PES University EC campus submission

examples/README.md

@@ -69,3 +69,9 @@ These examples demonstrates Swarm learning using a Reverse proxy server. [For de
 - ML platform is Tensorflow
 - SL-ML pairs are spawned using SWOP
 
+### [3D Point-Cloud detection ](/examples/point_clouds)
+This example demonstrates Swarm learning use case on following setup. 
+- Based on 4 hosts  
+- kitti 3d object detection dataset
+- ML platform is Pytorch
+- SL-ML pairs are spawned using run-sl script

examples/point_clouds/README.md

@@ -0,0 +1,137 @@
+# Swarm Learning In Autonomous Driving: A Privacy Preserving Approach
+## Introduction
+New cutting-edge models for computer vision and
+deep learning have accelerated the development of autonomous
+driving technology, but privacy has always been a concern.
+Autonomous driving vehicles are equipped with a variety of
+sensors and cameras that capture all the data around them, in-
+cluding sensitive information such as location-specific landmarks
+and personally identifiable information, raising concerns about
+privacy breaches. Identifying and obfuscating such data during
+preprocessing is an expensive process. This paper puts forth a
+Swarm Learning based training approach that offers perfor-
+mance on par with that of traditional training approaches while
+addressing the privacy component, and the improvements over
+other distributed machine learning methods such as Federated
+Learning.
+
+Note: This project utilizes Azure VMs and Swarm to run distributed machine learning training on point cloud data.
+
+The below image illustrates the setup for this following example
+<img src="./model/docs/4_Node_SD.png">
+
+The model used is [complex yolo v4 model](https://github.com/maudzung/Complex-YOLOv4-Pytorch) using pytorch and the dataset used is the kitti 3d dataset which is publicly available.For more details on model refer [paper](https://arxiv.org/pdf/1803.06199.pdf)
+
+
+
+## Data Preparation
+Download the 3D KITTI detection dataset from [here](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).
+
+The downloaded data includes:
+
+- Velodyne point clouds _**(29 GB)**_: input data to the Complex-YOLO model
+- Training labels of object data set _**(5 MB)**_: input label to the Complex-YOLO model
+- Camera calibration matrices of object data set _**(16 MB)**_: for visualization of predictions
+- Left color images of object data set _**(12 GB)**_: for visualization of predictions
+
+## Setup
+1. Create two hosts/workspace folders on Azure VM 1 and add the code in each folder:
+    ```bash
+    mkdir workspace_host1
+    ```
+    ```bash
+    mkdir workspace_host2
+    ```
+2. Similarly, create two workspace folders on VM 2 and add the code in each folder:
+    ```bash
+    mkdir workspace_host3
+    ```
+    ```bash
+    mkdir workspace_host4
+    ```
+3. Run the gen-cert utility to generate certificates for each Swarm workspace:
+     ```bash
+     ./workspace_host1/point_clouds/gen-cert -e point_clouds -i 1
+     ```
+     ```bash
+     ./workspace_host2/point_clouds/gen-cert -e point_clouds -i 2
+     ```
+     ```bash
+     ./workspace_host3/point_clouds/gen-cert -e point_clouds -i 3
+     ```
+     ```bash
+     ./workspace_host4/point_clouds/gen-cert -e point_clouds -i 4
+     ```
+4. Share the CA certificates among all other hosts using the scp command:
+     ```bash
+     scp ./workspace_host1/point_clouds/ca.pem <user>@<host2>:/home/<user>/workspace_host2/point_clouds
+     ```
+     ```bash
+     scp ./workspace_host1/point_clouds/ca.pem <user>@<host3>:/home/<user>/workspace_host3/point_clouds
+     ```
+     ```bash
+     scp ./workspace_host1/point_clouds/ca.pem <user>@<host4>:/home/<user>/workspace_host4/point_clouds
+     ```
+    Similarly, share the certificates among all other hosts using the scp command:
+5. Dataset
+    - Download the Kitti dataset and equally divide it among the hosts.
+    - The dataset is divided into 4 parts and each part is stored in a separate folder on each host.
+    - Copy Swarm Learning wheel file inside the build context and build a Docker image for ML that contains an environment to run Swarm training of user models on each VM:
+         ```bash
+         docker build -t user-ml-env-pc workspace_host1/point_clouds/ml-context
+         ```
+         ```bash
+         docker build -t user-ml-env-pc workspace_host2/point_clouds/ml-context
+         ```
+         ```bash
+         docker build -t user-ml-env-pc workspace_host3/point_clouds/ml-context
+         ```
+         ```bash
+         docker build -t user-ml-env-pc workspace_host4/point_clouds/ml-context
+         ```
+
+## Running on Azure VM1
+1. Start the SN node:
+     ```bash
+     ./scripts/bin/run-sn -d --rm --name=sn1 --host-ip=10.15.150.102 --sentinel --sn-api-port=30304 --key=workspace_host1/point_clouds/cert/sn-1-key.pem --cert=workspace_host1/point_clouds/cert/sn-1-cert.pem --capath=workspace_host1/point_clouds/cert/ca/capath --apls-ip=10.15.150.102
+     ```
+2. Start the SL nodes on each host
+     ```bash
+     ./scripts/bin/run-sl --name=sl1 --host-ip=10.15.150.102 --sn-ip=10.15.150.102 --sn-api-port=30304 --sl-fs-port=16000 --key=workspace_host1/point_clouds/cert/sl-1-key.pem --cert=workspace_host1/point_clouds/cert/sl-1-cert.pem --capath=workspace_host1/point_clouds/cert/ca/capath --ml-it --ml-image=user-ml-env-pc --ml-name=ml1 --ml-w=/tmp/test --ml-entrypoint=python3 --ml-cmd=model/src/train.py --ml-v=workspace_host1/point_clouds/model:/tmp/test/model --ml-e DATA_DIR=app-data --ml-e MODEL_DIR=model
+     ```
+
+Note: The SN node are started only on the first VM. The SL nodes are started on all the VMs.
+
+## Running on Azure VM2
+1. Start the SL node:
+     ```bash
+     ./scripts/bin/run-sl --name=sl3 --host-ip=10.15.150.102 --sn-ip=10.15.150.102 --sn-api-port=30304 --sl-fs-port=16000 --key=workspace_host3/point_clouds/cert/sl-3-key.pem --cert=workspace_host3/point_clouds/cert/sl-3-cert.pem --capath=workspace_host3/point_clouds/cert/ca/capath --ml-it --ml-image=user-ml-env-pc --ml-name=ml3 --ml-w=/tmp/test --ml-entrypoint=python3 --ml-cmd=model/src/train.py --ml-v=workspace_host1/point_clouds/model:/tmp/test/model --ml-e DATA_DIR=app-data --ml-e MODEL_DIR=model
+     ```
+2. Same steps are followed for the other hosts.
+
+## Model Saving
+The config file in the src code can be used to change the number of epochs after which the model is saved (currently the model is saved in the `checkpoints` folder after every 10 epochs).
+
+## Model Testing
+1. The model can be tested on the test dataset by running the `test.py` file in the `model/src` folder:
+    ```bash
+    python test.py --gpu_idx 0 --pretrained_path <path>\workspace\point_clouds\model\checkpoints\complexer_yolo\azure\Model_complexer_yolo_epoch_<num>.pth --show_image
+    ```
+This will run the test script using GPU index 0, specifying the path to the pretrained model, and showing the image output.
+
+## Contact
+For any queries, please contact:<br>
+    - [Abhishek Mishra](mailto:[email protected])<br>
+    - [Joy Jefferson](mailto:[email protected])<br>
+    - [Pradish Kapur](mailto:[email protected])<br>
+    - [Kiran Kannur](mailto:[email protected])
+
+## License
+This project is licensed under the MIT License - see the LICENSE file for details
+
+
+
+
+
+
+

