| 45 | |
| 46 | |
| 47 | class conv_bn_relu(nn.Module): |
| 48 | def __init__(self,in_channels, out_channels, kernel_size = 3, padding = 1, stride = 1): |
| 49 | super(conv_bn_relu,self).__init__() |
| 50 | self.conv = nn.Conv2d(in_channels= in_channels, out_channels= out_channels, kernel_size= kernel_size, padding= padding, stride = stride) |
| 51 | self.bn = nn.BatchNorm2d(out_channels) |
| 52 | self.relu = nn.ReLU() |
| 53 | |
| 54 | def forward(self,x): |
| 55 | x = self.conv(x) |
| 56 | x = self.bn(x) |
| 57 | x = self.relu(x) |
| 58 | return x |
| 59 | |
| 60 | |
| 61 |