surveillance_monitoring/dependence/PaddleDetection/static/docs/tutorials/GETTING_STARTED.md

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Getting Started

For setting up the running environment, please refer to installation instructions.

Training/Evaluation/Inference

PaddleDetection provides scripots for training, evalution and inference with various features according to different configure.

# set PYTHONPATH
export PYTHONPATH=$PYTHONPATH:.
# training in single-GPU and multi-GPU. specify different GPU numbers by CUDA_VISIBLE_DEVICES
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python tools/train.py -c configs/faster_rcnn_r50_1x.yml
# GPU evalution
export CUDA_VISIBLE_DEVICES=0
python tools/eval.py -c configs/faster_rcnn_r50_1x.yml
# Inference
python tools/infer.py -c configs/faster_rcnn_r50_1x.yml --infer_img=demo/000000570688.jpg

Optional argument list

list below can be viewed by --help

FLAG	script supported	description	default	remark
-c	ALL	Select config file	None	The description of configure can refer to CONFIG.md
-o	ALL	Set parameters in configure file	None	-o has higher priority to file configured by -c. Such as -o use_gpu=False max_iter=10000
-r/--resume_checkpoint	train	Checkpoint path for resuming training	None	-r output/faster_rcnn_r50_1x/10000
--eval	train	Whether to perform evaluation in training	False
--output_eval	train/eval	json path in evalution	current path	--output_eval ./json_result
--fp16	train	Whether to enable mixed precision training	False	GPU training is required
--loss_scale	train	Loss scaling factor for mixed precision training	8.0	enable when --fp16 is True
--json_eval	eval	Whether to evaluate with already existed bbox.json or mask.json	False	json path is set in --output_eval
--output_dir	infer	Directory for storing the output visualization files	./output	--output_dir output
--draw_threshold	infer	Threshold to reserve the result for visualization	0.5	--draw_threshold 0.7
--infer_dir	infer	Directory for images to perform inference on	None
--infer_img	infer	Image path	None	higher priority over --infer_dir
--use_vdl	train/infer	Whether to record the data with VisualDL, so as to display in VisualDL	False	VisualDL requires Python>=3.5
--vdl_log_dir	train/infer	VisualDL logging directory for image	train:vdl_log_dir/scalar infer: vdl_log_dir/image	VisualDL requires Python>=3.5

Examples

Training

• Perform evaluation in training

  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
  python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml --eval

Perform training and evalution alternatively and evaluate at each snapshot_iter. Meanwhile, the best model with highest MAP is saved at each snapshot_iter which has the same path as model_final.

If evaluation dataset is large, we suggest decreasing evaluation times or evaluating after training.

• Fine-tune other task

When using pre-trained model to fine-tune other task, pretrain_weights can be used directly. The parameters with different shape will be ignored automatically. For example:

  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
  # If the shape of parameters in program is different from pretrain_weights,
  # then PaddleDetection will not use such parameters.
  python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml \
                           -o pretrain_weights=output/faster_rcnn_r50_1x/model_final \

Besides, the name of parameters which need to ignore can be specified explicitly as well. Two methods can be used:

1. The excluded pre-trained parameters can be set by finetune_exclude_pretrained_params in YAML config
2. Set -o finetune_exclude_pretrained_params in the arguments.

  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
  python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml \
                           -o pretrain_weights=output/faster_rcnn_r50_1x/model_final \
                              finetune_exclude_pretrained_params = ['cls_score','bbox_pred']

• Training YOLOv3 with fine grained YOLOv3 loss built by Paddle OPs in python

In order to facilitate the redesign of YOLOv3 loss function, we also provide fine grained YOLOv3 loss function building in python code by common Paddle OPs instead of using fluid.layers.yolov3_loss, training YOLOv3 with python loss function as follows:

  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
  python -u tools/train.py -c configs/yolov3_darknet.yml \
                           -o use_fine_grained_loss=true

Fine grained YOLOv3 loss code is defined in ppdet/modeling/losses/yolo_loss.py.

NOTES

• CUDA_VISIBLE_DEVICES can specify different gpu numbers. Such as: export CUDA_VISIBLE_DEVICES=0,1,2,3. GPU calculation rules can refer FAQ
• Dataset will be downloaded automatically and cached in ~/.cache/paddle/dataset if not be found locally.
• Pretrained model is downloaded automatically and cached in ~/.cache/paddle/weights.
• Checkpoints are saved in output by default, and can be revised from save_dir in configure files.
• RCNN models training on CPU is not supported on PaddlePaddle<=1.5.1 and will be fixed on later version.

Mixed Precision Training

Mixed precision training can be enabled with --fp16 flag. Currently Faster-FPN, Mask-FPN and Yolov3 have been verified to be working with little to no loss of precision (less than 0.2 mAP)

To speed up mixed precision training, it is recommended to train in multi-process mode, for example

python -m paddle.distributed.launch --selected_gpus 0,1,2,3,4,5,6,7 tools/train.py --fp16 -c configs/faster_rcnn_r50_fpn_1x.yml

If loss becomes NaN during training, try tweak the --loss_scale value. Please refer to the Nvidia documentation on mixed precision training for details.

Also, please note mixed precision training currently requires changing norm_type from affine_channel to bn.

Evaluation

• Evaluate by specified weights path and dataset path

  export CUDA_VISIBLE_DEVICES=0
  python -u tools/eval.py -c configs/faster_rcnn_r50_1x.yml \
                          -o weights=https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar \

The path of model to be evaluted can be both local path and link in MODEL_ZOO.

• Evaluate with json

  export CUDA_VISIBLE_DEVICES=0
  python tools/eval.py -c configs/faster_rcnn_r50_1x.yml \
             --json_eval \
             -f evaluation/

The json file must be named bbox.json or mask.json, placed in the evaluation/ directory.

NOTES

• Multi-GPU evaluation for R-CNN and SSD models is not supported at the moment, but it is a planned feature

Inference

• Output specified directory && Set up threshold

  export CUDA_VISIBLE_DEVICES=0
  python tools/infer.py -c configs/faster_rcnn_r50_1x.yml \
                      --infer_img=demo/000000570688.jpg \
                      --output_dir=infer_output/ \
                      --draw_threshold=0.5 \
                      -o weights=output/faster_rcnn_r50_1x/model_final \
                      --use_vdl=Ture

--draw_threshold is an optional argument. Default is 0.5. Different thresholds will produce different results depending on the calculation of NMS.

• Export model

  python tools/export_model.py -c configs/faster_rcnn_r50_1x.yml \
                      --output_dir=inference_model \
                      -o weights=output/faster_rcnn_r50_1x/model_final \
                         FasterRCNNTestFeed.image_shape=[3,800,1333]

Save inference model tools/export_model.py, which can be loaded by PaddlePaddle predict library.