  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.
