总结来说:在某些情况下,分组卷积能带来的模型效果确实要优于标准的2D 卷积,是因为组卷积的方式能够增加相邻层filter之间的对角相关性,而且能够减少训练参数,不容易过拟合,这类似于正则的效果。 4. 代码 代码的话很简单,就是nn.Conv2d里面的一个参数:group,如下图所示: import torch import torch.nn as nn if _
output_dim,kernel_size=3,padding=1,groups=1,bias=False,padding_mode='replicate')print(f'groups=1时,卷积核的形状为:{conv1.weight.shape}')withtorch.no_grad():conv1.weight[:4,:,:,:]=torch.ones(4,4,3,3
conv = nn.Conv2d( in_channels=in_ch, out_channels=out_ch, kernel_size=3, stride=1, padding=1, groups=groups ) def forward(self, input): out = self.conv(input) return out conv = Group_Conv(16, 32, 4) conv = conv.to(device) print(summary(conv, (16, 64, 64), batch_size=...
Group Convolution顾名思义,则是对输入feature map进行分组,然后每组分别卷积。假设输入feature map的尺寸仍为C ∗ H ∗ W ,输出feature map的数量为N个,如果设定要分成G个groups,则每组的输入feature map数量为 三、代码实例 import torch x = torch.randn(3,1,5,4) print(x) conv = torch.nn.Conv2d...
1.当设置group=1时: conv = nn.Conv2d(in_channels=6, out_channels=6, kernel_size=1, groups=1) conv.weight.data.size() 返回: torch.Size([6, 6, 1, 1]) 另一个例子: co
{G}GN,卷积核只与其同组的输入map进行卷积,卷积核的总参数量为N ∗ C G ∗ K ∗ K N∗\frac{C}{G}∗K∗KN∗GC∗K∗K,可见,总参数量减少为原来的1 G \frac{1}{G}G1,其连接方式如下图右所示,group1输出map数为2,有2个卷积核,每个卷积核的channel数为4,与group1的...
weight.shape) print("\n") #分组卷积 print("--group conv--") conv_group = nn.Conv2d(in_channels=64,out_channels=32,kernel_size=3,groups=8) group_out = conv_group(features) print("group_out.shape:",group_out.shape) print("conv_group.weight.shape:",conv_group.weight.shape) print...
nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(256, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3, padding=1...
nn.Conv2d的功能是:对由多个输入平面组成的输入信号进行二维卷积。输入信号的形式为: (1)参数说明: stride(步长):步长,默认为1,可以设为1个int型数或者一个(int, int)型的tuple。 kernel_size:卷积核的宽度和长度,单个整数或由两个整数构成的list/tuple。如为单个整数,则表示在各个空间维度的相同长度。
(inplace=True)self.gnorm1 = nn.GroupNorm(num_groups=num_groups, num_channels=C)self.gnorm2 = nn.GroupNorm(num_groups=num_groups, num_channels=C)self.conv1 = nn.Conv2d(C, C, kernel_size=3, padding=1)self.conv2 = nn....