# Compute the dense keypoint scorescPa=self.relu(self.convPa(x))scores=self.convPb(cPa)# DIM: N x 65 x H/8 x W/8scores=torch.nn.functional.softmax(scores,1)[:,:-1]# DIM: N x 64 x H/8 x W/8b,_,h,w=scores.shapescores=scores.permute(0,2,3,1).reshape(b,h,w,8,8)...
class Net(nn.Module): def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim): super(Net, self).__init__() self.layer = nn.Sequential( nn.Linear(in_dim, n_hidden_1), nn.ReLU(True) ) self.layer2 = nn.Sequential( nn.Linear(n_hidden_1, n_hidden_2), nn.ReLU(Tru...
class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = con...
append(nn.ConvTranspose2d(in_channels=self.inplanes, out_channels=planes, kernel_size=kernel, stride=2, padding=padding, output_padding=output_padding, bias=self.deconv_with_bias)) layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) self.inplanes ...
ReLU(inplace=True)) layers.append(MetaLinear(84, n_out)) self.fc_layers = nn.Sequential(*layers) def forward(self, x): x = self.main(x) x = x.view(-1, 120) return self.fc_layers(x).squeeze() class MetaMLP(MetaModule): def __init__(self, dim): super(MetaMLP, self).__...
nn.ReLU(inplace=True), nn.Linear(channel// reduction, channel, bias=False), nn.Sigmoid() )defforward(self, x): b, c, _, _=x.size() y=self.avg_pool(x).view(b, c) y= self.fc(y).view(b, c, 1, 1)returnx *y.expand_as(x)classHybridSN(nn.Module):def__init__(self):...
regardless of the input. The second auxiliary loss is the covariance lossLcovwhich reduces the correlation between the dimensions in the output vectors. The covariance loss is in place to maximize the information yield of the output vectors, which has been shown to improve the feature vector quali...
ARTICLE https://doi.org/10.1038/s41467-021-27713-7 OPEN Self-directed online machine learning for topology optimization Changyu Deng 1, Yizhou Wang 2, Can Qin2, Yun Fu2 & Wei Lu 1,3✉ Topology optimization by optimally distributing materials in a given domain requires non- gradient ...
[GCC 7.3.0] Paddle compiled with cuda: True NVCC: Build cuda_11.2.r11.2/compiler.29618528_0 cudnn: 8.2 GPUs used: 1 CUDA_VISIBLE_DEVICES: None GPU: ['GPU 0: Tesla V100-SXM2-32GB'] GCC: gcc (Ubuntu 7.5.0-3ubuntu1~16.04) 7.5.0 PaddleSeg: develop PaddlePaddle: 2.4.0 OpenCV: 4.6...
最后输出尺度大小为 \frac{1}{8}H*\frac{1}{8}W*128 的Feature map。共享编码器的主要作用是从输入图像中提取多尺度的特征,这些特征将被传递到两个解码器,分别用于关键点检测和描述符生成。 self.relu = torch.nn.ReLU(inplace=True) self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2) c1,...