  Channel pruning is one of the major compression approaches for deep neural
networks. While previous pruning methods have mostly focused on identifying
unimportant channels, channel pruning is considered as a special case of neural
architecture search in recent years. However, existing methods are either
complicated or prone to sub-optimal pruning. In this paper, we propose a
pruning framework that adaptively determines the number of each layer's
channels as well as the wights inheritance criteria for sub-network. Firstly,
evaluate the importance of each block in the network based on the mean of the
scaling parameters of the BN layers. Secondly, use the bisection method to
quickly find the compact sub-network satisfying the budget. Finally, adaptively
and efficiently choose the weight inheritance criterion that fits the current
architecture and fine-tune the pruned network to recover performance. AdaPruner
allows to obtain pruned network quickly, accurately and efficiently, taking
into account both the structure and initialization weights. We prune the
currently popular CNN models (VGG, ResNet, MobileNetV2) on different image
classification datasets, and the experimental results demonstrate the
effectiveness of our proposed method. On ImageNet, we reduce 32.8% FLOPs of
MobileNetV2 with only 0.62% decrease for top-1 accuracy, which exceeds all
previous state-of-the-art channel pruning methods. The code will be released.
