A Close Look at Spatial Modeling: From Attention to Convolution [arXiv]

by Xu Ma, Huan Wang, Can Qin, Kunpeng Li, Xingchen Zhao, Jie Fu, Yun Fu

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Motivation

Figure 1: Attention map visualizations of Vision Transformers. For each pair, we show the query point and its corresponding attention map (of last block and last head). We randomly selected images and the query points for illustration. The right color bar identifies the value of normalized attention maps.

👀 ‼️ Observations & Motivations:

• 🔸 Query-irrelevant behavior. The attention maps consistently show a query-irrelevant (and even head-irrelevant) behavior. Visually, the attention maps appear to be nearly identical for each testing model and image, regardless of the query patch. This is a departure from the design philosophy of self-attention that each patch should exhibit a distinct attention map, indicating that a global context may be concealed behind the attention mechanism.
• 🔸 Sparse attention & Convolution helps. The attention weights (see ViT-B, ViT- L, and DeiT-B) are relatively sparse, indicating that only several patches dominate the attention. By introducing the knowledge from convolution, the attention weights (see DeiT-B-Distill) are largely smoothed, and the performance is significantly improved as well (83.4% of DeiT-B-Distill vs. 81.8% of DeiT-B top-1 accuracy on ImageNet-1K validation set).

Solution: From Attention to Convolution

Figure 2: Illustration of an FCViT block. Following MetaFormer, FCViT considers the block as a combination of token-mixer and channel-mixer, with residual connection and layer normalization (LN). In the token-mixers, we dynamically integrate the global context with input tokens by the token-global similarity. A depth-wise convolution is employed to fuse local information. To improve the generalization ability of the global context, we introduce a competition-driven information bottleneck structure.

Figure 3: Visual comparisons of FCViT-B12 similarity and ViT-B attention map. We plot all the outputs of the last block for the two models (8 groups for FCViT and 12 heads for ViT). Compared to ViT, the results indicate that: 1), FCViT focuses more on the objects; 2), FCViT presents more diversities than multi-head attention, whose attention maps from different heads are nearly the same.

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Image Classification

1. Requirements

torch>=1.7.0; torchvision>=0.8.0; pyyaml; apex-amp (if you want to use fp16); timm (pip install git+https://github.com/rwightman/pytorch-image-models.git@9d6aad44f8fd32e89e5cca503efe3ada5071cc2a)

data prepare: ImageNet with the following folder structure, you can extract ImageNet by this script.

│imagenet/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

2. FCViT Models

Model	#params	Image resolution	Top1 Acc	Download
FCViT-tiny	4.6M	224	74.9	download
FCViT-B12	14M	224	80.9	download
FCViT-B24	25.7M	224	82.5	download
FCViT-B48	49.1M	224	83.6	download

3. Validation

To evaluate our FCViT models, run:

MODEL=fcvit_tiny #{tiny, b12, b24, b48}
python3 validate.py /path/to/imagenet  --model $MODEL -b 128 --checkpoint {/path/to/checkpoint} 

4. Train

We show how to train FCViT on 8 GPUs. The relation between learning rate and batch size is lr=bs/1024*1e-3. For convenience, assuming the batch size is 1024, then the learning rate is set as 1e-3 (for batch size of 1024, setting the learning rate as 2e-3 sometimes sees better performance).

MODEL=fcvit_tiny # fcvit_{tiny, b12, b24, b48}
DROP_PATH=0.1 # drop path rates [0.1, 0.1, 0,1, 0.2] responding to model [tiny, b12, b24, b48]
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet \
  --model $MODEL -b 128 --lr 1e-3 --drop-path $DROP_PATH --apex-amp

5. Detection and Segmentation

For detection and segmentation tasks, please see here: [detection & instance segmentation] and [semantic segmentation].

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Acknowledgment

Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.

poolformer, pytorch-image-models, mmdetection, mmsegmentation.

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Citation

@article{ma2022fcvit,
  author      = {Ma, Xu and Wang, Huan and Qin, Can and Li, Kunpeng and Zhao, Xingchen and Fu, Jie and Fu, Yun},
  title       = {A Close Look at Spatial Modeling: From Attention to Convolution},
  publisher   = {arXiv},
  year        = {2022},
}