pytorch报错 RuntimeError: The size of tensor a (25) must match the size of tensor b (50) at non-singleton dimension 1 怎么解决? 简介:这个错误提示表明,在进行某个操作时,张量a和b在第1个非单例维(即除了1以外的维度)上的大小不一致。例如,如果a是一个形状为(5, 5)的张量,而b是一个形状为(...
importtorch.nn.functionalasFa=torch.zeros(2,2,1)print(a)# tensor([[[0.],# [0.]],# [[0.],# [0.]]])print(a.size())# torch.Size([2, 2, 1])a=torch.zeros(2,2,1)a=F.pad(a,pad=(1,2),mode='constant',value=1)# 在倒数第一个维度上,左边填充 1 个维数,右边填充 2 个...
device=torch.device('cuda:0')) # creates a torch.cuda.DoubleTensor tensor([[ 0.1111, 0.2222, 0.3333]], dtype=torch.float64, device='cuda:0') >>> torch.tensor(3.14159) # Create a scalar (zero-dimensional tensor) tensor(3.1416) >>> torch.tensor([]) # Create an empty tensor (of s...
File"<stdin>", line 1,in<module>RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 1 >>> >>> b=torch.rand(4)>>> a+b tensor([[1.0229, 1.0478, 1.5048, 0.1652], [0.7158, 1.5854, 0.9578, 1.0546], [0.8643, 1.6602, 1.1447, ...
报错:RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 93 and 89 in dimension 1 at /Users/soumith/code/builder/wheel/pytorch-src/aten/src/TH/generic/THTensorMath.cpp:3616 可能的原因:dataloader的__getitem__函数中,返回的图片形状不一致,导致无法stack ...
print(cat_ab.size()) ''' Traceback (most recent call last): File "/home/chenkc/code/pytorch/repeat_function.py", line 9, in <module> cat_ab = torch.cat([a, b], dim = 0) RuntimeError: Sizes of tensors must match except in dimension 0. Got 1 and 3 in dimension 1 ...
torch.nonzero(tensor).size(0) # number of non-zero elements torch.nonzero(tensor == 0).size(0) # number of zero elements 1. 2. 3. 4. 4. 判断两个张量相等 torch.allclose(tensor1, tensor2) # float tensor torch.equal(tensor1, tensor2) # int tensor ...
1、对于pytorch的深度学习框架,其基本的数据类型属于张量数据类型,即Tensor数据类型,对于python里面的int,float,int array,flaot array对应于pytorch里面即在前面加一个Tensor即可——intTensor ,Float tensor,IntTensor of size [d1,d2...], FloatTensor of size[d1,d2,...] ...
Shape of tensor: torch.Size([3, 4]) # 维数Datatype of tensor: torch.float32 # 数据类型Device tensor is stored on: cpu # 存储设备 四、张量的运算: 检查当前运行环境是否支持Pytorch,检查代码: # 判断当前环境GPU是否可用, 然后将tensor导入GPU内运行if torch.cuda.is_available():tensor = tensor....
numpy array:[[-123][456]]tensor:tensor([[-1,2,3],[4,5,6]],dtype=torch.int32) 二、依据数值创建 2.1 torch.zeros() 功能:依size 创建全 0 张量 size : 张量的形状 , 如 (3,3),(3,224,224) out : 输出的张量 layout 内存中布局形式 , 有strided(默认), sparse_coo(这个通常稀疏矩阵时...