Reorganize folder structure of multiedgetracking.

Signed-off-by: Vittorio Cozzolino <vittorio.cozzolino@huawei.com>

Reorganize folder structure of multiedgetracking.
Remove sync function with edge for multiedgertacking controllers (not used currently).
Edit README.md and Dockerfiles.
Remove some explicit parameters from YAML files (use default value).
Add empty line at the end of the YAML and Python files.
Add comments to the multiedgeinference code.
Minor edits to the run.sh script.
Update Docker image version number in YAML files.

Signed-off-by: Vittorio Cozzolino <vittorio.cozzolino@huawei.com>

examples/README.md

@@ -7,7 +7,7 @@ This repository is home to the following features of examples:
 * [Incremental Learning](#incremental-learning)
 * [Federated Learning](#federated-learning)
 * [Lifelong Learning](#lifelong-learning)
-* [Pedestrians ReID](#pedestrian-reid)
+* [Multi-Edge Inference](#multi-edge-inference)
 * [Shared Storage](#shared-storage)
 
 ### Joint Inference
@@ -22,8 +22,8 @@ Example: [Using Federated Learning Job in Surface Defect Detection Scenario](./f
 ### Lifelong Learning
 Example: [Using Lifelong Learning Job in Thermal Comfort Prediction Scenario](./lifelong_learning/atcii/README.md)
 
-### Pedestrian ReID
-Example: [Using ReID to Track an Infected COVID-19 Carrier in Pandemic Scenario](./multiedgetracking/tutorial/README.md)
+### Multi-Edge Inference
+Example: [Using ReID to Track an Infected COVID-19 Carrier in Pandemic Scenario](./multiedgeinference/pedestrian_tracking/README.md)
 
 ### Shared Storage
 | Support Protocols  |Support Features| Examples |Release|

examples/build_image.sh

@@ -57,15 +57,15 @@ fi
 
 cd "$(dirname "${BASH_SOURCE[0]}")"
 
-IMAGE_TAG=${IMAGE_TAG:-v0.4.0} 
+IMAGE_TAG=${GIT_TAG:-v0.5.0} 
 EXAMPLE_REPO_PREFIX=${IMAGE_REPO}/sedna-example-
 
-dockerfiles_multiedgetracking=(
-multi-edge-tracking-feature-extraction.Dockerfile
-# multi-edge-tracking-gpu-feature-extraction.Dockerfile
-# multi-edge-tracking-gpu-videoanalytics.Dockerfile
-multi-edge-tracking-reid.Dockerfile
-multi-edge-tracking-videoanalytics.Dockerfile
+dockerfiles_multiedgeinference=(
+multi-edge-inference-feature-extraction.Dockerfile
+# multi-edge-inference-gpu-feature-extraction.Dockerfile
+# multi-edge-inference-gpu-videoanalytics.Dockerfile
+multi-edge-inference-reid.Dockerfile
+multi-edge-inference-videoanalytics.Dockerfile
 )
 
 dockerfiles_federated_learning=(
@@ -92,7 +92,7 @@ incremental-learning-helmet-detection.Dockerfile
 for tp in ${type[@]}; do
    if [[ "$tp" == "all" ]]; then
       dockerfiles+=(
-         "${dockerfiles_multiedgetracking[@]}"
+         "${dockerfiles_multiedgeinference[@]}"
          "${dockerfiles_federated_learning[@]}"
          "${dockerfiles_joint_inference[@]}"
          "${dockerfiles_lifelong_learning[@]}"

examples/multi-edge-tracking-feature-extraction.Dockerfile → examples/multi-edge-inference-feature-extraction.Dockerfile

@@ -32,10 +32,10 @@ WORKDIR /home/work
 COPY ./lib /home/lib
 
 # Add M3L imports
-COPY examples/multiedgetracking/feature_extraction /home/work
+COPY examples/multiedgeinference/pedestrian_tracking/feature_extraction /home/work
 
 ENV PYTHONPATH "${PYTHONPATH}:/home/work"
 ENV LOG_LEVEL="INFO"
 
 ENTRYPOINT ["python"]
-CMD ["worker.py"]
+CMD ["worker.py"]

examples/multi-edge-tracking-gpu-feature-extraction.Dockerfile → examples/multi-edge-inference-gpu-feature-extraction.Dockerfile

@@ -38,10 +38,10 @@ WORKDIR /home/work
 COPY ./lib /home/lib
 
 # Add M3L imports
-COPY examples/multiedgetracking/feature_extraction /home/work
+COPY examples/multiedgeinference/pedestrian_tracking/feature_extraction /home/work
 
 ENV PYTHONPATH "${PYTHONPATH}:/home/work"
 ENV LOG_LEVEL="INFO"
 
 ENTRYPOINT ["python3.8"]
-CMD ["worker.py"]
+CMD ["worker.py"]

examples/multi-edge-tracking-gpu-videoanalytics.Dockerfile → examples/multi-edge-inference-gpu-videoanalytics.Dockerfile

@@ -98,9 +98,9 @@ RUN pip install onnx protobuf==3.16.0
 WORKDIR /home/work
 COPY ./lib /home/lib
 
-COPY examples/multiedgetracking/detection/ /home/work/
+COPY examples/multiedgeinference/pedestrian_tracking/detection/ /home/work/
 
 ENV LOG_LEVEL="INFO"
 
 ENTRYPOINT ["python"]
-CMD ["worker.py"]
+CMD ["worker.py"]

examples/multi-edge-tracking-reid.Dockerfile → examples/multi-edge-inference-reid.Dockerfile

@@ -28,9 +28,9 @@ ENV PYTHONPATH "${PYTHONPATH}:/home/lib"
 WORKDIR /home/work
 COPY ./lib /home/lib
 
-COPY examples/multiedgetracking/reid /home/work/
+COPY examples/multiedgeinference/pedestrian_tracking/reid /home/work/
 
 ENV LOG_LEVEL="INFO"
 
 ENTRYPOINT ["python"]
-CMD ["worker.py"]
+CMD ["worker.py"]

examples/multi-edge-tracking-videoanalytics.Dockerfile → examples/multi-edge-inference-videoanalytics.Dockerfile

@@ -36,9 +36,9 @@ ENV PYTHONPATH "${PYTHONPATH}:/home/lib"
 WORKDIR /home/work
 COPY ./lib /home/lib
 
-COPY examples/multiedgetracking/detection  /home/work/
+COPY examples/multiedgeinference/pedestrian_tracking/detection  /home/work/
 
 ENV LOG_LEVEL="DEBUG"
 
 ENTRYPOINT ["python"]
-CMD ["worker.py"]
+CMD ["worker.py"]

examples/multiedgetracking/tutorial/README.md → examples/multiedgeinference/pedestrian_tracking/README.md

@@ -5,8 +5,8 @@ Estimated completion time: ~60-100 mins.
 Requirements:
 - K8s cluster
 - Sedna
+- KubeEdge
 - Internet connection to download the containers images
-- Optional: Kubeedge
 - Optional: multi-node cluster
 
 # Introduction
@@ -17,43 +17,43 @@ However, our ReID solution is much more advanced as it does not require a galler
 
 The example images below show the ability of our system to re-identify a potential carrier of the virus and detect close contact proximity risk.
 
-![image info](./1.jpg) ![image info](./2.jpg)
+![image info](tutorial/1.jpg) ![image info](tutorial/2.jpg)
 
 # System Architecture and Components
 
 The image below shows the system architecture and its simplified workflow:
 
-![image info](./arch.png)
+![image info](tutorial/arch.png)
 
 ## Components
 
 **ReID Job**: it performs the ReID.
 
 - Available for CPU only.
-- Folder with specific implementation `examples/multiedgetracking/reid`.
-- Component specs in `lib/sedna/core/multi_edge_tracking/components/reid.py`.
-- Defined by the Dockerfile `multi-edge-tracking-reid.Dockerfile`.
+- Folder with specific implementation `examples/multiedgeinference/pedestrian_tracking/reid`.
+- Component specs in `lib/sedna/core/multi_edge_inference/components/reid.py`.
+- Defined by the Dockerfile `multi-edge-inference-reid.Dockerfile`.
 
 **Feature Extraction Service**: it performs the extraction of the features necessary for the ReID step.
 
 - Available for CPU and GPU.
-- Folder with specific implementation details `examples/multiedgetracking/feature_extraction`.
-- Component specs in `lib/sedna/core/multi_edge_tracking/components/feature_extraction.py`.
-- Defined by the Dockerfile `multi-edge-tracking-feature-extraction.Dockerfile` or `multi-edge-tracking-gpu-feature-extraction.Dockerfile`.
+- Folder with specific implementation details `examples/multiedgeinference/pedestrian_tracking/feature_extraction`.
+- Component specs in `lib/sedna/core/multi_edge_inference/components/feature_extraction.py`.
+- Defined by the Dockerfile `multi-edge-inference-feature-extraction.Dockerfile` or `multi-edge-inference-gpu-feature-extraction.Dockerfile`.
 - It loads the model defined by the CRD in the YAML file `yaml/models/model_m3l.yaml`.
 
