https://github.com/tum-vision/lgm

Implementation of Layered Graphical Model with demo code
https://github.com/tum-vision/lgm

cpp graphical-models machine-learning python pytorch

Last synced: 6 months ago
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Implementation of Layered Graphical Model with demo code

README

# the LGM package

by _Yuesong Shen_

This repository contains the demo code (as a python package) for the paper:

"Probabilistic Discriminative Learning with Layered Graphical Models" by
Yuesong Shen, Tao Wu, Csaba Domokos and Daniel Cremers

The code is released under GPL v3 or later. For any questions please contact:
yuesong.shen@tum.de

## setup instructions:

Tested environment: Ubuntu 16.04; Python 3.6; gcc 5.4.0.

Required dependencies: Python 3.5+ along with pip; ABI compatible C++ compiler.

- In terminal, change to current directory.

- Install dependencies: "pip install -r requirements.txt"

- Install locally the demo package: "pip install -e ."

## usage instructions:

Demo scripts are inside the folder "example/".

- "demo_lgm.py" is the demo script for LGM models

Run "python demo_lgm.py -h" for possible arguments

Examples:

- Run Conv model with TRW and FashionMNIST. Use cuda:

"python demo_lgm.py -m conv -i trw -d FashionMNIST -g"

- run Dense model with LBP (2 inference iterations) and MNIST for 10 epochs.
Use cpu only:

"python demo_lgm.py -m dense -i loopy -n 2 -d MNIST -e 10"

- "demo_nn.py" is the demo script for NN baselines

Run "python demo_nn.py -h" for possible arguments

Examples:

- Run Conv model with FashionMNIST and sigmoid activation. Use cuda:

"python demo_nn.py -m conv -a sigmoid -d FashionMNIST -g"

- run Dense model with relu and MNIST for 10 epochs. Use cpu only:

"python demo_nn.py -m dense -a relu -d MNIST -e 10"