Deep Learning Mini Project

General Information

Creator: Elia Fantini, Kieran Vaudaux, Kaan Okumuş

Environment:

This project has been developed and tested with python 3.8. The required libraries are:

• PyTorch: for the implementation of deep learning models, methods. If you don't have it, you can download it by following the instructions here.

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Project Description & Guidance for Reproducing the Project:

The goal of the mini-projects is to implement a Noise2Noise model. A Noise2Noise model is an image denoising network trained without a clean reference image. The original paper can be found at here.

The project has two parts, focusing on two different facets of deep learning. The first one is to build a network that denoises using the PyTorch framework, in particular the torch.nn modules and autograd. The second one is to understand and build a framework, its constituent modules, that are the standard building blocks of deep networks without PyTorch’s autograd.

Dataset:

• Dataset is available here.
• Please put train_data.pkl and val_data.pkl inside both Miniproject_1\others\dataset and Miniproject_2\others\dataset folders.

Project Structure:

├── Miniproject_1
│    ├── __init__.py
│    ├── model.py
│    ├── bestmodel.pth
│    ├── Report_1.pdf
│    ├── results.pkl
│    ├── Experiments.ipynb
│    └── others
│         ├── Config.py
│         ├── dataset
│         │    ├── train_data.pkl
│         │    └── val_data.pkl
│         ├── dataset.py
│         └── nets
│              ├── DeepLabV3.py
│              ├── unet.py
│              ├── unet2.py
│              └── unet3.py
└── Miniproject_2
     ├── __init__.py
     ├── model.py
     ├── bestmodel.pth
     ├── Report_2.pdf
     ├── results.pkl
     ├── Experiments.ipynb
     └── others
          ├── Config.py
          ├── dataset
          │    ├── train_data.pkl
          │    └── val_data.pkl
          ├── dataset.py
          ├── helpers_functional.py
          ├── helpers_layer.py
          ├── dataset
          ├── nets
          │    └── unet.py
          └── testing_custom_blocks
               ├── testing_conv2d.py
               └── testing_convtranspose2d.py

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Sample Run of the Model

Put your test.py in the base directory and Run python3 test.py -p "./PROJ_336006_SCIPER2_287703" -d "./PROJ_336006_SCIPER2_287703/Miniproject_1/others/dataset/" in your terminal. This command does all your testings.

You can also test Conv2D function by comparing with PyTorch one. To do so, Run python3 test.py -p "./PROJ_336006_SCIPER2_287703" -d "./PROJ_SCIPER1_SCIPER2_287703/Miniproject_1/others/dataset/"

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Report

Reports for Mini Project 1 and 2 can be found in Miniproject_1\Report_1.pdf and Miniproject_2\Report_2.pdf respectively.