argmax(1)) plt.scatter(center_x, center_y, c='r') plt.grid() plt.show() # [epoch 1997] loss: 0.30143806636333464 # [epoch 1998] loss: 0.3013251159588496 # [epoch 1999] loss: 0.30121227165063225 # tensor([[-0.5655, -0.1659, 3.1287], # [-1.3081, 1.4970, -0.8085]], requires_grad=...
if np.max(P_cls[0, ii, :]) < self.confidence or np.argmax(P_cls[0, ii, :]) == (P_cls.shape[2] - 1): continue label = np.argmax(P_cls[0, ii, :]) (x, y, w, h) = ROIs[0, ii, :] cls_num = np.argmax(P_cls[0, ii, :]) (tx, ty, tw, th) = P_regr...
argmax() V = eigvec[:,idx] q[i] = torch.stack([V[3],V[0],V[1],V[2]]) return q Example #9Source File: entity_ranking.py From kge with MIT License 6 votes def _get_ranks_and_num_ties( scores: torch.Tensor, true_scores: torch.Tensor ) -> (torch.Tensor, torch.Tensor)...
preds = output.argmax(dim=1) correct = preds.eq(target).sum().item() total = target.size(0) returncorrect / total Cross-Entropy Loss: 在分类问题中,交叉熵损失也是一个常用的评价指标。它计算的是模型预测的类别分布与真实类别分布之间的差距。 python复制代码 criterion = nn.CrossEntropyLoss() Me...
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argmax(self.adv_dict["adv_labels"],axis=1)[self.sample_num] # doing this so that it is consistent with all other datasets # to return a PIL Image if self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target)...
ctx.argmax = argmaxreturnoutput 开发者ID:open-mmlab,项目名称:mmdetection,代码行数:19,代码来源:roi_pool.py 示例2: __init__ ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importint[as 别名]def__init__(self, backbone: BackboneBase, num_classes:int, pooler_mode...
probs = nnf.softmax(logits) row_idx = torch.arange(0, n).type(torch.LongTensor).unsqueeze(1).expand(n, n_sample) sample_idx = torch.multinomial(probs, n_sample, replacement=True) sample_srcs = src[row_idx, sample_idx.data.cpu()] ...
(), a.max(), a.min(), a.argmax()) self.common(fn, (torch.full((4,), float("-inf")),)) def test_prod(self): def fn(a): return a.prod(0), a.prod(1), a.prod() self.common(fn, (torch.rand((10, 10)),)) self.common(fn, (torch.rand((1, 2050))...
(x_test,[-1,28,28,1])y_pred=tf.argmax(model(x_test),axis=1)y_pred=tf.cast(y_pred,tf.int32)correct=tf.cast((y_pred==y_test),tf.int32)correct=tf.reduce_sum(correct)total_correct+=int(correct)total_num+=x_test.shape[0]accuracy=total_correct/total_numprint('accuracy: ',...