synml/face-parsing-pytorch

Face parsing implementation based on PyTorch

Face-parsing-pytorch (작성 중... 설명 불일치)

PyTorch implementation of semantic segmentation models.

This repository aims to implement semantic segmentation models with PyTorch.

Table of Contents

• Requirements
• Models
• Datasets
• How-to-use
• Module-description
• Credits
• Contribution
• License

Requirements

• Hardware (Developer environment)
  • CPU: Intel Core i7 9700
  • RAM: 32GiB
  • GPU: Nvidia Geforce RTX 3090
  • Storage: Samsung SSD 970 Pro 512GB
• Software
  • OS: Ubuntu (Primary), Windows (Secondary)
  • Miniconda (Python 3.8)
  • PyTorch 1.8.1 (CUDA 11.1)
• Dependent packages
  • Matplotlib
  • PyYAML
  • Scikit-learn
  • Tensorboard
  • Tqdm
• Useful packages
  • pytorch-summary
  • flops-counter.pytorch
  • Netron : Visualizer for neural network models. (web version: Netron)

Models

This repository supports these semantic segmentation models as follows:

• (U-Net) Convolutional Networks for Biomedical Image Segmentation [Paper]
• (AR U-Net) Atrous Residual U-Net for Semantic Segmentation in Urban Street Scenes [Paper]
• (DeepLab V3) Rethinking Atrous Convolution for Semantic Image Segmentation [Paper]
• (DeepLab V3+) Encoder-Decoder with Atrous Separable Convolution for Semantic Segmentation [Paper]

Datasets

This repository supports these datasets as follows:

• Cityscapes

  1. Download dataset files. (leftImg8bit_trainvaltest.zip and gtFine_trainvaltest.zip).

  2. Extract downloaded files. The structure of dataset is as follows:

     |-- data
     |  |-- cityscapes
     |  |  |-- gtFine
     |  |  |  |-- test
     |  |  |  |-- train
     |  |  |  |-- val
     |  |  |-- leftImg8bit
     |  |  |  |-- test
     |  |  |  |-- train
     |  |  |  |-- val
     |  |  |-- license.txt
     |  |  |-- README
     

  3. Download cityscapesScripts for inspection, preparation, and evaluation. (or clone this repo)

  4. Edit the script labels.py to specify the label number.

  5. Edit the script createTrainIdLabelImgs.py to set cityscapes path.

  6. Run the script createTrainIdLabelImgs.py to create annotations based on the training labels.

• Pascal VOC 2012

How-to-use

1. Clone this repository.

   git clone https://github.com/synml/pytorch-semantic-segmentation

2. Create and activate a new virtual environment with Miniconda.

   conda create -n [env_name, ex: torch] python=3.8
   conda activate [env_name, ex: torch]

3. Install PyTorch.

   • https://pytorch.org/get-started/locally/

4. Install the dependent packages mentioned above.

   conda install matplotlib pyyaml scikit-learn tensorboard tqdm

5. Prepare datasets.

   • Please refer to Datasets section.

6. Customize the configuration file. (config.yaml)

   dataset:
     image_size: 400x800	# rows x cols
     num_classes: 20	# 19 + 1 (background)
     num_workers: 8	# number of CPU cores
     root: ../../data/cityscapes	# dataset path
   
   model: UNet		# options [UNet, AR_UNet, DeepLabV3, DeepLabV3plus]
   amp_enabled: True	# Automatic Mixed Precision
   
   UNet:	# Match model name
     batch_size: 16
     epoch: 100
     optimizer:
       name: Adam	# options [SGD, Adam]
       lr: 0.001
       weight_decay: 0.00001
       <optimizer_keyarg1>:<value>
     scheduler:
       name: ReduceLROnPlateau
       factor: 0.5
       patience: 5
       min_lr: 0.00005
     pretrained_weights: weights/UNet_best.pth
   
   Backbone:
     batch_size: 16
     epoch: 100
     optimizer:
       name: Adam
       lr: 0.001
       weight_decay: 0.00001
     scheduler:
       name: ReduceLROnPlateau
       factor: 0.5
       patience: 5
       min_lr: 0.00005
     pretrained_weights: weights/Backbone_val_best.pth
   
   Proposed:
     batch_size: 8
     epoch: 100
     optimizer:
       name: Adam
       lr: 0.0005
       weight_decay: 0.00001
     scheduler:
       name: ReduceLROnPlateau
       factor: 0.5
       patience: 5
       min_lr: 0.00005
     pretrained_weights: weights/Proposed_best.pth
   

Module-description

• backup.py

• clean.py

• demo.py

• eval.py

• exec_tensorboard.py

• featurte_visualizer.py

• train.py

• train_interupter.ini

Credits

• https://github.com/meetshah1995/pytorch-semseg
• https://github.com/warmspringwinds/pytorch-segmentation-detection
• https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks
• https://github.com/milesial/Pytorch-UNet

Contribution

1. Fork this repository.
2. Create a new branch or use the master branch.
3. Commit modifications.
4. Push on the selected branch.
5. Please send a pull request.

License

You can find more information in LICENSE.