torch.Tensor 默认整数类型是 int64 , 默认浮点数类型是 float64 数据类型转换可通过 mindspore.common.tensor中的 Tensor() 方法实现 example: import mindspore as ms from mindspore.common.tensor import Tensor a = ms.Tensor([1]) print("a.dtype: ",a.dtype) b = Tensor(a ,ms.float32) # 数据类型...
方法一:使用.float()方法 .float()方法是PyTorch提供的一个便捷方法,用于将张量转换为float32类型。 python import torch # 创建一个整数类型的张量 tensor = torch.tensor([1, 2, 3], dtype=torch.int64) # 使用.float()方法转换为float32类型 float_tensor = tensor.float() print(float_tensor) print(...
🐛 Describe the bug Hi there, I ran the following code on CPU or GPU, and observed that torch.tensor([0.01], dtype=torch.float16) * torch.tensor(65536, dtype=torch.float32) returns INF. The second scalar operand (torch.tensor(65536, dtype...
print(a, a.dtype) print(b, b.dtype) 【运行结果】 torch的浮点数与整数的默认数据类型 tensor([1, 2, 3]) torch.int64 tensor([1., 2., 3.]) torch.float32 1.2 dtype修改变量类型 a = torch.tensor([1,2,3], dtype=torch.int8) b = torch.tensor([1.,2.,3.], dtype=torch.float64)...
X = torch.arange(12, dtype=torch.float32).reshape((3,4)) Y = torch.tensor([[2.0, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]]) torch.cat((X,Y),dim=0),torch.cat((X,Y),dim=1) Out: (tensor([[ 0., 1., 2., 3.], ...
要将 float32 类型的张量转换为 int64 类型的张量,可以按以下方式操作: import torch # 创建一个示例的浮点数张量 float_tensor = torch.tensor([1.5, 2.7, 3.2], dtype=torch.float32) # 将浮点数张量转换为整数类型(int64) int_tensor = float_tensor.to(torch.int64) print("浮点数张量:", float_...
指定了Tensor的数据类型 (默认为:torch.float32) Tensor dtype的切换: data2 = th.ones(4, 2, dtype=th.float32) # 使用create-fucntion创建时指定dtype data_float32 = th.tensor([[1,2], [3,4]]).double() #强行cast到指定类型 data_float32_2 = th.tensor([[1,2], [3,4]]).to(th.dou...
x = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float64) y = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.int64) print(x + y) # tensor([[ 2., 4., 6.], # [ 8., 10., 12.]], dtype=torch.float64) 与C/C++ 一致,向上兼容数据类型。 另外值得一提...
dtype = torch.float64 long_dtype = torch.int64 device = torch.device('cpu')ifnotuse_cudaelsetorch.device(torch.cuda.device_count() -1) indices = torch.tensor(([0], [2]), dtype=long_dtype)ifuse_tensor_idxelse([0], [2])
🐛 Bug >>> import torch >>> import numpy >>> t = torch.tensor(numpy.float64()) >>> t.dtype torch.float32 Should be torch.float64. test_dataloader.py has test_numpy_scalars which was supposed to test for this, but that test historically ra...