 **VideoAnalytics Job**: it performs tracking of objects (pedestrians) in a video.
 
 - Available for CPU and GPU.
-- Folder with specific implementation details `examples/multiedgetracking/detection`.
-- AI model code in `examples/multiedgetracking/detection/estimator/bytetracker.py`.
-- Component specs in `lib/sedna/core/multi_edge_tracking/components/detection.py`.
-- Defined by the Dockerfile `multi-edge-tracking-videoanalytics.Dockerfile` or `multi-edge-tracking-gpu-videoanalytics.Dockerfile`.
+- Folder with specific implementation details `examples/multiedgeinference/pedestrian_tracking/detection`.
+- AI model code in `examples/multiedgeinference/detection/estimator/bytetracker.py`.
+- Component specs in `lib/sedna/core/multi_edge_inference/components/detection.py`.
+- Defined by the Dockerfile `multi-edge-inference-videoanalytics.Dockerfile` or `multi-edge-inference-gpu-videoanalytics.Dockerfile`.
 - It loads the model defined by the CRD in the YAML file `yaml/models/model_detection.yaml`.
 
 # Build Phase
 
-Go to the `sedna/examples` directory and run: `./build_image.sh -r <your-docker-private-repo> multiedgetracking` to build the Docker images. Remember to **push** the images to your own Docker repository!
+Go to the `sedna/examples` directory and run: `./build_image.sh -r <your-docker-private-repo> multiedgeinference` to build the Docker images. Remember to **push** the images to your own Docker repository!
 
 Run `make crds` in the `SEDNA_HOME` and then register the new CRD in the K8S cluster with `make install crds` or:
 - `kubectl create -f sedna/build/crd/sedna.io_featureextractionservices.yaml`
@@ -161,7 +161,7 @@ The provided YAML files are configured to run the feature extraction and ReID po
 
