Webtorch.cumsum(input, dim, *, dtype=None, out=None) → Tensor Returns the cumulative sum of elements of input in the dimension dim. For example, if input is a vector of size N, the result will also be a vector of size N, with elements. y_i = x_1 + x_2 + x_3 + \dots + x_i yi = x1 +x2 +x3 +⋯+xi Parameters: input ( Tensor) – the input tensor. WebFeb 20, 2024 · For a general solution working on any dimension, I implemented tile based on the .repeat method of torch’s tensors: def tile (a, dim, n_tile): init_dim = a.size (dim) repeat_idx = [1] * a.dim () repeat_idx [dim] = n_tile a = a.repeat (* (repeat_idx)) order_index = torch.LongTensor (np.concatenate ( [init_dim * np.arange (n_tile) + i for i in ...
Did you know?
WebFeb 2, 2024 · Suppose a tensor is of dimension (9,10), say it A, A.repeat(1,1) would produce same tensor as A. Calling A.repeat(1,1,10) produces tensor of dimension 1,9,100 Again calling A.repeat(1,2,1) produces 1,18,10. It look likes that from right to left, element wise multiplication is happening from the input of repeat WebDec 11, 2024 · Are you trying to create a multigraph (where multiple edges may exist between the same node pair)? If so, please specify multigraph=True. If not, currently …
WebDec 9, 2024 · def construct_negative_graph ( graph, k ): src, dst = graph. edges () neg_src = src. repeat_interleave ( k ) neg_dst = torch. randint ( 0, graph. num_nodes (), ( len ( src) * k ,)) return dgl. graph ( ( neg_src, neg_dst ), num_nodes=graph. num_nodes ()) 预测边得分的模型和边分类/回归模型中的预测边得分模型相同。 class Model ( nn. WebThe function is commonly used as a *readout* function on a batch of graphs to generate graph-level representation. Thus, the result tensor shape depends on the batch size of …
WebJul 1, 2024 · Say, mask is of shape N, T, S, then with torch.repeat_interleave (mask, num_heads, dim=0) each mask instance (in total there are N instances) is repeated num_heads times and stacked to form num_heads, T, S shape array. Repeating this for all such N masks we'll finally get an array of shape: WebTensor.repeat_interleave(repeats, dim=None, *, output_size=None) → Tensor. See torch.repeat_interleave (). Next Previous. © Copyright 2024, PyTorch Contributors. Built …
WebDec 7, 2024 · 1 Answer Sorted by: 1 Provided you're using PyTorch >= 1.1.0 you can use torch.repeat_interleave. repeat_tensor = torch.tensor (num_repeats).to (X.device, torch.int64) X_dup = torch.repeat_interleave (X, repeat_tensor, dim=1) Share Improve this answer Follow edited Dec 7, 2024 at 19:36 answered Dec 7, 2024 at 15:07 jodag 18.6k 5 …
Web133 g_repeat = g.repeat(n_nodes, 1, 1) g_repeat_interleave gets {g1,g1,…,g1,g2,g2,…,g2,...} where each node embedding is repeated n_nodes times. 138 g_repeat_interleave = g.repeat_interleave(n_nodes, dim=0) Now we concatenate to get {g1∥g1,g1∥g2,…,g1∥gN,g2∥g1,g2∥g2,…,g2∥gN,...} 146 g_concat = torch.cat( … ray skillman buick gmc south staffWebdgl.remove_self_loop¶ dgl. remove_self_loop (g, etype = None) [source] ¶ Remove self-loops for each node in the graph and return a new graph. Parameters. g – The graph. … ray skillman auto mall westWebGo to DGL/examples folder. Run semisupervised eample. DGL Version (e.g., 1.0): 0.6.1. Backend Library & Version (e.g., PyTorch 0.4.1, MXNet/Gluon 1.3):1.11.0. OS (e.g., … ray skillman buick gmc southsideWebOct 18, 2024 · hg = dgl.heterograph ( { ('a', 'etype_1', 'a'): ( [0,1,2], [1,2,3]), ('a', 'etype_2', 'a'): ( [1,2,3], [0,1,2]), }) sampler = dgl.dataloading.MultiLayerFullNeighborSampler (1,return_eids=True) collator = dgl.dataloading.NodeCollator (hg, {'a': [1]}, sampler) dataloader = torch.utils.data.DataLoader ( collator.dataset, collate_fn=collator.collate, … ray skillman buick northeastWebg_r_repeat_interleave gets {gr1,gr1,…,gr1,gr2,gr2,…,gr2,...} where each node embedding is repeated n_nodes times. 184 g_r_repeat_interleave = g_r.repeat_interleave(n_nodes, dim=0) Now we add the two tensors to get {gl1 + gr1,gl1 + gr2,…,gl1 +grN,gl2 + gr1,gl2 + gr2,…,gl2 + grN,...} 192 g_sum = g_l_repeat + g_r_repeat_interleave ray skillman body shop indianapolisWebSep 29, 2024 · Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric. - 3DInfomax/qmugs_dataset.py at master · HannesStark/3DInfomax ray skillman buy here pay here lotWebOct 1, 2024 · However, the function torch.repeat_interleave () is not found: x = torch.tensor ( [1, 2, 3]) x.repeat_interleave (2) gives AttributeError: 'Tensor' object has no attribute … simply dutch kitchens