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_...
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
trainer.callback_metrics.update(apply_to_collection(callback_metrics,np.ndarray,lambdax:torch.tensor(x))) callback_metrics_bytes=extra["callback_metrics_bytes"] callback_metrics=torch.load(io.BytesIO(callback_metrics_bytes)) trainer.callback_metrics.update(callback_metrics) ...
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*...
Pytorch常用创建Tensor方法总结 2019-12-20 15:23 −1、import from numpy / list 方法:torch.from_numpy(ndarray) 常见的初始化有torch.tensor和torch.Tensor ... JiangXiaoKun 0 13402 Pytorch个人心得(一)---Tensor基本使用 2019-12-18 21:11 −最近在学习Pytorch,在这里分享一些心得,其实如果也是入门的...
ndarray) return UnquantizedTensor(self.ndarray.astype(dtype)) def to_ggml(self) -> Self: return self def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: r = self.ndarray.shape[0] // 3 return UnquantizedTensor(permute(self.ndarray[r *...
return False def qkv_concat_hf2mg(qkv_weights: np.ndarray, num_heads, n_kv_heads, hidden_size): """ convert qkv_concat weight with huggingface format to megatron format.""" qkv_dim = len(qkv_weights.shape) if qkv_dim == 2: w, h = qkv_weights.shape ...
bins = torch.tensor(bins, device=probs.device) TypeError: can't convert np.ndarray of type numpy.uint16. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool. ...