 Download the sample video and query images which will be placed in the NFS folder:
 ```
-wget http://obs.eu-central-201.myhuaweicloud.com/bucket-sedna.obs.eu-central-201.myhuaweicloud.com:80/test_video.zip?AWSAccessKeyId=GWQRKFEFDMSUHM36WN7V&Expires=1652284916&Signature=IFO8%2B55%2BxsOuqozaRipSoCuFUYA%3D
+wget https://drive.google.com/file/d/1HTpzY09bQJe-68d09We3fUrd7REXK5j5/view?usp=sharing -o test_video.zip
 unzip test_video.zip
 sudo cp -r test_video/query /data/network_shared/reid/query #copy sample images to query folder
 sudo cp test_video/test_video.mp4 /data/network_shared/reid/video/test_video.mp4 #copy sample video to video folder
@@ -192,11 +192,11 @@ Following, the application workflow is divided in 2 parts: analysis of the video
 # Cleanup
 
 Don't forget to delete the jobs once they are completed:
-- `k delete -f multiedgetracking/yaml/video-analytics-job.yaml`
-- `k delete -f multiedgetracking/yaml/reid-job.yaml`
+- `k delete -f multiedgeinference/pedestrian_tracking/yaml/video-analytics-job.yaml`
+- `k delete -f multiedgeinference/pedestrian_tracking/yaml/reid-job.yaml`
 
 To also delete the feature extraction service:
-- `k delete -f multiedgetracking/yaml/feature-extraction.yaml`
+- `k delete -f multiedgeinference/pedestrian_tracking/yaml/feature-extraction.yaml`
 
 # **Automated Installation**
 

examples/multiedgetracking/detection/estimator/__init__.py → examples/multiedgeinference/pedestrian_tracking/detection/model/__init__.py

@@ -16,15 +16,15 @@ import importlib
 
 from sedna.common.log import LOGGER
 
-def str_to_estimator_class(module_name=".", estimator_class="ByteTracker"):
+def str_to_class(module_name=".", class_name="ByteTracker"):
     """Return a class type from a string reference"""
-    LOGGER.info(f"Dynamically loading estimator class {estimator_class}")
+    LOGGER.info(f"Dynamically loading class {class_name}")
     try:
-        module_ = importlib.import_module(module_name + estimator_class.lower(), package="estimator")
+        module_ = importlib.import_module(module_name + class_name.lower(), package="model")
         try:
-            class_ = getattr(module_, estimator_class)
+            class_ = getattr(module_, class_name)
         except AttributeError:
-            LOGGER.error('Estimator class does not exist')
+            LOGGER.error('Class does not exist')
     except ImportError:
         LOGGER.error('Module does not exist')
     return class_ or None

examples/multiedgetracking/detection/estimator/bytetracker.py → examples/multiedgeinference/pedestrian_tracking/detection/model/bytetracker.py

@@ -18,9 +18,9 @@ import torch
 import numpy as np
 import cv2
 
-from sedna.core.multi_edge_tracking.plugins import PluggableModel
-from sedna.core.multi_edge_tracking.data_classes import OP_MODE, DetTrackResult
-from sedna.core.multi_edge_tracking.utils import get_parameters
+from sedna.core.multi_edge_inference.plugins import PluggableModel
+from sedna.core.multi_edge_inference.data_classes import OP_MODE, DetTrackResult
+from sedna.core.multi_edge_inference.utils import get_parameters
 from sedna.common.log import LOGGER
 
 # YOLOX imports
@@ -103,7 +103,9 @@ class ByteTracker(PluggableModel):
 
 
     def forward(self, data):
-        """ Data is of List images type"""
+        """ Perform forward pass on with the provided nerual network.
+            'data' is a List of images.
+        """
         
         img_info = {}
 
@@ -179,6 +181,10 @@ class ByteTracker(PluggableModel):
         
 
     def detection(self, data, output, img_info, det_time, frame_nr):
+        """
+        Performs object detection.
+        """
+
         result = None
 
         # Prepare image with boxes overlaid
@@ -213,6 +219,10 @@ class ByteTracker(PluggableModel):
 
 
     def tracking(self, data, output, img_info, det_time, frame_nr):
+        """
+        Performs object tracking.
+        """
+
         # initialize placeholders for the tracking data
         online_tlwhs = []
         online_ids = []
@@ -272,6 +282,9 @@ class ByteTracker(PluggableModel):
         return self.tracking(data, outputs, img_info, det_time, frame_nr)
 
     def predict(self, data, **kwargs):
+        """
+        Main prediction function.
+        """
         tresult = []
 
         start = time.time()

examples/multiedgetracking/detection/worker.py → examples/multiedgeinference/pedestrian_tracking/detection/worker.py

@@ -18,24 +18,24 @@ import os
 import time
 import cv2
 from urllib.request import Request, urlopen
-from estimator import str_to_estimator_class
+from model import str_to_class
 from sedna.common.log import LOGGER
-from sedna.core.multi_edge_tracking.utils import get_parameters
+from sedna.core.multi_edge_inference.utils import get_parameters
 from sedna.datasources.obs.connector import OBSClientWrapper
-from sedna.core.multi_edge_tracking.components.detector import ObjectDetector
+from sedna.core.multi_edge_inference.components.detector import ObjectDetector
 
 class Bootstrapper():
     def __init__(self) -> None:
         LOGGER.info("Creating Detection/Tracking Bootstrapper module")
 
-        self.estimator_class = get_parameters('estimator_class', "ByteTracker")
+        self.model = get_parameters('model', "ByteTracker")
         self.hostname = get_parameters('hostname', "unknown")
         self.fps = float(get_parameters('fps', 25))
         self.batch_size = int(get_parameters('batch_size', 1))
         self.video_id = get_parameters('video_id', 0)
         self.video_address = get_parameters('video_address', "") 
 
-        self.eclass = str_to_estimator_class(estimator_class=self.estimator_class)
+        self.model_class = str_to_class(class_name=self.model)
 
         self.enable_obs =  bool(util.strtobool(get_parameters('ENABLE_OBS', "False")))
         
@@ -45,6 +45,11 @@ class Bootstrapper():
         self.service = None
         
     def run(self):
+        """
+        Entry point for the component. It decides how to process the video
+        source based on its kind.
+        """
+
         protocol = self.video_address.split(":")[0]
         LOGGER.info(f"Detected video source protocol {protocol} for video source {self.video_address}.")
 
@@ -66,6 +71,9 @@ class Bootstrapper():
         self.close()
 
     def download_video(self):
+        """
+        Downloads a video given its address.
+        """
         try:
             req = Request(self.video_address, headers={'User-Agent': 'Mozilla/5.0'})
             LOGGER.info("Video download complete")
@@ -80,6 +88,9 @@ class Bootstrapper():
             LOGGER.error(f"Unable to download video file {ex}")
 
     def connect_to_camera(self, stream_address):
+        """
+        Connects to the video source camera.
+        """
         camera = None
         while camera == None or not camera.isOpened():
             try:
@@ -92,8 +103,11 @@ class Bootstrapper():
         return camera
 
     def process_video_from_disk(self, filename, timeout=20):
-        selected_estimator=self.eclass(video_id=self.video_id)
-        self.service = ObjectDetector(models=[selected_estimator])
+        """
+        Processes a video loaded from disk.
+        """
+        model=self.model_class(video_id=self.video_id)
+        self.service = ObjectDetector(models=[model])
 
         cap = cv2.VideoCapture(filename)
         index = 0
@@ -115,8 +129,11 @@ class Bootstrapper():
         cap.release()
 
     def process_video_from_stream(self, timeout=20):
-        selected_estimator=self.eclass(video_id=self.video_id)
-        self.service=ObjectDetector(models=[selected_estimator], asynchronous=True)
+        """
+        Processes a video accessible through a stream.
+        """
+        model=self.model_class(video_id=self.video_id)
+        self.service=ObjectDetector(models=[model], asynchronous=True)
 
         nframe = 0
         grabbed = False
@@ -144,6 +161,9 @@ class Bootstrapper():
                 camera.release()
     
     def close(self, timeout=20):
+        """
+        Exits the worker.
+        """
         while (time.time() - self.service.heartbeat) <= timeout:
             LOGGER.debug(f"Waiting for more data from the feature extraction service..")
             time.sleep(1)
@@ -154,4 +174,4 @@ class Bootstrapper():
 
 if __name__ == '__main__':
     bs = Bootstrapper()
-    bs.run()
+    bs.run()

examples/multiedgeinference/pedestrian_tracking/detection/yolox/exps/yolox_s_mix_det.py

@@ -0,0 +1,25 @@
+# encoding: utf-8
+import os
+from yolox.yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.num_classes = 1
+        self.depth = 0.33
+        self.width = 0.50
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.train_ann = "train.json"
+        self.val_ann = "train.json"
+        self.input_size = (608, 1088)
+        self.test_size = (608, 1088)
+        self.random_size = (12, 26)
+        self.max_epoch = 80
+        self.print_interval = 20
+        self.eval_interval = 5
+        self.test_conf = 0.001
+        self.nmsthre = 0.7
+        self.no_aug_epochs = 10
+        self.basic_lr_per_img = 0.001 / 64.0
+        self.warmup_epochs = 1

examples/multiedgetracking/detection/yolox/yolox/exp/base_exp.py → examples/multiedgeinference/pedestrian_tracking/detection/yolox/yolox/exp/base_exp.py

@@ -16,6 +16,9 @@ class BaseExp(metaclass=ABCMeta):
 
     def __init__(self):
         self.seed = None
+        self.output_dir = "./YOLOX_outputs"
+        self.print_interval = 100
+        self.eval_interval = 10
 
     @abstractmethod
     def get_model(self) -> Module:
@@ -29,4 +32,3 @@ class BaseExp(metaclass=ABCMeta):
             if not k.startswith("_")
         ]
         return None
-

examples/multiedgeinference/pedestrian_tracking/detection/yolox/yolox/exp/yolox_base.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+
+import os
+
+from .base_exp import BaseExp
+
+
+class Exp(BaseExp):
+    def __init__(self):
+        super().__init__()
+
+        # ---------------- model config ---------------- #
+        self.num_classes = 80
+        self.depth = 1.00
+        self.width = 1.00
+
+        # ---------------- dataloader config ---------------- #
+        # set worker to 4 for shorter dataloader init time
+        self.data_num_workers = 4
+        self.input_size = (640, 640)
+        self.random_size = (14, 26)
+        self.train_ann = "instances_train2017.json"
+        self.val_ann = "instances_val2017.json"
