(PyCFunction)(void(*)(void))THPVariable_arange,METH_VARARGS|METH_KEYWORDS|METH_STATIC,NULL},{"as_tensor",(PyCFunction)(void(*)(void))THPVariable_as_tensor,METH_VARARGS|METH_KEYWORDS|METH_STATIC,NULL},{"dsmm",(P
Tensor is a "generic" object holding a pointer to the underlying TensorImpl object, which has an embedded reference count. In this way, Tensor is similar to boost::intrusive_ptr. 通过注释可知,Tensor本质上是一个采用引用计数的对象,多个Tensor可以同时指向同一个TensorImpl。首先简单介绍几个Tensor相关...
importtorcha=torch.Tensor([[1,2],[3,4]])#定义一个2*2的张量,数值为1,2,3,4print(a)Out[]:tensor([[1.,2.],[3.,4.]])b=torch.Tensor(2,2)#制定形状2*2print(b)Out[]:tensor([[6.2106e-42,0.0000e+00],[nan,nan]]) 稀疏张量实例: i = torch.tensor([[0, 1, 2], [0, 1,...
self.seq=nn.Sequential(nn.Conv2d(3,self.inplanes,kernel_size=7,stride=2,padding=3,bias=False),self._norm_layer(self.inplanes),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=3,stride=2,padding=1),self._make_layer(64,3),self._make_layer(128,4,stride=2)).to(self.device)forminse...
elements 15 ['a', 'b', 'c', 'd', 'e'] 16 >>> ''.join(sorted(c.elements())) # list elements with repetitions 17 'aaaaabbbbcccdde' 18 >>> sum(c.values()) # total of all counts 19 20 >>> c['a'] # count of letter 'a' 21 >>> for elem in 'shazam': # update ...
torch.nonzero(tensor) # index of non-zero elementstorch.nonzero(tensor==0) # index of zero elementstorch.nonzero(tensor).size(0) # number of non-zero elementstorch.nonzero(tensor == 0).size(0) # number of zero elements 判断两个张量相等 ...
一、pytorch Tensor详解 首先,让我们导入PyTorch模块。我们还将添加Python的math模块来简化一些示例。 import torch import math 1. 2. Creating Tensors 最简单创建 tensor 的方法是调用torch.empty() : x = torch.empty(3, 4) print(type(x)) print(x) ...
def run_worker(rank, world_size):r"""A wrapper function that initializes RPC, calls the function, and shuts downRPC."""# We need to use different port numbers in TCP init_method for init_rpc and# init_process_group to avoid port conflicts.rpc_backend_options = TensorPipeRpcBackendOption...
def resize_as(self, tensor): def split(self, split_size, dim=0): def unique(self, sorted=True, return_inverse=False, return_counts=False, dim=None): def unique_consecutive(self, return_inverse=False, return_counts=False, dim=None): ...
torch.nonzero(tensor).size(0) # Number of non-zero elements torch.nonzero(tensor ==0).size(0) # Number of zero elements 判断两个张量相等 torch.allclose(tensor1, tensor2) #floattensor torch.equal(tensor1, tensor2) # int tensor