| 29 | |
| 30 | |
| 31 | class InceptionBlock(nn.Module): |
| 32 | def __init__(self, inplanes, outplanes, stride=1, pool_size=3): |
| 33 | self.inplanes = inplanes |
| 34 | self.outplanes = outplanes |
| 35 | self.stride = stride |
| 36 | self.pool_size = pool_size |
| 37 | super(InceptionBlock, self).__init__() |
| 38 | |
| 39 | self.pw_block = point_wise_block(self.inplanes, self.outplanes//4) |
| 40 | self.mp_layer = nn.MaxPool2d(kernel_size=self.pool_size, stride=stride, padding=1) |
| 41 | self.conv3_block = conv_block(self.outplanes//4, self.outplanes//4, kernel_size=3, stride=1, padding=1) |
| 42 | self.conv5_block = conv_block(self.outplanes//4, self.outplanes//4, kernel_size=5, stride=1, padding=2) |
| 43 | |
| 44 | def forward(self, x): |
| 45 | x1 = self.pw_block(x) |
| 46 | |
| 47 | x2 = self.pw_block(x) |
| 48 | x2 = self.conv3_block(x2) |
| 49 | |
| 50 | x3 = self.pw_block(x) |
| 51 | x3 = self.conv5_block(x3) |
| 52 | |
| 53 | x4 = self.mp_layer(x) |
| 54 | x4 = self.pw_block(x4) |
| 55 | |
| 56 | x = torch.cat((x1, x2, x3, x4), dim=1) |
| 57 | return x |
| 58 | |
| 59 | |
| 60 | class SELayer(nn.Module): |