+
+        # --------------- transform config ----------------- #
+        self.degrees = 10.0
+        self.translate = 0.1
+        self.scale = (0.1, 2)
+        self.mscale = (0.8, 1.6)
+        self.shear = 2.0
+        self.perspective = 0.0
+        self.enable_mixup = True
+
+        # --------------  training config --------------------- #
+        self.warmup_epochs = 5
+        self.max_epoch = 300
+        self.warmup_lr = 0
+        self.basic_lr_per_img = 0.01 / 64.0
+        self.scheduler = "yoloxwarmcos"
+        self.no_aug_epochs = 15
+        self.min_lr_ratio = 0.05
+        self.ema = True
+
+        self.weight_decay = 5e-4
+        self.momentum = 0.9
+        self.print_interval = 10
+        self.eval_interval = 10
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+
+        # -----------------  testing config ------------------ #
+        self.test_size = (640, 640)
+        self.test_conf = 0.001
+        self.nmsthre = 0.65
+
+    def get_model(self):
+        from yolox.yolox.models import YOLOPAFPN, YOLOX, YOLOXHead
+
+        def init_yolo(M):
+            for m in M.modules():
+                if isinstance(m, nn.BatchNorm2d):
+                    m.eps = 1e-3
+                    m.momentum = 0.03
+
+        if getattr(self, "model", None) is None:
+            in_channels = [256, 512, 1024]
+            backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels)
+            head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels)
+            self.model = YOLOX(backbone, head)
+
+        self.model.apply(init_yolo)
+        self.model.head.initialize_biases(1e-2)
+        return self.model

examples/multiedgetracking/feature_extraction/worker.py → examples/multiedgeinference/pedestrian_tracking/feature_extraction/worker.py

@@ -24,17 +24,20 @@ from functools import reduce
 from threading import Thread
 
 from sedna.common.log import LOGGER
-from sedna.core.multi_edge_tracking.utils import get_parameters
-from sedna.core.multi_edge_tracking.plugins import PluggableModel
-from sedna.core.multi_edge_tracking.data_classes import DetTrackResult, Target
-from sedna.core.multi_edge_tracking.components.feature_extraction import FEService
+from sedna.core.multi_edge_inference.utils import get_parameters
+from sedna.core.multi_edge_inference.plugins import PluggableModel
+from sedna.core.multi_edge_inference.data_classes import DetTrackResult, Target
+from sedna.core.multi_edge_inference.components.feature_extraction import FEService
 
 os.environ['BACKEND_TYPE'] = 'TORCH'
 
 class FeatureExtractionAI(PluggableModel):
 
     def __init__(self, **kwargs):
-        # Initialize feature extraction module
+        """
+        Initialize feature extraction module
+        """
+        
         self.model = None
 
         # Device and input parameters
@@ -73,6 +76,9 @@ class FeatureExtractionAI(PluggableModel):
         self.model.eval()
 
     def extract_features(self, data : List[DetTrackResult] ):
+        """
+        Extract ReID features from the provided image.
+        """
         input_batch = None
         j = 0
         offset = 0
@@ -201,4 +207,4 @@ class Bootstrapper(Thread):
 # Starting the FE service.
 if __name__ == '__main__':
     bs = Bootstrapper()
-    bs.run()
+    bs.run()

examples/multiedgetracking/reid/store_result.py → examples/multiedgeinference/pedestrian_tracking/reid/store_result.py

@@ -18,7 +18,7 @@ import cv2
 from PIL import Image
 
 from sedna.common.log import LOGGER
-from sedna.core.multi_edge_tracking.data_classes import DetTrackResult
+from sedna.core.multi_edge_inference.data_classes import DetTrackResult
 from sedna.algorithms.reid.close_contact_estimation import ContactTracker
 
 def create_results_folder(folder):

examples/multiedgetracking/reid/worker.py → examples/multiedgeinference/pedestrian_tracking/reid/worker.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from distutils import util
+import pathlib
 import time
 import torch
 import numpy as np
@@ -23,11 +24,13 @@ from store_result import save_image
 from sedna.algorithms.reid.multi_img_matching import match_query_to_targets
 from sedna.algorithms.reid.close_contact_estimation import ContactTracker
 from sedna.common.log import LOGGER
-from sedna.core.multi_edge_tracking.components.reid import ReID
-from sedna.core.multi_edge_tracking.data_classes import DetTrackResult, OP_MODE, Target
-from sedna.core.multi_edge_tracking.utils import get_parameters
+from sedna.core.multi_edge_inference.components.reid import ReID
+from sedna.core.multi_edge_inference.data_classes import DetTrackResult, OP_MODE, Target
+from sedna.core.multi_edge_inference.utils import get_parameters
 from sedna.datasources.obs.connector import OBSClientWrapper
 
+MOUNT_PATH="/data/network_shared/reid"
+
 class ReIDWorker():
 
     def __init__(self, **kwargs):       
@@ -35,12 +38,12 @@ class ReIDWorker():
         self.op_mode = OP_MODE(get_parameters('op_mode', 'covid19')) 
         self.threshold = get_parameters('match_threshold', 0.75)
         self.user_id = get_parameters('user_id', "DEFAULT")
-        self.query_images = str(get_parameters('query_images', "/data/query/sample.png")).split("|")
+        self.query_images = str(get_parameters('query_images', f"{MOUNT_PATH}/query/sample.png")).split("|")
 
         self.target = None
         self.targets_list : List[Target] = []
 
-        self.results_base_folder = "/data/images/"
+        self.results_base_folder = f"{MOUNT_PATH}/images/"
 
         self.CT = ContactTracker(draw_top_view=False)
         
@@ -67,6 +70,9 @@ class ReIDWorker():
         return [data]
 
     def update_target(self, ldata):
+        """
+            Updates the target for the ReID.       
+        """
         LOGGER.info(f"Target updated for user {ldata[0].userid} with {len(ldata[0].features)} feature vectors!")
         self.targets_list = ldata 
 
@@ -114,6 +120,10 @@ class ReIDWorker():
         return None
 
     def tracking_no_gallery(self, det_track : DetTrackResult):
+        """
+            Performs ReID without gallery using the results from the 
+            tracking and feature extraction component.       
+        """
         det_track.targetID = [-1] * len(det_track.bbox_coord) 
         
         for target in self.targets_list:
@@ -141,6 +151,9 @@ class ReIDWorker():
         return det_track
 
     def store_result(self, det_track : DetTrackResult):
+        """
+            Stores ReID result on disk (and OBS, if enabled).     
+        """
         try:
             filename = save_image(det_track, self.CT, folder=f"{self.results_base_folder}{det_track.userID}/")
             if self.enable_obs:
@@ -153,7 +166,6 @@ class Bootstrapper(Thread):
         super().__init__()
 
         self.daemon = True
-        self.folder = "/data/"
         self.retry = 3
         self.job = ReID(models=[ReIDWorker()], asynchronous=False)
 
@@ -161,16 +173,17 @@ class Bootstrapper(Thread):
         LOGGER.info("Loading data from disk.")
 
         while self.retry > 0:
-            files = self.job.get_files_list(self.folder)
+            files = self.job.get_files_list(f"{MOUNT_PATH}/")
 
             if files:
                 LOGGER.debug(f"Loaded {len(files)} files.")
                 for filename in files:
-                    data = self.job.read_from_disk(filename)
-                    if data:
-                        LOGGER.debug(f"File {filename} loaded!")
-                        self.job.put(data)
-                        self.job.delete_from_disk(filename)
+                    if pathlib.Path(filename).suffix == '.dat':
+                        data = self.job.read_from_disk(filename)
+                        if data:
+                            LOGGER.debug(f"File {filename} loaded!")
+                            self.job.put(data)
+                            self.job.delete_from_disk(filename)
                 break
             else:
                 LOGGER.warning("No data available to process!")
@@ -182,4 +195,4 @@ class Bootstrapper(Thread):
 # Start the ReID job.
 if __name__ == '__main__':
     bs = Bootstrapper()
-    bs.run()
+    bs.run()

examples/multiedgetracking/tutorial/run.sh → examples/multiedgeinference/pedestrian_tracking/tutorial/run.sh

@@ -86,7 +86,7 @@ do
   sleep 0.2
 done
 
-echo -n "" && echo "🟢 VideoAnalytics job has completed."
+echo "" && echo "🟢 VideoAnalytics job has completed."
 echo "⚪ Clean-up VideoAnalytics job resources."
 kubectl delete -f ../yaml/video-analytics-job.yaml
 
@@ -112,7 +112,7 @@ do
   sleep 0.2
 done
 
-echo "" &&  echo "🟢 ReID job has completed."
+echo "" && echo "🟢 ReID job has completed."
 echo "⚪ Clean-up ReID job resources."
 kubectl delete -f ../yaml/reid-job.yaml
 

examples/multiedgetracking/yaml/feature-extraction-service.yaml → examples/multiedgeinference/pedestrian_tracking/yaml/feature-extraction-service.yaml

@@ -18,7 +18,7 @@ spec:
       nodeSelector:
         node-role.kubernetes.io/master: ''
       containers:
-      - image: registry-cbu.huawei.com/kubeedge/sedna-example-multi-edge-tracking-feature-extraction:sase
+      - image: registry-cbu.huawei.com/kubeedge/sedna-example-multi-edge-inference-feature-extraction:v0.5.0
         imagePullPolicy: Always
         name:  feature-extraction
         env:

examples/multiedgetracking/yaml/models/model_detection.yaml → examples/multiedgeinference/pedestrian_tracking/yaml/models/model_detection.yaml

@@ -6,10 +6,10 @@ metadata:
 spec:
   url: "/data/ai_models/object_detection/pedestrians/yolox.pth"
   format: "pth"
-  # description: "YoloX model to detect pedestrians."
-  # purpose: "detection"
-  # classes:
-  #   - "pedestrian"
-  # extra:
-  #   - name: "model_size"
-  #     value: "bytetrack_s_mot17"
+  description: "YoloX model to detect pedestrians."
+  purpose: "detection"
+  classes:
+    - "pedestrian"
+  extra:
+    - name: "model_size"
+      value: "bytetrack_s_mot17"

examples/multiedgetracking/yaml/models/model_m3l.yaml → examples/multiedgeinference/pedestrian_tracking/yaml/models/model_m3l.yaml

@@ -6,5 +6,5 @@ metadata:
 spec:
   url: "/data/ai_models/m3l/m3l.pth"
   format: "pth"
-  # description: "M3L to extract ReID features."
-  # purpose: "feature_extraction"
+  description: "M3L to extract ReID features."
+  purpose: "feature_extraction"

examples/multiedgetracking/yaml/reid-job.yaml → examples/multiedgeinference/pedestrian_tracking/yaml/reid-job.yaml

