Pocketflow的简介：https://pocketflow.github.io/
由两个modules组成：Learner module 负责利用随机hyperparameters压缩并fine-tuning，optimizer module 负责依据随机参数对应的结果确定最佳的压缩参数。Learn module 遍历的hyperparameter来自于：
| Name | Description |
|---|---|
ChannelPrunedLearner | channel pruning with LASSO-based channel selection (He et al., 2017) |
DisChnPrunedLearner | discrimination-aware channel pruning (Zhuang et al., 2018) |
WeightSparseLearner | weight sparsification with dynamic pruning schedule (Zhu & Gupta, 2017) |
UniformQuantLearner | weight quantization with uniform reconstruction levels (Jacob et al., 2018) |
UniformQuantTFLearner | weight quantization with uniform reconstruction levels and TensorFlow APIs |
NonUniformQuantLearner | weight quantization with non-uniform reconstruction levels (Han et al., 2016) |
压缩后可以快速训练收敛。
optimizer module 的策略来自于
Gaussian Processes (GP, Mockus, 1975), Tree-structured Parzen Estimator (TPE, Bergstra et al., 2013), and Deterministic Deep Policy Gradients (DDPG, Lillicrap et al., 2016) 以及当前hyperparameters的reward
安装Pocketflow：https://pocketflow.github.io/installation/
本地安装或在docker环境中安装
本地安装：
配置文件 path.conf 说明数据集路径(.tfrecord)以及预训练模型的路径
首先安装 anaconda 和 tensorflow，注意python 版本不能高于3.6
一句话就可训练：./scripts/run_local.sh nets/resnet_at_cifar10_run.py
docker环境安装：
官方推荐 horovod docker ，因其支持多GPU分布
一句话训练：./scripts/run_docker.sh nets/resnet_at_cifar10_run.py
压缩方法：
参考https://pocketflow.github.io/tutorial/
输出：*.tflite 文件
Step 1: 准备数据成TFRecord格式
Step 2: 根据上面的步骤训练或直接下载 .ckpt 模型 保存在 models
Step 3: 选择压缩方法 开始调优压缩后的模型
./scripts/run_local.sh nets/resnet_at_ilsvrc12_run.py \ --learner dis-chn-pruned
压缩方法与arg对应：
| Learner name | Learner class | Note |
|---|---|---|
full-prec | FullPrecLearner | No model compression |
channel | ChannelPrunedLearner | Channel pruning with LASSO-based channel selection (He et al., 2017) |
dis-chn-pruned | DisChnPrunedLearner | Discrimination-aware channel pruning (Zhuang et al., 2018) |
weight-sparse | WeightSparseLearner | Weight sparsification with dynamic pruning schedule (Zhu & Gupta, 2017) |
uniform | UniformQuantLearner | Weight quantization with uniform reconstruction levels (Jacob et al., 2018) |
uniform-tf | UniformQuantTFLearner | Weight quantization with uniform reconstruction levels and TensorFlow APIs |
non-uniform | NonUniformQuantLearner | Weight quantization with non-uniform reconstruction levels (Han et al., 2016) |
保存在models_dcp_eval文件夹
Step 3: 转化成 tflite 格式：
python tools/conversion/export_pb_tflite_models.py \ --model_dir models_dcp_eval
保存在 models_dcp_eval
)
