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Model Export

The trained model needs to be exported as a prediction model before deployment.

This tutorial will show how to export a trained model。

Acquire the Pre-training Model

In this example，BiseNetV2 model will be used. Run the following command or click link to download the pretrained model.

mkdir bisenet && cd bisenet
wget https://paddleseg.bj.bcebos.com/dygraph/cityscapes/bisenet_cityscapes_1024x1024_160k/model.pdparams
cd ..

Export the prediction Model

Make sure you have installed PaddleSeg and are in the PaddleSeg directory.

Run the following command, and the prediction model will be saved in output directory.

export CUDA_VISIBLE_DEVICES=0 # Set a usable GPU.
# If on windows, Run the following command：
# set CUDA_VISIBLE_DEVICES=0
python export.py \
       --config configs/bisenet/bisenet_cityscapes_1024x1024_160k.yml \
       --model_path bisenet/model.pdparams\
       --save_dir output
       --input_shape 1 3 512 1024

Description of Exported Script Parameters

parammeter	purpose	is needed	default
config	Config file	yes	-
save_dir	Save root path for model and VisualDL log files	no	output
model_path	Path of pre-training model parameters	no	The value in config file
with_softmax	Add softmax operator at the end of the network. Since PaddleSeg networking returns Logits by default, you can set it to True if you want the deployment model to get the probability value	no	False
without_argmax	Whether or not to add argmax operator at the end of the network. Since PaddleSeg networking returns Logits by default, we add argmax operator at the end of the network by default in order to directly obtain the prediction results for the deployment model	no	False
input_shape	Set the input shape of exported model, such as --input_shape 1 3 1024 1024。if input_shape is not provided，the Default input shape of exported model is [-1, 3, -1, -1]	no (If the image shape in prediction is consistent, you should set the input_shape)	None

If you encounter shape-relevant issue, please try to set the input_shape.

Prediction Model Files

output
  ├── deploy.yaml            # Config file of deployment
  ├── model.pdiparams        # Paramters of static model
  ├── model.pdiparams.info   # Additional information witch is not concerned generally
  └── model.pdmodel          # Static model file

After exporting prediction model, it can be deployed by the following methods.

Deployment scenarios	Inference library	Tutorial
Server (Nvidia GPU and X86 CPU) Python deployment	Paddle Inference	doc
Server (Nvidia GPU and X86 CPU) C++ deployment	Paddle Inference	doc
Mobile deployment	Paddle Lite	doc
Service-oriented deployment	Paddle Serving	doc
Web deployment	Paddle JS	doc