关于concatFusionFactor的问题 #22
Description
您好，我的数据是768*768,。请问我把concat替换为concatFusionFactor后，运行yolo.py，报如下的错误。应该还要改哪些地方呢？
D:\ProgramData\Anaconda3\envs\py38\python.exe D:/1bishe/yolov5-master2/models/yolo.py
models\yolo: cfg=yolov5s-biformer2-scale.yaml, batch_size=1, device=0, profile=False, line_profile=False, test=False
YOLOv5 2023-12-11 Python-3.8.18 torch-1.13.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3060 Laptop GPU, 6144MiB)
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 1055744 models.Biformer.BiLevelRoutingAttention [512]
10 -1 1 656896 models.common.SPPF [512, 512, 5]
11 -1 1 131584 models.common.Conv [512, 256, 1, 1]
12 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
13 [-1, 6] 1 1 models.common.ConcatFusionFactor [1]
14 -1 1 427520 models.common.C3 [768, 256, 1, False]
15 -1 1 33024 models.common.Conv [256, 128, 1, 1]
16 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
17 [-1, 4] 1 1 models.common.ConcatFusionFactor [1]
18 -1 1 107264 models.common.C3 [384, 128, 1, False]
19 -1 1 147712 models.common.Conv [128, 128, 3, 2]
20 [-1, 15] 1 1 models.common.ConcatFusionFactor [1]
21 -1 1 296448 models.common.C3 [256, 256, 1, False]
22 -1 1 590336 models.common.Conv [256, 256, 3, 2]
23 [-1, 11] 1 1 models.common.ConcatFusionFactor [1]
24 -1 1 1182720 models.common.C3 [512, 512, 1, False]
25 [18, 21, 24] 1 229245 Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Traceback (most recent call last):
File "D:/1bishe/yolov5-master2/models/yolo.py", line 391, in
model = Model(opt.cfg).to(device)
File "D:/1bishe/yolov5-master2/models/yolo.py", line 200, in init
m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward
File "D:/1bishe/yolov5-master2/models/yolo.py", line 199, in
forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
File "D:/1bishe/yolov5-master2/models/yolo.py", line 214, in forward
return self._forward_once(x, profile, visualize) # single-scale inference, train
File "D:/1bishe/yolov5-master2/models/yolo.py", line 125, in _forward_once
x = m(x) # run
File "D:\ProgramData\Anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "D:\1bishe\yolov5-master2\models\common.py", line 228, in forward
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
File "D:\ProgramData\Anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "D:\1bishe\yolov5-master2\models\common.py", line 68, in forward
return self.act(self.bn(self.conv(x)))
File "D:\ProgramData\Anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "D:\ProgramData\Anaconda3\envs\py38\lib\site-packages\torch\nn\modules\conv.py", line 463, in forward
return self._conv_forward(input, self.weight, self.bias)
File "D:\ProgramData\Anaconda3\envs\py38\lib\site-packages\torch\nn\modules\conv.py", line 459, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [128, 768, 1, 1], expected input[1, 512, 48, 48] to have 768 channels, but got 512 channels instead