1.1 Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True) 1.1.1 参数解释 in_channels:输入向量特征维度 out_channels:输入向量经过Conv1d后的特征维度,out_channels等于几,就有几个卷积的kernel. kernel_size:卷积核大小 stride:步长 padding:输入向量的...
importtorch.nnasnnclassDoubleConv(nn.Module):"""(convolution => [BN] => ReLU) * 2"""def__init__(self,in_channels,out_channels):super().__init__()self.double_conv=nn.Sequential(nn.Conv2d(in_channels,out_channels,kernel_size=3,padding=0),nn.BatchNorm2d(out_channels),nn.ReLU(inpl...
out_channels=8,kernel_size=3,stride=2)# out_width = (32-3)/2+1 = 29/2+1 = 15self.enc2=nn.Conv2d(in_channels=8,out_channels=16,kernel_size=3,stride=2)self.enc3=nn.Flatten(start_dim=1)self.enc4=nn.Linear(7*7*16,d)# ...
nn.Linear(参数) 对信号进行线性组合 in_features:输入节点数 out_features:输出节点数 bias :是否需要偏置 nn.Conv2d(参数) 对多个二维信号进行二维卷积 in_channels:输入通道数 out_channels:输出通道数,等价于卷积核个数 kernel_size:卷积核尺寸 stride:步长 padding :填充个数 dilation:空洞卷积大小 groups:分...
Tensor通道排列顺序是:[batch, channel, height, width],首先我们看一下Pytorch中Conv2d的各参数: torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias…
in_channels(int)—输入数据的通道数。在文本分类中,即为句子中单个词的词向量的维度。(word_vector_num) out_channels(int)—输出数据的通道数。设置 N 个输出通道数,就有 N 个1维卷积核。(new word_vector_num) kernel_size(int or tuple)—卷积核的长度,1维卷积中卷积核的实际大小维度是(in_channels,...
self.conv1 = GraphConv(in_channels, out_channels) self.conv2 = GraphConv(out_channels, out_channels) def forward(self, x, edge_index): x = self.conv1(x, edge_index) x = nn.functional.relu(x) x = self.conv2(x, edge_index) return x 在上面的例子中,GraphConv类是用来实现GCN卷积...
class ConvNextBlock(nn.Module):def __init__(self,in_channels,out_channels,mult=2,time_embedding_dim=None,norm=True,group=8,):super().__init__()self.mlp = (nn.Sequential(nn.GELU(), nn.Linear(time_embedding_dim, in_channels))if time_embedding...
def init(self, inchannels, outchannels, kernel_size, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1):super(DeformConv2d, self).__init()self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels, kernel_size, kernel_size))self.bias = nn.Parameter(torch.Tensor(...
block_in_channels = block_out_channels //2#更新下一个dense block的in_channelsself.avg_pool = nn.AdaptiveAvgPool2d(output_size=(1,1)) self.fc = nn.Linear(block_in_channels,num_classes)defforward(self,x): out = self.conv1(x)