在尝试将NumPy数组转换为张量(tensor)时遇到“unsupported object type int”错误,通常意味着NumPy数组中的数据类型不是张量库(如PyTorch或TensorFlow)所期望的。基于你提供的提示,我将逐步解释如何解决这个问题。 1. 确认NumPy数组的数据类型 首先,我们需要检查NumPy数组的数据类型。这可以通过numpy.ndarray.dtype属性来...
Use Tensor.cpu(),我尝试将 intrinsic_normal 改成 intrinsic_normal.cuda().data.cpu().numpy(),继续报新的错: 'numpy.ndarray' object has no attribute 'cuda' 参考'numpy.ndarray' object has no attribute 'cuda' , 将 intrinsic_normal 转化成tensor类型 intrinsic_normal = torch.tensor(intrinsic_...
torch.Tensor(predictions), torch.Tensor(labels) TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool. The model was able to finish 1 epoch and print it'...
I've just noticed that s = torch.Size(np.array([1, 2, 3])) type(s[0]) returns <class 'numpy.int64'> whereas s = torch.Size(torch.tensor([1, 2, 3])) type(s[0]) gives a int. These two things are not interchangeable, yet it seems np.ndarray...
'numpy.ndarray' object has no attribute 'cuda' 1. 参考'numpy.ndarray' object has no attribute 'cuda', 将 intrinsic_normal 转化成tensor类型 intrinsic_normal = torch.tensor(intrinsic_normal).cuda().data.cpu().numpy() 1. 成功解决
TypeError:can't convertCUDAtensor to numpy.Use Tensor.cpu()to copy the tensor to host memory first. 意思是:如果想把CUDA tensor格式的数据改成numpy时,需要先将其转换成cpu float-tensor随后再转到numpy格式。 numpy不能读取CUDA tensor 需要将它转化为 CPU tensor 将predict.data.numpy()改为predict.data...
def convert_to_tensor(self, sample): tensor_sample = {} for k, v in sample.items(): if v is None: continue elif isinstance(v, str): tensor_sample[k] = v elif isinstance(v, np.ndarray): if len(v.shape) == 3: tensor_sample[k] = torch.from_numpy(np.transpose(v, [2, 0,...
ndarray, top_k:int = 5): """ Posprocess model results. This function applied sofrmax on output tensor and returns specified top_k number of labels with highest probability Parameters: output_tensor (np.ndarray): model output tensor with probabilities top_k (int, *optional...
:param np.ndarray batch: The batch to concatenate :param int device: The device to send to :param Tuple[int,int] padding: The padding to use :return: (inputs, targets) :rtype (torch.Tensor, torch.Tensor) """x, t = convert.concat_examples(batch, padding=padding) ...
🐛 Bug I ran into the following error when trying to convert an integer numpy ndarray to a torch tensor: >>> torch.from_numpy(b) Traceback (most recent call last): File "", line 1, in TypeError: can't convert np.ndarray of type numpy.int3...