luissalgueiro / dsrl

Implementation of CVPR 2020 Dual Super-Resolution Learning for Semantic Segmentation

Dual super-resolution learning for semantic segmentation

2020-01-02 Subpixel Update

Happy new year! The 2020-12-29 update of SISR with subpixel conv performs bad in my experiment so I did some changes to it.

The former version is depreciated now. Click here to see more.

2020-12-29 New branch: subpixel

• In this new branch, SISR path changes to follow the design of Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, CVPR 2016. The main branch still uses Deconv so if you prefer the older version you can simply ignore this update.
• I haven't run a full test on this new framework yet so I'm still not sure about it's performance on validation set. Please let me know if you find this new framework performs better. Thank you. :)

2020-12-15 Pretrained Weights Uploaded (Only for the main branch)

• See Google Drive (Please note that you don't have to unzip this file.)

2020-10-31 Good News! I achieved an mIoU of 0.6787 in the newest experiment(the experiment is still running and the final mIoU may be even higher)!

• So the FA module should be places after each path's final output.
• The FTM should be 19 channel -> 3 channel
• Hyper-Parameter fine-tuning

It's amazing that the final model converges at a extremely fast speed. Now the codes are all set, just clone this repo and run train.py!

And thanks for the reminder of @XinruiYuan, currently this repo also differs from the original paper in the architecture of SISR path. I will be working on it after finishing my homework.

2020-10-22 First commit

I implemented the framework proposed in this paper since the authors' code is still under legal scan and i just can't wait to see the results. This repo is based on Deeplab v3+ and Cityscapes, and i still have problems about the FA module.

• so the code is runnable? yes. just run train.py directly and you can see DSRL starts training.(of course change the dataset path. See insturctions in the Deeplab v3+ part below.)

• any difference from the paper's proposed method? Actually yes. It's mainly about the FA module. I tried several mothods such as:

  • 19 channel SSSR output -> feature transform module -> 3 channel output -> calculate FAloss with 3 channel SISR output. Result is like a disaster
  • 19 channel SSSR last_conv(see the code and you'll know what it is) feature -> feature transform module -> calculate FAloss with 19 channel SISR last_conv feature. still disaster.
  • 19 channel SSSR last_conv(see the code and you'll know what it is) feature -> feature transform module -> calculate FAloss with 19 channel SISR last_conv feature, but no more normalization in the FA module. Seems not bad, but still cannot surpass simple original Deeplab v3+
  • Besides, this project use a square input(default 512*512) which is cropped from the original image.

• so my results? mIoU about 0.6669 when use the original Deeplab v3+. 0.6638 when i add the SISR path but no FA module. and about 0.62 after i added the FA module.

The result doesn't look good, but this may because of the differences of the FA module.(but why the mIoU decreased after i added the SISR path)

Currently the code doesn't use normalization in FA module. If you want to try using them, please cancel the comment of line 16,18,23,25 in 'utils/fa_loss.py'

Please imform me if you have any questions about the code.

below are discriptions about Deeplab v3+(from the original repo).

---

pytorch-deeplab-xception

Update on 2018/12/06. Provide model trained on VOC and SBD datasets.

Update on 2018/11/24. Release newest version code, which fix some previous issues and also add support for new backbones and multi-gpu training. For previous code, please see in previous branch

TODO

• Support different backbones
• Support VOC, SBD, Cityscapes and COCO datasets
• Multi-GPU training

Backbone	train/eval os	mIoU in val	Pretrained Model
ResNet	16/16	78.43%	google drive
MobileNet	16/16	70.81%	google drive
DRN	16/16	78.87%	google drive

Introduction

This is a PyTorch(0.4.1) implementation of DeepLab-V3-Plus. It can use Modified Aligned Xception and ResNet as backbone. Currently, we train DeepLab V3 Plus using Pascal VOC 2012, SBD and Cityscapes datasets.

Installation

The code was tested with Anaconda and Python 3.6. After installing the Anaconda environment:

0. Clone the repo:

   git clone https://github.com/jfzhang95/pytorch-deeplab-xception.git
   cd pytorch-deeplab-xception

1. Install dependencies:

   For PyTorch dependency, see pytorch.org for more details.

   For custom dependencies:

   pip install matplotlib pillow tensorboardX tqdm

Training

Follow steps below to train your model:

0. Configure your dataset path in mypath.py.

1. Input arguments: (see full input arguments via python train.py --help):

   usage: train.py [-h] [--backbone {resnet,xception,drn,mobilenet}]
               [--out-stride OUT_STRIDE] [--dataset {pascal,coco,cityscapes}]
               [--use-sbd] [--workers N] [--base-size BASE_SIZE]
               [--crop-size CROP_SIZE] [--sync-bn SYNC_BN]
               [--freeze-bn FREEZE_BN] [--loss-type {ce,focal}] [--epochs N]
               [--start_epoch N] [--batch-size N] [--test-batch-size N]
               [--use-balanced-weights] [--lr LR]
               [--lr-scheduler {poly,step,cos}] [--momentum M]
               [--weight-decay M] [--nesterov] [--no-cuda]
               [--gpu-ids GPU_IDS] [--seed S] [--resume RESUME]
               [--checkname CHECKNAME] [--ft] [--eval-interval EVAL_INTERVAL]
               [--no-val]
   

2. To train deeplabv3+ using Pascal VOC dataset and ResNet as backbone:

   bash train_voc.sh

3. To train deeplabv3+ using COCO dataset and ResNet as backbone:

   bash train_coco.sh

Acknowledgement

PyTorch-Encoding

Synchronized-BatchNorm-PyTorch

drn