bruinxiong / learntopayattention

PyTorch implementation of ICLR 2018 paper Learn To Pay Attention (and some modification)

LearnToPayAttention

PyTorch implementation of ICLR 2018 paper Learn To Pay Attention

My implementation is based on "(VGG-att3)-concat-pc" in the paper, and I trained the model on CIFAR-100 DATASET.
I implemented two version of the model, the only difference is whether to insert the attention module before or after the corresponding max-pooling layer.

(New!) Pre-trained models

Google drive link
Alternative link(Baidu Cloud Disk)

Dependences

• PyTorch (>=0.4.1)
• OpenCV
• tensorboardX

NOTE If you are using PyTorch < 0.4.1, then replace torch.nn.functional.interpolate by torch.nn.Upsample. (Modify the code in utilities.py).

Training

1. Pay attention before max-pooling layers

python train.py --attn_mode before --outf logs_before --normalize_attn --log_images

2. Pay attention after max-pooling layers

python train.py --attn_mode after --outf logs_after --normalize_attn --log_images

Results

Training curve - loss

The x-axis is # iter

1. Pay attention before max-pooling layers
   

2. Pay attention after max-pooling layers
   

3. Plot in one figure
   

Training curve - accuracy on test data

The x-axis is # epoch

1. Pay attention before max-pooling layers
   

2. Pay attention after max-pooling layers
   

3. Plot in one figure
   

Quantitative results (on test data of CIFAR-100)

Method	VGG (Simonyan&Zisserman,2014)	(VGG-att3)-concat-pc (ICLR 2018)	attn-before-pooling (my code)	attn-after-pooling (my code)
Top-1 error	30.62	22.97	22.62	22.92

Attention map visualization (on test data of CIFAR-100)

From left to right: L1, L2, L3, original images

1. Pay attention before max-pooling layers
   

2. Pay attention after max-pooling layers