Network compression has been widely studied since it is able to reduce the
memory and computation cost during inference. However, previous methods seldom
deal with complicated structures like residual connections, group/depth-wise
convolution and feature pyramid network, where channels of multiple layers are
coupled and need to be pruned simultaneously. In this paper, we present a
general channel pruning approach that can be applied to various complicated
structures. Particularly, we propose a layer grouping algorithm to find coupled
channels automatically. Then we derive a unified metric based on Fisher
information to evaluate the importance of a single channel and coupled
channels. Moreover, we find that inference speedup on GPUs is more correlated
with the reduction of memory rather than FLOPs, and thus we employ the memory
reduction of each channel to normalize the importance. Our method can be used
to prune any structures including those with coupled channels. We conduct
extensive experiments on various backbones, including the classic ResNet and
ResNeXt, mobile-friendly MobileNetV2, and the NAS-based RegNet, both on image
classification and object detection which is under-explored. Experimental
results validate that our method can effectively prune sophisticated networks,
boosting inference speed without sacrificing accuracy.

本文提出了一种通道剪枝的方法，可以应用于各种复杂的结构，包括具有耦合通道的结构，在各种主干网络上进行了大量的实验，证明该方法可以有效地提高推断速度而不影响准确性。同时纳入了 GPU 推断速度关联性比 FLOPs 更高的记忆占用量的考量。