YOLOv5：修改backbone为SPD-Conv
前言
前提条件
相关介绍
SPD-Conv
以下是使用Python实现SPD-Conv的简单例子，目的是方便大家理解SPD-Conv的操作。
import cv2
import torch
from torch import nn
############## SPD-Conv ##############
class space_to_depth(nn.Module):
# Changing the dimension of the Tensor
def __init__(self, dimension=1):
super().__init__()
self.d = dimension
def forward(self, x):
return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
############## SPD-Conv ##############
if __name__=="__main__":
img_tensor = torch.Tensor([[[[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12],
[ 13, 14, 15, 16]],
[[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12],
[ 13, 14, 15, 16]],
[[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12],
[ 13, 14, 15, 16]]]])
# print('img_tensor:',img_tensor)
print('img_tensor.shape:',img_tensor.shape)
spd = space_to_depth()
res = spd.forward(img_tensor)
# print('res:',res)
print('res.shape:',res.shape)
img_tensor.shape: torch.Size([1, 3, 4, 4])
res: tensor([[[[ 1., 3.],
[ 9., 11.]],
[[ 1., 3.],
[ 9., 11.]],
[[ 1., 3.],
[ 9., 11.]],
[[ 5., 7.],
[13., 15.]],
[[ 5., 7.],
[13., 15.]],
[[ 5., 7.],
[13., 15.]],
[[ 2., 4.],
[10., 12.]],
[[ 2., 4.],
[10., 12.]],
[[ 2., 4.],
[10., 12.]],
[[ 6., 8.],
[14., 16.]],
[[ 6., 8.],
[14., 16.]],
[[ 6., 8.],
[14., 16.]]]])
res.shape: torch.Size([1, 12, 2, 2])
YOLOv5修改backbone为SPD-Conv
修改common.py
将以下代码，添加进common.py。
############## SPD-Conv ##############
class space_to_depth(nn.Module):
# Changing the dimension of the Tensor
def __init__(self, dimension=1):
super().__init__()
self.d = dimension
def forward(self, x):
return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
############## SPD-Conv ##############
修改yolo.py
elif m is space_to_depth:
c2 = 4 * ch[f]
修改yolov5.yaml配置
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
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 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 1]], # 1
[-1,1,space_to_depth,[1]], # 2 -P2/4
[-1, 3, C3, [128]], # 3
[-1, 1, Conv, [256, 3, 1]], # 4
[-1,1,space_to_depth,[1]], # 5 -P3/8
[-1, 6, C3, [256]], # 6
[-1, 1, Conv, [512, 3, 1]], # 7-P4/16
[-1,1,space_to_depth,[1]], # 8 -P4/16
[-1, 9, C3, [512]], # 9
[-1, 1, Conv, [1024, 3, 1]], # 10-P5/32
[-1,1,space_to_depth,[1]], # 11 -P5/32
[-1, 3, C3, [1024]], # 12
[-1, 1, SPPF, [1024, 5]], # 13
]
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]], # 14
[-1, 1, nn.Upsample, [None, 2, 'nearest']], # 15
[[-1, 9], 1, Concat, [1]], # 16 cat backbone P4
[-1, 3, C3, [512, False]], # 17
[-1, 1, Conv, [256, 1, 1]], # 18
[-1, 1, nn.Upsample, [None, 2, 'nearest']], # 19
[[-1, 6], 1, Concat, [1]], # 20 cat backbone P3
[-1, 3, C3, [256, False]], # 21 (P3/8-small)
[-1, 1, Conv, [256, 3, 1]], # 22
[-1,1,space_to_depth,[1]], # 23 -P2/4
[[-1, 18], 1, Concat, [1]], # 24 cat head P4
[-1, 3, C3, [512, False]], # 25 (P4/16-medium)
[-1, 1, Conv, [512, 3, 1]], # 26
[-1,1,space_to_depth,[1]], # 27 -P2/4
[[-1, 14], 1, Concat, [1]], # 28 cat head P5
[-1, 3, C3, [1024, False]], # 29 (P5/32-large)
[[21, 25, 29], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
参考
[1] Raja Sunkara, Tie Luo. No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects. 2022
[2] https://github.com/LabSAINT/SPD-Conv
[3] https://github.com/ultralytics/yolov5.git
