论文标题
考虑输入分辨率
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution
论文作者
论文摘要
我们提出了一个有效的一度预算预算的修剪框架（OFARPRUNING），以在早期培训阶段找到许多紧凑型网络结构，这些网络结构与赢家票相近，考虑到在修剪过程中输入分辨率的效果。在结构搜索阶段，我们利用余弦相似性来测量修剪面罩的相似性，以获取具有低能量和时间消耗的高质量网络结构。在结构搜索阶段之后，我们提出的方法随机对具有不同修剪速率和输入分辨率的紧凑结构进行采样，以实现关节优化。最终，我们可以获得适应各种分辨率的紧凑型网络的队列，以仅在一次训练的情况下满足不同边缘设备上的动态拖放约束。基于图像分类和对象检测的实验表明，与曾经使用的所有压缩方法（例如US-NET和MUTUALNET）（较少的Flops更好1-2％）具有更高的准确性，并且与常规修剪方法相同的精度甚至更高的准确性（72.6％vs. 72.6％vs. 70.5％vs. 70.5％vsibilenetv2 periomilenetv2 persialenetv2 persialenetv2 perifition conse 170 mfrops上的效率均高得多）。
We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network structures close to winner tickets in the early training stage considering the effect of input resolution during the pruning process. In structure searching stage, we utilize cosine similarity to measure the similarity of the pruning mask to get high-quality network structures with low energy and time consumption. After structure searching stage, our proposed method randomly sample the compact structures with different pruning rates and input resolution to achieve joint optimization. Ultimately, we can obtain a cohort of compact networks adaptive to various resolution to meet dynamic FLOPs constraints on different edge devices with only once training. The experiments based on image classification and object detection show that OFARPruning has a higher accuracy than the once-for-all compression methods such as US-Net and MutualNet (1-2% better with less FLOPs), and achieve the same even higher accuracy as the conventional pruning methods (72.6% vs. 70.5% on MobileNetv2 under 170 MFLOPs) with much higher efficiency.
