huihui308
commented
6 months ago I also meet the same issue when convert yolov9, how to solve it?
Overriding model.yaml nc=80 with nc=7
Overriding model.yaml anchors with anchors=3
from n params module arguments
0 -1 1 0 models.common.Silence []
1 -1 1 1856 models.common.Conv [3, 64, 3, 2]
2 -1 1 73984 models.common.Conv [64, 128, 3, 2]
3 -1 1 212864 models.common.RepNCSPELAN4 [128, 256, 128, 64, 1]
4 -1 1 164352 models.common.ADown [256, 256]
5 -1 1 847616 models.common.RepNCSPELAN4 [256, 512, 256, 128, 1]
6 -1 1 656384 models.common.ADown [512, 512]
7 -1 1 2857472 models.common.RepNCSPELAN4 [512, 512, 512, 256, 1]
8 -1 1 656384 models.common.ADown [512, 512]
9 -1 1 2857472 models.common.RepNCSPELAN4 [512, 512, 512, 256, 1]
10 -1 1 656896 models.common.SPPELAN [512, 512, 256]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 7] 1 0 models.common.Concat [1]
13 -1 1 3119616 models.common.RepNCSPELAN4 [1024, 512, 512, 256, 1]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 5] 1 0 models.common.Concat [1]
16 -1 1 912640 models.common.RepNCSPELAN4 [1024, 256, 256, 128, 1]
17 -1 1 164352 models.common.ADown [256, 256]
18 [-1, 13] 1 0 models.common.Concat [1]
19 -1 1 2988544 models.common.RepNCSPELAN4 [768, 512, 512, 256, 1]
20 -1 1 656384 models.common.ADown [512, 512]
21 [-1, 10] 1 0 models.common.Concat [1]
22 -1 1 3119616 models.common.RepNCSPELAN4 [1024, 512, 512, 256, 1]
23 5 1 131328 models.common.CBLinear [512, [256]]
24 7 1 393984 models.common.CBLinear [512, [256, 512]]
25 9 1 656640 models.common.CBLinear [512, [256, 512, 512]]
26 0 1 1856 models.common.Conv [3, 64, 3, 2]
27 -1 1 73984 models.common.Conv [64, 128, 3, 2]
28 -1 1 212864 models.common.RepNCSPELAN4 [128, 256, 128, 64, 1]
29 -1 1 164352 models.common.ADown [256, 256]
30 [23, 24, 25, -1] 1 0 models.common.CBFuse [[0, 0, 0]]
31 -1 1 847616 models.common.RepNCSPELAN4 [256, 512, 256, 128, 1]
32 -1 1 656384 models.common.ADown [512, 512]
33 [24, 25, -1] 1 0 models.common.CBFuse [[1, 1]]
34 -1 1 2857472 models.common.RepNCSPELAN4 [512, 512, 512, 256, 1]
35 -1 1 656384 models.common.ADown [512, 512]
36 [25, -1] 1 0 models.common.CBFuse [[2]]
37 -1 1 2857472 models.common.RepNCSPELAN4 [512, 512, 512, 256, 1]
38[31, 34, 37, 16, 19, 22] 1 21556682 models.yolo.DualDDetect [7, [512, 512, 512, 256, 512, 512]]
yolov9-c summary: 962 layers, 51013450 parameters, 51013418 gradients, 238.9 GFLOPs
Traceback (most recent call last):
File "yolo9c.py", line 48, in <module>
model.state_dict()[k] += ckpt['model'].state_dict()[kr]
RuntimeError: The size of tensor a (3) must match the size of tensor b (64) at non-singleton dimension 1
WongKinYiu
AndreSchakkal
AnhDai1997
I run reparameterization.ipynb with custom model but get error. How to fix this? thank you
this is my config
this is gelan-e.yaml
YOLOv9
parameters
nc: 35 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple
activation: nn.LeakyReLU(0.1)
activation: nn.ReLU()
anchors
anchors: 3
gelan backbone
backbone: [ [-1, 1, Silence, []],
conv down
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
conv down
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
elan-1 block
[-1, 1, RepNCSPELAN4, [256, 128, 64, 2]], # 3
avg-conv down
[-1, 1, ADown, [256]], # 4-P3/8
elan-2 block
[-1, 1, RepNCSPELAN4, [512, 256, 128, 2]], # 5
avg-conv down
[-1, 1, ADown, [512]], # 6-P4/16
elan-2 block
[-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]], # 7
avg-conv down
[-1, 1, ADown, [1024]], # 8-P5/32
elan-2 block
[-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]], # 9
routing
[1, 1, CBLinear, [[64]]], # 10 [3, 1, CBLinear, [[64, 128]]], # 11 [5, 1, CBLinear, [[64, 128, 256]]], # 12 [7, 1, CBLinear, [[64, 128, 256, 512]]], # 13 [9, 1, CBLinear, [[64, 128, 256, 512, 1024]]], # 14
conv down fuse
[0, 1, Conv, [64, 3, 2]], # 15-P1/2 [[10, 11, 12, 13, 14, -1], 1, CBFuse, [[0, 0, 0, 0, 0]]], # 16
conv down fuse
[-1, 1, Conv, [128, 3, 2]], # 17-P2/4 [[11, 12, 13, 14, -1], 1, CBFuse, [[1, 1, 1, 1]]], # 18
elan-1 block
[-1, 1, RepNCSPELAN4, [256, 128, 64, 2]], # 19
avg-conv down fuse
[-1, 1, ADown, [256]], # 20-P3/8 [[12, 13, 14, -1], 1, CBFuse, [[2, 2, 2]]], # 21
elan-2 block
[-1, 1, RepNCSPELAN4, [512, 256, 128, 2]], # 22
avg-conv down fuse
[-1, 1, ADown, [512]], # 23-P4/16 [[13, 14, -1], 1, CBFuse, [[3, 3]]], # 24
elan-2 block
[-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]], # 25
avg-conv down fuse
[-1, 1, ADown, [1024]], # 26-P5/32 [[14, -1], 1, CBFuse, [[4]]], # 27
elan-2 block
[-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]], # 28 ]
gelan head
head: [
elan-spp block
[28, 1, SPPELAN, [512, 256]], # 29
up-concat merge
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 25], 1, Concat, [1]], # cat backbone P4
elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 2]], # 32
up-concat merge
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 22], 1, Concat, [1]], # cat backbone P3
elan-2 block
[-1, 1, RepNCSPELAN4, [256, 256, 128, 2]], # 35 (P3/8-small)
avg-conv-down merge
[-1, 1, ADown, [256]], [[-1, 32], 1, Concat, [1]], # cat head P4
elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 2]], # 38 (P4/16-medium)
avg-conv-down merge
[-1, 1, ADown, [512]], [[-1, 29], 1, Concat, [1]], # cat head P5
elan-2 block
[-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]], # 41 (P5/32-large)
detect
[[35, 38, 41], 1, DDetect, [nc]], # Detect(P3, P4, P5) ]