b = torch.mean(a, [0, 2], keepdim=True) print(a.shape) print(b.shape) print(b) 结果输出: torch.Size([3, 2, 3]) torch.Size([1, 2, 1]) tensor([[[2.], [5.]]]) 保留a的第1维,沿着a的第0和第2维做均值,a的维度为[3, 2, 3],输出b的维度变为[1, 2, 1]。b中2.是...
defsoftmax(x):e_x = np.exp(x - np.max(x, axis=1, keepdims=True))returne_x / np.sum(e_x, axis=1, keepdims=True) conf_scores = softmax(scores) class_preds = np.argmax(conf_scores, axis=1) print("predicted classes:", ([(class_idx, classes[class_idx])forclass_idxinclass_...
Provide feedback We read every piece of feedback, and take your input very seriously. Include my email address so I can be contacted Cancel Submit feedback Saved searches Use saved searches to filter your results more quickly Cancel Create saved search Sign in Sign up {...
We read every piece of feedback, and take your input very seriously. Include my email address so I can be contacted Cancel Submit feedback Saved searches Use saved searches to filter your results more quickly Cancel Create saved search Sign in Sign up Reseting focus {...
sum(torch.square(f - down_mean), dim=0)) up = (f_hat - f) * S return torch.sum(torch.square(up / down)) def loss_fn(self, pred, label): mask = ~torch.isnan(label) if self.loss == "mse": return self.mse(pred[mask], label[mask]) raise ValueError("unknown ...
self.fc = nn.Conv2D(channels, channels, kernel_size=1, padding=0) self.act = get_act_fn(act) if act is None or isinstance(act, (str, dict)) else act def forward(self, x): x_se = x.mean((2, 3), keepdim=True) x_se = self.fc(x_se) ...
defforward(self,x):x_se=x.mean((2,3),keepdim=True)x_se=self.fc(x_se)returnx*self.act(x_se) 4、CSPNet结构 CSPNet的主要思想还是Partial Dense Block,设计Partial Dense Block的目的是: 增加梯度路径:通过分裂合并策略,可以使梯度路径的数目翻倍。由于采用了跨阶段的策略,可以减轻使用显式特征映射复制...
Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. ...
当X大于时才显示ChartJS数据是一种基于条件判断的数据展示方式。ChartJS是一款流行的前端数据可视化库,可以用于生成各种类型的图表,如折线图、柱状图、饼图等。通过设置条件,只有当满足条件时,才...
norm_x = torch.mean(hidden_states * hidden_states, dim=-1, keepdim=True) x_normed = hidden_states * torch.rsqrt(norm_x + self.eps) return self.weight * x_normed else: input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=...