torch.argsort:仅返回排序后的indices,不返回排序后的数据本身。 通过阅读代码实现可以发现,这两个算子实际上都是基于torch.sort实现的。因此,它们的延迟(latency)与torch.sort相同,并未针对返回结果较少的特点进行优化。换句话说,torch.msort和torch.argsort仍然执行了完整的排序操作,而不是
i = 0 new_edge_indices = [] #new_edge_indices 用于记录即将被合并的边的索引 edge_index_cpu = edge_index.cpu() for edge_idx in edge_argsort.tolist(): #edge_argsort的第i个元素是分数第i大的边的索引,这个索引是相对于edge_index_cpu 来论, #edge_index_cpu 是输入模型时边的排列顺序。
)defforward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out)returnoutclassBottleneck(nn.Module): expansion =4def__init__(self, in_planes, planes, stride=1):super(Bottleneck, self).__init__() self...
ind = np.argpartition(array, -k)[-k:] return ind[np.argsort(array[ind])][::-1]tokenized, _, _ = st.tokenize_sentences([sentence]) prob = model(tokenized)[0] emoji_ids = top_elements(prob, top_n) emojis = map(lambda x: EMOJIS[x], emoji_ids) return emoji.emojize(f"{sentence...
argsort(loss_1.data.cpu()) loss_1_sorted = loss_1[ind_1_sorted] # model1对同一个batch的samples的预测的loss从小到大排序 loss_2 = F.cross_entropy(y_2, t, reduce = False) ind_2_sorted = np.argsort(loss_2.data.cpu()) loss_2_sorted = loss_2[ind_2_sorted] # model2 对同一...
return ind[np.argsort(array[ind])][::-1]tokenized, _, _ = st.tokenize_sentences([sentence]) prob = model(tokenized)[0] emoji_ids = top_elements(prob, top_n) emojis= map(lambda x: EMOJIS[x], emoji_ids) return emoji.emojize(f"{sentence} {' '.join(emojis)}", use_aliases=True...