sedna/examples/helmet_detection_incremental_train

• Using Incremental Learning Job in Helmet Detection Scenario
• • Helmet Detection Experiment
  • • Prepare Worker Image
    • Install Neptune
    • Prepare Data and Model
    • Prepare Script
    • Create Incremental Job
    • Mock Video Stream for Inference in Edge Side
    • Check Incremental Learning Job

Using Incremental Learning Job in Helmet Detection Scenario

This document introduces how to use incremental learning job in helmet detectioni scenario.
Using the incremental learning job, our application can automatically retrains, evaluates,
and updates models based on the data generated at the edge.

Helmet Detection Experiment

Prepare Worker Image

Build the worker image by referring to the dockerfile
and put the image to the gm-config.yaml's imageHub in Install Neptune
In this demo, we need to replace the requirement.txt to

flask==1.1.2
keras==2.4.3
opencv-python==4.4.0.44
websockets==8.1
Pillow==8.0.1
requests==2.24.0
tqdm==4.56.0
matplotlib==3.3.3

Install Neptune

Follow the Neptune installation document to install Neptune.

Prepare Data and Model

• step 1: create dataset directory:

mkdir -p /data/helmet_detection

• step 2: download base model

mkdir /model
cd /model
wget https://edgeai-neptune.obs.cn-north-1.myhuaweicloud.com/examples/helmet-detection/dataset.tar.gz
tar -zxvf model.tar.gz

Prepare Script

Download the scripts to the path code of your node

Create Incremental Job

Create Namespace kubectl create ns neptune-test

Create Dataset

kubectl create -f - <<EOF
apiVersion: neptune.io/v1alpha1
kind: Dataset
metadata:
  name: incremental-dataset
  namespace: neptune-test
spec:
  url: "/data/helmet_detection/dataset/data.txt"
  format: "txt"
  nodeName: "cloud0"
EOF

Create Initial Model to simulate the initial model in incremental learning scenario.

kubectl create -f - <<EOF
apiVersion: neptune.io/v1alpha1
kind: Model
metadata:
  name: initial-model
  namespace: neptune-test
spec:
  url : "/model/base_model"
  format: "ckpt"
EOF

Create Deploy Model

kubectl create -f - <<EOF
apiVersion: neptune.io/v1alpha1
kind: Model
metadata:
  name: deploy-model
  namespace: neptune-test
spec:
  url : "/deploy/model.pb"
  format: "pb"
EOF

Start The Incremental Learning Job

kubectl create -f - <<EOF
apiVersion: neptune.io/v1alpha1
kind: IncrementalLearningJob
metadata:
  name: helmet-detection-demo
  namespace: neptune-test
spec:
  initialModel:
    name: "initial-model"
  dataset:
    name: "incremental-dataset"
    trainProb: 0.8
  trainSpec:
    workerSpec:
      scriptDir: "/code"
      scriptBootFile: "train.py"
      frameworkType: "tensorflow"
      frameworkVersion: "1.15"
      parameters:
        - key: "batch_size"
          value: "32"
        - key: "epochs"
          value: "1"
        - key: "input_shape"
          value: "352,640"
        - key: "class_names"
          value: "person,helmet,helmet-on,helmet-off"
        - key: "nms_threshold"
          value: "0.4"
        - key: "obj_threshold"
          value: "0.3"
    trigger:
      checkPeriodSeconds: 60
      timer:
        start: 02:00
        end: 04:00
      condition:
        operator: ">"
        threshold: 500
        metric: num_of_samples
  evalSpec:
    workerSpec:
      scriptDir: "/code"
      scriptBootFile: "eval.py"
      frameworkType: "tensorflow"
      frameworkVersion: "1.15"
      parameters:
        - key: "input_shape"
          value: "352,640"
        - key: "class_names"
          value: "person,helmet,helmet-on,helmet-off"
  deploySpec:
    model:
      name: "deploy-model"
    trigger:
      condition:
        operator: ">"
        threshold: 0.1
        metric: precision_delta
    nodeName: "cloud0"
    hardExampleMining:
      name: "IBT"
    workerSpec:
      scriptDir: "/code"
      scriptBootFile: "inference.py"
      frameworkType: "tensorflow"
      frameworkVersion: "1.15"
      parameters:
        - key: "input_shape"
          value: "352,640"
        - key: "video_url"
          value: "rtsp://localhost/video"
        - key: "HE_SAVED_URL" 
          value: "/he_saved_url"
  nodeName: "cloud0"
  outputDir: "/output"
EOF

1. The Dataset describes data with labels and HE_SAVED_URL indicates the address of the deploy container for uploading hard examples. Users will mark label for the hard examples in the address.
2. Ensure that the path of outputDir in the YAML file exists on your node. This path will be directly mounted to the container.

Mock Video Stream for Inference in Edge Side

• step 1: install the open source video streaming server EasyDarwin.
• step 2: start EasyDarwin server.
• step 3: download video.
• step 4: push a video stream to the url (e.g., rtsp://localhost/video) that the inference service can connect.

wget https://github.com/EasyDarwin/EasyDarwin/releases/download/v8.1.0/EasyDarwin-linux-8.1.0-1901141151.tar.gz --no-check-certificate
tar -zxvf EasyDarwin-linux-8.1.0-1901141151.tar.gz
cd EasyDarwin-linux-8.1.0-1901141151
./start.sh

mkdir -p /data/video
cd /data/video
tar -zxvf video.tar.gz
ffmpeg -re -i /data/video/helmet-detection.mp4 -vcodec libx264 -f rtsp rtsp://localhost/video

Check Incremental Learning Job

query the service status

kubectl get incrementallearningjob helmet-detection-demo -n neptune-test

In the IncrementalLearningJob resource helmet-detection-demo, the following trigger is configured:

trigger:
  checkPeriodSeconds: 60
  timer:
    start: 02:00
    end: 04:00
  condition:
    operator: ">"
    threshold: 500
    metric: num_of_samples

In a real word, we need to label the hard examples in HE_SAVED_URL with annotation tools and then put the examples to Dataset's url.
Without annotation tools, we can simulate the condition of num_of_samples in the following ways:
Download dataset to our cloud0 node.

cd /data/helmet_detection
wget  https://edgeai-neptune.obs.cn-north-1.myhuaweicloud.com/examples/helmet-detection/dataset.tar.gz
tar -zxvf dataset.tar.gz

The LocalController component will check the number of the sample, realize trigger conditions are met and notice the GlobalManager Component to start train worker.
When the train worker finish, we can view the updated model in the /output directory in cloud0 node.
Then the eval worker will start to evaluate the model that train worker generated.

If the eval result satisfy the deploySpec's trigger

trigger:
  condition:
    operator: ">"
    threshold: 0.1
    metric: precision_delta

the deploy worker will load the new model and provide service.