我们提出了一个有效的“一劳永逸”的预算修剪框架（OFARPruning），以便在早期训练阶段考虑修剪过程中输入分辨率的影响，找到许多靠近获胜者门票的紧凑型网络结构。在结构搜索阶段，我们利用余弦相似度来测量修剪掩码的相似度，从而获得低能耗，低时间消耗的高质量网络结构。..

An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution

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.