Making changes to yolov3.yaml & understanding it
sabitatasnim opened this issue · 3 comments
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Question
I am experimenting with the yolov3 architecture to customize it for some custom hardware platform. To do this, I have been making some changes to the yaml file by adding layers such as convolutions, bottlenecks and upsampling. However, I am running into tensor mismatch problems. It is very possible that I am misunderstanding the conversion from the original .cfg file from a few revisions ago to the now present yaml files. Can someone explain how these yaml files work ?
The following shows the errors I am currently running into:
from n params module arguments
0 -1 1 928 models.common.Conv [3, 32, 3, 1]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 20672 models.common.Bottleneck [64, 64]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 164608 models.common.Bottleneck [128, 128]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 8 2627584 models.common.Bottleneck [256, 256]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 8 10498048 models.common.Bottleneck [512, 512]
9 -1 1 4720640 models.common.Conv [512, 1024, 3, 2]
10 -1 4 20983808 models.common.Bottleneck [1024, 1024]
11 -1 1 5245952 models.common.Bottleneck [1024, 1024, False]
12 -1 1 525312 models.common.Conv [1024, 512, 1, 1]
13 -1 1 4720640 models.common.Conv [512, 1024, 3, 1]
14 -1 1 525312 models.common.Conv [1024, 512, 1, 1]
15 -1 1 4720640 models.common.Conv [512, 1024, 3, 1]
16 -2 1 131584 models.common.Conv [512, 256, 1, 1]
17 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
18 [-1, 8] 1 0 models.common.Concat [1]
19 -1 1 1377792 models.common.Bottleneck [768, 512, False]
20 -1 1 1312256 models.common.Bottleneck [512, 512, False]
21 -1 1 131584 models.common.Conv [512, 256, 1, 1]
22 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
23 [-1, 8] 1 0 models.common.Concat [1]
24 -1 1 3539968 models.common.Conv [768, 512, 3, 1]
25 -2 1 98560 models.common.Conv [768, 128, 1, 1]
26 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
27 [-1, 6] 1 0 models.common.Concat [1]
28 -1 1 344832 models.common.Bottleneck [384, 256, False]
29 -1 2 656896 models.common.Bottleneck [256, 256, False]
30 [27, 22, 15] 1 425085 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [384, 256, 1024]]
Traceback (most recent call last):
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/train.py", line 635, in <module>
main(opt)
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/train.py", line 528, in main
train(opt.hyp, opt, device, callbacks)
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/train.py", line 124, in train
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/models/yolo.py", line 194, in __init__
m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/models/yolo.py", line 193, in <lambda>
forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/models/yolo.py", line 208, in forward
return self._forward_once(x, profile, visualize) # single-scale inference, train
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/models/yolo.py", line 120, in _forward_once
x = m(x) # run
File "/home/user/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/models/common.py", line 313, in forward
return torch.cat(x, self.d)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 32 but got size 16 for tensor number 1 in the list.
Traceback (most recent call last):
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/train.py", line 635, in <module>
main(opt)
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/train.py", line 528, in main
train(opt.hyp, opt, device, callbacks)
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/train.py", line 124, in train
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/models/yolo.py", line 194, in __init__
m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/models/yolo.py", line 193, in <lambda>
forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/models/yolo.py", line 208, in forward
return self._forward_once(x, profile, visualize) # single-scale inference, train
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/models/yolo.py", line 120, in _forward_once
x = m(x) # run
File "/home/user/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/user/Documents/dir1/dir2/ultralytics/yolov3/models/common.py", line 313, in forward
return torch.cat(x, self.d)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 32 but got size 16 for tensor number 1 in the list.
The following screenshot shows the changes I made to yolov3.yaml on lines 42 and 43:
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@sabitatasnim hi there,
It looks like there may be a mismatch of tensor sizes with the changes you've made to the yolov3.yaml file. Without seeing the specific changes you made to the file, it's a bit difficult to provide a definitive solution.
One potential approach to debugging this issue would be to carefully review the changes made on lines 42 and 43 in the yolov3.yaml file to ensure that the tensor sizes are consistent with the rest of the network architecture.
If you're still encountering issues, I would recommend consulting the YOLO community or the Ultralytics team for further assistance with customizing the YOLOv3 architecture for your hardware platform.
Hope this helps!