YOLOv8实战-模型剪枝_yolov8剪枝

        模型剪枝是用在模型的一种优化技术，旨在减少神经网络中不必要的参数，从而降低模型的复杂性和计算负载，进一步提高模型的效率。

        模型剪枝的流程：约束训练（constained training）、剪枝（prune）、回调训练（finetune)

        本篇主要记录自己YOLOv8模型剪枝的全过程，主要参考：YOLOv8剪枝全过程

目 录

一、约束训练（constrained training）

1、参数设置

2、稀疏训练

二、剪枝（prune）

三、回调训练（finetune)

1、代码修改

2、再训练

---

一、约束训练（constrained training）

1、参数设置

         设置./ultralytics/cfg/default.yaml中的amp=False

2、稀疏训练

        主要方式：在BN层添加L1正则化

        具体步骤：在./ultralytics/engine/trainer.py中添加以下内容：

                # Backward
                self.scaler.scale(self.loss).backward()
 
                # ========== added（新增） ==========
                # 1 constrained training
                l1_lambda = 1e-2 * (1 - 0.9 * epoch / self.epochs)
                for k, m in self.model.named_modules():
                    if isinstance(m, nn.BatchNorm2d):
                        m.weight.grad.data.add_(l1_lambda * torch.sign(m.weight.data))
                        m.bias.grad.data.add_(1e-2 * torch.sign(m.bias.data))
                # ========== added（新增） ==========
 
                # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
                if ni - last_opt_step >= self.accumulate:
                    self.optimizer_step()
                    last_opt_step = ni

        然后启动训练（/yolov8/train.py）：

from ultralytics import YOLO
 
model = YOLO('yolov8n.yaml')
 
results = model.train(data='./data/data_nc5/data_nc5.yaml', batch=8, epochs=300, save=True)

二、剪枝（prune）

        一该部分选用上一步训练得到的模型./runs/detect/train2/weight/last.pt进行剪枝处理。在/yolov8/下新建文件prune.py，具体内容如下：

from ultralytics import YOLO
import torch
from ultralytics.nn.modules import Bottleneck, Conv, C2f, SPPF, Detect
 
# Load a model
yolo = YOLO("./runs/detect/train2/weights/last.pt")
model = yolo.model
 
ws = []
bs = []
 
for name, m in model.named_modules():
    if isinstance(m, torch.nn.BatchNorm2d):
        w = m.weight.abs().detach()
        b = m.bias.abs().detach()
        ws.append(w)
        bs.append(b)
        # print(name, w.max().item(), w.min().item(), b.max().item(), b.min().item())
 
# keep
factor = 0.8
ws = torch.cat(ws)
threshold = torch.sort(ws, descending=True)[0][int(len(ws) * factor)]
print(threshold)
 
 
def prune_conv(conv1: Conv, conv2: Conv):
    gamma = conv1.bn.weight.data.detach()
    beta = conv1.bn.bias.data.detach()
    keep_idxs = []
    local_threshold = threshold
    while len(keep_idxs) < 8:
        keep_idxs = torch.where(gamma.abs() >= local_threshold)[0]
        local_threshold = local_threshold * 0.5
    n = len(keep_idxs)
    # n = max(int(len(idxs) * 0.8), p)
    # print(n / len(gamma) * 100)
    # scale = len(idxs) / n
    conv1.bn.weight.data = gamma[keep_idxs]
    conv1.bn.bias.data = beta[keep_idxs]
    conv1.bn.running_var.data = conv1.bn.running_var.data[keep_idxs]
    conv1.bn.running_mean.data = conv1.bn.running_mean.data[keep_idxs]
    conv1.bn.num_features = n
    conv1.conv.weight.data = conv1.conv.weight.data[keep_idxs]
    conv1.conv.out_channels = n
 
    if conv1.conv.bias is not None:
        conv1.conv.bias.data = conv1.conv.bias.data[keep_idxs]
 
    if not isinstance(conv2, list):
        conv2 = [conv2]
 
    for item in conv2:
        if item is not None:
            if isinstance(item, Conv):
                conv = item.conv
            else:
                conv = item
            conv.in_channels = n
            conv.weight.data = conv.weight.data[:, keep_idxs]
 
