ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()解释一下这段代码
时间: 2024-05-22 13:13:11 浏览: 24
This code converts the input 'grid', which is a torch tensor, into a numpy array 'ndarr'.
First, it multiplies every element of the tensor by 255 and adds 0.5 to it. Then, it clamps the resulting tensor between 0 and 255. This is done to ensure that the tensor contains only valid pixel values.
Next, it permutes the dimensions of the tensor from (height, width, channels) to (channels, height, width).
Finally, it converts the tensor to a numpy array of unsigned 8-bit integers and moves it to the CPU. The resulting numpy array 'ndarr' can be used for further image processing or visualization.
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mask_pro_label = torch.mul(label, mask_labels) pos_lab= torch.mul(pre_label, mask_pro_label)#积极标签 neg_label=torch.abs(label-1) mask_neg_label = torch.mul(neg_label, mask_labels) neg_lab= torch.mul(pre_label, mask_neg_label)#消极标签 neg_l
abel= torch.mul(neg_label, mask_labels) neg_lab= torch.mul(pre_label, mask_neg_label)#消极标签
这段代码是用来生成积极和消极标签的。首先,通过 torch.mul(label, mask_labels) 将原始标签 label 和掩码 mask_labels 相乘,得到积极标签 mask_pro_label。然后,通过 torch.mul(pre_label, mask_pro_label) 将预测标签 pre_label 和积极标签 mask_pro_label 相乘,得到最终的积极标签 pos_lab。
接着,通过 torch.abs(label-1) 将原始标签 label 取反得到消极标签 neg_label。再通过 torch.mul(neg_label, mask_labels) 将消极标签 neg_label 和掩码 mask_labels 相乘,得到消极标签的掩码 mask_neg_label。最后,通过 torch.mul(pre_label, mask_neg_label) 将预测标签 pre_label 和消极标签的掩码 mask_neg_label 相乘,得到最终的消极标签 neg_lab。
解释下面这段代码: for i, edge_index in enumerate(edge_index_sets): edge_num = edge_index.shape[1] cache_edge_index = self.cache_edge_index_sets[i] if cache_edge_index is None or cache_edge_index.shape[1] != edge_num*batch_num: self.cache_edge_index_sets[i] = get_batch_edge_index(edge_index, batch_num, node_num).to(device) batch_edge_index = self.cache_edge_index_sets[i] all_embeddings = self.embedding(torch.arange(node_num).to(device)) weights_arr = all_embeddings.detach().clone() all_embeddings = all_embeddings.repeat(batch_num, 1) weights = weights_arr.view(node_num, -1) cos_ji_mat = torch.matmul(weights, weights.T) normed_mat = torch.matmul(weights.norm(dim=-1).view(-1,1), weights.norm(dim=-1).view(1,-1)) cos_ji_mat = cos_ji_mat / normed_mat dim = weights.shape[-1] topk_num = self.topk topk_indices_ji = torch.topk(cos_ji_mat, topk_num, dim=-1)[1] self.learned_graph = topk_indices_ji gated_i = torch.arange(0, node_num).T.unsqueeze(1).repeat(1, topk_num).flatten().to(device).unsqueeze(0) gated_j = topk_indices_ji.flatten().unsqueeze(0) gated_edge_index = torch.cat((gated_j, gated_i), dim=0) batch_gated_edge_index = get_batch_edge_index(gated_edge_index, batch_num, node_num).to(device) gcn_out = self.gnn_layers[i](x, batch_gated_edge_index, node_num=node_num*batch_num, embedding=all_embeddings) gcn_outs.append(gcn_out) x = torch.cat(gcn_outs, dim=1) x = x.view(batch_num, node_num, -1) indexes = torch.arange(0,node_num).to(device) out = torch.mul(x, self.embedding(indexes)) out = out.permute(0,2,1) out = F.relu(self.bn_outlayer_in(out)) out = out.permute(0,2,1) out = self.dp(out) out = self.out_layer(out) out = out.view(-1, node_num) return out
这部分代码是一个Graph Convolutional Network (GCN)的前向函数。首先,对于一个图中每个连接的边,将其变换为针对batch中所有节点的连接边。然后,对于每个节点,通过GCN层和学习到的邻居节点之间的注意力矩阵,将其信息聚合到其邻居节点上。最后,对于每个节点都会得到一个向量表示,通过与输入的向量嵌入相乘来进行特征融合。最后一步则是将所有节点的结果收集起来并且分批次处理。