>>>深度学习Tricks，第一时间送达<<<

目录

RepVGG——极简架构，SOTA性能！！！

（一）前沿介绍

1.RepVGGBlock模块

2.相关实验结果

（二）YOLOv5/YOLOv7改进之结合​RepVGG

1.配置common.py文件

2.配置yolo.py文件

3.配置yolov5/yolov7_​​RepVGG.yaml文件

RepVGG——极简架构，SOTA性能！！！

（一）前沿介绍

论文题目：RepVGG: Making VGG-style ConvNets Great Again

论文地址：https://arxiv.org/abs/2101.03697

代码地址：https://github.com/DingXiaoH/RepVGG

作者提出了一个简单但功能强大的卷积神经网络架构，该架构推理时候具有类似于VGG的骨干结构，该主体仅由3 x 3卷积和ReLU堆叠组成，而训练时候模型采用多分支拓扑结构。 训练和推理架构的这种解耦是通过结构重参数化技术实现的，因此该模型称为RepVGG。 在ImageNet上，据我们所知，RepVGG的top-1准确性达到80％以上，这是老模型首次实现该精度。 在NVIDIA 1080Ti GPU上，RepVGG模型的运行速度比ResNet-50快83％，比ResNet-101快101％，并且具有更高的精度，与诸如EfficientNet和RegNet的最新模型相比，RepVGG显示出良好的精度-速度权衡。效果对比如下图所示。

该论文主要有以下三点贡献：

1.更快

除了Winograd conv带来的加速之外，FLOPs和速度之间的差异可以归因于两个重要因素，它们对速度有很大影响，但FLOPs并未考虑这些因素：内存访问成本（MAC）和并行度。 另一方面，在相同的FLOPs下，具有高并行度的模型可能比具有低并行度的模型快得多。因此简单的推理结构可以避免多分支的零碎计算。

2.更省内存

如图（A）所示的Residual模块，假设卷积层不改变channel的数量，那么在主分支和shortcut分支上都要保存各自的特征图或者称Activation，那么在add操作前占用的内存大概是输入Activation的两倍，而图（B）的Plain结构占用内存始终不变。

VGG是一个直筒性单路结构，由上述分析可知，单路结构会占有更少的内存，因为不需要保存其中间结果，同时，单路架构非常快，因为并行度高。同样的计算量，大而整的运算效率远超小而碎的运算。

3.更加灵活

作者在论文中提到了模型优化的剪枝问题，对于多分支的模型，结构限制较多剪枝很麻烦，而对于Plain结构的模型就相对灵活很多，剪枝也更加方便。

1.RepVGGBlock模块

2.相关实验结果

（二）YOLOv5/YOLOv7改进之结合​RepVGG

改进方法和其他模块一样，分三步走：

1.配置common.py文件

#RepVGGBlock
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 = deployself.groups = groupsself.in_channels = in_channelspadding_11 = padding - kernel_size // 2self.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 Noneself.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 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 + biasiddef _pad_1x1_to_3x3_tensor(self, kernel1x1):if kernel1x1 is None:return 0else:return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])def _fuse_bn_tensor(self, branch):if branch is None:return 0, 0if isinstance(branch, nn.Sequential):kernel = branch.conv.weightrunning_mean = branch.bn.running_meanrunning_var = branch.bn.running_vargamma = branch.bn.weightbeta = branch.bn.biaseps = branch.bn.epselse:assert isinstance(branch, nn.BatchNorm2d)if not hasattr(self, 'id_tensor'):input_dim = self.in_channels // self.groupskernel_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] = 1self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)kernel = self.id_tensorrunning_mean = branch.running_meanrunning_var = branch.running_vargamma = branch.weightbeta = branch.biaseps = branch.epsstd = (running_var + eps).sqrt()t = (gamma / std).reshape(-1, 1, 1, 1)return kernel * t, beta - running_mean * gamma / stddef forward(self, inputs):if hasattr(self, 'rbr_reparam'):return self.nonlinearity(self.se(self.rbr_reparam(inputs)))if self.rbr_identity is None:id_out = 0else:id_out = self.rbr_identity(inputs)return self.nonlinearity(self.se(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out))def fusevggforward(self, x):return self.nonlinearity(self.rbr_dense(x))

2.配置yolo.py文件

加入RepVGGBlock模块。

3.配置yolov5/yolov7_​​RepVGG.yaml文件

# anchors
anchors:- [10,13, 16,30, 33,23]  # P3/8- [30,61, 62,45, 59,119]  # P4/16- [116,90, 156,198, 373,326]  # P5/32# YOLOv5 backbone
backbone:# [from, number, module, args][[-1, 1, RepVGGBlock, [64, 3, 2]],  # 0-P1/2[-1, 1, RepVGGBlock, [64, 3, 2]], # 1-P2/4[-1, 1, RepVGGBlock, [64, 3, 1]], # 2-P2/4[-1, 1, RepVGGBlock, [128, 3, 2]], # 3-P3/8[-1, 3, RepVGGBlock, [128, 3, 1]],[-1, 1, RepVGGBlock, [256, 3, 2]], # 5-P4/16[-1, 13, RepVGGBlock, [256, 3, 1]],[-1, 1, RepVGGBlock, [512, 3, 2]], # 7-P4/16]
# YOLOv5 head
head:[[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]],  # cat backbone P4[-1, 1, C3, [256, False]],  # 11[-1, 1, Conv, [128, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]],  # cat backbone P3[-1, 1, C3, [128, False]],  # 15 (P3/8-small)[-1, 1, Conv, [128, 3, 2]],[[-1, 12], 1, Concat, [1]],  # cat head P4[-1, 1, C3, [256, False]],  # 18 (P4/16-medium)[-1, 1, Conv, [256, 3, 2]],[[-1, 8], 1, Concat, [1]],  # cat head P5[-1, 1, C3, [512, False]],  # 21 (P5/32-large)[[15, 18, 21], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

关于YOLO算法改进及论文投稿可关注并留言博主的CSDN/QQ

>>>一起交流！互相学习！共同进步！<<<