WebMay 28, 2024 · 2. repeat_interleave. This function returns the tensor obtained by repeating each item separately along the specified dimension rather than as a whole tensor. torch.Tensor.repeat_interleave(repeat ... Webpos_score = torch.sum (src_emb * dst_emb, dim=-1) if src_emb.shape != neg_dst_emb.shape: src_emb = torch.repeat_interleave ( src_emb, neg_dst_emb.shape [-2], dim=-2 ).reshape (neg_dst_emb.shape) neg_score = torch.sum (src_emb * neg_dst_emb, dim=-1) return pos_score, neg_score
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WebApr 28, 2024 · About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators ... Webdgl.broadcast_edges(graph, graph_feat, *, etype=None) [source] Generate an edge feature equal to the graph-level feature graph_feat. The operation is similar to numpy.repeat (or torch.repeat_interleave ). It is commonly used to normalize edge features by a global vector. For example, to normalize edge features across graph to range [ 0 1):
Webg_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 WebAbout Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators ...
WebOct 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, … 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.
WebSep 13, 2012 · You could use repeat: import numpy as np def slow (a): b = np.array (zip (a.T,a.T)) b.shape = (2*len (a [0]), 2) return b.T def fast (a): return a.repeat (2).reshape (2, 2*len (a [0])) def faster (a): # compliments of WW return a.repeat (2, axis=1) gives
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. iot data share/serverWebdgl.reverse¶ dgl. reverse (g, copy_ndata = True, copy_edata = False, *, share_ndata = None, share_edata = None) [source] ¶ Return a new graph with every edges being the … iotdb compactionWebDec 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 … iot data ingestion architectureWeb133 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( … iotdb selectWebSep 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 iot data security strategyWebOct 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 … iotdb elasticsearchWebdgl.broadcast_edges¶ dgl. broadcast_edges (graph, graph_feat, *, etype = None) [source] ¶ Generate an edge feature equal to the graph-level feature graph_feat.. The operation is … iotdb connection reset