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
MediaPipeで姿勢推定を行い、Tokyo2020オリンピック風のピクトグラムを表示するデモ

Tokyo2020-Pictogram-using-MediaPipe MediaPipeで姿勢推定を行い、Tokyo2020オリンピック風のピクトグラムを表示するデモです。 Tokyo2020Pictgram02.mp4 Requirement mediapipe 0.8.6 or later O

KazuhitoTakahashi 295 Dec 26, 2022
A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

Evan 1.3k Jan 02, 2023
Research on Event Accumulator Settings for Event-Based SLAM

Research on Event Accumulator Settings for Event-Based SLAM This is the source code for paper "Research on Event Accumulator Settings for Event-Based

Robin Shaun 26 Dec 21, 2022
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable.

Diffrax Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. Diffrax is a JAX-based library providing numerical differe

Patrick Kidger 717 Jan 09, 2023
PyTorch Autoencoders - Implementing a Variational Autoencoder (VAE) Series in Pytorch.

PyTorch Autoencoders Implementing a Variational Autoencoder (VAE) Series in Pytorch. Inspired by this repository Model List check model paper conferen

Subin An 8 Nov 21, 2022
implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning"

MarginGAN This repository is the implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning". 1."preliminary" is the imp

Van 7 Dec 23, 2022
Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Hiroshechka Y 33 Dec 26, 2022
Yolo algorithm for detection + centroid tracker to track vehicles

Vehicle Tracking using Centroid tracker Algorithm used : Yolo algorithm for detection + centroid tracker to track vehicles Backend : opencv and python

6 Dec 21, 2022
Code for the paper "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks"

ON-LSTM This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree

Yikang Shen 572 Nov 21, 2022
A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generative Modeling" (ICCV 2021)

Manifold Matching via Deep Metric Learning for Generative Modeling A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generat

69 Dec 10, 2022
A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis

A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis This is the pytorch implementation for our MICCAI 2021 paper. A Mul

Jiarong Ye 7 Apr 04, 2022
DTCN SMP Challenge - Sequential prediction learning framework and algorithm

DTCN This is the implementation of our paper "Sequential Prediction of Social Me

Bobby 2 Jan 24, 2022
SuperSonic, a new open-source framework to allow compiler developers to integrate RL into compilers easily, regardless of their RL expertise

Automating reinforcement learning architecture design for code optimization. Che

113 Nov 28, 2022
Code for "Solving Graph-based Public Good Games with Tree Search and Imitation Learning"

Code for "Solving Graph-based Public Good Games with Tree Search and Imitation Learning" This is the code for the paper Solving Graph-based Public Goo

Victor-Alexandru Darvariu 3 Dec 05, 2022
PyTorch implementation of some learning rate schedulers for deep learning researcher.

pytorch-lr-scheduler PyTorch implementation of some learning rate schedulers for deep learning researcher. Usage WarmupReduceLROnPlateauScheduler Visu

Soohwan Kim 59 Dec 08, 2022
ACV is a python library that provides explanations for any machine learning model or data.

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based mod

Salim Amoukou 85 Dec 27, 2022
Cancer metastasis detection with neural conditional random field (NCRF)

NCRF Prerequisites Data Whole slide images Annotations Patch images Model Training Testing Tissue mask Probability map Tumor localization FROC evaluat

Baidu Research 731 Jan 01, 2023
source code of Adversarial Feedback Loop Paper

Adversarial Feedback Loop [ArXiv] [project page] Official repository of Adversarial Feedback Loop paper Firas Shama, Roey Mechrez, Alon Shoshan, Lihi

17 Jul 20, 2022
AfriBERTa: Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages

AfriBERTa: Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages This repository contains the code for the pa

Kelechi 40 Nov 24, 2022
Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

tonne 1.4k Dec 29, 2022