PaddleDetection Model Export Tutorial

一、Model Export

This section describes how to use the tools/export_model.py script to export models.

Export model input and output description

• Input variables and input shapes are as follows:

  Input Name	Input Shape	Meaning
  image	[None, 3, H, W]	Enter the network image. None indicates the Batch dimension. If the input image size is variable length, H and W are None
  im_shape	[None, 2]	The size of the image after resize is expressed as H,W, and None represents the Batch dimension
  scale_factor	[None, 2]	The input image size is larger than the real image size, denoted byscale_y, scale_x

AttentionFor details about the preprocessing method, see the Test Reader section in the configuration file.

-The output of the dynamic and static derived model in Paddle Detection is unified as follows:

• bbox, the output of NMS, in the shape of [N, 6], where N is the number of prediction boxes, and 6 is [class_id, score, x1, y1, x2, y2].
• bbox_num, Each picture corresponds to the number of prediction boxes. For example, batch size is 2 and the output is [N1, N2], indicating that the first picture contains N1 prediction boxes and the second picture contains N2 prediction boxes, and the total number of prediction boxes is the same as the first dimension N output by NMS
• mask, If the network contains a mask, the mask branch is printed

AttentionThe model-to-static export does not support cases where numpy operations are included in the model structure.

2、Start Parameters

3、Example

Using the trained model for trial use, the script is as follows:

# The YOLOv3 model is exported
python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --output_dir=./inference_model \
 -o weights=weights/yolov3_darknet53_270e_coco.pdparams

The prediction model will be exported to the inference_model/yolov3_darknet53_270e_coco directory. infer_cfg.yml, model.pdiparams, model.pdiparams.info, model.pdmodel respectively.

4、Sets the input size of the export model

When using Fluid TensorRT for prediction, since <= TensorRT 5.1 only supports fixed-length input, the image size of the data layer of the saved model needs to be the same as the actual input image size. Fluid C++ prediction engine does not have this limitation. Setting image_shape in Test Reader changes the size of the input image in the saved model. The following is an example:

#Export the YOLOv3 model with the input 3x640x640
python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --output_dir=./inference_model \
 -o weights=weights/yolov3_darknet53_270e_coco.pdparams TestReader.inputs_def.image_shape=[3,640,640]