PSPNet in Chainer

Related tags

Deep Learningpythondeep-learningchainerimagenetconvolutional-neural-networkssemantic-segmentationpascal-voccityscapespspnetade20k
Overview

PSPNet

This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer.

Training

Requirement

  • Python 3.4.4+
    • Chainer 3.0.0b1+
    • ChainerMN master
    • CuPy 2.0.0b1+
    • ChainerCV 0.6.0+
    • NumPy 1.12.0+
    • tqdm 4.11.0+
pip install chainer --pre
pip install cupy --pre
pip install git+git://github.com/chainer/chainermn
pip install git+git://github.com/chainer/chainercv
pip install tqdm

Inference using converted weights

Requirement

  • Python 3.4.4+
    • Chainer 3.0.0b1+
    • ChainerCV 0.6.0+
    • Matplotlib 2.0.0+
    • CuPy 2.0.0b1+
    • tqdm 4.11.0+

1. Run demo.py

Cityscapes

$ python demo.py -g 0 -m cityscapes -f aachen_000000_000019_leftImg8bit.png

Pascal VOC2012

$ python demo.py -g 0 -m voc2012 -f 2008_000005.jpg

ADE20K

$ python demo.py -g 0 -m ade20k -f ADE_val_00000001.jpg

FAQ

If you get RuntimeError: Invalid DISPLAY variable, how about specifying the matplotlib's backend by an environment variable?

$ MPLBACKEND=Agg python demo.py -g 0 -m cityscapes -f aachen_000000_000019_leftImg8bit.png

Convert weights by yourself

Caffe is NOT needed to convert .caffemodel to Chainer model. Use caffe_pb2.py.

Requirement

  • Python 3.4.4+
    • protobuf 3.2.0+
    • Chainer 3.0.0b1+
    • NumPy 1.12.0+

1. Download the original weights

Please download the weights below from the author's repository:

and then put them into weights directory.

2. Convert weights

$ python convert.py

Reference

  • The original implementation by authors is: hszhao/PSPNet
  • The original paper is:
    • Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, "Pyramid Scene Parsing Network", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
You might also like...
Comments
  • Training failes with ModuleNotFoundError when using train_mn.py

    Training failes with ModuleNotFoundError when using train_mn.py

    Hi, I got following error when I tried to train PSP net with your train_mn.py How can I train my PSPNet model?

    [email protected]:/yendo/oss/chainer-pspnet# python3 train_mn.py --result_dir result configs/cityscapes/pspnet.yml
Warning: using naive communicator because only naive supports CPU-only execution
==========================================
Num process (COMM_WORLD): 1
Using single_node communicator
Chainer version: 3.4.0
ChainerMN version: 1.2.0
cuda: True, cudnn: True
result_dir: result
Traceback (most recent call last):
  File "train_mn.py", line 504, in <module>
    trainer = get_trainer(args)
  File "train_mn.py", line 374, in get_trainer
    model = get_model_from_config(config, comm)
  File "train_mn.py", line 239, in get_model_from_config
    loss.module, loss.name, loss.args, comm)
  File "train_mn.py", line 219, in get_model
    mod = import_module(loss_module)
  File "/root/.pyenv/versions/anaconda3-5.0.1/lib/python3.6/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 994, in _gcd_import
  File "<frozen importlib._bootstrap>", line 971, in _find_and_load
  File "<frozen importlib._bootstrap>", line 941, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 994, in _gcd_import
  File "<frozen importlib._bootstrap>", line 971, in _find_and_load
  File "<frozen importlib._bootstrap>", line 953, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'loss'
    opened by jo7ueb 0
  • Training Fails with IndexError when using train.py

    Training Fails with IndexError when using train.py

    Hi, I got following error when I tried to train PSP net with your train.py How can I train my PSPNet model?

    [email protected]:/yendo/oss/chainer-pspnet# python3 train.py --gpu --result_dir result configs/cityscapes/pspnet.yml
==========================================
Chainer version: 3.4.0
CuPy version: 2.4.0
Traceback (most recent call last):
  File "train.py", line 483, in <module>
    trainer = get_trainer(args)
  File "train.py", line 339, in get_trainer
    chainer.cuda.available, chainer.cuda.cudnn_enabled, ))
IndexError: tuple index out of range
    opened by jo7ueb 0
  • could you actually train a new model?

    could you actually train a new model?

