PyTorch是一个开源的机器学习框架,它提供了丰富的工具和库,用于构建和训练深度神经网络模型。在PyTorch中,torch.int32和torch.long都是数据类型,用于表示整数。 torch.int32是32位有符号整数类型,可以存储范围在-2^31到2^31-1之间的整数。它通常用于处理需要较小范围整数的任务,例如计数器、索引等。
torch.int8 : np.dtype(np.int8), torch.int16 : np.dtype(np.int16), torch.short : np.dtype(np.int16), torch.int32: np.dtype(np.int32), torch.int : np.dtype(np.int32), torch.int64 : np.dtype(np.int64), torch.long : np.dtype(np.int64), torch.float16 : np.dtype(np.flo...
to(torch.int64), num_classes=opt.classification_number)) #打包训练数据集 Data_iter_train = dataloader.DataLoader(dataset=Dataset_train, batch_size=opt.batch_size, shuffle=True) Dataset_test = dataset.TensorDataset(torch.tensor(x_test), one_hot(torch.tensor(y_test).to(torch.int64), num_...
Wait(Int64, Int32) Causes the current thread to wait until it is awakened, typically by being notified or interrupted, or until a certain amount of real time has elapsed. (Inherited from Object) Wait(Int64) Causes the current thread to wait until it is awakened, typically by being...
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_...
int64 numpy转化为tensor importtorchimportnumpyasnp b= np.array([1,2,3])# b = b.astype(np.float)print(b.dtype) c = torch.from_numpy(b)print(c.dtype) AI代码助手复制代码 int32 torch.int32 可以看到,torch默认int型是64位的,numpy默认int型是32位的 ...
int64 tensor([1., 2., 3.]) torch.float32 torch.float64 方式-2 import torch a = torch.tensor([1,2,3],dtype=torch.int32) print(a) a = torch.tensor([1,2,3],dtype=torch.int64) print(a) a = torch.tensor([1,2,3],dtype=torch.float32) print(a) a = torch.tensor([1,2,3...
When converting fromint32toint64throughtensor_object.long()(final graphhere), we don't get any warning. However, when we try NOT to convert (final graphhere), we get the following warning (which is practically an error for us since we want the model to run inference): ...
# 需要导入模块: import torch [as 别名]# 或者: from torch importint[as 别名]def_graph_fn_call(self, inputs):ifself.backend =="python"orget_backend() =="python":ifisinstance(inputs, list): inputs = np.asarray(inputs)returninputs.astype(dtype=util.convert_dtype(self.to_dtype, to=...
numpy使⽤astype转化数据类型，float默认转化为64位，可以使⽤np.float32指定为32位 #numpy转化float类型 a= np.array([1,2,3])a = a.astype(np.float)print(a)print(a.dtype)[1. 2. 3.]float64 不要使⽤a.dtype指定数据类型，会使数据丢失 #numpy转化float类型 b= np.array([1,2,3])b....