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PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model

Reference

Juncai Peng, Yi Liu, Shiyu Tang, Yuying Hao, Lutao Chu, Guowei Chen, Zewu Wu, Zeyu Chen, Zhiliang Yu, Yuning Du, Qingqing Dang,Baohua Lai, Qiwen Liu, Xiaoguang Hu, Dianhai Yu, Yanjun Ma. PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model. https://arxiv.org/abs/2204.02681

Overview

We propose PP-LiteSeg, a novel lightweight model for the real-time semantic segmentation task. Specifically, we present a Flexible and Lightweight Decoder (FLD) to reduce computation overhead of previous decoder. To strengthen feature representations, we propose a Unified Attention Fusion Module (UAFM), which takes advantage of spatial and channel attention to produce a weight and then fuses the input features with the weight. Moreover, a Simple Pyramid Pooling Module (SPPM) is proposed to aggregate global context with low computation cost.

arch

Training

Prepare:

  • Install gpu driver, cuda toolkit and cudnn
  • Install Paddle and PaddleSeg (doc)
  • Download dataset and link it to PaddleSeg/data (Cityscapes, CamVid) 
    PaddleSeg/data
├── cityscapes
│   ├── gtFine
│   ├── infer.list
│   ├── leftImg8bit
│   ├── test.list
│   ├── train.list
│   ├── trainval.list
│   └── val.list
├── camvid
│   ├── annot
│   ├── images
│   ├── README.md
│   ├── test.txt
│   ├── train.txt
│   └── val.txt

Training:

The config files of PP-LiteSeg are under PaddleSeg/configs/pp_liteseg/.

Based on the train.py script, we set the config file and start training model.

export CUDA_VISIBLE_DEVICES=0,1,2,3
export model=pp_liteseg_stdc1_cityscapes_1024x512_scale0.5_160k     # test resolution is 1024*512
# export model=pp_liteseg_stdc1_cityscapes_1024x512_scale0.75_160k  # test resolution is 1536x768
# export model=pp_liteseg_stdc1_cityscapes_1024x512_scale1.0_160k   # test resolution is 2048x1024
# export model=pp_liteseg_stdc2_cityscapes_1024x512_scale0.5_160k
# export model=pp_liteseg_stdc2_cityscapes_1024x512_scale0.75_160k
# export model=pp_liteseg_stdc2_cityscapes_1024x512_scale1.0_160k
# export model=pp_liteseg_stdc1_camvid_960x720_10k
# export model=pp_liteseg_stdc2_camvid_960x720_10k
python -m paddle.distributed.launch train.py \
    --config configs/pp_liteseg/${model}.yml \
    --save_dir output/${model} \
    --save_interval 1000 \
    --num_workers 3 \
    --do_eval \
    --use_vdl

After the training, the weights are saved in PaddleSeg/output/xxx/best_model/model.pdparams.

Refer to doc for the detailed usage of training.

Evaluation

With the config file and trained weights, we use the val.py script to evaluate the model.

Refer to doc for the detailed usage of evalution.

export CUDA_VISIBLE_DEVICES=0
export model=pp_liteseg_stdc1_cityscapes_1024x512_scale0.5_160k
# export other model
python val.py \
    --config configs/pp_liteseg/${model}.yml \
    --model_path output/${model}/best_model/model.pdparams \
    --num_workers 3

Deployment

Using ONNX+TRT

Prepare:

  • Install gpu driver, cuda toolkit and cudnn
  • Download TensorRT 7 tar file from Nvidia. We provide cuda10.2-cudnn8.0-trt7.1
  • Install the TensorRT whl in the tar file, i.e., pip install TensorRT-7.1.3.4/python/xx.whl
  • Set Path, i.e., export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:TensorRT-7.1.3.4/lib
  • Install Paddle and PaddleSeg (doc)
  • Run pip install 'pycuda>=2019.1.1'
  • Run pip install paddle2onnx onnx onnxruntime

We measure the inference speed with infer_onnx_trt.py, which first exports the Paddle model as ONNX and then infers the ONNX model by TRT. Sometimes, the adaptive average pooling op can not be converted to ONNX. To solve the problem, you can adjust the input shape of the model as a multiple of 128.

python deploy/python/infer_onnx_trt.py \
    --config configs/pp_liteseg/pp_liteseg_xxx.yml
    --width 1024 \
    --height 512

Please refer to infer_onnx_trt.py for the detailed usage.

