类型错误：无法将 cuda:0 设备类型张量转换为 numpy。首先使用 Tensor.cpu() 将张量复制到主机内存（使用 yolact 边缘进行分割）

Question

我正在 yolac Edge 上运行分段。我正在尝试使用我自己的算法找到掩模的最小和最大 x 和 y 像素坐标。我正在尝试将元组的值转换为 numpy。但是我收到以下错误

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

代码

    xmin = []
    xmax = []
    y = []
    print(np.shape(t[3]))
    print(type(t[3][:][:][:]))
    #row = (t[3][1][360][:]==1).nonzero(as_tuple=True) 
    for i in range (0, 2):
        t_cpu = t[3].clone().detach().cpu()
        horizontal_translation = torch.where(t[3][i][:][:]==1)
        print(horizontal_translation)
        horizontal_translation_numpy = np.asarray(horizontal_translation[1])
        x_min = np.amin(horizontal_translation_numpy)
        x_max = np.amax(horizontal_translation_numpy)
        np.append(xmin,x_min)
        np.append(xmax, x_max)
    print(xmin)
    print(xmax)

注意：t是默认程序输出的pytorch张量，其中包含t[3]中的掩码数据。我该如何解决？

输出：

torch.Size([2, 720, 1280])
<class 'torch.Tensor'>
(tensor([105, 105, 105,  ..., 503, 503, 503]), tensor([427, 428, 429,  ..., 468, 469, 470]))
Traceback (most recent call last):
  File "eval.py", line 1303, in <module>
    evaluate(net, dataset)
  File "eval.py", line 928, in evaluate
    evalimage(net, inp, out, detections=detections, image_id="0")
  File "eval.py", line 621, in evalimage
    img_numpy = prep_display(preds, frame, None, None, undo_transform=False)
  File "eval.py", line 198, in prep_display
    horizontal_translation_numpy = np.asarray(horizontal_translation[1])
  File "/home/nvidia/.local/lib/python3.6/site-packages/numpy/core/_asarray.py", line 83, in asarray
    return array(a, dtype, copy=False, order=order)
  File "/home/nvidia/.local/lib/python3.6/site-packages/torch/tensor.py", line 480, in __array__
    return self.numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

Answer 1

这应该有效

xmin = []
xmax = []
y = []
print(np.shape(t[3]))
print(type(t[3][:][:][:]))
#row = (t[3][1][360][:]==1).nonzero(as_tuple=True) 
for i in range (0, 2):
    t_cpu = t[3].clone().detach().cpu()
    horizontal_translation = torch.where(t[3][i][:][:]==1)
    print(horizontal_translation)
    horizontal_translation_numpy = horizontal_translation.cpu().numpy()
    x_min = np.amin(horizontal_translation_numpy)
    x_max = np.amax(horizontal_translation_numpy)
    np.append(xmin,x_min)
    np.append(xmax, x_max)
print(xmin)
print(xmax)