@@ -10,14 +10,12 @@ spec:
       nodeSelector:
         node-role.kubernetes.io/master: ''
       containers:
-        - image: registry-cbu.huawei.com/kubeedge/sedna-example-multi-edge-tracking-reid:sase
+        - image: registry-cbu.huawei.com/kubeedge/sedna-example-multi-edge-inference-reid:v0.5.0
           name:  reid
           imagePullPolicy: Always
           env:
           - name: OBS_TOKEN
             value: ""
-          - name: op_mode # can be one of covid19, tracking, or detection
-            value: covid19
           - name: match_thresh # the matching threshold for the reid
             value: "0.35"
           - name: user_id # the user running the job

examples/multiedgetracking/yaml/video-analytics-job.yaml → examples/multiedgeinference/pedestrian_tracking/yaml/video-analytics-job.yaml

@@ -12,7 +12,7 @@ spec:
       nodeSelector:
         node-role.kubernetes.io/master: ''
       containers:
-        - image: registry-cbu.huawei.com/kubeedge/sedna-example-multi-edge-tracking-videoanalytics:sase
+        - image: registry-cbu.huawei.com/kubeedge/sedna-example-multi-edge-inference-videoanalytics:sase
           imagePullPolicy: Always
           name:  detection
           env:
@@ -22,18 +22,12 @@ spec:
             value: "MASTER_NODE_IP|kafka-service"
           - name: KAFKA_BIND_PORTS # a list of Kafka brokers port, separated by pipe (|)
             value: "9092|9092" 
-          - name: "estimator_class" # only ByteTracker is currently supported
-            value: "ByteTracker"
-          - name: confidence_thr # ByteTracker NMS confidence threshold
-            value: "0.7"
           - name: fps # video sampling rate, used only when processing a network stream
             value: "5"
-          - name: "video_id" # the video id, can be any value
+          - name: video_id # the video id, can be any value
             value: "0000-1111-2222"
-          - name: "video_address" # the video source can be rtsp (recommended), http, or a file loaded from NFS
+          - name: video_address # the video source can be rtsp (recommended), http, or a file loaded from NFS
             value: "rtsp://RTSP_SERVER_IP/video/0" #rtsp://7.182.8.79/video/0
-          - name: op_mode # covid19
-            value: covid19
           - name: hostname
             valueFrom:
               fieldRef:
@@ -51,4 +45,4 @@ spec:
       volumes:
         - name: vol1
           persistentVolumeClaim:
-            claimName: reid-pvc
+            claimName: reid-pvc

examples/multiedgetracking/detection/yolox/exps/yolox_s_mix_det.py

@@ -1,13 +0,0 @@
-# encoding: utf-8
-import os
-from yolox.yolox.exp import Exp as MyExp
-
-
-class Exp(MyExp):
-    def __init__(self):
-        super(Exp, self).__init__()
-        self.num_classes = 1
-        self.depth = 0.33
-        self.width = 0.50
-        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
-

examples/multiedgetracking/detection/yolox/yolox/exp/yolox_base.py

@@ -1,41 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
-
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-
-import os
-
-from .base_exp import BaseExp
-
-
-class Exp(BaseExp):
-    def __init__(self):
-        super().__init__()
-
-        # ---------------- model config ---------------- #
-        self.num_classes = 80
-        self.depth = 1.00
-        self.width = 1.00
-
-
-    def get_model(self):
-        from yolox.yolox.models import YOLOPAFPN, YOLOX, YOLOXHead
-
-        def init_yolo(M):
-            for m in M.modules():
-                if isinstance(m, nn.BatchNorm2d):
-                    m.eps = 1e-3
-                    m.momentum = 0.03
-
-        if getattr(self, "model", None) is None:
-            in_channels = [256, 512, 1024]
-            backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels)
-            head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels)
-            self.model = YOLOX(backbone, head)
-
-        self.model.apply(init_yolo)
-        self.model.head.initialize_biases(1e-2)
-        return self.model

lib/requirements.dev.txt

@@ -2,5 +2,5 @@
 plato-learn~=0.26  # Apache-2.0
 # lifelong_learning
 scikit-learn~=0.24.1  # BSD
-# multi_edge_tracking
-kafka-python~=2.0.2 # Apache-2.0
+# multi_edge_inference
+kafka-python~=2.0.2 # Apache-2.0

lib/sedna/algorithms/__init__.py

@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from . import aggregation  # federated_learning
-from . import hard_example_mining  # joint_inference incremental_learning
-from . import multi_task_learning  # lifelong_learning
-from . import unseen_task_detect  # lifelong_learning
-from . import optical_flow  # multi_edge_tracking
-from . import reid # multi_edge_tracking
+from . import aggregation
+from . import hard_example_mining
+from . import multi_task_learning
+from . import unseen_task_detect
+from . import optical_flow
+from . import reid

lib/sedna/algorithms/optical_flow/__init__.py

@@ -24,7 +24,7 @@ __all__ = ('LukasKanade')
 
 
 class BaseFilter(metaclass=abc.ABCMeta):
-    """The base class to define unified interface."""
+    """The base class to define an unified interface."""
 
     def __call__(self, old_frame=None, current_frame=None):
         """predict function, and it must be implemented by
@@ -38,6 +38,9 @@ class BaseFilter(metaclass=abc.ABCMeta):
 
 @ClassFactory.register(ClassType.OF, alias="LukasKanadeOF")
 class LukasKanade(BaseFilter, abc.ABC):
+    """
+        Class to detect movement between two consecutive images.
+    """
     def __init__(self, **kwargs):
         # Parameters for ShiTomasi corner detection
         self.feature_params = dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)
@@ -83,6 +86,9 @@ class LukasKanade(BaseFilter, abc.ABC):
         
 @ClassFactory.register(ClassType.OF, alias="LukasKanadeOF_CUDA")
 class LukasKanadeCUDA(BaseFilter, abc.ABC):
+    """
+        Class to detect movement between two consecutive images (GPU implementation).
+    """
     def __init__(self, **kwargs):
         # Parameters for ShiTomasi corner detection
         self.feature_params = dict(srcType= cv2.CV_8UC1, maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)
@@ -133,4 +139,4 @@ class LukasKanadeCUDA(BaseFilter, abc.ABC):
         except Exception as ex:
             LOGGER.error(f"Error during the execution of the optical flow estimation! [{ex}]")
 
-        return movement
+        return movement

lib/sedna/backend/torch/__init__.py

@@ -59,4 +59,4 @@ class TorchBackend(BackendBase):
         else:
             LOGGER.info("Path to model does not exists!")
 
-        self.has_load = True
+        self.has_load = True

lib/sedna/core/base.py

@@ -106,8 +106,6 @@ class ModelLoadingThread(threading.Thread):
 
 class JobBase:
     """ sedna feature base class """
-    parameters = Context
-
     def __init__(self, estimator, config=None):
         self.config = BaseConfig()
         if config:
@@ -119,6 +117,7 @@ class JobBase:
         self.worker_name = self.config.worker_name or self.job_name
         self.namespace = self.config.namespace or self.job_name
         self.lc_server = self.config.lc_server
+
         if str(
                 self.get_parameters("MODEL_HOT_UPDATE", "False")
         ).lower() == "true":
@@ -159,7 +158,7 @@ class JobBase:
         return callback_func(res) if callback_func else res
 
     def get_parameters(self, param, default=None):
-        return self.parameters.get_parameters(param=param, default=default)
+        return Context.get_parameters(param=param, default=default)
 
     def report_task_info(self, task_info, status, results=None, kind="train"):
         message = {
@@ -177,4 +176,4 @@ class JobBase:
         try:
             LCClient.send(self.lc_server, self.worker_name, message)
         except Exception as err:
-            self.log.error(err)
+            self.log.error(err)

lib/sedna/core/multi_edge_tracking/components/__init__.py → lib/sedna/core/multi_edge_inference/components/__init__.py

@@ -24,15 +24,15 @@ import traceback
 import uuid
 from typing import List
 from sedna.common.log import LOGGER
-from sedna.core.multi_edge_tracking.plugins import PLUGIN, PluggableModel, PluggableNetworkService
-from sedna.core.multi_edge_tracking.utils import get_parameters
+from sedna.core.multi_edge_inference.plugins import PLUGIN, PluggableModel, PluggableNetworkService
+from sedna.core.multi_edge_inference.utils import get_parameters
 from sedna.datasources.kafka.kafka_manager import KafkaConsumerThread, KafkaProducer
 from distutils import util
 from collections import deque
 
 class BaseService(ABC):
     """
-    Base wrapper for video analytics, feature extraction, and reid services
+    Base MultiEdgeInference wrapper for video analytics, feature extraction, and reid components.
     """
 
     def __init__(self, consumer_topics = [], producer_topics = [], plugins : List[PluggableNetworkService] = [], models : List[PluggableModel] = [], timeout = 10, asynchronous = False):
@@ -69,6 +69,10 @@ class BaseService(ABC):
         self._post_init()
 
     def _init_kafka_connection(self, consumer_topics , producer_topics):
+        """
+        Initializes the Kafka backend, if enabled.
+        """
+
         self.kafka_enabled = bool(util.strtobool(get_parameters("KAFKA_ENABLED", "False")))
 
         if self.kafka_enabled:
@@ -94,12 +98,19 @@ class BaseService(ABC):
 
 
     def _post_init(self):
+        """
+        It starts the main data acquisition loop in a separate thread. It can be overridden
+        to add some post initialization calls.
+        """
         threading.Thread(target=self.fetch_data, daemon=True).start()
         return
 
-    # Use asynch mode for ingesting a stream (e.g., RTSP).
-    # Use synch mode when reading from disk (e.g., a video file).
     def put(self, data):
+        """
+        Call this function to push data into the component. For example, after you extract a 
+        frame from a video stream, you can call put(image). Depending on the value of the
+        'asynchronous' parameter, the data will be put into a different data structure.
+        """
         data = self.preprocess(data)
         if data:
             return self._put_data_asynchronous(data) if self.asynchronous else self._put_data_synchronous(data)
@@ -174,6 +185,11 @@ class BaseService(ABC):
         return
         
