静态剪枝让分割模型更快，速度提升34%且精度不降。
StaticSegFormer: An Efficient High-Performance Semantic Segmentation Based on Static Structured Pruning
- 采用静态结构化剪枝，专注优化注意力层参数。
- 在Cityscapes上帧率最高提升34%，FLOPs降低且mIoU不变。
- 适合小模型和大图像场景，部署效率高。
结构化剪枝通过推理时移除参数组来提升深度神经网络效率。以往方法多聚焦降低计算量（FLOPs），但语义分割性能（mIoU）略有下降。近期动态结构化剪枝试图缓解性能损失并进一步降低FLOPs，但在ADE20K和Cityscapes基准测试中，我们发现其在GPU平台上帧率远低于简单静态方法，而mIoU与FLOPs表现相近。为此，我们提出一种针对注意力层的静态结构化剪枝方法——StaticSegFormer，可在降低FLOPs的同时显著提升帧率，在Cityscapes数据集上最高提升34%，且无mIoU性能损失。该方法对小型编码器和大图像场景尤为有效。
原文摘要 · Abstract (English)
Structured pruning enhances the efficiency of deep neural networks (DNNs) by eliminating groups of parameters during inference. Previous methods mostly reduce computational complexity (FLOPs), while semantic segmentation performance (mIoU) slightly drops. Accordingly, recent dynamic structured pruning methods aim at reducing the performance drop, while lowering the FLOPs even more. However, on the ADE20K and Cityscapes benchmarks, our study reveals that on a GPU platform such dynamic methods exhibit a surprisingly low frame rate far below a simple static approach, while having comparable results in mIoU and FLOPs. To address this issue, we propose a static structured pruning method for attention layers, that achieves both, a lower FLOPs and a high frame rate [fps] of the SegFormer network, the latter increased by up to 34% relative on the Cityscapes dataset, while having no mIoU performance drop at all. Our so-called StaticSegFormer method is strongest for small encoders and large images.
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