examples/point_clouds/ml-context/Dockerfile

@@ -0,0 +1,24 @@
+######################################################################
+# (C)Copyright 2021,2022 Hewlett Packard Enterprise Development LP
+######################################################################
+# This is a sample docker file to build the user ml container
+
+FROM pytorch/pytorch:1.5-cuda10.1-cudnn7-runtime
+
+COPY requirements.txt swarmlearning-client-py3-none-manylinux_2_24_x86_64.whl /tmp/swarmlib/
+
+# Setup conda dev environment
+RUN apt-get update && apt-get install \
+    build-essential python3-dev python3-pip \
+    python3-setuptools --no-install-recommends -y
+
+RUN apt-get install libglib2.0-0 -y
+
+# Install conda
+RUN conda install pip
+
+# Install ml environment packages
+RUN pip3 install --upgrade pip && pip3 install -r /tmp/swarmlib/requirements.txt
+
+# Install SwarmLearning package
+RUN pip3 install /tmp/swarmlib/swarmlearning-client-py3-none-manylinux_2_24_x86_64.whl

examples/point_clouds/ml-context/requirements.txt

@@ -0,0 +1,16 @@
+######################################################################
+# (C)Copyright 2021,2022 Hewlett Packard Enterprise Development LP
+######################################################################
+### Dependent python packages for user's model training ###
+
+easydict==1.9
+opencv-python
+opencv-contrib-python-headless
+numpy==1.18.3
+tensorboard==2.2.1
+scikit-learn==0.22.2
+matplotlib
+pandas
+sklearn
+protobuf==3.15.6
+Shapely

examples/point_clouds/model/dataset/kitti/ImageSets/test.txt

@@ -0,0 +1 @@
+000012