     def get_plugin(self, plugin_key : PLUGIN):
+        """
+        This function allows to select the network service to communicate to based
+        on its name (given that is has been registered before).The list of registered
+        plugins can be found in plugins/registered.py.
+        """
         try:
             ls = list(filter(lambda n: n.kind == plugin_key.name, self.plugins))[0]
         except IndexError as ie:
@@ -189,22 +205,41 @@ class BaseService(ABC):
 
     # Distributes the data in the queue to the models associated to this service
     def distribute_data(self, data = [], **kwargs):
+        """
+        This function sends the data to all the AI models passed to with this
+        component during the initialization phase.
+        """
         for ai in self.models:
             self.process_data(ai, data)
         return
 
     @abstractmethod
     def process_data(self, ai, data, **kwargs):
+        """
+        The user needs to implement this function to call the main processing
+        function of the AI model and decide what to do with the result.
+        """
         return
     
     @abstractmethod
     def update_operational_mode(self, status):
+        """
+        The user needs to trigger updates to the AI model, if necessary.
+        """
         return
 
     def preprocess(self, data, **kwargs):
+        """
+        The user can override this function to inject some preprocessing
+        operation to be executed before the data is added to the data
+        structure by the 'put()' function.
+        """
         return data
 
 class FileOperations:
+    """
+    Class containing file operations to read/write from disk.
+    """
     def read_from_disk(self, path):
         data = []
         try:
@@ -229,4 +264,4 @@ class FileOperations:
 
 
     def get_files_list(self, folder):
-        return [join(folder, f) for f in os.listdir(folder) if isfile(join(folder, f))]
+        return [join(folder, f) for f in os.listdir(folder) if isfile(join(folder, f))]

lib/sedna/core/multi_edge_tracking/components/detector.py → lib/sedna/core/multi_edge_inference/components/detector.py

@@ -19,14 +19,57 @@ import numpy as np
 from sedna.algorithms.optical_flow import LukasKanade, LukasKanadeCUDA
 from sedna.common.log import LOGGER
 
-from sedna.core.multi_edge_tracking.plugins import PLUGIN, PluggableModel, PluggableNetworkService
-from sedna.core.multi_edge_tracking.plugins.registered import Feature_Extraction_I, VideoAnalytics
-from sedna.core.multi_edge_tracking.components import BaseService, FileOperations
+from sedna.core.multi_edge_inference.plugins import PLUGIN, PluggableModel, PluggableNetworkService
+from sedna.core.multi_edge_inference.plugins.registered import Feature_Extraction_I, VideoAnalytics
+from sedna.core.multi_edge_inference.components import BaseService, FileOperations
 
 class ObjectDetector(BaseService, FileOperations):
     """
-    Object Detection/Tracking service.
+    In MultiEdgeInference, the Object Detection/Tracking component
+    is deployed as a service at the edge and it used to detect or track objects
+    (for example, pedestrians) and send the result to the cloud for
+    further processing using Kafka or REST API.
+
+    Parameters
+    ----------
+    consumer_topics : List
+        A list of Kafka topics used to communicate with the Feature
+        Extraction service (to receive data from it).
+        This is accessed only if the Kafka backend is in use.
+    producer_topics : List
+        A list of Kafka topics used to communicate with the Feature
+        Extraction service (to send data to it).
+        This is accessed only if the Kafka backend is in use.
+    plugins : List
+        A list of PluggableNetworkService. It can be left empty
+        as the ObjectDetector service is already preconfigured
+        to connect to the correct network services.
+    models : List
+        A list of PluggableModel. By passing a specific instance
+        of the model, it is possible to customize the ObjectDetector
+        to, for example, track different objects as long as the 
+        PluggableModel interface is respected.
+    timeout: int
+        It sets a timeout condition to terminate the main fetch loop after the specified
+        amount of seconds has passed since we received the last frame.
+    asynchronous: bool
+        If True, the AI processing will be decoupled from the data acquisition step.
+        If False, the processing will be sequential. In general, set it to True when
+        ingesting a stream (e.g., RTSP) and to False when reading from disk
+        (e.g., a video file).
+
+
+    Examples
+    --------
+    >>> model = ByteTracker() # A class implementing the PluggableModel abstract class (example in pedestrian_tracking/detector/model/bytetracker.py)
+    >>> objecttracking_service = ObjectDetector(models=[model], asynchronous=True)
+
+    Notes
+    -----
+    For the parameters described above, only 'models' has to be defined, while 
+    for others the default value will work in most cases.
     """
+
     def __init__(self, consumer_topics = ["enriched_object"], producer_topics=["object_detection"], plugins: List[PluggableNetworkService] = [], models: List[PluggableModel] = [], timeout = 10, asynchronous = False):
         merged_plugins = [VideoAnalytics(wrapper=self), Feature_Extraction_I()] + plugins
         super().__init__(consumer_topics, producer_topics, merged_plugins, models, timeout, asynchronous)
@@ -52,7 +95,7 @@ class ObjectDetector(BaseService, FileOperations):
 
     # We change the preprocess function to add the optical flow analysis
     def preprocess(self, data):
-        # This is risky, we should create a specific class to represent data processed by FE or create a flag in the detrack object.
+        # TODO: Improve this check, this is not reliable.
         if isinstance(data, List):
             self.data_counter += len(data)
             LOGGER.info(f"Received data from FE module (counter={self.data_counter}). Writing to local storage")

lib/sedna/core/multi_edge_inference/components/feature_extraction.py

@@ -0,0 +1,90 @@
+# Copyright 2021 The KubeEdge Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List
+from sedna.core.multi_edge_inference.components import BaseService
+from sedna.core.multi_edge_inference.plugins import PLUGIN, PluggableModel, PluggableNetworkService
+from sedna.core.multi_edge_inference.plugins.registered import Feature_Extraction, VideoAnalytics_I
+
+class FEService(BaseService):
+    """
+    In MultiEdgeInference, the Feature Extraction component
+    is deployed in the edge or the cloud and it used to extract
+    ReID features from frames received by the ObjectDetector component
+    and send back to it the enriched data using Kafka or REST API.
+
+    Parameters
+    ----------
+    consumer_topics : List
+        A list of Kafka topics used to communicate with the Object
+        Detector service (to receive data from it).
+        This is accessed only if the Kafka backend is in use.
+    producer_topics : List
+        A list of Kafka topics used to communicate with the Object
+        Detector service (to send data to it).
+        This is accessed only if the Kafka backend is in use.
+    plugins : List
+        A list of PluggableNetworkService. It can be left empty
+        as the FeatureExtraction service is already preconfigured
+        to connect to the correct network services.
+    models : List
+        A list of PluggableModel. By passing a specific instance
+        of the model, it is possible to customize the FeatureExtraction
+        component to, for example, extract differently the objects 
+        features.
+    timeout: int
+        It sets a timeout condition to terminate the main fetch loop after the specified
+        amount of seconds has passed since we received the last frame.
+    asynchronous: bool
+        If True, the AI processing will be decoupled from the data acquisition step.
+        If False, the processing will be sequential. In general, set it to True when
+        ingesting a stream (e.g., RTSP) and to False when reading from disk
+        (e.g., a video file).
+
+
+    Examples
+    --------
+    >>> model = FeatureExtractionAI() # A class implementing the PluggableModel abstract class (example in pedestrian_tracking/feature_extraction/worker.py)
+    >>> fe_service = FEService(models=[model], asynchronous=False)
+
+    Notes
+    -----
+    For the parameters described above, only 'models' has to be defined, while 
+    for others the default value will work in most cases.
+    """
+
+
+    def __init__(self, consumer_topics = ["object_detection"], producer_topics=["enriched_object"], plugins: List[PluggableNetworkService] = [], models: List[PluggableModel] = [], timeout = 10, asynchronous = False):
+        merged_plugins =  [VideoAnalytics_I(), Feature_Extraction(wrapper=self)] + plugins
+        super().__init__(consumer_topics, producer_topics, merged_plugins, models, timeout, asynchronous)
+
+    def process_data(self, ai, data, **kwargs):
+        for ai in self.models:
+            result = ai.inference(data)
+
+        if result != []:
+            if self.kafka_enabled:
+                for d in result:
+                    self.producer.write_result(d)
+            else:
+                plg = self.get_plugin(PLUGIN.VIDEO_ANALYTICS_I)
+                plg.plugin_api.transmit(result, **kwargs)
+
+    def update_operational_mode(self, status):
+        pass
+
+    def get_target_features(self, ldata):
+        # TODO: Fix this workaround, we need a function to select a model based on its name
+        fe_ai = self.models[0]
+        return fe_ai.get_target_features(ldata)

lib/sedna/core/multi_edge_tracking/components/reid.py → lib/sedna/core/multi_edge_inference/components/reid.py

@@ -15,13 +15,49 @@
 from typing import List
 from sedna.common.log import LOGGER
 
-from sedna.core.multi_edge_tracking.components import BaseService, FileOperations
-from sedna.core.multi_edge_tracking.plugins import PLUGIN, PluggableModel, PluggableNetworkService
-from sedna.core.multi_edge_tracking.plugins.registered import Feature_Extraction_I, ReID_Server
+from sedna.core.multi_edge_inference.components import BaseService, FileOperations
+from sedna.core.multi_edge_inference.plugins import PLUGIN, PluggableModel, PluggableNetworkService
