最新创新点改进推荐
-💡统一使用 YOLO 代码框架, 结合不同模块来构建不同的YOLO目标检测模型。
🔥 《芒果书》系列改进专栏内的改进文章,均包含多种模型改进方式,均适用于 YOLOv3
、 YOLOv4
、 YOLOR
、 YOLOX
、 YOLOv5
、 YOLOv7
、 YOLOv8
改进(重点)!!!
🔥 专栏创新点教程 均有不少同学反应和我说已经在自己的数据集上有效涨点啦!! 包括COCO数据集也能涨点
所有文章博客均包含 改进源代码部分,一键训练即可
🔥 对应专栏订阅的越早,就可以越早使用 原创创新点
去改进模型,抢先一步
芒果书 点击以下链接 查看文章目录详情🔗
- 💡🎈☁️:一、CSDN原创《芒果改进YOLO高阶指南》强烈改进涨点推荐!📚推荐指数:🌟🌟🌟🌟🌟
- 💡🎈☁️:二、CSDN原创YOLO进阶 | 《芒果改进YOLO进阶指南》改进涨点推荐!📚推荐指数:🌟🌟🌟🌟🌟
- 💡🎈☁️:三、CSDN独家全网首发专栏 | 《目标检测YOLO改进指南》改进涨点推荐!推荐指数:🌟🌟🌟🌟🌟
本篇是《RepVGG结构🚀》的修改 演示
使用YOLOv7网络🚀作为示范,可以无缝加入到 YOLOv7、YOLOX、YOLOR、YOLOv4、Scaled_YOLOv4、YOLOv3等一系列YOLO算法模块
文章目录
*
– 最新创新点改进推荐
– 点击查看详情:[YOLOv5改进、YOLOv7改进|YOLO改进超过50种注意力机制,全篇共计30万字(内附改进源代码),原创改进50种Attention注意力机制和Transformer自注意力机制](https://blog.csdn.net/qq_38668236/article/details/129137111)
– 本篇是《RepVGG结构🚀》的修改 演示
– 1.RepVGG模型理论部分
–
+ 模型定义
+ 结构重参数化让VGG再次伟大
– 2.在YOLOv7中加入RepVGG模块🚀
–
+ 新增YOLOv7的yaml配置文件
+ common.py配置
+ yolo.py配置
+ 训练yolov7_RepVGGBlock模型
+ 推理过程效果
1.RepVGG模型理论部分
论文参考:最新RepVGG结构: Paper
; 模型定义
我们所说的”VGG式”指的是:
- 没有任何分支结构。即通常所说的plain或feed-forward架构。
- 仅使用3×3卷积。
- 仅使用ReLU作为激活函数。
结构重参数化让VGG再次伟大
相比于各种多分支架构(如ResNet,Inception,DenseNet,各种NAS架构),近年来VGG式模型鲜有关注,主要自然是因为性能差。例如,有研究[1]认为,ResNet性能好的一种解释是ResNet的分支结构(shortcut)产生了一个大量子模型的隐式ensemble(因为每遇到一次分支,总的路径就变成两倍),单路架构显然不具备这种特点。
; 2.在YOLOv7中加入RepVGG模块🚀
使用YOLOv7算法🚀作为演示,模块可以无缝插入到YOLOv7、YOLOv5、YOLOv4、Scaled_YOLOv4、YOLOv3、YOLOR等一系列YOLO算法中
新增YOLOv7的yaml配置文件
首先增加以下yolov7_RepVGG.yaml文件,作为改进演示
代码
YOLOv7 🚀, GPL-3.0 license
parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 1.0 # layer channel multiple
anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
yolov7 backbone by yoloair
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 1, RepVGGBlock, [128, 3, 2]], # 5-P4/16
[-1, 1, Conv, [256, 3, 2]],
[-1, 1, MP, []],
[-1, 1, Conv, [128, 1, 1]],
[-3, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 16-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, MP, []],
[-1, 1, Conv, [256, 1, 1]],
[-3, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 2]],
[[-1, -3], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [1024, 1, 1]],
[-1, 1, MP, []],
[-1, 1, Conv, [512, 1, 1]],
[-3, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 2]],
[[-1, -3], 1, Concat, [1]],
[-1, 1, C3C2, [1024]],
[-1, 1, Conv, [256, 3, 1]],
]
yolov7 head by yoloair
head:
[[-1, 1, SPPCSPC, [512]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[31, 1, Conv, [256, 1, 1]],
[[-1, -2], 1, Concat, [1]],
[-1, 1, C3C2, [128]],
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[18, 1, Conv, [128, 1, 1]],
[[-1, -2], 1, Concat, [1]],
[-1, 1, C3C2, [128]],
[-1, 1, MP, []],
[-1, 1, Conv, [128, 1, 1]],
[-3, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 2]],
[[-1, -3, 44], 1, Concat, [1]],
[-1, 1, C3C2, [256]],
[-1, 1, MP, []],
[-1, 1, Conv, [256, 1, 1]],
[-3, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 2]],
[[-1, -3, 39], 1, Concat, [1]],
[-1, 3, C3C2, [512]],
检测头 -----------------------------
[49, 1, RepConv, [256, 3, 1]],
[55, 1, RepConv, [512, 3, 1]],
[61, 1, RepConv, [1024, 3, 1]],
[[62,63,64], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
]
当需要修改yaml配置文件,将xx模块 加到你想加入的位置(层数);
首先基于一个可以成功运行的.yaml模型配置文件,进行新增或者减少层数 之后,那么该层网络后续的层的编号都会发生改变,对应的一些层都需要针对性的修改,以匹配通道和层数的关系
common.py配置
在./models/common.py文件中增加以下模块,直接复制即可
class RepVGGBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3,
stride=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False):
super(RepVGGBlock, self).__init__()
self.deploy = deploy
self.groups = groups
self.in_channels = in_channels
padding_11 = padding - kernel_size // 2
self.nonlinearity = nn.SiLU()
# self.nonlinearity = nn.ReLU()
if use_se:
