NLE practical session for PAISS 2018
This code requires Python 3 and Pytorch 0.4. Follow the instructions below to install all the necessary dependencies.
First, download and install the appropriate version of miniconda following the instructions for MacOS or Linux.
Then run the following commands:
source $HOME/miniconda3/bin/activate #Activates your conda environment
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.
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
NOTE: The FAISS package is not supported on Windows. Participants with Windows machines must follow the product quantization exercise with their neighbours.
First, clone this repository:
cd $HOME/my_projects
git clone https://github.com/almazan/paiss.git
Then, you will need to download 4 files:
- oxbuild_images.tgz (1.8GB)
- gt_files_170407.tgz (280KB)
- features.tgz (579MB)
- models.tgz (328MB)
and store them in the appropriate paths.
Note: All paths in this section are relative to the root directory of this repository.
Oxford dataset
On Linux/MacOS, execute the following:
cd $HOME/my_projects/paiss
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
On Windows, perform the following:
- Download www.robots.ox.ac.uk/~vgg/data/oxbuildings/oxbuild_images.tgz
- create directory
data/oxford5k/jpg/ - uncompress oxbuild_images.tgz and store in
data/oxford5k/jpg/ - Download www.robots.ox.ac.uk/~vgg/data/oxbuildings/gt_files_170407.tgz
- create directory
data/oxford5k/lab/ - uncompress gt_files_170407.tgz and store in
data/oxford5k/lab/
Features and models
On Linux/MacOS, execute the following:
cd $HOME/my_projects/paiss
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
On Windows, perform the following:
- Download https://www.dropbox.com/s/gr404xlfr4021pw/features.tgz?dl=1
- Uncompress features.tgz and store in
data/ - Download https://www.dropbox.com/s/mr4risqu7t9neel/models.tgz?dl=1
- Uncompress models.tgz and store in
data/
Execute:
source $HOME/miniconda3/bin/activate
cd $HOME/my_projects/paiss
python demo.py --qidx 42 --topk 5
and you should see the following ouput:
