>>>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 = 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 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 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))
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)
]
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