Conditional Automated Channel Pruning for Deep Neural Networks

Channel pruning has become one of the predominant compression methods to deploy deep models on resource-constrained devices. Most channel pruning methods often use a fixed compression rate for all the layers of the model, which, however, may not be optimal. To address this issue, given a specific target compression rate, one can search for the optimal compression rate for each layer via some automated methods. Nevertheless, when we consider multiple compression rates, these methods have to repeat the channel pruning process multiple times, once for each rate, which can be unnecessary and inefficient. To tackle the problem, we propose a Conditional Automated Channel Pruning (CACP) method which simultaneously produces compressed models under different compression rates through a single channel pruning process. Specifically, CACP takes a set of compression rates and the original model as its input, and outputs the feasible compressed models that satisfy the considered compression rates. To learn CACP, we cast the layer-by-layer channel pruning process into a Markov decision process (MDP), in which we seek to solve a series of decision-making problems. Based on MDP, we develop a reinforcement learning (RL) framework with deep deterministic policy gradient (DDPG) to learn the optimal policy. To satisfy the constraint items in the optimization problem, we design a constraint-guaranteed method, which guides the agent to search for compressed models that satisfy the computational constraints by limiting the action space. Extensive experiments on CIFAR-10 and ImageNet datasets demonstrate the superiority of our method over existing methods.