RuntimeError: The size of tensor a (6) must match the size of tensor b (7) at non-singleton dimension 2

[YounghoJo01]

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Question

whdudgh@whdudgh-G5-KE:~/yolov5$ python3 load_dataset.py
Scanning /home/whdudgh/datasets/my_dataset/labels/train.cache... 1038 images, 12
Overriding model.yaml nc=80 with nc=3

             from  n    params  module                                  arguments                     

0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
2 -1 2 65280 models.common.C3 [96, 96, 2]
3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
4 -1 4 444672 models.common.C3 [192, 192, 4]
5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
6 -1 6 2512896 models.common.C3 [384, 384, 6]
7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
8 -1 2 4134912 models.common.C3 [768, 768, 2]
9 -1 1 1476864 models.common.SPPF [768, 768, 5]
10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 2 1182720 models.common.C3 [768, 384, 2, False]
14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 2 296448 models.common.C3 [384, 192, 2, False]
18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
24 [17, 20, 23] 1 32328 models.yolo.Detect [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
YOLOv5m summary: 291 layers, 20879400 parameters, 20879400 gradients, 48.2 GFLOPs

Processed Shapes: tensor([1080., 1920., 1080., 1920.], device='cuda:0')
Labels size: torch.Size([1, 3, 6])
Labels: tensor([[[0.00000, 2.00000, 0.35182, 0.43099, 0.03490, 0.01302],
[0.00000, 2.00000, 0.50338, 0.42891, 0.02656, 0.00990],
[0.00000, 2.00000, 0.55469, 0.42630, 0.02500, 0.01094]]], device='cuda:0')
targets shape: torch.Size([3, 5])
gain shape: torch.Size([7])
targets: tensor([[0.00000, 2.00000, 0.35182, 0.43099, 0.03490],
[0.00000, 2.00000, 0.50338, 0.42891, 0.02656],
[0.00000, 2.00000, 0.55469, 0.42630, 0.02500]], device='cuda:0')
Traceback (most recent call last):
File "load_dataset.py", line 73, in
loss, loss_items = compute_loss(outputs, labels)
File "/home/whdudgh/yolov5/utils/loss.py", line 144, in call
tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
File "/home/whdudgh/yolov5/utils/loss.py", line 240, in build_targets
t = targets * gain # shape(3,n,7)
RuntimeError: The size of tensor a (6) must match the size of tensor b (7) at non-singleton dimension 2

Additional

No response

[UltralyticsAssistant]

👋 Hello @YounghoJo01, thank you for reaching out and your interest in YOLOv5 🚀!

It looks like you've encountered a runtime error while working with tensor dimensions. Don't worry, an Ultralytics engineer will assist you shortly. In the meantime, could you please provide a minimum reproducible example (MRE) to help us better understand and debug the issue? This should include any relevant code snippets and specific configurations you’re using.

To ensure everything is set up correctly, please check that your environment meets the following requirements:

Requirements

Python>=3.8.0 with all requirements.txt installed, including PyTorch>=1.8. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 can be run in several verified environments with pre-installed dependencies:

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

While you wait, you might also be interested in trying out our latest model:

Introducing YOLOv8 🚀

Check out YOLOv8, our state-of-the-art model for 2023, featuring improvements in speed and accuracy for various tasks. Get started with:

pip install ultralytics

Thanks again for your patience and detailed report! 😊