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,而不是利用更轻量的计算方式来减少...
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...
(out_channels * pruned_prob) #pruning_index = np.argsort(L1_norm)[:num_pruned].tolist() # remove filters with small L1-Norm strategy = tp.strategy.L1Strategy() pruning_index = strategy(conv.weight, amount=amount, round_to=round_to) plan = DG.get_pruning_plan(conv, tp.prune_conv, ...
Torch.argsort()“返回结果错误” 最近对于torch.argsort()理解不太到位 记录一下疑问以及正确解释 错误理解 之前误以为torch.argsort()返回的是 该元素的值的排名,比如[1,2,3]的argsort排名为[0,1,2]表示第一个元素在向量中排名为0 带着这个错误理解,去查阅torch.argsort官方文档: 但是example中第一...torch...
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=Tru...
edge_argsort = torch.argsort(edge_score, descending=True) #edge_score是一维张量,edge_argsort 是edge_score中按降序排列的个元素的原始索引,不改变edge_score # Iterate through all edges, selecting it if it is not incident to # another already chosen edge. ...
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...
avg)) video_pred = [np.argmax(x[0]) for x in output] video_pred_top5 = [np.argsort...