+from sedna.core.multi_edge_inference.plugins.registered import Feature_Extraction_I, ReID_Server
 
 class ReID(BaseService, FileOperations):
     """
-    ReID service.
+    In MultiEdgeInference, the ReID component is deployed in the cloud
+    and it used to identify a target by compairing its features
+    with the ones genereated from the Feature Extraction component.
+
+    Parameters
+    ----------
+    consumer_topics : List
+        Leave empty.
+    producer_topics : List
+        Leave empty.
+    plugins : List
+        A list of PluggableNetworkService. It can be left empty
+        as the ReID component is already preconfigured
+        to connect to the correct network services.
+    models : List
+        A list of PluggableModel. In this case we abuse of the term
+        model as the ReID doesn't really use an AI model but rather
+        a wrapper for the ReID functions.
+    timeout: int
+        It sets a timeout condition to terminate the main fetch loop after the specified
+        amount of seconds has passed since we received the last frame.
+    asynchronous: bool
+        If True, the AI processing will be decoupled from the data acquisition step.
+        If False, the processing will be sequential. In general, set it to True when
+        ingesting a stream (e.g., RTSP) and to False when reading from disk
+        (e.g., a video file).
+
+
+    Examples
+    --------
+    >>> model = ReIDWorker() # A class implementing the PluggableModel abstract class (example in pedestrian_tracking/reid/worker.py)
+    >>> self.job = ReID(models=[model], asynchronous=False)
+
+    Notes
+    -----
+    For the parameters described above, only 'models' has to be defined, while 
+    for others the default value will work in most cases.
     """
 
     def __init__(self, consumer_topics = [], producer_topics=[], plugins: List[PluggableNetworkService] = [], models: List[PluggableModel] = [], timeout = 10, asynchronous = True):
@@ -52,4 +88,4 @@ class ReID(BaseService, FileOperations):
     def get_target_features(self, ldata):
         feature_extraction_plugin = self.get_plugin(PLUGIN.FEATURE_EXTRACTION_I)
         features = feature_extraction_plugin.plugin_api.get_target_features(ldata)
-        return features
+        return features

lib/sedna/core/multi_edge_tracking/data_classes.py → lib/sedna/core/multi_edge_inference/data_classes.py

@@ -25,6 +25,10 @@ class OP_MODE(Enum):
 
 # Class defining the output of a ReID service.
 class DetTrackResult:
+    """
+    Base data object exchanged by the MultiEdgeInference components.
+    """
+
     def __init__(self, frame_index : int = 0, bbox : List = None, scene = None, confidence : List = None, detection_time : List = None, camera : int = 0, bbox_coord : List = [], tracking_ids : List = [], features : List = [], is_target=False, ID : List = []):
         self.userID = "DEFAULT" # Name of the enduser using the application, used to bound the user to the results
         self.frame_index = frame_index # Video frame index number

lib/sedna/core/multi_edge_tracking/plugins/__init__.py → lib/sedna/core/multi_edge_inference/plugins/__init__.py

@@ -30,7 +30,7 @@ from sedna.common.config import BaseConfig
 from sedna.common.file_ops import FileOps
 from sedna.common.log import LOGGER
 
-from sedna.core.multi_edge_tracking.utils import get_parameters
+from sedna.core.multi_edge_inference.utils import get_parameters
 
 
 # Class defining the possible plugin services.
@@ -45,6 +45,9 @@ class PLUGIN(Enum):
     VIDEO_ANALYTICS_I = "VideoAnalytics_I"
 
 class PluggableNetworkService(ABC):
+    """
+    Abstract class to wrap a REST service.
+    """
     def __init__(self, ip, port, plugin_api : object = None):
         self.ip = ip
         self.port = port
@@ -66,23 +69,10 @@ class PluggableNetworkService(ABC):
             if callable(start):
                 threading.Thread(target=self.plugin_api.start, daemon=True).start()
 
-class PluggableDatasource(ABC):
-    def __init__(self, **kwargs) -> None:
-        super().__init__()
-
-        self.kind = PLUGIN(self.__class__.__name__).name
-
-        LOGGER.info(f"Created PluggableDatasource of kind {self.kind}")
-
-    @abstractmethod
-    def write(self, data, **kwargs):
-        return
-
-    @abstractmethod
-    def read(self, **kwargs):
-        return
-
 class PluggableModel(ABC):
+    """
+    Abstract class to wrap and AI model.
+    """
     def __init__(self) -> None:
         self.config = BaseConfig()
         self.model_backend = self._set_backend()
@@ -126,7 +116,7 @@ class PluggableModel(ABC):
         raise NotImplementedError
 
     def inference(self, data=None, post_process=None, **kwargs):
-
+        """Calls the model 'predict' function"""
         res = self.model_backend.predict(data, **kwargs)
         callback_func = None
         if callable(post_process):
@@ -147,7 +137,7 @@ class PluggableModel(ABC):
         return callback_func(res) if callback_func else res
 
     def _set_backend(self):
-        """Create Trainer class"""
+        """Configure AI backend parameters based on model type."""
         use_cuda = False
         backend_type = os.getenv(
             'BACKEND_TYPE', self.config.get("backend_type", "UNKNOWN")
@@ -181,4 +171,4 @@ class PluggableModel(ABC):
             model_save_path=base_model_save,
             model_name=model_save_name,
             model_save_url=model_save_url
-        )
+        )

lib/sedna/core/multi_edge_tracking/plugins/registered.py → lib/sedna/core/multi_edge_inference/plugins/registered.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 
 from sedna.common.utils import get_host_ip
-from sedna.core.multi_edge_tracking.plugins import PluggableNetworkService
-from sedna.core.multi_edge_tracking.utils import get_parameters
+from sedna.core.multi_edge_inference.plugins import PluggableNetworkService
+from sedna.core.multi_edge_inference.utils import get_parameters
 from sedna.service.multi_edge_tracking.interface import *
 from sedna.service.multi_edge_tracking.server import *
 
@@ -78,4 +78,4 @@ class VideoAnalytics_I(PluggableNetworkService):
             ip,
             port,
             Detection("video_analytics", ip=ip, port=int(port))
-        )
+        )

lib/sedna/core/multi_edge_tracking/components/feature_extraction.py

@@ -1,47 +0,0 @@
-# Copyright 2021 The KubeEdge Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import List
-from sedna.core.multi_edge_tracking.components import BaseService
-from sedna.core.multi_edge_tracking.plugins import PLUGIN, PluggableModel, PluggableNetworkService
-from sedna.core.multi_edge_tracking.plugins.registered import Feature_Extraction, VideoAnalytics_I
-
-class FEService(BaseService):
-    """
-   Feature Extraction service.
-   """
-
-    def __init__(self, consumer_topics = ["object_detection"], producer_topics=["enriched_object"], plugins: List[PluggableNetworkService] = [], models: List[PluggableModel] = [], timeout = 10, asynchronous = False):
-        merged_plugins =  [VideoAnalytics_I(), Feature_Extraction(wrapper=self)] + plugins
-        super().__init__(consumer_topics, producer_topics, merged_plugins, models, timeout, asynchronous)
-
-    def process_data(self, ai, data, **kwargs):
-        for ai in self.models:
-            result = ai.inference(data)
-
-        if result != []:
-            if self.kafka_enabled:
-                for d in result:
-                    self.producer.write_result(d)
-            else:
-                plg = self.get_plugin(PLUGIN.VIDEO_ANALYTICS_I)
-                plg.plugin_api.transmit(result, **kwargs)
-
-    def update_operational_mode(self, status):
-        pass
-
-    def get_target_features(self, ldata):
-        # TODO: Fix this workaround, we need a function to select a model based on its name
-        fe_ai = self.models[0]
-        return fe_ai.get_target_features(ldata)

lib/sedna/datasources/kafka/kafka_manager.py

@@ -43,4 +43,4 @@ class KafkaConsumerThread(Thread):
         while not self.consumer.disconnected:
             data = self.consumer.consume_messages_poll()
             if data:
-                self.callback(data)
+                self.callback(data)

lib/sedna/datasources/kafka/producer.py

@@ -66,4 +66,4 @@ class Producer(Client):
 
     def close(self):
         LOGGER.debug("Shutting down producer")
-        self.producer.close()
+        self.producer.close()

lib/sedna/service/multi_edge_tracking/interface/detection_endpoint.py → lib/sedna/service/multi_edge_inference/interface/detection_endpoint.py

@@ -16,7 +16,7 @@ import pickle
 from sedna.service.client import http_request
 
 class Detection:
-    """Endpoint to trigger detection"""
+    """Endpoint to trigger the Object Tracking component"""
 
     def __init__(self, service_name, version="",
                  ip="127.0.0.1", port="8080", protocol="http"):

lib/sedna/service/multi_edge_tracking/interface/reid_endpoint.py → lib/sedna/service/multi_edge_inference/interface/reid_endpoint.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import pickle
-from sedna.core.multi_edge_tracking.data_classes import DetTrackResult
+from sedna.core.multi_edge_inference.data_classes import DetTrackResult
 from sedna.service.client import http_request
 
 class ReID_Endpoint:
@@ -30,4 +30,4 @@ class ReID_Endpoint:
     def transmit(self, x : DetTrackResult, **kwargs):
         """Transfer feature vector to ReID worker"""
         _url = f"{self.endpoint}/reid"
-        return http_request(url=_url, method="POST", data=pickle.dumps(x))
+        return http_request(url=_url, method="POST", data=pickle.dumps(x))

lib/sedna/service/multi_edge_tracking/server/detection.py → lib/sedna/service/multi_edge_inference/server/detection.py

@@ -24,7 +24,7 @@ __all__ = ('DetectionServer', )
 
 class DetectionServer(BaseServer):  # pylint: disable=too-many-arguments
     """
-    rest api server for detection
+    REST api server for object detection component
     """
 
     def __init__(

lib/sedna/service/multi_edge_tracking/server/reid.py → lib/sedna/service/multi_edge_inference/server/reid.py

@@ -24,7 +24,7 @@ __all__ = ('ReIDServer', )
 
 class ReIDServer(BaseServer):  # pylint: disable=too-many-arguments
     """
-    rest api server for reid
+    REST api server for reid
     """
 
     def __init__(
@@ -73,4 +73,4 @@ class ReIDServer(BaseServer):  # pylint: disable=too-many-arguments
