Python

credit Introduction At the start of 2022, we have a new pure convolution architecture (ConvNext)[1] that challenges the transformer architectures as a generic vision backbone. The new Visual Attention Network (VAN)[2] is yet another pure and simplistic convolution architecture that its creators claim to have achieved SOTA results with fewer parameters. Source: [2] What ConvNext tries to achieve is modernizing a standard ConvNet (ResNet) without introducing any attention-based modules. VAN still has attention-based modules, but the attention weights are obtained from a large kernel convolution instead of a self-attention block. To overcome the high computation costs brought by a large kernel convolution, it is decomposed into three components: a spatial local convolution (depth-wise convolution), a spatial long-range convolution (depth-wise dilation convolution), and a channel convolution (1x1 point-wise convolution). ...

credit Introduction Hierarchical Transformers (e.g., Swin Transformers[1]) has made Transformers highly competitive as a generic vision backbone and in a wide variety of vision tasks. A new paper from Facebook AI Research — “A ConvNet for the 2020s”[2] — gradually and systematically “modernizes” a standard ResNet[3] toward the design of a vision Transformer. The result is a family of pure ConvNet models dubbed ConvNeXt that compete favorably with Transformers in terms of accuracy and scalability. ...

Photo Credit Introduction Parallel programming is hard, and you probably should not use any low-level API to do it in most cases (I’d argue that Python’s built-in multiprocessing package is low-level). I’ve been using Joblib’s Parallel class for tasks that are embarrassingly parallel and it works wonderfully. However, sometimes the task at hand is not simple enough for the Parallel class (e.g., you need to share something from the main process that is not pickle-able, or you want to maintain states in each child process). I’ve recently found this library — MPIRE (MultiProcessing Is Really Easy) — that significantly mitigates this problem of not having enough flexibility, while still having a high-level and user-friendly API. ...

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. ...