first commit

README.md

 # mobilenet_prune
+## 模型介绍
+基于torch-pruning对mobilenetv2、mobilenetv3、efficientnet进行剪枝。
+## 模型结构
+模型结构见官方论文：
+EfficientNet
+https://arxiv.org/pdf/1905.11946.pdf
+mobilenetv2
+https://arxiv.org/pdf/1801.04381.pdf
+mobilenetv3
+https://arxiv.org/pdf/1905.02244.pdf
+## 数据集
+torchvision.datasets.CIFAR10
+## 剪枝及微调
+### 环境配置
+在[光源](https://www.sourcefind.cn/#/service-details)可拉取剪枝及微调的docker镜像，在[光合开发者社区](https://cancon.hpccube.com:65024/4/main/)可下载torch安装包。mobilenet_prune推荐的镜像如下：
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04-py37-latest
+```
+获取最新的dtk并安装依赖
+```
+cd /opt
+wget http://10.0.50.210:8000/jenkins/rocm/23.04.1/centos7/DTK-23.04.1-rc4-centos7-x86_64.tar.gz
+tar -zxvf DTK-23.04.1-rc4-centos7-x86_64.tar.gz
+source /opt/dtk-23.04.1/env.sh
+cd mobilenet_prune
+pip3 install -r requirements.txt
+```
+
+### 剪枝
+efficientnet
+```
+python3 main.py --mode prune --model efficientnet --batch-size 128 --restore ./checkpoints/efficientnet.pt --dataset cifar10  --method l1 --speed-up 2.11 --global-pruning
+```
+mobilenetv2
+```
+python3 main.py --mode prune --model mobilenetv2 --batch-size 128 --restore ./checkpoints/mobilenetv2.pt --dataset cifar10  --method l1 --speed-up 2.11 --global-pruning
+```
+mobilenetv3
+```
+python3 main.py --mode prune --model mobilenetv3 --batch-size 128 --restore ./checkpoints/mobilenetv3.pt --dataset cifar10  --method l1 --speed-up 2.11 --global-pruning
+```
+### 测试
+efficientnet
+```
+python3 main.py --mode test --model efficientnet --batch-size 128 --restore ./run/cifar10/prune/cifar10-global-l1-efficientnet/cifar10_efficientnet_l1.pt --dataset cifar10  --method l1 --speed-up 2.11 --global-pruning
+```
+mobilenetv2
+```
+python3 main.py --mode test --model mobilenetv2 --batch-size 128 --restore ./run/cifar10/prune/cifar10-global-l1-mobilenetv2/cifar10_mobilenetv2_l1.pt --dataset cifar10  --method l1 --speed-up 2.11 --global-pruning
+```
+mobilenetv3
+```
+python3 main.py --mode test --model mobilenetv3 --batch-size 128 --restore ./run/cifar10/prune/cifar10-global-l1-mobilenetv3/cifar10_mobilenetv3_l1.pt --dataset cifar10  --method l1 --speed-up 2.11 --global-pruning
+```
+## 性能和准确率数据
+method l1
+| model | Base Acc | Pruned Acc | infer time(ms)| pruned infer time(ms)| 
+| :------: | :------: |:------: |:------: |:------: |
+| efficientnet | 0.9300 | 0.9248 | 0.00023220938958693296 | 0.00023220938958693296 | 
+| mobilenetv2 | 0.9222 | 0.9205 | 0.00018807348320260643 | 0.00018807348320260643 | 
+| mobilenetv3 | 0.9034 | 0.8963 | 0.00019642823620233683 | 0.00019642823620233683 | 
+
+method lamp
+| model | Base Acc | Pruned Acc | infer time(ms)| pruned infer time(ms)| 
+| :------: | :------: |:------: |:------: |:------: |
+| efficientnet | 0.9300 | 0.9256 | 0.00023166481370572002 | 0.00023166481370572002 | 
+| mobilenetv2 | 0.9222 | 0.9225 | 0.00021445175451226533 | 0.00021445175451226533 | 
+| mobilenetv3 | 0.9034 | 0.9044 | 0.0001933462681947276 | 0.0001933462681947276 | 
+
+method group_norm
+| model | Base Acc | Pruned Acc | infer time(ms)| pruned infer time(ms)| 
+| :------: | :------: |:------: |:------: |:------: |
+| efficientnet | 0.9300 | 0.8977 | 0.00022248145109042525 | 0.00022248145109042525 | 
+| mobilenetv2 | 0.9222 | 0.8953 | 0.00020749805739615112 | 0.00020749805739615112 | 
+| mobilenetv3 | 0.9034 | 0.8852 | 0.00019550215750932694 | 0.00019550215750932694 | 
+
+## 源码仓库及问题反馈
+https://developer.hpccube.com/codes/modelzoo/mobilenet_prune
+## 参考
+* [Torch-Pruning](https://github.com/VainF/Torch-Pruning)
 

engine/__init__.py

+from . import models, utils

engine/models/__init__.py

+from . import cifar
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engine/models/cifar/__init__.py

+from . import (
+    mobilenetv2,
+    mobilenetv3,
+    efficientnet,
+)
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