Semantic Segmentation with Pytorch-Lightning

Overview

Lightning Kitti

Semantic Segmentation with Pytorch-Lightning

Introduction

This is a simple demo for performing semantic segmentation on the Kitti dataset using Pytorch-Lightning and optimizing the neural network by monitoring and comparing runs with Weights & Biases.

Pytorch-Ligthning includes a logger for W&B that can be called simply with:

from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning import Trainer

wandb_logger = WandbLogger()
trainer = Trainer(logger=wandb_logger)

Refer to the documentation for more details.

Hyper-parameters can be defined manually and every run is automatically logged onto Weights & Biases for easier analysis/interpretation of results and how to optimize the architecture.

You can also run sweeps to optimize automatically hyper-parameters.

Note: this example has been adapted from Pytorch-Lightning examples.

Usage

Notebook

  • A quick way to run the training scrip is to go to the notebook/tutorial.ipynb and play with it.

Script

  1. Clone this repository.

  2. Download Kitti dataset

  3. The dataset will be downloaded in the form of a zip file namely data_semantics.zip. Unzip the dataset inside the lightning-kitti/data_semantic/ folder.

  4. Install dependencies through requirements.txt, Pipfile or manually (Pytorch, Pytorch-Lightning & Wandb)

  5. Log in or sign up for an account -> wandb login

  6. Run python train.py and add any optional args

  7. Visualize and compare your runs through generated link

    alt text

Sweeps for hyper-parameter tuning

W&B Sweeps can be defined in multiple ways:

  • with a YAML file - best for distributed sweeps and runs from command line
  • with a Python object - best for notebooks

In this project we use a YAML file. You can refer to W&B documentation for more Pytorch-Lightning examples.

  1. Run wandb sweep sweep.yaml

  2. Run wandb agent where is given by previous command

  3. Visualize and compare the sweep runs

    alt text

Results

After running the script a few times, you will be able to compare quickly a large combination of hyperparameters.

Feel free to modify the script and define your own hyperparameters.

See the live report →

Owner
Boris Dayma
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Boris Dayma
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