This is a training framework based on PyTorch, which is used to rapidly build the model, without caring about the training process (such as DDP or DP, Tensorboard, et al.). The demo for use can be found in FrameworkTemplate (https://github.com/Archaic-Atom/Template-jf). if you have any questions, please send an e-mail to [email protected]

Document：https://www.wolai.com/archaic-atom/rqKJVi7M1x44mPT8CdM1TL

Demo Project: https://github.com/Archaic-Atom/Demo-jf

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1. Software Environment

1) OS Environment

$ os >= linux 16.04
$ cudaToolKit >= 10.1
$ cudnn >= 7.3.6

2) Python Environment

$ python == 3.8.5
$ pythorch >= 1.15.0
$ numpy == 1.14.5
$ opencv == 3.4.0
$ PIL == 5.1.0

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2. Hardware Environment

This framework is only used in GPUs.

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3. How to use our framework:

1) Build env

$ conda env create -f environment.yml
$ conda activate JackFramework-torch2.3.1

2) Install the JackFramework lib

$ ./install.sh

3) Check the version (optional)

$ python -c "import JackFramework as jf; print(jf.version())"

4) the template for using the JackFramework

you can find the template project in: https://github.com/Archaic-Atom/FameworkTemplate

you can find the demo project in: https://github.com/Archaic-Atom/Demo-jf

Related Arguments for training or testing process

Args	Type	Description	Default
mode	[str]	train or test	train
gpu	[int]	the number of gpus	2
auto_save_num	[int]	the number of interval save	1
dataloaderNum	[int]	the number of dataloader	8
pretrain	[bool]	is a new training process	False
ip	[str]	used for distributed training	127.0.0.1
port	[str]	used for distributed training	8086
dist	[bool]	distributed training (DDP)	True
trainListPath	[str]	the list for training or testing	./Datasets/*.csv
valListPath	[str]	the list for validate process	./Datasets/*.csv
outputDir	[str]	the folder for log file	./Result/
modelDir	[str]	the folder for saving model	./Checkpoint/
resultImgDir	[str]	the folder for output	./ResultImg/
log	[str]	the folder for tensorboard	./log/
sampleNum	[int]	the number of sample for data	1
batchSize	[int]	batch size	4
lr	[float]	learning rate	0.001
maxEpochs	[int]	training epoch	30
imgWidth	[int]	the cutting width	512
imgHeight	[int]	the cutting height	256
imgNum	[int]	the number of images for training	35354
valImgNum	[int]	the number of images for val	200
modelName	[str]	the model's name	NLCA-Net
dataset	[str]	the dataset's name	SceneFlow
web_cmd	[str]	cmd for django	'main.py runserver 0.0.0.0:8000'

5) Clean the project (if you want to clean generating files)

$ ./clean.sh

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3. File Structure

.
├── Source # source code
│   ├── JackFramework/
|   |   ├── Contrib/
|   |   ├── DatasetReader/
|   |   ├── Evalution/
|   |   ├── NN/
|   |   ├── Proc/
|   |   ├── SysBasic/
|   |   ├── UserTemplate/
|   |   ├── FileHandler/ 
|   |   ├── Web/ 
│   |   └── ...
│   ├── setup.py
│   └── ...
├── LICENSE
└── README.md

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To do

2021-07-10

1. rewirte the readme;
2. code refacotoring for contrib;
3. refactor the code;

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Update log

2021-07-01

1. Add action for gitHub;
2. Add some information for JackFramework;
3. Write the ReadMe.

2021-05-28

1. Write ReadMe;
2. Add setup.py;