Using PaddleInference

Export the trained model as inference model (doc).

Use PaddleInference to deploy the inference model on Nvidia GPU and X86 CPU(python api doc, cpp api doc).

Performance

Cityscapes

Model Backbone Training Iters Train Resolution Test Resolution mIoU mIoU (flip) mIoU (ms+flip) Links
PP-LiteSeg-T STDC1 160000 1024x512 1025x512 73.10% 73.89% - config|model|log|vdl
PP-LiteSeg-T STDC1 160000 1024x512 1536x768 76.03% 76.74% - config|model|log|vdl
PP-LiteSeg-T STDC1 160000 1024x512 2048x1024 77.04% 77.73% 77.46% config|model|log|vdl
PP-LiteSeg-B STDC2 160000 1024x512 1024x512 75.25% 75.65% - config|model|log|vdl
PP-LiteSeg-B STDC2 160000 1024x512 1536x768 78.75% 79.23% - config|model|log|vdl
PP-LiteSeg-B STDC2 160000 1024x512 2048x1024 79.04% 79.52% 79.85% config|model|log|vdl

Note that:

  • Use infer_onnx_trt.py to measure the inference speed.
  • The flip denotes flip_horizontal, the ms denotes multi scale, i.e (0.75, 1.0, 1.25) * test_resolution.
  • Simliar to other models in PaddleSeg, the mIoU in above table refer to the evaluation of PP-LiteSeg on Cityscapes validation set.
  • You can download the trained model in above table and use it in evaluation.

The comparisons with state-of-the-art real-time methods on Cityscapes as follows.

Model Encoder Resolution mIoU(Val) mIoU(Test) FPS
ENet - 512x1024 - 58.3 76.9
ICNet PSPNet50 1024x2048 - 69.5 30.3
ESPNet ESPNet 512x1024 - 60.3 112.9
ESPNetV2 ESPNetV2 512x1024 66.4 66.2 -
SwiftNet ResNet18 1024x2048 75.4 75.5 39.9
BiSeNetV1 Xception39 768x1536 69.0 68.4 105.8
BiSeNetV1-L ResNet18 768x1536 74.8 74.7 65.5
BiSeNetV2 - 512x1024 73.4 72.6 156
BiSeNetV2-L - 512x1024 75.8 75.3 47.3
FasterSeg - 1024x2048 73.1 71.5 163.9
SFNet DF1 1024x2048 - 74.5 121
STDC1-Seg50 STDC1 512x1024 72.2 71.9 250.4
STDC2-Seg50 STDC2 512x1024 74.2 73.4 188.6
STDC1-Seg75 STDC1 768x1536 74.5 75.3 126.7
STDC2-Seg75 STDC2 768x1536 77.0 76.8 97.0
PP-LiteSeg-T1 STDC1 512x1024 73.1 72.0 273.6
PP-LiteSeg-B1 STDC2 512x1024 75.3 73.9 195.3
PP-LiteSeg-T2 STDC1 768x1536 76.0 74.9 143.6
PP-LiteSeg-B2 STDC2 768x1536 78.2 77.5 102.6
iou_fps

CamVid

Model Backbone Training Iters Train Resolution Test Resolution mIoU mIoU (flip) mIoU (ms+flip) Links
PP-LiteSeg-T STDC1 10000 960x720 960x720 73.30% 73.89% 73.66% config|model|log|vdl
PP-LiteSeg-B STDC2 10000 960x720 960x720 75.10% 75.85% 75.48% config|model|log|vdl

Note:

  • The flip denotes flip_horizontal, the ms denotes multi scale, i.e (0.75, 1.0, 1.25) * test_resolution.
  • The mIoU in above table refer to the evaluation of PP-LiteSeg on CamVid test set.