MSCG-Net for Semantic Segmentation

Introduce

This repository contains MSCG-Net models (MSCG-Net-50 and MSCG-Net-101) for semantic segmentation in Agriculture-Vision Challenge and Workshop (CVPR 2020), and the pipeline of training and testing models, implemented in PyTorch. Please refer to our paper for details: Multi-view SelfConstructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation

Code structure

├── config		# config code
├── data		# dataset loader and pre-processing code
├── tools		# train and test code, ckpt and model_load
├── lib			# model block, loss, utils code, etc
└── ckpt 		# output check point, trained weights, log files, etc

Environments

• python 3.5+
• pytorch 1.4.0
• opencv 3.4+
• tensorboardx 1.9
• albumentations 0.4.0
• pretrainedmodels 0.7.4
• others (see requirements.txt)

Dataset prepare

1. change DATASET_ROOT to your dataset path in ./data/AgricultureVision/pre_process.py

DATASET_ROOT = '/your/path/to/Agriculture-Vision'

2. keep the dataset structure as the same with the official structure shown as below

Agriculture-Vision
|-- train
|   |-- masks
|   |-- labels
|   |-- boundaries
|   |-- images
|   |   |-- nir
|   |   |-- rgb
|-- val
|   |-- masks
|   |-- labels
|   |-- boundaries
|   |-- images
|   |   |-- nir
|   |   |-- rgb
|-- test
|   |-- boundaries
|   |-- images
|   |   |-- nir
|   |   |-- rgb
|   |-- masks

Train with a single GPU

CUDA_VISIBLE_DEVICES=0 python ./tools/train_R50.py  # trained weights ckpt1
# train_R101.py 								 # trained weights, ckpt2
# train_R101_k31.py 							  # trained weights, ckpt3

Please note that: we first train these models using Adam combined with Lookahead as the optimizer for the first 10k iterations (around 7~10 epochs) and then change the optimizer to SGD in the remaining iterations. So you will have to manually change the code to switch the optimizer to SGD as follows:

# Change line 48: Copy the file name ('----.pth') of the best ckpt trained with Adam
train_args.snapshot = '-------.pth'
...
# Comment line 92
# base_optimizer = optim.Adam(params, amsgrad=True)

# uncomment line 93
base_optimizer = optim.SGD(params, momentum=train_args.momentum, nesterov=True)

Test with a single GPU

# To reproduce the leaderboard results (0.608), download the trained-weights ckpt1,2,3
# and save them with the original names into ./ckpt folder before run test_submission.py
CUDA_VISIBLE_DEVICES=0 python ./tools/test_submission.py

Trained weights for 3 models download (save them to ./ckpt before run test_submission)

ckpt1,ckpt2,ckpt3

Results Summary

Models	mIoU (%)	Background	Cloud shadow	Double plant	Planter skip	Standing water	Waterway	Weed cluster
MSCG-Net-50 (ckpt1)	54.7	78.0	50.7	46.6	34.3	68.8	51.3	53.0
MSCG-Net-101 (ckpt2)	55.0	79.8	44.8	55.0	30.5	65.4	59.2	50.6
MSCG-Net-101_k31 (ckpt3)	54.1	79.6	46.2	54.6	9.1	74.3	62.4	52.1
Ensemble_TTA (ckpt1,2)	59.9	80.1	50.3	57.6	52.0	69.6	56.0	53.8
Ensemble_TTA (ckpt1,2,3)	60.8	80.5	51.0	58.6	49.8	72.0	59.8	53.8
Ensemble_TTA (new_5model)	62.2	80.6	48.7	62.4	58.7	71.3	60.1	53.4

Please note that all our single model's scores are computed with just single-scale (512x512) and single feed-forward inference without TTA. TTA denotes test time augmentation (e.g. flip and mirror). Ensemble_TTA (ckpt1,2) denotes two models (ckpt1, and ckpt2) ensemble with TTA, and (ckpt1, 2, 3) denotes three models ensemble.

Model Size

Models	Backbones	Parameters	GFLOPs	Inference time (CPU/GPU )
MSCG-Net-50	Se_ResNext50_32x4d	9.59	18.21	522 / 26 ms
MSCG-Net-101	Se_ResNext101_32x4d	30.99	37.86	752 / 45 ms
MSCG-Net-101_k31	Se_ResNext101_32x4d	30.99	37.86	752 / 45 ms

Please note that all backbones used pretrained weights on ImageNet that can be imported and downloaded from the link. And MSCG-Net-101_k31 has exactly the same architecture wit MSCG-Net-101, while it is trained with extra 1/3 validation set (4,431) instead of just using the official training images (12,901).

Citation:

Please consider citing our work if you find the code helps you

Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation

@InProceedings{Liu_2020_CVPR_Workshops,
author = {Liu, Qinghui and Kampffmeyer, Michael C. and Jenssen, Robert and Salberg, Arnt-Borre},
title = {Multi-View Self-Constructing Graph Convolutional Networks With Adaptive Class Weighting Loss for Semantic Segmentation},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}

Self-Constructing Graph Convolutional Networks for Semantic Labeling

@inproceedings{liu2020scg,
  title={Self-Constructing Graph Convolutional Networks for Semantic Labeling},
  author={Qinghui Liu and Michael Kampffmeyer and Robert Jenssen and Arnt-Børre Salberg},
  booktitle={Proceedings of IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium},
  year={2020}
}