 
def prune(m1, m2):
    if isinstance(m1, C2f):  # C2f as a top conv
        m1 = m1.cv2
 
    if not isinstance(m2, list):  # m2 is just one module
        m2 = [m2]
 
    for i, item in enumerate(m2):
        if isinstance(item, C2f) or isinstance(item, SPPF):
            m2[i] = item.cv1
 
    prune_conv(m1, m2)
 
 
for name, m in model.named_modules():
    if isinstance(m, Bottleneck):
        prune_conv(m.cv1, m.cv2)
 
seq = model.model
for i in range(3, 9):
    if i in [6, 4, 9]: continue
    prune(seq[i], seq[i + 1])
 
detect: Detect = seq[-1]
last_inputs = [seq[15], seq[18], seq[21]]
colasts = [seq[16], seq[19], None]
for last_input, colast, cv2, cv3 in zip(last_inputs, colasts, detect.cv2, detect.cv3):
    prune(last_input, [colast, cv2[0], cv3[0]])
    prune(cv2[0], cv2[1])
    prune(cv2[1], cv2[2])
    prune(cv3[0], cv3[1])
    prune(cv3[1], cv3[2])
 
for name, p in yolo.model.named_parameters():
    p.requires_grad = True
 
yolo.val()  # 剪枝模型进行验证 yolo.val(workers=0)
yolo.export(format="onnx")  # 导出为onnx文件
# yolo.train(data="./data/data_nc5/data_nc5.yaml", epochs=100)  # 剪枝后直接训练微调
 
torch.save(yolo.ckpt, "./runs/detect/train2/weights/prune.pt")
print("done")

其中，factor=0.8 表示的是保持率，factor越小，裁剪的就越多，一般不建议裁剪太多。

        运行prune.py，可得到剪枝后的模型prune.pt，保存在./runs/detect/train2/weight/中。同文件夹下，还有last.onnx，可以看到onnx文件的大小比剪枝前变小了，具体结构（onnx模型结构查看）也和剪枝前的onnx相比有了轻微变化。

三、回调训练（finetune)

1、代码修改

        首先，将先前在./ultralytics/engine/trainer.py中添加的L1正则化部分注释掉：

                # Backward
                self.scaler.scale(self.loss).backward()
 
                # # ========== added（新增） ==========
                # # 1 constrained training
                # l1_lambda = 1e-2 * (1 - 0.9 * epoch / self.epochs)
                # for k, m in self.model.named_modules():
                #     if isinstance(m, nn.BatchNorm2d):
                #         m.weight.grad.data.add_(l1_lambda * torch.sign(m.weight.data))
                #         m.bias.grad.data.add_(1e-2 * torch.sign(m.bias.data))
                # # ========== added（新增） ==========
 
                # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
                if ni - last_opt_step >= self.accumulate:
                    self.optimizer_step()
                    last_opt_step = ni

        然后，在该文件第543行左右添加代码 “self.model = weights” ：

    def setup_model(self):
        """Load/create/download model for any task."""
        if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
            return
 
        model, weights = self.model, None
        ckpt = None
        if str(model).endswith(".pt"):
            weights, ckpt = attempt_load_one_weight(model)
            cfg = weights.yaml
        else:
            cfg = model
        self.model = self.get_model(cfg=cfg, weights=weights, verbose=RANK == -1)  # calls Model(cfg, weights)
        # ========== added（新增） ==========
        # 2 finetune 回调训练
        self.model = weights
        # ========== added（新增） ==========
        return ckpt

2、再训练

         利用已经剪枝好的模型prune.pt，我们再次启动训练（/yolov8/train.py）：

from ultralytics import YOLO
 
model = YOLO('./runs/detect/train5/weights/prune.pt')
results = model.train(data='./data/data_nc5/data_nc5.yaml', batch=8, epochs=100, save=True)

注意，这里把model改成了"prune.pt"，而不是原来的"yolov8n.yaml"

        训练后新的模型保存在“./runs/detect/train3/weight/”中。后面可按需要进一步进行模型的推理和部署。

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