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AutoNUE

README.md

AutoNUE@CVPR 2021 Challenge

Implementation of the 1st solution for AutoNUE@CVPR 2021 Challenge Semenatic Segmentation Track based on PaddlePaddle.

Installation

step 1. Install PaddlePaddle

System Requirements:

  • PaddlePaddle == 2.0.2
  • Python >= 3.6+

Highly recommend you install the GPU version of PaddlePaddle, due to large overhead of segmentation models, otherwise it could be out of memory while running the models. For more detailed installation tutorials, please refer to the official website of PaddlePaddle。

step 2. Install PaddleSeg

You should use API Calling method to install PaddleSeg for flexible development.

pip install paddleseg==2.5.0

Data Preparation

Firstly, you need to to download and convert the India Driving Dataset following the instructions of Segmentation Track. IDD_Dectection dataset also need for pseudo-labeling.

And then, you need to organize data following the below structure.

IDD_Segmentation
|
|--leftImg8bit
|  |--train
|  |--val
|  |--test
|
|--gtFine
|  |--train
|  |--val
|  |--test

We make three contributions and managed to rank 1st.

  • Progressively Segmentation
  • Leverage IDD_Detection Dataset to generate extre training samples by pseudo-labeling.
  • Decoder-enhanced Swin Transformer

Training

Baseline

  1. Download pretrained weights on Mapillary.
mkdir -p pretrain && cd pretrain
wget https://bj.bcebos.com/paddleseg/dygraph/cityscapes/ocrnet_hrnetw48_mapillary/pretrained.pdparams
cd ..
  1. Modify scripts/train.py line 27 with from core.val import evaluate
  2. Run the training script.
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -u -m paddle.distributed.launch train.py \
--config configs/sscale_auto_nue_map+city@1920.yml --use_vdl \
--save_dir saved_model/sscale_auto_nue_map+city@1920 --save_interval 2000 --num_workers 2 --do_eval

Regional progressive segmentation

  1. Replace scripts/train.py line 27 'from core.val import evaluate' with from core.val_crop import evaluate
  2. Run
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -u -m paddle.distributed.launch train.py \
--config configs/auto_nue_map+city_crop.yml --use_vdl \
--save_dir saved_model/auto_nue_map+city_crop --save_interval 2000 --num_workers 2 --do_eval

Pseudo-labeling

First you need to organize the IDD_Detection dataset as follow:

IDD_Detection
|
|--JPEGImages
|--Annotations

where JPEGImages and Annotation are images and xml files collected from IDD_Detection/FrontFar and IDD_Detection/FrontNear two folders.

And Then:

  1. Replace AutoNUE21/predict.py line 22 from paddleseg.core import predict with from core.predict_generate_autolabel.py import predictAutolabel
  2. Modity AutoNUE21/predict.py line 156 predict( with predictAutolabel(
  3. Run
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m paddle.distributed.launch  predict.py --config configs/sscale_auto_nue_map+city@1920.yml  --model_path saved_model/sscale_auto_nue_map+city@1920/best_model/model.pdparams --image_path data/IDD_Detection/JPEGImages --save_dir detection_out --aug_pred --scales 1.0 1.5 2.0 --flip_horizontal
  1. Auto-box traffic lights and traffic sign two classes from bounding box annotation by running tools/IDD_labeling.py
  2. Put the generated pred_refine folder under data/IDD_Detection
  3. Modify scripts/train.py line 27 with from core.val import evaluate
  4. Train these pseudo labels with fine-annotated sample:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -u -m paddle.distributed.launch train.py \
--config configs/auto_nue_auto_label.yml --use_vdl \
--save_dir saved_model/auto_nue_auto_label --save_interval 2000 --num_workers 2 --do_eval

Decoder-enhanced Swin Transformer

  1. Download pretrained weights on Mapillary.
cd pretrain
wget https://bj.bcebos.com/paddleseg/dygraph/cityscapes/swin_mla_p4w7_mapillary/pretrained_swin.pdparams
cd ..
  1. Run the training script.
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -u -m paddle.distributed.launch train.py \
--config configs/swin_transformer_mla_base_patch4_window7_160k_autonue.yml --use_vdl \
--save_dir saved_model/swin_transformer_mla_autonue --save_interval 2000 --num_workers 2 --do_eval
  1. Run the testing script.
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m paddle.distributed.launch  predict.py --config configs/swin_transformer_mla_base_patch4_window7_160k_autonue.yml  --model_path saved_model/swin_transformer_mla_autonue/best_model/model.pdparams --image_path data/IDD_Segmentation/leftImg8bit/test/ --save_dir test_out_swin --aug_pred --scales 1.0 1.5 2.0 --flip_horizontal

Ensemble Testing

We provide a predict script for ensembling baseline, pseudo-labeling and rps. Just running:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m paddle.distributed.launch  predict_ensemble_three.py --config configs/sscale_auto_nue_map+city@1920.yml  --config_1 configs/auto_nue_auto_label.yml --config_crop configs/auto_nue_map+city_crop.yml --model_path saved_model/sscale_auto_nue_map+city@1920/best_model/model.pdparams  --model_path_1 saved_model/auto_nue_auto_label/best_model/model.pdparams  --model_path_crop saved_model/auto_nue_map+city_crop/best_model/model.pdparams --image_path data/IDD_Segmentation/leftImg8bit/test/ --save_dir test_out --aug_pred --scales 1.0 1.5 2.0 --flip_horizontal