PyTorch Implementation of Non-Local Neural Network
This repository contains my implementation of Non-Local Neural Netowrks (CVPR 2018).
To understand more about the structure of this paper, you may refer to this slide and video which is in Korean.
The experiment was run on CIFAR-10 dataset for the sake of ensuring that the code runs without error.
Implementation Details
The original paper used ResNet-50 as its backbone structure for conducting experiment on video datasets such as Kinetics, Charades.
As an inital study, I adopted ResNet-56 strucutre for CIFAR-10 dataset which is a 2D classification. The architecture is implemented in models/resnet2D.py.
Original baseline model from the paper called C2D uses ResNet-50 as its backbone and 1 non-local block after the 4th residual block. This structure is implemented in models/resnet3D.py. The detail of the architecture is shown in the below figure:
The four different pairwise functions discussed in the paper are implemented accordingly in models/non_local.py. You can simply pass one of the operation as an argument. The details of the non-local block is shown in the below figure:
Finally, the original experiment of activity recognition was similarly replicated in 3D_experiment folder. The necessary data preprocessing code was borrowed from https://github.com/kenshohara/3D-ResNets-PyTorch. The training is run without error but I didn't have enough time to compare the performance boost from the addition of non-local block.
Training
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To start training for CIFAR-10 with ResNet-56, you can simply execute
run.sh. -
To start training for HMDB51 dataset with C2D, you first need to prepare the HMDB51 dataset as instructed in the
3D_experimentfolder. Then, executerun.sh. It seems like use of multiple GPU(s) may be need due to memory issues.
Results
Trained on CIFAR-10 for 200 epochs using the command shown in run.sh. The training was conducted using single 1080ti GPU.
The result showed that there wasn't a huge performance boost for image classification task on CIFAR-10. The below graph illustrates the loss curves for two different networks.
The Top-1 validation accuracy for ResNet-56 without non-local block was 93.97% while the one with non-local block had 93.98% validation accuracy.
This could be due to two reasons: 1) the proposed task was mainly for video classification 2) the input size of CIFAR-10 is too small so may not maintain spatial information after the second resnet block.
TO DO
- Compare the result of baseline model and that of non-local model for CIFAR-10
- Prepare video dataset (e.g. UCF-101, HMDB-51)
- Modify the model code to adapt to spatiotemporal settings
- Run test on some video datasets
- Run test on image segmentation dataset (e.g. COCO)
Reference
This repo is an adaptation from several other exisitng works.