         s = await request.body()
         self.model.inference(pickle.loads(s), post_process=None)
 
-        return 200
+        return 200

pkg/globalmanager/controllers/featureextraction/downstream.go

@@ -17,32 +17,14 @@ limitations under the License.
 package featureextraction
 
 import (
-	"fmt"
-
 	"k8s.io/apimachinery/pkg/watch"
 
-	sednav1 "github.com/kubeedge/sedna/pkg/apis/sedna/v1alpha1"
 	"github.com/kubeedge/sedna/pkg/globalmanager/runtime"
 )
 
+// NOTE: For this job we don't need synchronization
 func (c *Controller) syncToEdge(eventType watch.EventType, obj interface{}) error {
-	service, ok := obj.(*sednav1.FeatureExtractionService)
-	if !ok {
-		return nil
-	}
-
-	// Since Kind may be empty,
-	// we need to fix the kind here if missing.
-	// more details at https://github.com/kubernetes/kubernetes/issues/3030
-	service.Kind = KindName
-
-	// Here only propagate to the nodes with non empty name
-	nodeName := service.Spec.Template.Spec.NodeName
-	if len(nodeName) == 0 {
-		return fmt.Errorf("empty node name")
-	}
-
-	return c.sendToEdgeFunc(nodeName, eventType, service)
+	return nil
 }
 
 func (c *Controller) SetDownstreamSendFunc(f runtime.DownstreamSendFunc) error {

pkg/globalmanager/controllers/featureextraction/featureextractionservice.go

@@ -49,17 +49,15 @@ import (
 	"github.com/kubeedge/sedna/pkg/globalmanager/runtime"
 )
 
-const (
-	FEWorker = "fe"
-	FEPort   = 6000
-)
-
 const (
 	// Name is this controller name
 	Name = "FeatureExtraction"
-
 	// KindName is the kind name of CR this controller controls
 	KindName = "FeatureExtractionService"
+	// VideoAnalyticsWorker is this name given to the worker pod
+	FEWorker = "fe"
+	// VideoAnalyticsPort is the port where the service will be exposed
+	FEPort = 6000
 )
 
 // FeatureExtractionServicerKind contains the schema.GroupVersionKind for this controller type.
@@ -184,7 +182,7 @@ func (c *Controller) updateDeployment(old, cur interface{}) {
 	c.addDeployment(curD)
 }
 
-// obj could be an *sednav1.ObjectSearchService, or a DeletionFinalStateUnknown marker item,
+// obj could be an *sednav1.FeatureExtractionService, or a DeletionFinalStateUnknown marker item,
 // immediate tells the controller to update the status right away, and should
 // happen ONLY when there was a successful pod run.
 func (c *Controller) enqueueController(obj interface{}, immediate bool) {
@@ -387,8 +385,6 @@ func (c *Controller) sync(key string) (bool, error) {
 
 	if failedPods > 0 || failedDeployment > 0 {
 		serviceFailed = true
-		// TODO: Split code to handle deployment failure separately
-		// TODO: get the failed worker, and knows that which worker fails, edge inference worker or cloud inference worker
 		reason = "workerFailed"
 		message = "the worker of FeatureExtractionService failed"
 		newCondtionType = sednav1.FeatureExtractionServiceCondFailed

pkg/globalmanager/controllers/featureextraction/upstream.go

@@ -21,11 +21,11 @@ import (
 	"encoding/json"
 	"fmt"
 
-	metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
 	"k8s.io/klog/v2"
 
 	sednav1 "github.com/kubeedge/sedna/pkg/apis/sedna/v1alpha1"
 	"github.com/kubeedge/sedna/pkg/globalmanager/runtime"
+	metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
 )
 
 // updateHandler handles the updates from LC(running at edge) to update the

pkg/globalmanager/controllers/reid/downstream.go

@@ -17,32 +17,14 @@ limitations under the License.
 package reid
 
 import (
-	"fmt"
-
 	"k8s.io/apimachinery/pkg/watch"
 
-	sednav1 "github.com/kubeedge/sedna/pkg/apis/sedna/v1alpha1"
 	"github.com/kubeedge/sedna/pkg/globalmanager/runtime"
 )
 
+// NOTE: For this job we don't need synchronization
 func (c *Controller) syncToEdge(eventType watch.EventType, obj interface{}) error {
-	job, ok := obj.(*sednav1.ReidJob)
-	if !ok {
-		return nil
-	}
-
-	// Since Kind may be empty,
-	// we need to fix the kind here if missing.
-	// more details at https://github.com/kubernetes/kubernetes/issues/3030
-	job.Kind = KindName
-
-	nodeName := job.Spec.Template.Spec.NodeName
-	if len(nodeName) == 0 {
-		return fmt.Errorf("empty node name")
-	}
-
-	return c.sendToEdgeFunc(nodeName, eventType, job)
-
+	return nil
 }
 
 func (c *Controller) SetDownstreamSendFunc(f runtime.DownstreamSendFunc) error {

pkg/globalmanager/controllers/reid/reidjob.go

@@ -492,7 +492,7 @@ func New(cc *runtime.ControllerContext) (runtime.FeatureControllerI, error) {
 	eventBroadcaster := record.NewBroadcaster()
 	eventBroadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: cc.KubeClient.CoreV1().Events("")})
 
-	fc := &Controller{
+	rc := &Controller{
 		kubeClient: cc.KubeClient,
 		client:     cc.SednaClient.SednaV1alpha1(),
 
@@ -503,38 +503,38 @@ func New(cc *runtime.ControllerContext) (runtime.FeatureControllerI, error) {
 
 	jobInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{
 		AddFunc: func(obj interface{}) {
-			fc.enqueueController(obj, true)
+			rc.enqueueController(obj, true)
 
-			// when a video analytics job is added,
+			// when a reid job is added,
 			// send it to edge's LC.
-			fc.syncToEdge(watch.Added, obj)
+			rc.syncToEdge(watch.Added, obj)
 		},
 		UpdateFunc: func(old, cur interface{}) {
-			fc.enqueueController(cur, true)
+			rc.enqueueController(cur, true)
 
-			// when a video analytics job is updated,
+			// when a reid job is updated,
 			// send it to edge's LC as Added event.
-			fc.syncToEdge(watch.Added, cur)
+			rc.syncToEdge(watch.Added, cur)
 		},
 		DeleteFunc: func(obj interface{}) {
-			fc.enqueueController(obj, true)
+			rc.enqueueController(obj, true)
 
-			// when a video analytics job is deleted,
+			// when a reid job is deleted,
 			// send it to edge's LC.
-			fc.syncToEdge(watch.Deleted, obj)
+			rc.syncToEdge(watch.Deleted, obj)
 		},
 	})
 
-	fc.jobLister = jobInformer.Lister()
-	fc.jobStoreSynced = jobInformer.Informer().HasSynced
+	rc.jobLister = jobInformer.Lister()
+	rc.jobStoreSynced = jobInformer.Informer().HasSynced
 
 	podInformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{
-		AddFunc:    fc.addPod,
-		UpdateFunc: fc.updatePod,
-		DeleteFunc: fc.deletePod,
+		AddFunc:    rc.addPod,
+		UpdateFunc: rc.updatePod,
+		DeleteFunc: rc.deletePod,
 	})
-	fc.podStore = podInformer.Lister()
-	fc.podStoreSynced = podInformer.Informer().HasSynced
+	rc.podStore = podInformer.Lister()
+	rc.podStoreSynced = podInformer.Informer().HasSynced
 
-	return fc, nil
+	return rc, nil
 }

pkg/globalmanager/controllers/reid/upstream.go

@@ -21,10 +21,11 @@ import (
 	"encoding/json"
 	"fmt"
 
+	"k8s.io/klog/v2"
+
 	sednav1 "github.com/kubeedge/sedna/pkg/apis/sedna/v1alpha1"
 	"github.com/kubeedge/sedna/pkg/globalmanager/runtime"
 	metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
-	"k8s.io/klog/v2"
 )
 
 const upstreamStatusUpdateRetries = 3
@@ -47,19 +48,6 @@ func newUnmarshalError(namespace, name, operation string, content []byte) error
 	return fmt.Errorf("Unable to unmarshal content for (%s/%s) operation: '%s', content: '%+v'", namespace, name, operation, string(content))
 }
 
-// retryUpdateStatus simply retries to call the status update func
-func retryUpdateStatus(name, namespace string, updateStatusFunc func() error) error {
-	var err error
-	for retry := 0; retry <= upstreamStatusUpdateRetries; retry++ {
-		err = updateStatusFunc()
-		if err == nil {
-			return nil
-		}
-		klog.Warningf("Error to update %s/%s status, retried %d times: %+v", namespace, name, retry, err)
-	}
-	return err
-}
-
 // updateFromEdge updates the reid job's status
 func (c *Controller) updateFromEdge(name, namespace, operation string, content []byte) (err error) {
 	// Output defines owner output information
@@ -68,7 +56,6 @@ func (c *Controller) updateFromEdge(name, namespace, operation string, content [
 	}
 
 	var status struct {
-		// Phase always should be "inference"
 		Phase  string  `json:"phase"`
 		Status string  `json:"status"`
 		Output *Output `json:"output"`

pkg/globalmanager/controllers/videoanalytics/downstream.go

@@ -17,32 +17,14 @@ limitations under the License.
 package videoanalytics
 
 import (
-	"fmt"
-
 	"k8s.io/apimachinery/pkg/watch"
 
-	sednav1 "github.com/kubeedge/sedna/pkg/apis/sedna/v1alpha1"
 	"github.com/kubeedge/sedna/pkg/globalmanager/runtime"
 )
 
+// NOTE: For this job we don't need synchronization with the edge
 func (c *Controller) syncToEdge(eventType watch.EventType, obj interface{}) error {
-	job, ok := obj.(*sednav1.VideoAnalyticsJob)
-	if !ok {
-		return nil
-	}
-
-	// Since Kind may be empty,
-	// we need to fix the kind here if missing.
-	// more details at https://github.com/kubernetes/kubernetes/issues/3030
-	job.Kind = KindName
-
-	nodeName := job.Spec.Template.Spec.NodeName
-	if len(nodeName) == 0 {
-		return fmt.Errorf("empty node name")
-	}
-
-	return c.sendToEdgeFunc(nodeName, eventType, job)
-
+	return nil
 }
 
 func (c *Controller) SetDownstreamSendFunc(f runtime.DownstreamSendFunc) error {

pkg/globalmanager/controllers/videoanalytics/upstream.go

@@ -21,10 +21,11 @@ import (
 	"encoding/json"
 	"fmt"
 
+	"k8s.io/klog/v2"
+
 	sednav1 "github.com/kubeedge/sedna/pkg/apis/sedna/v1alpha1"
 	"github.com/kubeedge/sedna/pkg/globalmanager/runtime"
 	metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
-	"k8s.io/klog/v2"
 )
 
 const upstreamStatusUpdateRetries = 3
@@ -71,19 +72,6 @@ func newUnmarshalError(namespace, name, operation string, content []byte) error
 	return fmt.Errorf("Unable to unmarshal content for (%s/%s) operation: '%s', content: '%+v'", namespace, name, operation, string(content))
 }
 
-// retryUpdateStatus simply retries to call the status update func
-func retryUpdateStatus(name, namespace string, updateStatusFunc func() error) error {
-	var err error
-	for retry := 0; retry <= upstreamStatusUpdateRetries; retry++ {
-		err = updateStatusFunc()
-		if err == nil {
-			return nil
-		}
-		klog.Warningf("Error to update %s/%s status, retried %d times: %+v", namespace, name, retry, err)
-	}
-	return err
-}
-
 // updateFromEdge updates the videoanalytics job's status
 func (c *Controller) updateFromEdge(name, namespace, operation string, content []byte) (err error) {
 	// Output defines owner output information
@@ -92,7 +80,6 @@ func (c *Controller) updateFromEdge(name, namespace, operation string, content [
 	}
 
 	var status struct {
-		// Phase always should be "inference"
 		Phase  string  `json:"phase"`
 		Status string  `json:"status"`
 		Output *Output `json:"output"`