DenseNet(原文中3Composite function部分): 在每个Dense Block中,使用BN-ReLU-Conv(3x3)来运算,transiton layer使用BN-Conv(1x1)-AvgPOOL(2x2) DenseNet-B(原文中3Bottleneck layers部分): 在每个Dense Block中,采用Bottleneck layers,使用BN-ReLU-Conv(1x1)-BN-ReLU-Conv(3x3)来运算,这样能减少运算量,在Conv(1x...
#self.bn1=nn.BatchNorm2d(planes)# ResNet XBNBlock self.bn1=GroupNorm(planes,num_groups=32)self.relu=nn.ReLU(inplace=True)self.conv2=conv3x3(planes,planes)self.bn2=nn.BatchNorm2d(planes)self.downsample=downsample self.stride=stride defforward(self,x):residual=x out=self.conv1(x)out=self...
代码运行次数:0 layer{name:"conv3_3_3x3/bn"type:"SyncBN"bottom:"conv3_3_3x3"top:"conv3_3_3x3/bn"param{lr_mult:1decay_mult:0}param{lr_mult:1decay_mult:0}param{lr_mult:0decay_mult:0}param{lr_mult:0decay_mult:0}bn_param{slope_filler{type:"constant"value:1}bias_filler{type:"c...
num_groups=32)self.relu = nn.ReLU(inplace=True)self.conv2 = conv3x3(planes, planes)self.bn2 = nn.BatchNorm2d(planes)self.downsample = downsampleself.stride = stridedef forward(self, x):residual = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out...
MaskedConv2d('B', feature_dim, feature_dim, 7, 1, 3, bias=False), nn.BatchNorm2d(feature_dim), nn.ReLU(True), nn.Conv2d(feature_dim, 256, 1)) network.to(device) 接着设置dataloader和优化器: train_data = data.DataLoader(datasets.MNIST('data', train=True, download=True, transform...
(planes) self.conv2=conv3x3(planes,planes) self.downsample=downsample self.stride=stride def forward(self,x): residual=x out=self.bn1(x) out=self.relu(out) out=self.conv1(out) out=self.bn2(out) out=self.relu(out) out=self.conv2(out) if self.downsample is not None: residual=self...
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0) 1. forward 然后在 forward 函数里引用该层级!在此例中,我传入了输入图像x,并向此层的输出应用了 ReLU 函数。 x = F.relu(self.conv1(x)) 1. 注意:可以将kernel_size和stride表示为数字或元组。你还可以设置...
self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride defforward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) ...
2. 仅使用 3x3 卷积和 ReLU 作为激活函数。 3. 模型的详细架构 (包括深度和通道数) 不经过架构搜索或者人工微调等过程。 RepVGG 模型的基本架构:将20多层 3x3 卷积堆起来,分成5个 stage,每个 stage 的第一层是 stride=2 的降采样,每个卷积层用 ReLU 作为激活函数。 3×3 卷积的优势: 3×3 卷积的优势是...
(out_planes,eps=1e-5,momentum=0.01,affine=True)ifbnelseNoneself.relu=nn.ReLU(inplace=True)ifreluelseNonedefforward(self,x):x=self.conv(x)ifself.bnisnotNone:x=self.bn(x)ifself.reluisnotNone:x=self.relu(x)returnx# RFB ArchitectureclassBasicRFB(nn.Module):def__init__(self,in_planes...