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.

中文翻译：

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深度神经网络的条件自动通道修剪


通道修剪已成为在资源受限设备上部署深度模型的主要压缩方法之一。大多数通道修剪方法通常对模型的所有层使用固定的压缩率，但这可能不是最佳的。为了解决这个问题，给定特定的目标压缩率，可以通过一些自动化方法搜索每一层的最佳压缩率。然而，当我们考虑多个压缩率时，这些方法必须多次重复通道修剪过程，每个速率一次，这可能是不必要的且效率低下。为了解决这个问题，我们提出了一种条件自动通道修剪（CACP）方法，该方法通过单通道修剪过程同时生成不同压缩率下的压缩模型。具体来说，CACP以一组压缩率和原始模型作为输入，并输出满足所考虑的压缩率的可行压缩模型。为了学习CACP，我们将逐层通道剪枝过程转化为马尔可夫决策过程（MDP），在其中我们寻求解决一系列决策问题。基于MDP，我们开发了一个具有深度确定性策略梯度（DDPG）的强化学习（RL）框架来学习最优策略。为了满足优化问题中的约束项，我们设计了一种约束保证方法，该方法引导智能体通过限制动作空间来搜索满足计算约束的压缩模型。对 CIFAR-10 和 ImageNet 数据集的大量实验证明了我们的方法相对于现有方法的优越性。