Dynamic Computational Time for Recurrent Attention Model (DT-RAM)
Torch implementation of DT-RAM form https://arxiv.org/pdf/1703.10332.pdf with training/testing scripts.
Requirements
- Install Torch on a machine with CUDA GPU
- Install cuDNN v4 or v5 and the Torch cuDNN bindings
- Download Trained ResNet on DT-RAM folder.
- Download the CUB_bird dataset and run
process.pyas:python process.py [Path to CUB_bird]
If you already have Torch installed, make sure you have installed rnn, dpnn,optim,dp,net-toolkit and cuimage.
Training
The training scripts come with several options, which can be listed with the --help flag.
th main.lua --help
To run the training, simply run demo.sh. By default, the script runs 3 step DT-RAM based ResNet-50 on CUB with 4 GPU and 4 data-loader threads.
sh demo.sh train.list val.list 3
Testing on MNIST
To view some example results, you can directly do as follow. It will run a 9 step DT-RAM on mnist dataset
cd mnist
th recurrent-visual-attention-dynamic.lua --testOnly --xpPath ../save/model_mnist.t7
Performance on Fine-Grained Recognition
We train and test DT-RAM on MNIST, CUB-200-2011 and Stanford Cars dataset. Performance on the three datasets are:
| MNIST | Error (%) |
|---|---|
| RAM 4 Steps | 1.54 |
| RAM 5 Steps | 1.34 |
| RAM 7 steps | 1.07 |
| DT-RAM 5.2 Steps | 1.12 |
| CUB-200-2011 | Accuracy (%) |
|---|---|
| ResNet-50 Baseline | 84.5 |
| RAM 3 Steps | 86.0 |
| DT-RAM 1.9 Steps | 86.0 |
| Stanford Cars | Accuracy (%) |
|---|---|
| ResNet-50 Baseline | 92.3 |
| RAM 3 Steps | 93.1 |
| DT-RAM 1.9 Steps | 93.1 |
reference
This implements training of DT-RAM based on RAM(Recurrent Models of Visual Attention) , and we use the framework fb.resnet.torch