self.se = SEBlock(out_channels, internal_neurons=out_channels // 16)
else:
self.se = nn.Identity()
if deploy:
self.rbr_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=True,
padding_mode=padding_mode)
else:
self.rbr_identity = nn.BatchNorm2d(
num_features=in_channels) if out_channels == in_channels and stride == 1 else None
self.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, groups=groups)
self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
padding=padding_11, groups=groups)
# print('RepVGG Block, identity = ', self.rbr_identity)
def switch_to_deploy(self):
if hasattr(self, 'rbr_1x1'):
kernel, bias = self.get_equivalent_kernel_bias()
self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.conv.in_channels, out_channels=self.rbr_dense.conv.out_channels,
kernel_size=self.rbr_dense.conv.kernel_size, stride=self.rbr_dense.conv.stride,
padding=self.rbr_dense.conv.padding, dilation=self.rbr_dense.conv.dilation, groups=self.rbr_dense.conv.groups, bias=True)
self.rbr_reparam.weight.data = kernel
self.rbr_reparam.bias.data = bias
for para in self.parameters():
para.detach_()
self.rbr_dense = self.rbr_reparam
# self.__delattr__('rbr_dense')
self.__delattr__('rbr_1x1')
if hasattr(self, 'rbr_identity'):
self.__delattr__('rbr_identity')
if hasattr(self, 'id_tensor'):
self.__delattr__('id_tensor')
self.deploy = True
def get_equivalent_kernel_bias(self):
kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
def _pad_1x1_to_3x3_tensor(self, kernel1x1):
if kernel1x1 is None:
return 0
else:
return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
def _fuse_bn_tensor(self, branch):
if branch is None:
return 0, 0
if isinstance(branch, nn.Sequential):
kernel = branch.conv.weight
running_mean = branch.bn.running_mean
running_var = branch.bn.running_var
gamma = branch.bn.weight
beta = branch.bn.bias
eps = branch.bn.eps
else:
assert isinstance(branch, nn.BatchNorm2d)
if not hasattr(self, 'id_tensor'):
input_dim = self.in_channels // self.groups
kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)
for i in range(self.in_channels):
kernel_value[i, i % input_dim, 1, 1] = 1
self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
kernel = self.id_tensor
running_mean = branch.running_mean
running_var = branch.running_var
gamma = branch.weight
beta = branch.bias
eps = branch.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta - running_mean * gamma / std
def forward(self, inputs):
if self.deploy:
return self.nonlinearity(self.rbr_dense(inputs))
if hasattr(self, 'rbr_reparam'):
return self.nonlinearity(self.se(self.rbr_reparam(inputs)))
if self.rbr_identity is None:
id_out = 0
else:
id_out = self.rbr_identity(inputs)
return self.nonlinearity(self.se(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out))
其中缺少的C3C2模块 需要补充,在Git中
yolo.py配置
然后找到./models/yolo.py文件下里的parse_model函数,将类名加入进去
在 models/yolo.py文件夹下
- parse_model函数中
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):
内部- 对应位置 下方只需要增加
RepVGGBlock
模块
参考代码
elif m is RepVGGBlock:
c1, c2 = ch[f], args[0]
if c2 != no: # if not output
c2 = make_divisible(c2 * gw, 8)
args = [c1, c2, *args[1:]]
训练yolov7_RepVGGBlock模型
python train.py --cfg yolov7_RepVGGBlock.yaml
推理过程效果
以下使用单独测试的RepVGG模块(基于v5)作为参考:
训练的时候代码
Model Summary: 375 layers, 5574845 parameters, 5574845 gradients, 16.2 GFLOPs
推理时候的代码
Model Summary: 284 layers, 5390365 parameters, 1567680 gradients, 15.7 GFLOPs
推理模型的数据相比于训练模型的数据
参数量、计算量、推理时间均有所减少
参考文献: 理论部分来自RepVGG作者的知乎文章:https://zhuanlan.zhihu.com/p/344324470
Original: https://blog.csdn.net/qq_38668236/article/details/126715391
Author: 芒果汁没有芒果
Title: 改进YOLOv7系列:25.YOLOv7 加入RepVGG模型结构,重参数化 极简架构
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