def parse_pruned_model

from yolov5prune.

commom.py中Concat_bifpn结构如下：

from yolov5prune.

yolov5s_bifpn.yaml结构如下：

parameters

nc: 2 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple

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, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, C3, [1024, False]], # 9
]

YOLOv5 head

head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1,6], 1, Concat_bifpn, [256,256]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13

[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat_bifpn, [128,128]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small)

[-1, 1, Conv, [512, 3, 2]], # 320, 640 #
[[-1, 6, 13], 1, Concat_bifpn, [256,256]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)

[-1, 1, Conv, [1024, 3, 2]], # 640, 1280 #
[[-1, 9], 1, Concat_bifpn, [512, 512]], # cat head P5 cat 20,20 #22
[-1, 3, C3, [1024, False]], # 25 (P5/32-large) # 1280, 1280 #23

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

from yolov5prune.

Hello @Hazel-Xiang , how did you finish the pruning? What is the code modification idea?

from yolov5prune.

你好，请问你有解决方法了吗？我现在也困在这里

from yolov5prune.