torch.masked_select(input, mask, out) output = input.masked_select(mask) selected_ele = torch.masked_select(input=imgs, mask=mask)#true表示selected,false则未选中,所以这里没有取反 #tensor([182., 92., 86., 157., 148., 56.]) 3)torch.masked_scatter(input, mask, source) 说明:将从inp...
input (Tensor) – 输入张量 mask (ByteTensor) – 掩码张量,包含了二元索引值 out (Tensor, optional) – 目标张量 实验现象 x = torch.randn(3,4)mask = torch.ByteTensor(x > 0) torch.masked_select(x,mask)注意: 返回的正是一维张量
tensor([0, 2])) print(t1) def tensor_maskSelect(): arr = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[1, 2, 3], [4, 5, 6], [7, 8, 9]]]) print(arr) t = torch.normal(1.0, 1.0, (3, 3)) print(...
select by mask:调用 pytorch学习手册【一】 axis specified by dim.torch.index_select(input, dim, index, out=None) →Tensor,类似于标准库slice函数的...squeeze(input, dim=None, out=None) →Tensor,将维度=1的那个维度(即只包含一个元素的维度)去掉,即所谓的压榨torch.stack(seq, dim=0, out 2020-...
ge(0.5) >>> mask tensor([[False, False, False, False], [False, True, True, True], [False, False, False, True]]) >>> torch.masked_select(x, mask) tensor([ 1.2252, 0.5002, 0.6248, 2.0139]) torch.narrow(input, dim, start, length)→ Tensor Returns a new tensor that is a ...
[Tensor] maskedSelect(mask) 。-- mask 是ByteTensor类型的掩码矩阵或者向量,元素为0或1. mask并不要求size和src相同,但元素个数必须相同。 。--返回的是mask中元素1对应的src中元素,长度和mask中1的个数相同,元素类型和src类型相同,ndim=1 d. maskedCopy ...
In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting torch.backends.cudnn.deterministic = Tr...
x = x * (torch.tanh(self.Feature_Mask)+1) Copy 那么我们着重考虑以下的代码: x = x.view(n*t,v*c) x = torch.index_select(x, 1, self.shift_in) x = x.view(n*t,v,c) Copy 第一行代码将特征图展开,如Fig 3所示,得到了25 × C 25 \times C25×C大小的特征向量。通过torch.index_...
dropout)self.n_head=config.n_headself.n_embd=config.n_embdself.dropout=config.dropout# select ...
torch.index_select(input, dim, index, out=None) → Tensor torch.narrow(input, dim, start, length) → Tensor torch.nonzero(input, *, out=None, as_tuple=False) → LongTensor or tuple of LongTensors torch.reshape(input, shape) → Tensor ...