Implementing Stand-Alone Self-Attention in Vision Models using Pytorch (13 Jun 2019)

Method

Attention Layer
- Equation 1:
Relative Position Embedding
- The row and column offsets are associated with an embedding and respectively each with dimension . The row and column offset embeddings are concatenated to form . This spatial-relative attention is now defined as below equation.
- Equation 2:
- I refer to the following paper when implementing this part.
  - Attention Augemnted Convolutional Networks paper

Replacing Spatial Convolutions
- A 2 × 2 average pooling with stride 2 operation follows the attention layer whenever spatial downsampling is required. - This work applies the transform on the ResNet family of architectures. The proposed transform swaps the 3 × 3 spatial convolution with a self-attention layer as defined in Equation 3.
Replacing the Convolutional Stem
- The initial layers of a CNN, sometimes referred to as the stem, play a critical role in learning local features such as edges, which later layers use to identify global objects. - The stem performs self-attention within each 4 × 4 spatial block of the original image, followed by batch normalization and a 4 × 4 max pool operation.

Spatial extent: 7
Attention heads: 8
Layers:
- ResNet 26: [1, 2, 4, 1]
- ResNet 38: [2, 3, 5, 2]
- ResNet 50: [3, 4, 6, 3]

Datasets	Model	Accuracy	Parameters (My Model, Paper Model)
CIFAR-10	ResNet 26	89.45%	8.30M, -
CIFAR-10	ResNet 26 + stem	89.29%	8.30M, -
CIFAR-10	ResNet 38 (WORK IN PROCESS)	89.46%	12.1M, -
CIFAR-10	ResNet 50 (WORK IN PROCESS)		16.0M, -
IMAGENET	ResNet 26 (WORK IN PROCESS)		10.3M, 10.3M
IMAGENET	ResNet 38 (WORK IN PROCESS)		14.1M, 14.1M
IMAGENET	ResNet 50 (WORK IN PROCESS)		18.0M, 18.0M