  Channel pruning is formulated as a neural architecture search (NAS) problem
recently. However, existing NAS-based methods are challenged by huge
computational cost and inflexibility of applications. How to deal with multiple
sparsity constraints simultaneously and speed up NAS-based channel pruning are
still open challenges. In this paper, we propose a novel Accurate and Automatic
Channel Pruning (AACP) method to address these problems. Firstly, AACP
represents the structure of a model as a structure vector and introduces a
pruning step vector to control the compressing granularity of each layer.
Secondly, AACP utilizes Pruned Structure Accuracy Estimator (PSAE) to speed up
the performance estimation process. Thirdly, AACP proposes Improved
Differential Evolution (IDE) algorithm to search the optimal structure vector
effectively. Because of IDE, AACP can deal with FLOPs constraint and model size
constraint simultaneously and efficiently. Our method can be easily applied to
various tasks and achieve state of the art performance. On CIFAR10, our method
reduces $65\%$ FLOPs of ResNet110 with an improvement of $0.26\%$ top-1
accuracy. On ImageNet, we reduce $42\%$ FLOPs of ResNet50 with a small loss of
$0.18\%$ top-1 accuracy and reduce $30\%$ FLOPs of MobileNetV2 with a small
loss of $0.7\%$ top-1 accuracy. The source code will be released after
publication.
