PSPNet in Chainer

Last update: Dec 12, 2022

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:

pspnet50_ADE20K.caffemodel: GoogleDrive
pspnet101_VOC2012.caffemodel: GoogleDrive
pspnet101_cityscapes.caffemodel: GoogleDrive

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

Comments

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

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?

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

Releases(ChainerCV_PSPNet)

ChainerCV_PSPNet(Aug 14, 2017)

Source code(tar.gz)
Source code(zip)
pspnet101_cityscapes_713_reference.npz(250.74 MB)
pspnet101_VOC2012_473_reference.npz(250.82 MB)
pspnet50_ADE20K_473_reference.npz(185.12 MB)
PSPNet_reference_weights(Aug 11, 2017)

Source code(tar.gz)
Source code(zip)
pspnet101_cityscapes_713_reference.chainer(233.76 MB)
pspnet101_VOC2012_473_reference.chainer(233.80 MB)
pspnet50_ADE20K_473_reference.chainer(166.00 MB)

Owner

Shunta Saito

Ph.D in Engineering, Researcher at Preferred Networks, Inc.

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PSPNet in Chainer

Related tags

Overview

PSPNet

Training

Requirement

Inference using converted weights

Requirement

1. Run demo.py

Cityscapes

Pascal VOC2012

ADE20K

FAQ

Convert weights by yourself

Requirement

1. Download the original weights

2. Convert weights

Reference

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Comments

Training failes with ModuleNotFoundError when using train_mn.py

Training Fails with IndexError when using train.py

could you actually train a new model?

Releases(ChainerCV_PSPNet)

ChainerCV_PSPNet(Aug 14, 2017)

PSPNet_reference_weights(Aug 11, 2017)

Owner

Shunta Saito

Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test.

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This is the open-source reference implementation of the SIGGRAPH 2021 paper Intersection-free Rigid Body Dynamics.

Official codebase for ICLR oral paper Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling

Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

Experimenting with computer vision techniques to generate annotated image datasets from gameplay recordings automatically.

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