x1,x2,x3 = torch.split(torch.index_select(verts,0, faces[:,0]) - torch.index_select(verts,0, faces[:,1]),1, dim =1) y1,y2,y3 = torch.split(torch.index_select(verts,0, faces[:,1]) - torch.index_select(verts,0, faces[:,2]),1, dim =1) a = (x2*y3 - x3*y2)**...
index_select(label.view(num_label_ft, -1), 1, lin_indices_2d[1:1+num_ind]) output.view(num_label_ft, -1)[:, lin_indices_3d[1:1+num_ind]] = vals return output Example #12Source File: eval_base.py From person-reid-lib with MIT License 6 votes def _feature_distance(self, ...
(torch.index_select/index)') # Display the actual z values for i in range(len(batch_sizes)): for j in range(len(feature_sizes)): plt.text(j, i, f'{index_select_times[i][j]/indexing_times[i][j]:.2f}', ha='center', va='center', color='white', path_effects=[pe.with...
torch.index_select(input, dim, index, out=None) → Tensor/index_select(dim, index) f非常重要的函数 torch.masked_select(input, mask, out=None) → Tensor/masked_select(mask) 注意所有mask都为torch.ByteTensor,同时需要注意mask的shape不一定要和tensor相同数量也不一定要相同,shape中必须有一个轴要和...
torch.index_select(input, dim, index, out=None)→ Tensor返回一个新的张量,其索引input 张量沿尺寸 dim使用的条目中index这是一个LongTensor。返回的Tensor具有与原始Tensor相同数量的尺寸。注意: 返回的张量不与原始张量共享内存空间。参数:input (Tensor) – 输入张量 dim (int) – 索引的轴 index (Long...
select(dim, index) → Tensor set_(source=None, storage_offset=0, size=None, stride=None) → Tensor share_memory_()[source] short() → Tensor sigmoid() → Tensor sigmoid_() → Tensor sign() → Tensor sign_() → Tensor sin() → Tensor ...
yield features.index_select(0, indexs), labels.index_select(0, indexs) 这就是自己构建数据管道的代码,模型训练的时候,直接遍历一下这个即可,没有用到Dataset和DataLoader。下面我们测试一下结果: for feature, label in data_iter(X, Y, batch_size=8): ...
I do not agree with the suggestion about "deprecating the argument", as the sorted=True does do something to sort the output values. If you set the argument sorted=False, the underlying code will save the time of sorting output values. One thing in mind is that picking topk values is no...
shape return torch.sparse.FloatTensor(edge_index, values, shape) Example #4Source File: utils.py From DeepRobust with MIT License 6 votes def degree_normalize_adj_tensor(adj, sparse=True): """degree_normalize_adj_tensor. """ device = torch.device("cuda" if adj.is_cuda else "cpu") ...
zeros(n_channel, n_sample_padded, dtype=dtype, device=device) # Set up the coefficients matrix # Flip coefficients' order a_coeffs_flipped = a_coeffs.flip(0) b_coeffs_flipped = b_coeffs.flip(0) # calculate windowed_input_signal in parallel # create indices of original with shape (n_...