Lightning Kitti
Semantic Segmentation with Pytorch-Lightning
Note: this example is now part of Pytorch-Lightning repo and directly maintained there.
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
-
Install dependencies through
requirements.txt,Pipfileor manually (Pytorch, Pytorch-Lightning & Wandb) -
Log in or sign up for an account ->
wandb login -
Run
python train.py -
Visualize and compare your runs through generated link
Sweeps
-
Run
wandb sweep sweep.yaml -
Run
wandb agent <sweep_id>where<sweep_id>is given by previous command -
Visualize and compare the sweep runs
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.