Our reproducibility effort based on the 2020 ML Reproducibility Challenge. We are reproducing the results of this CVPR 2020 paper: Learning Representations by Predicting Bags of Visual Words by Gidaris et al S. Gidaris, A. Bursuc, N. Komodakis, P. Pérez, and M. Cord, “Learning Representations by Predicting Bags of Visual Words,” ArXiv, 27-Feb-2020. [Online]. Available: https://arxiv.org/abs/2002.12247. [Accessed: 15-Nov-2020].
Group project for UWaterloo course SYDE 671 - Advanced Image Processing by Harry Nguyen, Stone Yun, Hisham Mohammad
Code base is implemented with PyTorch. Dataloader is adapted from Github released by authors of the RotNet paper: https://github.com/gidariss/FeatureLearningRotNet
Our model definitions are in model.py. Custom loss and layer class definitions are in layers.py
See dependencies.txt for list of libraries that need to be installed. Pip install or conda install both work
Before running the experiments:
Inside the project code, create a folder ./datasets/CIFAR, download the dataset CIFAR100 from https://www.cs.toronto.edu/~kriz/cifar.html and put in the folder.
Pretrained weights of BowNet and RotNet from our best results are in saved_weights directory.
To generate your own RotNet checkpoint, running rotation_prediction_training.py will train a new RotNet from scratch. The checkpoint is saved as rotnet1_checkpoint.pt
To run rotnet_linearclf.py or rotnet_nonlinearclf.py, you need to have the checkpoint file of pretrained RotNet, download here (eg. saved_weights/rotnet.pt). These scripts load the pretrained RotNet and use its feature maps to train a classifier on CIFAR-100 prediction.
$python rotnet_linearclf.py --checkpoint /path/to/checkpoint
$python rotnet_nonlinearclf.py --checkpoint /path/to/checkpoint
bownet_plus_linearclf_cifar_training.py takes pretrained BowNet and uses feature maps to train linear classifier on CIFAR-100. kmeans_cluster_and_bownet_training.py loads pretrained RotNet, performs KMeans clustering of feature map, then trains BowNet on BOW reconstruction. Thus, you'll need pretrained BowNet and RotNet checkpoints respectively.
We also include a pre-computed RotNet codebook for K = 2048 clusters. If you include the path to it for kmeans_cluster_and_bownet_training.py the script will skip the codebook generation step and go straight to BOW reconstruction training
$python bownet_plus_linearclf_cifar_training.py --checkpoint /path/to/bownet/checkpoint
$python kmeans_cluster_and_bownet_training.p --checkpoint /path/to/rotnet/checkpoint [optional: --rotnet_vocab /path/to/rotnet/vocab.npy]