Awesome
PSPNet with PyTorch
Unofficial implementation of "Pyramid Scene Parsing Network" (https://arxiv.org/abs/1612.01105). This repository is just for caffe to pytorch model conversion and evaluation.
Requirements
- pytorch
- click
- addict
- pydensecrf
- protobuf
Preparation
Instead of building the author's caffe implementation, you can convert off-the-shelf caffemodels to pytorch models via the caffe.proto
.
1. Compile the caffe.proto
for Python API
This step can be skipped. FYI.<br>
Download the author's caffe.proto
into the libs
, not the one in the original caffe.
# For protoc command
pip install protobuf
# This generates ./caffe_pb2.py
protoc --python_out=. caffe.proto
2. Model conversion
- Find the caffemodels on the author's page (e.g. pspnet50_ADE20K.caffemodel) and store them to the
data/models/
directory. - Convert the caffemodels to
.pth
file.
python convert.py -c <PATH TO YAML>
Demo
python demo.py -c <PATH TO YAML> -i <PATH TO IMAGE>
- With a
--no-cuda
option, this runs on CPU. - With a
--crf
option, you can perform a CRF postprocessing.
Evaluation
PASCAL VOC2012 only. Please set the dataset path in config/voc12.yaml
.
python eval.py -c config/voc12.yaml
88.1% mIoU (SS) and 88.6% mIoU (MS) on validation set.<br> NOTE: 3 points lower than caffe implementation. WIP
- SS: averaged prediction with flipping (2x)
- MS: averaged prediction with multi-scaling (6x) and flipping (2x)
- Both: No CRF post-processing
References
- Official implementation: https://github.com/hszhao/PSPNet
- Chainer implementation: https://github.com/mitmul/chainer-pspnet