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We propose an efficient once-for-all budgeted pruning framework (OFARPruning)to find many compact network structures close to winner tickets in the earlytraining stage considering the effect of input resolution during the pruningprocess. In structure searching stage, we utilize cosine similarity to measurethe similarity of the pruning mask to get high-quality network structures withlow energy and time consumption. After structure searching stage, our proposedmethod randomly sample the compact structures with different pruning rates andinput resolution to achieve joint optimization. Ultimately, we can obtain acohort of compact networks adaptive to various resolution to meet dynamic FLOPsconstraints on different edge devices with only once training. The experimentsbased on image classification and object detection show that OFARPruning has ahigher accuracy than the once-for-all compression methods such as US-Net andMutualNet (1-2% better with less FLOPs), and achieve the same even higheraccuracy as the conventional pruning methods (72.6% vs. 70.5% on MobileNetv2under 170 MFLOPs) with much higher efficiency.

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