Python

Photo Credit In Part 1, we introduced the XCiT architecture and reviewed the implementation of the Cross-Covariance Attention(XCA) block. In this Part 2, we’ll review the implementation of the Local Patch Interaction(LPI) block and the Class Attention layer. from [1] Local Patch Interaction(LPI) Because there is no explicit communication between patches(tokens) in XCA, a layer consisting of two depth-wise 3×3 convolutional layers with Batch Normalization with GELU non-linearity is added to enable explicit communication. ...

credit Overview XCiT: Cross-Covariance Image Transformers[1] is a paper from Facebook AI that proposes a “transposed” version of self-attention that operates across feature channels rather than tokens. This cross-covariance attention has linear complexity in the number of tokens (the original self-attention has quadratic complexity). When used on images as in vision transformers, this linear complexity allows the model to process images of higher resolutions and split the images into smaller patches, which are both shown to improve performance. ...

Photo Credit Motivation Sphinx is a tool that helps you create intelligent and beautiful documentation. I use it to generate documentation for the pytorch-lightning-spells project and publish it on readthedocs.io for free (if the project is open-source). Documentation is tremendously helpful to users of your project (including yourselves). As long as you maintain the good habit of writing docstrings in your code, Sphinx will convert the docstrings into webpages for you, drastically reducing the manual labor required from you. ...

Photo Credit Streamlit an increasingly popular tool that allows Python developers to turn data scripts into interactive web applications in a few lines of code. I recently developed and deployed a semantic search app for news articles in Chinese, and I made a mistake not caching the model loading code. The performance was abysmal, and the memory footprint was huge for a TinyBERT-4L model (had to allocate 1GB of memory for the app). ...

Photo Credit Motivation I recently came across this new image data augmentation technique called SnapMix. It looks like a very sensible improvement over CutMix, so I was eager to give it a try. The SnapMix author provides a PyTorch implementation. I made some adjustments to improve the numeric stability and converted it to a callback in PyTorch Lightning. I encountered one major obstacle during the process — SnapMix uses Class Activation Mapping(CAM) to calculate an augmented example’s label weights. It requires access to the final linear classifier’s weight and the model activations before the pooling operation. Some PyTorch pre-trained CV models do implement methods to access these two things, but the namings are inconsistent. We need a unified API to do this. ...