    Hi, I am currently trying to train the cityscapes dataset with your code, but the result is miserable: still 0.5263158 (=1/19) class accuracy after 120 epochs. Apparently, the loss of training data is converged correctly, so it seems like a perfect over fitting. Since I used the same settings as yours, i am wondering how you managed to reproduce the results(maybe i need less learning rate?). thanks in advance!

    opened by suzukikbp 0
Owner
Shunta Saito
Ph.D in Engineering, Researcher at Preferred Networks, Inc.
Shunta Saito
GitHub Repository
Robot Servers and Server Manager software for robo-gym

robo-gym-server-modules Robot Servers and Server Manager software for robo-gym. For info on how to use this package please visit the robo-gym website

JR ROBOTICS 4 Aug 16, 2021
Conditional Gradients For The Approximately Vanishing Ideal

Conditional Gradients For The Approximately Vanishing Ideal Code for the paper: Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Appro

IOL Lab @ ZIB 0 May 25, 2022
Official Pytorch implementation of Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations

Scene Representation Networks This is the official implementation of the NeurIPS submission "Scene Representation Networks: Continuous 3D-Structure-Aw

Vincent Sitzmann 365 Jan 06, 2023
Deep Learning Pipelines for Apache Spark

Deep Learning Pipelines for Apache Spark The repo only contains HorovodRunner code for local CI and API docs. To use HorovodRunner for distributed tra

Databricks 2k Jan 08, 2023
Hashformers is a framework for hashtag segmentation with transformers.

Hashtag segmentation is the task of automatically inserting the missing spaces between the words in a hashtag. Hashformers applies Transformer models

Ruan Chaves 41 Nov 09, 2022
This project helps to colorize grayscale images using multiple exemplars.

Multiple Exemplar-based Deep Colorization (Pytorch Implementation) Pretrained Model [Jitendra Chautharia](IIT Jodhpur)1,3, Prerequisites Python 3.6+ N

jitendra chautharia 3 Aug 05, 2022
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight

Revisiting RCAN: Improved Training for Image Super-Resolution Introduction Image super-resolution (SR) is a fast-moving field with novel architectures

Zudi Lin 76 Dec 01, 2022
Calibrated Hyperspectral Image Reconstruction via Graph-based Self-Tuning Network.

mask-uncertainty-in-HSI This repository contains the testing code and pre-trained models for the paper Calibrated Hyperspectral Image Reconstruction v

JIAMIAN WANG 9 Dec 29, 2022
Code repo for "FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation" (ICCV 2021)

FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation (ICCV 2021) This repository contains the implementation of th

Yuhang Zang 21 Dec 17, 2022
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection

Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection Introduction This repository includes codes and models of "Effect of De

Amir Abbasi 5 Sep 05, 2022
imbalanced-DL: Deep Imbalanced Learning in Python

imbalanced-DL: Deep Imbalanced Learning in Python Overview imbalanced-DL (imported as imbalanceddl) is a Python package designed to make deep imbalanc

NTUCSIE CLLab 19 Dec 28, 2022
Luminous is a framework for testing the performance of Embodied AI (EAI) models in indoor tasks.

Luminous is a framework for testing the performance of Embodied AI (EAI) models in indoor tasks. Generally, we intergrete different kind of functional

28 Jan 08, 2023
Explore extreme compression for pre-trained language models

Code for paper "Exploring extreme parameter compression for pre-trained language models ICLR2022"

twinkle 16 Nov 14, 2022
Stacked Recurrent Hourglass Network for Stereo Matching

SRH-Net: Stacked Recurrent Hourglass Introduction This repository is supplementary material of our RA-L submission, which helps reviewers to understan

28 Jan 03, 2023
Auto-updating data to assist in investment to NEPSE

Symbol Ratios Summary Sector LTP Undervalued Bonus % MEGA Strong Commercial Banks 368 5 10 JBBL Strong Development Banks 568 5 10 SIFC Strong Finance

Amit Chaudhary 16 Nov 01, 2022
Deep High-Resolution Representation Learning for Human Pose Estimation

Deep High-Resolution Representation Learning for Human Pose Estimation (accepted to CVPR2019) News If you are interested in internship or research pos

HRNet 167 Dec 27, 2022
Use VITS and Opencpop to develop singing voice synthesis; Maybe it will VISinger.

Init Use VITS and Opencpop to develop singing voice synthesis; Maybe it will VISinger. 本项目基于 https://github.com/jaywalnut310/vits https://github.com/S

AmorTX 107 Dec 23, 2022
A script written in Python that returns a consensus string and profile matrix of a given DNA string(s) in FASTA format.

A script written in Python that returns a consensus string and profile matrix of a given DNA string(s) in FASTA format.

Zain 1 Feb 01, 2022
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model

Only a Matter of Style: Age Transformation Using a Style-Based Regression Model The task of age transformation illustrates the change of an individual

444 Dec 30, 2022
A ssl analyzer which could analyzer target domain's certificate.

ssl_analyzer A ssl analyzer which could analyzer target domain's certificate. Analyze the domain name ssl certificate information according to the inp

vincent 17 Dec 12, 2022