between the different mapping te

Photogrammetry & Robotics Bonn 305 Dec 21, 2022
Real-Time High-Resolution Background Matting

Real-Time High-Resolution Background Matting Official repository for the paper Real-Time High-Resolution Background Matting. Our model requires captur

Peter Lin 6.1k Jan 03, 2023
PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models

octconv.pytorch PyTorch implementation of Octave Convolution in Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octa

Duo Li 273 Dec 18, 2022
Spam your friends and famly and when you do your famly will disown you and you will have no friends.

SpamBot9000 Spam your friends and family and when you do your family will disown you and you will have no friends. Terms of Use Disclaimer: Please onl

DJ15 0 Jun 09, 2022
Code release for our paper, "SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo"

SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo Thomas Kollar, Michael Laskey, Kevin Stone, Brijen Thananjeyan

68 Dec 14, 2022
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Benedek Rozemberczki 619 Dec 14, 2022
The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue. How do I cite D-REX? For now, cite

Alon Albalak 6 Mar 31, 2022
ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Ibai Gorordo 14 Dec 09, 2022
This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

TransFG: A Transformer Architecture for Fine-grained Recognition Official PyTorch code for the paper: TransFG: A Transformer Architecture for Fine-gra

Ju He 307 Jan 03, 2023
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity

[ICLR 2022] Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity by Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elen

VITA 18 Dec 31, 2022