adilek / paiss

NLE practical session for PAISS 2018

PAISS18

NLE practical session for PAISS 2018

Installation

Linux / MacOS

First, download and install the appropriate version of miniconda following the instructions for MacOS or Linux.

Then run the following commands:

conda install numpy matplotlib ipython scikit-learn
conda install pytorch torchvision faiss-cpu -c pytorch

On MacOS there’s a bug for faiss related to libomp (facebookresearch/faiss#485): run “brew install libomp” (see https://brew.sh/ to install brew) to resolve this bug.

Windows

Install anaconda on windows (launch .exe file downloaded from the conda website). It has to be python 3 (pytorch doesn’t support 2.7 on windows)

In the anaconda prompt, run:

conda create -n pytorch
activate pytorch
conda install pytorch-cpu -c pytorch
pip install torchvision --no-deps
conda install pillow

Donwload the dataset and models

Oxford dataset:

wget www.robots.ox.ac.uk/~vgg/data/oxbuildings/oxbuild_images.tgz -O images.tgz
mkdir -p data/oxford5k/jpg && tar -xzf images.tgz -C data/oxford5k/jpg
wget www.robots.ox.ac.uk/~vgg/data/oxbuildings/gt_files_170407.tgz -O gt_files.tgz
mkdir -p data/oxford5k/lab && tar -xzf gt_files.tgz -C data/oxford5k/lab

Features and models:

wget https://www.dropbox.com/s/gr404xlfr4021pw/features.tgz?dl=1 -O features.tgz
tar -xzf features.tgz -C data
wget https://www.dropbox.com/s/mr4risqu7t9neel/models.tgz?dl=1 -O models.tgz
tar -xzf models.tgz -C data

Demo

Run python demo.py --qidx 42 --topk 5 and you should see the following ouput: