TypeError: tuple indices must be integers or slices, not tuple

[yangboz]

Search before asking

• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Training

Bug

Traceback (most recent call last):
File "/home/xibaoda/yolov5_ws/train.py", line 664, in
main(opt)
File "/home/xibaoda/yolov5_ws/train.py", line 559, in main
train(opt.hyp, opt, device, callbacks)
File "/home/xibaoda/yolov5_ws/train.py", line 347, in train
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
File "/home/xibaoda/yolov5_ws/utils/loss.py", line 125, in call
tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
File "/home/xibaoda/yolov5_ws/utils/loss.py", line 205, in build_targets
r = t[..., 4:6] / anchors[:, None] # wh ratio
TypeError: tuple indices must be integers or slices, not tuple

Environment

No response

Minimal Reproducible Example

CUDA_VISIBLE_DEVICES=0 python train.py --weight=yolov5s.pt --data=dataset4xyz.yaml --img=480

Additional

loss.py

dataset4xyz.yaml copy from default [dataset.yaml]
python 3.9
torch 1.12.1
torchvision 0.13.1

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

👋 Hello @yangboz, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

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

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• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@yangboz there might be something wrong with your model yaml or dataset format. See Train Custom Data tutorial to get started:

Tutorials

• Train Custom Data  🚀 RECOMMENDED
• Tips for Best Training Results  ☘️
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• Multi-GPU Training
• PyTorch Hub 🌟 NEW
• TFLite, ONNX, CoreML, TensorRT Export 🚀
• Test-Time Augmentation (TTA)
• Model Ensembling
• Model Pruning/Sparsity
• Hyperparameter Evolution
• Transfer Learning with Frozen Layers
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Good luck 🍀 and let us know if you have any other questions!

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv5 🚀 resources:

• Wiki – https://github.com/ultralytics/yolov5/wiki
• Tutorials – https://docs.ultralytics.com/yolov5
• Docs – https://docs.ultralytics.com

Access additional Ultralytics ⚡ resources:

• Ultralytics HUB – https://ultralytics.com/hub
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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!

[HaHalr]

你好，请问解决了这个问题吗

[glenn-jocher]

@HaHalr 你好！看起来你是在寻找有关之前讨论的问题的解决方案。如果你遇到的是特定的错误消息或问题，我建议首先检查你的代码是否最新，并确认你遵循了所有的数据准备和训练步骤。如果问题仍然存在，请提供更具体的错误描述和代码上下文，这样我们可以更准确地帮助你。感谢你对YOLOv5的支持！🌟

[HaHalr]

@glenn-jocher Hello Author, I have followed the data preparation and training steps, but I still find that the problem exists. I am at a loss on how to deal with it and have not found any relevant solutions, so I am seeking help. Below is the error information prompted after I executed train.py:

home/liu/anaconda3/yes/envs/myolov5/lib/python3.7/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Model Summary: 363 layers, 7760090 parameters, 7760090 gradients, 23.9 GFLOPS

Transferred 354/450 items from ./yolov5s.pt
Scaled weight_decay = 0.0005625000000000001
Optimizer groups: 77 .bias, 79 conv.weight, 73 other
train: Scanning 'data/citys/detdata/labels/train.cache' images and labels... 2975 found, 0 missing, 1 empty, 118 corrupt
val: Scanning 'data/citys/detdata/labels/val.cache' images and labels... 500 found, 0 missing, 3 empty, 11 corrupted: 10
Plotting labels...
Found 500 images in the folder ./data/citys/leftImg8bit/val
BaseDataset: base_size 1024, crop_size (832, 416)
Random scale low: 0.65, high: 3, sample_std: 25
Found 2975 images in the folder ./data/citys/leftImg8bit/train
Image sizes 832 train, 832 test
Using 8 dataloader workers
Logging results to runs/train/exp54
Starting training for 200 epochs...
accumulate: 4

 Epoch   gpu_mem       box       obj       cls     total       seg    labels  img_size

0%| | 0/159 [00:12<?, ?it/s]
0%| | 0/159 [00:12<?, ?it/s]
Traceback (most recent call last):
File "train.py", line 635, in
train(hyp, opt, device, tb_writer)
File "train.py", line 365, in train
loss, loss_items = compute_loss(pred[0], targets.to(device)) # loss scaled by batch_size
File "/media/liu/Liu/code/multiyolov5/utils/loss.py", line 118, in call
tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
File "/media/liu/Liu/code/multiyolov5/utils/loss.py", line 186, in build_targets
r = t[:, :, 4:6] / anchors[:, None] # 元
TypeError: tuple indices must be integers or slices, not tuple

. I hope you can give me some direction or method to solve this problem. I would be very grateful.

[glenn-jocher]

@HaHalr Hi there! It looks like the issue is related to improper indexing or data types when handling the anchors in your loss.py. This type of error usually happens when the expected object structure is different from what is actually provided.

Please ensure that anchors is a tensor. If it's being loaded as a different datatype (e.g., a list or a numpy array), you might need to convert it to a PyTorch tensor before performing operations on it. You can convert anchors to a tensor using:

anchors = torch.tensor(anchors).float().to(device)  # Assuming you have a device defined

Also, double-check the shape and type of t and anchors just before the line causing the error to ensure they are compatible for the operations you're trying to perform.

If the problem persists, it might be helpful to print out the types and shapes of variables involved in the operation causing the error right before it occurs. This can give you more insight into why the type error is happening.

Keep going, debugging issues like this can be tricky but you're on the right path! 🚀

[HaHalr]

@glenn-jocher Dear author, thank you for your valuable suggestions. After repeatedly checking the loss.py file, I found two errors and have corrected them. I am very grateful for your recent responses. I would like to share the joy of solving the problem with you. Thank you once again!

[glenn-jocher]

@HaHalr That's fantastic news! 🎉 I'm glad to hear you were able to solve the issues by making those corrections in the loss.py file. Thanks for sharing your success - it's always rewarding to see problems being solved, and your perseverance is commendable. Keep up the great work, and don't hesitate to reach out if you run into further questions or need assistance down the road. Happy coding! 😊