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...
torch.masked_select(input,mask,out=None) 函数返回一个根据布尔掩码 (boolean mask) 索引输入张量的 1D 张量,其中布尔掩码和输入张量就是 torch.masked_select(input, mask, out = None) 函数的两个关键参数,函数的参数有: 触摸壹缕阳光 2020/12/02 4.3K0 强的离谱,16个Pytorch核心操作!! pytorchtorch函数...
🐛 Describe the bug In the following example, torch.masked_select produces a too large tensor, the first part of which is filled with the correct values while the rest is filled with some trash. import torch import PIL.Image as Image impo...
[n] = mask_pred[n, l] # else: # inds = rois_mask_label.data + \ # torch.arange(0, n_rois * n_classes, n_classes).long().cuda(rois_mask_label.data.get_device()) # mask_pred_select = mask_pred.view(-1, cfg.MRCNN.RESOLUTION, # cfg.MRCNN.RESOLUTION)[inds] # loss = F...
index_select(mask, 1, idx) own_input = torch.index_select(inputs, 1, idx) own_hidden = own_hidden.repeat(1, self.evi_num, 1, 1) own_mask = own_mask.repeat(1, self.evi_num, 1) own_input = own_input.repeat(1, self.evi_num, 1) hiddens_norm = F.normalize(inputs_hiddens,...
I think it will be more convenient if we can have a "dim" parameter intorch.masked_select. There is a scenario as we have these three tensors, labels: [N] scores: [N] boxes: [N,4], and we can get the indexes like,bool_mask = labels== 2. ...
fake_score = torch.masked_select(torch.exp(fake_dis - right_dis), fake_diff_mask) margin_score = F.relu(torch.log(fake_score +1) - self.margin) loss_fake = torch.sum(margin_score) loss_dis += loss_fake loss_norm += fake.norm() ...
[Tensor] tensor:maskedSelect(mask) 或者是[Tensor] tensor1:maskedSelect(tensor2,mask):返回一个新的Tensor,其中包含所有与相应mask中的1对齐所有的元素。mask是0和1的torch.ByteTensor。mask和Tensor必须包含有相同数量的元素。生成的Tensor是与Tensor相同类型的一维张量,其大小为mask:sum() ...
masked_select(mask) → Tensor matmul(tensor2) → Tensor matrix_power(n) → Tensor max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor) mean(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor) median(dim=None, keepdim=False) -> (Tensor, LongTensor) ...
[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 ...