CSE252C HW1

0. Homework instructions

1. Attempt all questions.

2. Please comment all your code adequately.

3. Include all relevant information such as text answers, output images in notebook.

4. Academic integrity: The homework must be completed individually.

5. Submission instructions:
   (a) Submit the notebook and its PDF version on Gradescope.
   (b) Rename your submission files as Lastname_Firstname.ipynb and Lastname_Firstname.pdf.
   (c) Correctly select pages for each answer on Gradescope to allow proper grading.

6. Due date: Assignments are due Mon, May 4, by 4pm PST.

Steps to access and complete homework

• Clone the homework repository
  • git clone https://github.com/eric-yyjau/cse252c_hw1.git
• The homework is in the Jupyter Notebook hw1-CSE252C.ipynb
• Follow the README (this file) for installation, data and compute instructions.

1. Installation instructions

1. Set up the environment

1. [Option 1] On your own machine

• (local) SSH into your machine
• Install SWIG
  • On Ubuntu: sudo apt-get install swig (sudo required)
  • On MacOS: brew install swig
    • You need to install Homebrew first with HomeBrew
• Install Python 3.X and Pip
• [Recommended] Create an environment (e.g. with Anaconda)
  • conda create --name py36 python=3.6 pip
  • conda activate py36
• Install Jupyter Notebook
  • conda install jupyter
• Install kernels for Jupter Notebook
  • conda install nb_conda
• Launch Jupyter Notebook server in the conda env of the cluster
  • jupyter notebook
  • You will be provided with a URL that you can open locally
  • In a opened notebook, change the kernel (on Menu: Kernel -> Change Kernel) to the name of the conda env you just created (in the case of this documentation it should be py36)

2. [Option 2] On the ieng6.ucsd.edu server

• (local) (IMPORTANT) Connect your UCSD VPN
• (local) Login with your credentials
  • ssh {USERNAME}@ieng6.ucsd.edu
• If you cannot launch a pod, set up the environment following these instructions
• Launch your pod. You should enter a node with 1 GPU
  • launch-scipy-ml.sh -i ucsdets/cse152-252-notebook:latest -g 1
• You will be provided with a URL that you can open locally:
  • Click on the link. Then natigate to the jupyter notebook for a question which you are going to git clone as follows

2. Pull the repo and install dependencies

• git clone https://github.com/eric-yyjau/cse252c_hw1.git
• Install dependencies (Python 3.X with Pip)
  • pip install -r requirements.txt --user
• Compile and install pyviso for the SfM question
  • cd pyviso/src/
  • pip install -e . --user

2. Data

On the ieng6.ucsd.edu server, the datasets are located at

• Q1: SfM
  • /datasets/cse152-252-sp20-public/dataset_SfM
  • Change the dataset path in jupyter notebooks to your paths
• Q5:
  • /datasets/cse152-252-sp20-public/sfmlearner_h128w416
  • /datasets/cse152-252-sp20-public/kitti

3. How to run

Q1: SfM - Working folder: ./pyviso

Launch Jupyter Notebook

There is a hw1-CSE252C.ipynb jupyter notebook file in the top-level directory cse252c_hw1.

Options

One toggle if_vis = True/False allows you to enable/disable the visualization. Disabling the visualization will make the for loop run significantly faster.

Output

The errors are printed and the visualizations are saved at vis/. The images should look like: To fetch the files you can use commands like scp to transfer files from the cluster to your local machine:

From your local machine:

scp -r <USERNAME>@dsmlp-login.ucsd.edu:/datasets/home/53/253/cs152sp20ta1/pyviso2/vis {LOCAL PATH}

Or from within server if your local machine has a fixed address or IP:

scp -r {REMOTE PATH TO THE vis FOLDER} <USERNAME>@<LOCAL ADDRESS>:{YOUR LOCAL PATH}

Q4: Optical Flow - Working folder: ./opticalFlow

4. [Extra] How to run training sessions

1. Set up the environment

[Option 1] On the ieng6.ucsd.edu server

• Login with your credentials

  • ssh {USERNAME}@ieng6.ucsd.edu

• Launch TMUX

  • Reconmended for session management: you can come back anytime after you disconnect your session. Otherwise you have to keep your connection on for hours while training.
  • Just run tmux
  • To detach and come back later, use ctrl + b then d. To attach next time, use ctrl + b then a.
  • For more TMUX usages please refer to online tutorials like https://linuxize.com/post/getting-started-with-tmux/

• Launch your pod

  • Follow Section 1.1.2

[Option 2] On your own server

Just launch TMUX.

2. Start training

Now you can create conda env and do your training in there following Section 1.1