Cuda-KNN

K nearest neighbors cuda-accelerated implementation using PyTorch. Usage example can be found in mnist.ipynb which applies the model to mnist dataset.

Usage

Start by installing the requirements in requirements.txt. To apply the model to a new dataset, first write some code to load your data in the load_data function in utils.py. Give a name to your dataset and wrap your code in a if dataset == $dataset_name. Then, the eval.py script allows to evaluate the model on fixed parameters and save a PCA and a CudaKNN model. CLI arguments:

python eval.py -h
usage: eval.py [-h] --dataset DATASET --test_size TEST_SIZE 
               --val_size VAL_SIZE --batch_size BATCH_SIZE
               [--flatten] [--cuda] --pca_components PCA_COMPONENTS 
               --neighbors NEIGHBORS --p_norm P_NORM
               [--verbose] --output_dir OUTPUT_DIR

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET, -d DATASET
                        name of dataset
  --test_size TEST_SIZE, -ts TEST_SIZE
                        number of test samples
  --val_size VAL_SIZE, -vs VAL_SIZE
                        number of validation samples
  --batch_size BATCH_SIZE, -bs BATCH_SIZE
                        batch size
  --flatten, -f         flatten data if flag specified
  --cuda, -c            use cuda if flag specified
  --pca_components PCA_COMPONENTS, -n PCA_COMPONENTS
                        number of components for PCA
  --neighbors NEIGHBORS, -k NEIGHBORS
                        number of neighbors for knn
  --p_norm P_NORM, -p P_NORM
                        norm parameter
  --verbose, -v         verbose activated if flag specified
  --output_dir OUTPUT_DIR, -o OUTPUT_DIR
                        model output directory

Example for mnist (11Gb of gpu memory):

python eval.py --dataset mnist \
               --test_size 10000 \
               --val_size 5000 \
               --batch_size 64 \
               --flatten \
               --cuda \
               --pca_components 100 \
               --neighbors 3 \
               --p_norm 2 \
               --verbose \
               --output_dir model