pytorch-cv

Convert the gluon-cv to pytorch. (And add more models from others)

Note: use pytorch-nightly （due to nn.SyncBatchNorm）

Enviroment

# 1. create new enviroment
conda env create -f environment.yml
source activate ptcv
# 2. install opencv 
conda install -c menpo opencv3
# 3. install pytorch (https://pytorch.org/get-started/locally/)
conda install pytorch-nightly cudatoolkit=9.0 -c pytorch  # choose your cuda version
# # 4. install mxnet and gluoncv (options---for convert pre-trained gluon model)
# pip install mxnet-cu92   # (https://beta.mxnet.io/install.html, choose your cuda version)
# pip install gluoncv --pre --upgrade

build cpp-extension (for object detection)

Note: it will delete after torchvision merge this pull-826

cd model
python setup.py build_ext develop

Supported Applications

Application	Illustration	Available Models
Image Classification: recognize an object in an image.		CIFAR10, IMAGENET
Object Detection: detect multiple objects with their bounding boxes in an image.		SSD, YOLO3, Faster-RCNN, CenterNet
Semantic Segmentation: associate each pixel of an image with a categorical label.		FCN, DeepLabv3, PSPNet, DANet, OCNet, BiSeNet, CCNet
Instance Segmentation: detect objects and associate each pixel inside object area with an instance label.		Mask RCNN, YOLACT
Pose Estimation: detect human pose from images.		Simple-Pose

Schedule

Classification:

	demo	eval	train		demo	eval	train
CIFAR10	✓	✓	✓	IMAGENET	✓	✓	✗

Semantic Segmentation：training performance nearly same as paper

	demo	eval	train
Segmentation	✓	✓	✓

Object Detection：the training performance nearly same as gluon-cv

	demo	eval	train		demo	eval	train
SSD	✓	✓	✓	YOLO	✓	✓	✓
Faster-RCNN	✓	✓	✗	CenterNet	✓	✗	✗

Instance Segmentation

	demo	eval	train		demo	eval	train
Mask-RCNN	✓	✓	✗	YOLACT	✓	✗	✗

Pose Estimation

	demo	eval	train		demo	eval	train
Simple-Pose	✓	✓	✗

TODO

• Provide converted models (BaiduYun and GoogleDrive)
• Compare the backbone performance with torchvison and pretrained-models.pytorch , and replace with better one.
• Improve performance
• Move sync BN through use convert_sync_batchnorm
• Add more training code and models
• Move convert code from master to branch