layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) conv6 = nn.Conv2d...
padding = 3 if kernel_size == 7 else 1 self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = torch.mean(x, dim=1, keepdim=True) max_out, _ = torch.max(x, dim=1, keepdim=True) x = torch....
max(1, keepdim=True) best_truth_overlap, best_truth_idx = overlap.max(0, keepdim=True) overlap的含义是7个groundtruth与6375个prior的交并比,所以best_prior_overlap的维度知道是什么样子的吗?代表的含义又是啥? best_prior_overlap的shape[7,1] best_prior_idx的shape[7,1],取值范围是[0,6375) ...
(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # 计算损失并对损失求和,返回浮点型 pred = output.argmax(dim=1, keepdim=True) """torch.argmax()中dim的值表示忽略某个维度,输出其他维度的最大值索引, 在二阶张量中,dim=1表示忽略列,比较每行得到每行的最大值的...
if batch_idx * BATCHSIZE >= N_VALID_EXAMPLES: break data, target = data.view(data.size(0), -1).to(DEVICE), target.to(DEVICE) output = model(data) # Get the index of the max log-probability. pred = output.argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)...
(self.device) output = self.model(img) output = torch.softmax(output, dim=1) # 每个预测值的概率 probability = output.cpu().detach().numpy() # 找出最大概率值的索引 output = torch.argmax(output, dim=1) index = output.cpu().numpy()[0] # 预测结果 pred = self.labels[index] print...
pred = output.data.max(1, keepdim=True)[1] correct += pred.eq(target.data.view_as(pred)).sum() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( ...
修改non_max_suppression类方法中针对单一检测类型时代码的处理逻辑。 else: # best class only tensor_ = torch.Tensor(x[:, 5:]) conf, j = tensor_.max(1, keepdim=True) # Tencent is pleased to support the open source community by making ncnn available. # # Copyright (C) 2020 THL A29 ...
norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps x = t.div(x, norm) out = self.weights.view(1, -1, 1, 1).expand_as(x) * x return out Prior box的选择 之前已经说过,6个检测层的特征图都分别定义了不同的先验框,那么这些先验框的尺寸和长宽比是如何得到的呢?