yolov5 backbone 更改为 mobilevit(即改即用)

在大佬的博客补充了一些小问题，按照如下修改，你的代码就能跑起来了

使用MobileViT替换YOLOv5主干网络

收费教程：YOLOv5更换骨干网络之 MobileViT-S / MobileViT-XS / MobileViT-XXS

知识储备

MobileViT模型简介

MobileViT、MobileViTv2、MobileViTv3学习笔记（自用）

MobileViTv1、MobileViTv2、MobileViTv3网络详解

准备工作：

我使用的是6.0 yolov5s

mobilevit

正式修改

1. 将mobilevit.py放在yolov5/models

2. 修改models/yolo.py

加入所有的模块，或者只加入MV2Block, MobileViTBlock

加入MV2Block, MobileViTBlock

3.修改yaml文件

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# Parameters
nc: 1 # 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 backbone
backbone:
  # [from, number, module, args] 640 x 640
#  [[-1, 1, Conv, [32, 6, 2, 2]],  # 0-P1/2  320 x 320
  [[-1, 1, Focus, [32, 3]],
   [-1, 1, MV2Block, [32, 1, 2]],  # 1-P2/4
   [-1, 1, MV2Block, [48, 2, 2]],  # 160 x 160
   [-1, 2, MV2Block, [48, 1, 2]],
   [-1, 1, MV2Block, [64, 2, 2]],  # 80 x 80
   [-1, 1, MobileViTBlock, [64,96, 2, 3, 2, 192]], # 5 out_dim,dim, depth, kernel_size, patch_size, mlp_dim
   [-1, 1, MV2Block, [80, 2, 2]],  # 40 x 40
   [-1, 1, MobileViTBlock, [80,120, 4, 3, 2, 480]], # 7
   [-1, 1, MV2Block, [96, 2, 2]],   # 20 x 20
   [-1, 1, MobileViTBlock, [96,144, 3, 3, 2, 576]], # 11-P2/4 # 9
  ]

# YOLOv5 head
head:
  [[-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 7], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [256, False]],  # 13

   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 5], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [128, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [128, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [256, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [512, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

1. 修改mobilevit.py

可以愉快的跑起来了！！！

END

谢谢观看，有用的话点个赞吧！

ADD

einops.EinopsError: Error while processing rearrange-reduction pattern “b d (h ph) (w pw) -> b (ph pw) (h w) d”.

Input tensor shape: torch.Size([1, 120, 42, 42]). Additional info: {‘ph’: 4, ‘pw’: 4}

1. 是因为输入输出不匹配造成

2. 记得关掉rect哦！一个是在参数里，另一个在下图。如果要在test或者val中跑，同样要改

特别感谢养乐多阿