零基础掌握yolov8剪枝

## 1. 环境要求 ```bash pip install -r requirements.txt ``` 测试环境: ``` Pytorch==1.12.1 Torchvision==0.13.1 ``` ## 2. YOLO v8 ```bash git clone https://github.com/ultralytics/ultralytics.git cp yolov8_pruning.py ultralytics/ cd ultralytics python yolov8_pruning.py ``` #### Screenshot for coco128 post-training: <img width="960" alt="image" src="https://user-images.githubusercontent.com/18592211/232287178-95825d66-c569-479d-8b6d-d433ff0d6739.png"> #### Outputs of yolov8_pruning.py: ``` DetectionModel( (model): Sequential( (0): Conv( (conv): Conv2d(3, 80, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): Conv( (conv): Conv2d(80, 160, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(160, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ... (2): Sequential( (0): Conv( (conv): Conv2d(640, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(320, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): Conv( (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(320, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (2): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1)) ) ) (dfl): DFL( (conv): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) ) ) ) ) DetectionModel( (model): Sequential( (0): Conv( (conv): Conv2d(3, 40, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(40, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): Conv( (conv): Conv2d(40, 80, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ... (2): Sequential( (0): Conv( (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(320, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): Conv( (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(320, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (2): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1)) ) ) (dfl): DFL( (conv): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False) ) ) ) ) Before Pruning: MACs=129.092051 G, #Params=68.229648 M After Pruning: MACs=41.741203 G, #Params=20.787528 M ```