English | [简体中文](./ppvehicle_violation.md) # Customized Vehicle Violation The secondary development of vehicle violation task mainly focuses on the task of lane line segmentation model. PP-LiteSeg model is used to get the lane line data set bdd100k through fine-tune. The process is referred to [PP-LiteSeg](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.7/configs/pp_liteseg/README.md)。 ## Data preparation ppvehicle violation analysis divides the lane line into 4 categories ``` 0 Background 1 double yellow line 2 Solid line 3 Dashed line ``` 1. For the bdd100k data set, we can combine the processing script provided by [lane_to_mask.py](../../../deploy/pipeline/tools/lane_to_mask.py) and bdd100k [repo](https://github.com/bdd100k/bdd100k) to process the data into the data format required for segmentation. ``` # clone bdd100k： git clone https://github.com/bdd100k/bdd100k.git # copy lane_to_mask.py to bdd100k/ cp PaddleDetection/deploy/pipeline/tools/lane_to_mask.py bdd100k/ # preparation bdd100k env cd bdd100k && pip install -r requirements.txt #bdd100k to mask python lane_to_mask.py -i dataset/labels/lane/polygons/lane_train.json -o /output_path # -i means input path for bdd100k dataset label json， # -o for output patn ``` 2. Organize data and store data in the following format: ``` dataset_root | |--images | |--train | |--image1.jpg | |--image2.jpg | |--... | |--val | |--image3.jpg | |--image4.jpg | |--... | |--test | |--image5.jpg | |--image6.jpg | |--... | |--labels | |--train | |--label1.jpg | |--label2.jpg | |--... | |--val | |--label3.jpg | |--label4.jpg | |--... | |--test | |--label5.jpg | |--label6.jpg | |--... | ``` run [create_dataset_list.py](../../../deploy/pipeline/tools/create_dataset_list.py) create txt file ``` python create_dataset_list.py #dataset path --type custom #dataset type，support cityscapes、custom ``` For other data and data annotation, please refer to PaddleSeg [Prepare Custom Datasets](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.7/docs/data/marker/marker_cn.md) ## model training clone PaddleSeg： ``` git clone https://github.com/PaddlePaddle/PaddleSeg.git ``` prepapation env： ``` cd PaddleSeg pip install -r requirements.txt ``` ### Prepare configuration file For details, please refer to PaddleSeg [prepare configuration file](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.7/docs/config/pre_config_cn.md). exp: pp_liteseg_stdc2_bdd100k_1024x512.yml ``` batch_size: 16 iters: 50000 train_dataset: type: Dataset dataset_root: data/bdd100k #dataset path train_path: data/bdd100k/train.txt #dataset train txt num_classes: 4 #lane classes mode: train transforms: - type: ResizeStepScaling min_scale_factor: 0.5 max_scale_factor: 2.0 scale_step_size: 0.25 - type: RandomPaddingCrop crop_size: [512, 1024] - type: RandomHorizontalFlip - type: RandomAffine - type: RandomDistort brightness_range: 0.5 contrast_range: 0.5 saturation_range: 0.5 - type: Normalize val_dataset: type: Dataset dataset_root: data/bdd100k #dataset path val_path: data/bdd100k/val.txt #dataset val txt num_classes: 4 mode: val transforms: - type: Normalize optimizer: type: sgd momentum: 0.9 weight_decay: 4.0e-5 lr_scheduler: type: PolynomialDecay learning_rate: 0.01 #0.01 end_lr: 0 power: 0.9 loss: types: - type: MixedLoss losses: - type: CrossEntropyLoss - type: LovaszSoftmaxLoss coef: [0.6, 0.4] - type: MixedLoss losses: - type: CrossEntropyLoss - type: LovaszSoftmaxLoss coef: [0.6, 0.4] - type: MixedLoss losses: - type: CrossEntropyLoss - type: LovaszSoftmaxLoss coef: [0.6, 0.4] coef: [1, 1,1] model: type: PPLiteSeg backbone: type: STDC2 pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz #Pre-training model ``` ### training model ``` #Single GPU training export CUDA_VISIBLE_DEVICES=0 # Linux # set CUDA_VISIBLE_DEVICES=0 # Windows python train.py \ --config configs/pp_liteseg/pp_liteseg_stdc2_bdd100k_1024x512.yml \ --do_eval \ --use_vdl \ --save_interval 500 \ --save_dir output ``` ### Explanation of training parameters ``` --do_eval Whether to start the evaluation when saving the model. When starting, the best model will be saved to best according to mIoU model --use_vdl Whether to enable visualdl to record training data --save_interval 500 Number of steps between model saving --save_dir output Model output path ``` ## 2、Multiple GPUs training if you want to use multiple gpus training, you need to set the environment variable CUDA_VISIBLE_DEVICES is specified as multiple gpus (if not specified, all gpus will be used by default), and the training script will be started using paddle.distributed.launch (because nccl is not supported under windows, multi-card training cannot be used): ``` export CUDA_VISIBLE_DEVICES=0,1,2,3 # 4 gpus python -m paddle.distributed.launch train.py \ --config configs/pp_liteseg/pp_liteseg_stdc2_bdd100k_1024x512.yml \ --do_eval \ --use_vdl \ --save_interval 500 \ --save_dir output ``` After training, you can execute the following commands for performance evaluation: ``` python val.py \ --config configs/pp_liteseg/pp_liteseg_stdc2_bdd100k_1024x512.yml \ --model_path output/iter_1000/model.pdparams ``` ### Model export Use the following command to export the trained model as a prediction deployment model. ``` python export.py \ --config configs/pp_liteseg/pp_liteseg_stdc2_bdd100k_1024x512.yml \ --model_path output/iter_1000/model.pdparams \ --save_dir output/inference_model ``` Profile in PP-Vehicle when used `./deploy/pipeline/config/infer_cfg_ppvehicle.yml` set `model_dir` in `LANE_SEG`. ``` LANE_SEG: lane_seg_config: deploy/pipeline/config/lane_seg_config.yml model_dir: output/inference_model ``` Then you can use -->to finish the task of updating the lane line segmentation model.