yolov5--权重稀疏化教程

yolov5--权重稀疏化教程

模型压缩分为两大类：模型连接剪枝，针对已训练的模型，将其中不重要的结构去除；权重稀疏化，训练过程中将不重要的权重置为0，使得权重分布更稀疏。权重稀疏化算法最著名的便是2015年Han发表的https://arxiv.org/abs/1510.00149，将裁剪、权值共享和量化、编码等方式运用在模型压缩上。

yolov5权重稀疏化

yolov5中的权重稀疏化做法,调用prune函数，去除30%的权重。

# Initialize/load model and set device
    training = model is not None
    if training:  # called by train.py
        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model

        half &= device.type != 'cpu'  # half precision only supported on CUDA
        model.half() if half else model.float()
from util.torch_utils import prune
prune(model,0.3)

稀疏化的结果，我们可以观察到，修剪后，我们的模型的稀疏率为30%，这意味着模型权重参数的30%在nn.Conv2d层中等于0。推理时间基本上保持不变，而模型的AP和AR分数略有减少。

val: data=./data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
YOLOv5  v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)

Fusing layers... 
Model Summary: 476 layers, 87730285 parameters, 0 gradients

val: Scanning '../datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2846.03it/s]
val: New cache created: ../datasets/coco/val2017.cache
               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100% 157/157 [02:30<00:00,  1.05it/s]
                 all       5000      36335      0.746      0.626       0.68       0.49
Speed: 0.1ms pre-process, 22.4ms inference, 1.4ms NMS per image at shape (32, 3, 640, 640)  # <--- baseline speed

evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...
...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504  # <--- baseline mAP
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.688
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.546
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.382
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.628
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.681  # <--- baseline mAR
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.826

 https://arxiv.org/abs/1608.04493 

《Dynamic Network Surgery for Efficient DNNs》中介绍了一种动态的模型裁剪方法，包括以下两个过程：pruning和splicing，pruning就是将认为不重要的weight去掉，但是往往无法直观的判断哪些weight是否重要，因此在这里增加了一个splicing的过程，将那些重要的被裁掉的weight再恢复回来，将重要的结构修补回来。该算法采取了剪枝与嫁接相结合、训练与压缩相同步的策略完成网络压缩任务。通过网络嫁接 *** 作的引入，避免了错误剪枝所造成的性能损失，从而在实际 *** 作中更好地逼近网络压缩的理论极限。