PaddleSeg/configs/pp_liteseg

• PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model
• • Reference
  • Overview
  • Training
  • Evaluation
  • Deployment
  • Performance
  • • Cityscapes
    • CamVid

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.

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

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.