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segmentation_guide.md

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Deployment and Test on models in PaddleSeg

Introduction

We start Deployment and Test from HRNet_Contrast_W48, a semantic segmentaion model in PaddleSeg.

Running evaluation and inference

Preparation

Setup development host and deployment target device according to NCSDK documents. Make sure basic IMGDNN test pass and tutorial example could be deployed successfully.

Development Host

  1. Install PaddlePaddle
  2. Compile PaddlePaddle prediction model into PowerVR deployment packages.
  3. Clone this repository.
  4. Download cityspace evaluate dataset from sftp server to EvalDatasetPath directory. The structure of the evaluation dataset is as follows:
    cityscapes
    |
    |--leftImg8bit
    |  |--val
    |
    |--gtFine
    |  |--val

Target device

  1. Copy python/engine/backend/pvr_grpc/* to GRPCServerPath directory on target devices;
  2. Copy PowerVR deployment package to DeploymentModelPath;
  3. Set base_name field in $GRPCServerPath/pvr_service_config.yml, e.g
field description values
base_name path to vm file $DeploymentModelPath/HRNet_Contrast_W48-AX2185-d16b16w16-ncsdk_2_8-arrch64_linux_gnu.ro
  1. Launch the gRPC server
python PVRInferServer.py

Running Test

Config File

Create a config file in Yaml, some fields are described below. Also refer to configs/image_segmentation/HRNet_Contrast_W48.yaml

field description values
Global.mode test mode evaluation,inference
Dataloader.dataset.name dataset class name to be instantialize CityScapesDataset
Dataloader.dataset.image_root directory of input mages of CityScapes dataset string
Dataloader.dataset.label_path directory of ground truth of CityScapes dataset string
Dataloader.sampler.batch_size set the batch size of inupt integer
Infer.infer_imgs path to test image or directory string
Infer.batch_size batch size of inference interger
Infer.PostProcess.save_path path to inference results of test image or directory string
Metric.Eval.mIou.num_classes number of semantic categories interger
Config PaddlePaddle backend

To inference with Paddle backend, the Pretrained Model need to be downloaded and exported to a prediction model before deployment. Besides, fields below need to be set.

field description values
Model.backend backend used to inference paddle
Model.Paddle.path path to Paddle model string
Model.Paddle.base_name base name of model files string
Config PowerVR standalone backend

To run all test code on target device, set the fields below

field description values
Model.backend backend used to inference powervr
Model.PowerVR.base_name path to vm file string
Model.PowerVR.input_name network input name string
Model.PowerVR.output_shape shape of output tensor list
Config PowerVR Distributed backend

To run test code on host and run inference on target device, set the fields below

field description values
Model.backend backend used to inference powervr_grpc
Model.PowerVR_gRPC.pvr_server IP of gRPC server IP address
Model.PowerVR.input_name network input name string
Model.PowerVR.output_shape shape of output tensor list

Running test scripts

python tools/test_egret.py -c ./configs/image_segmentation/HRNet_Contrast_W48.yaml

Some field could be override at command line, e.g. to override the batch_size

python tools/test_egret.py -c ./configs/image_segmentation/HRNet_Contrast_W48.yaml \
-o DataLoader.Eval.sampler.batch_size=1

Performance

Image Sematic Segmentation

Model mIoU (with 5 images) time(ms)
bs=1		 time(ms)
bs=4		 Download
Address
HRNet_Contrast_W48
(d16-w16-b16)		 0.593 null null sftp://transfer.imgtec.com/paddle_models/paddle_segmentation/HRNet_Contrast_W48-AX2185-d16b16w16-ncsdk_2_8-arrch64_linux_gnu.ro
HRNet_Contrast_W48
(non-quant)		 0.593 null null link