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Asymmetric metric learning

This is the official code that enables the reproduction of the results from our paper:

Asymmetric metric learning for knowledge transfer, Budnik M., Avrithis Y. [arXiv]

Content

This repository provides the means to train and test all the models presented in the paper. This includes:

  1. Code to train the models with and without the teacher (asymmetric and symmetric).
  2. Code to do symmetric and asymmetric testing on rOxford and rParis datasets.
  3. Best pre-trainend models (including whitening).

Dependencies

  1. Python3 (tested on version 3.6)
  2. Numpy 1.19
  3. PyTorch (tested on version 1.4.0)
  4. Datasets and base models will be downloaded automatically.

Training and testing the networks

To train a model use the following script:

python main.py [-h] [--training-dataset DATASET] [--directory EXPORT_DIR] [--no-val]
                  [--test-datasets DATASETS] [--test-whiten DATASET]
                  [--val-freq N] [--save-freq N] [--arch ARCH] [--pool POOL]
                  [--local-whitening] [--regional] [--whitening]
                  [--not-pretrained] [--loss LOSS] [--loss-margin LM] 
                  [--mode MODE] [--teacher TEACHER] [--sym]
                  [--feat-path FEAT] [--feat-val-path FEATVAL]
                  [--image-size N] [--neg-num N] [--query-size N]
                  [--pool-size N] [--gpu-id N] [--workers N] [--epochs N]
                  [--batch-size N] [--optimizer OPTIMIZER] [--lr LR]
                  [--momentum M] [--weight-decay W] [--print-freq N]
                  [--resume FILENAME] [--comment COMMENT]

Most parameters are the same as in CNN Image Retrieval in PyTorch. Here, we describe parameters added or modified in this work, namely:
--arch - architecture of the model to be trained, in our case the student.
--mode - is the training mode, which determines how the dataset is handled, e.g. are the tuples constructed randomly or with mining; which examples are coming from the teacher vs student, etc. So for example while the --loss is set to 'contrastive', 'ts' enables standard student-teacher training (includes mining), 'ts_self' trains using the Contr+ approach, 'reg' uses the regression. When using 'rand' or 'reg' no mining is used. With 'std' it follows the original training protocol from here (the teacher model is not used).
--teacher - the model of the teacher(vgg16 or resnet101), note that this param makes the last layer of the student match that of the teacher. Therefore, this can be used even in a standard symmetric training.
--sym - a flag that indicates if the training should be symmetric or asymmetric.
--feat-path and --feat-val-path - a path to the extracted teacher features used to train the student. The features can be extracted using the extract_features.py script.

To perform a symmetric test of the model that is already trained:

python test.py [-h] (--network-path NETWORK | --network-offtheshelf NETWORK)
               [--datasets DATASETS] [--image-size N] [--multiscale MULTISCALE] 
               [--whitening WHITENING] [--teacher TEACHER]

For the asymmetric testing:

python test.py [-h] (--network-path NETWORK | --network-offtheshelf NETWORK)
               [--datasets DATASETS] [--image-size N] [--multiscale MULTISCALE] 
               [--whitening WHITENING] [--teacher TEACHER] [--asym]

Examples:

Perform a symmetric test with a pre-trained model:

python test.py -npath  mobilenet-v2-gem-contr-vgg16 -d 'roxford5k,rparis6k' -ms '[1, 1/2**(1/2), 1/2]' -w retrieval-SfM-120k --teacher vgg16

For an asymmetric test:

python test.py -npath  mobilenet-v2-gem-contr-vgg16 -d 'roxford5k,rparis6k' -ms '[1, 1/2**(1/2), 1/2]' -w retrieval-SfM-120k --teacher vgg16 --asym

If you are interested in just the trained models, you can find the links to them in the test.py file.

Acknowledgements

This code is adapted and modified based on the amazing repository by F. Radenović called CNN Image Retrieval in PyTorch: Training and evaluating CNNs for Image Retrieval in PyTorch