can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first

[WestbrookZero]

when i start to train , An error is as follows:

[github-actions]

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[hehe03]

Hello @WestbrookZero , I've run into the same problem, and I'm wondering how did you reslove it, thanks!

[oukohou]

+1 same problem, what's the workaround?

[glenn-jocher]

@hehe91 @oukohou If you believe you have a reproducible issue, we suggest you close this issue and raise a new one using the 🐛 Bug Report template, providing screenshots and a minimum reproducible example to help us better understand and diagnose your problem. Thank you!

[oukohou]

@glenn-jocher well, no bother, that's a tiny carelesses of old-versioned codes, just simply update the repo can fix it.
In case someone still uses the old codes, a tiny modification can fix it:
in utils/general.py's output_to_target function, just add one more type assert:

def output_to_target(output, width, height):
    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
    if isinstance(output, torch.Tensor):
        output = output.cpu().numpy()

    targets = []
    for i, o in enumerate(output):
        if o is not None:
            # sometimes output can be a list of tensor, so here ensure the type again, this fixes the error.
            if isinstance(o, torch.Tensor):
                o = o.cpu().numpy()
            for pred in o:
                box = pred[:4]
                w = (box[2] - box[0]) / width
                h = (box[3] - box[1]) / height
                x = box[0] / width + w / 2
                y = box[1] / height + h / 2
                conf = pred[4]
                cls = int(pred[5])

                targets.append([i, cls, x, y, w, h, conf])

    return np.array(targets)

@hehe91 hope this helps.
and @glenn-jocher thanks for your great work again!

[jingruhou]

@glenn-jocher well, no bother, that's a tiny carelesses of old-versioned codes, just simply update the repo can fix it. In case someone still uses the old codes, a tiny modification can fix it: in utils/general.py's output_to_target function, just add one more type assert:

def output_to_target(output, width, height):
    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
    if isinstance(output, torch.Tensor):
        output = output.cpu().numpy()

    targets = []
    for i, o in enumerate(output):
        if o is not None:
            # sometimes output can be a list of tensor, so here ensure the type again, this fixes the error.
            if isinstance(o, torch.Tensor):
                o = o.cpu().numpy()
            for pred in o:
                box = pred[:4]
                w = (box[2] - box[0]) / width
                h = (box[3] - box[1]) / height
                x = box[0] / width + w / 2
                y = box[1] / height + h / 2
                conf = pred[4]
                cls = int(pred[5])

                targets.append([i, cls, x, y, w, h, conf])

    return np.array(targets)

@hehe91 hope this helps. and @glenn-jocher thanks for your great work again!

thanks，resolved my question.