'list' object has no attribute 'shape'

[HouNAiL]

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/hounl/Data/yolov5/train.py", line 848, in
main(opt)
File "/home/hounl/Data/yolov5/train.py", line 623, in main
train(opt.hyp, opt, device, callbacks)
File "/home/hounl/Data/yolov5/train.py", line 383, in train
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
File "/data1/hounl/yolov5/utils/loss.py", line 139, in call
tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
File "/data1/hounl/yolov5/utils/loss.py", line 218, in build_targets
anchors, shape = self.anchors[i], p[i].shape
AttributeError: 'list' object has no attribute 'shape'

Environment

No response

Minimal Reproducible Example

for i in range(self.nl):
anchors, shape = self.anchors[i], p[i].shape
gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain

        # Match targets to anchors
        t = targets * gain  # shape(3,n,7)
        if nt:
            # Matches
            r = t[..., 4:6] / anchors[:, None]  # wh ratio
            j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"]  # compare
            # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
            t = t[j]  # filter

            # Offsets
            gxy = t[:, 2:4]  # grid xy
            gxi = gain[[2, 3]] - gxy  # inverse
            j, k = ((gxy % 1 < g) & (gxy > 1)).T
            l, m = ((gxi % 1 < g) & (gxi > 1)).T
            j = torch.stack((torch.ones_like(j), j, k, l, m))
            t = t.repeat((5, 1, 1))[j]
            offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
        else:
            t = targets[0]
            offsets = 0

        # Define
        bc, gxy, gwh, a = t.chunk(4, 1)  # (image, class), grid xy, grid wh, anchors
        a, (b, c) = a.long().view(-1), bc.long().T  # anchors, image, class
        gij = (gxy - offsets).long()
        gi, gj = gij.T  # grid indices

Additional

No response

Are you willing to submit a PR?

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

[github-actions]

👋 Hello @HouNAiL, 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>=3.8.0 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

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Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

[glenn-jocher]

Hello @HouNAiL,

Thank you for your detailed bug report and for your willingness to contribute a PR! The error you're encountering, 'list' object has no attribute 'shape', suggests that one of the elements in your list p is not a tensor, which is causing the issue when trying to access the shape attribute.

To help us investigate further, could you please provide a minimal reproducible example? This will allow us to replicate the issue on our end and identify the root cause more effectively. You can find guidelines for creating a minimal reproducible example here: Minimum Reproducible Example.

Additionally, please ensure that you are using the latest versions of torch and the YOLOv5 repository. Sometimes, issues are resolved in newer releases, and updating might resolve your problem.

Here's a quick checklist to help us move forward:

1. Minimal Reproducible Example: Provide a snippet of code that reproduces the issue.
2. Environment Details: Share the versions of torch, YOLOv5, and any other relevant libraries.
3. Latest Versions: Confirm that you have tested with the latest versions of torch and YOLOv5.

Once we have this information, we can dive deeper into the issue. Thank you for your cooperation and for being a part of the YOLO community!