| 88 | return torch.tanh(self.final(x)) |
| 89 | |
| 90 | class Discriminator(torch.nn.Module): |
| 91 | def __init__(self,in_channels = 3,features = [64,64,128,128,256,256,512,512]): |
| 92 | super(Discriminator, self).__init__() |
| 93 | blocks = [] |
| 94 | for idx,feature in enumerate(features): |
| 95 | blocks.append( |
| 96 | ConvBlock( |
| 97 | in_channels, |
| 98 | feature, |
| 99 | kernel_size = (3,3), |
| 100 | stride = (1 + idx % 2,1 + idx % 2), |
| 101 | padding = (1,1), |
| 102 | discriminator=True, |
| 103 | use_act=True, |
| 104 | use_bn=False if idx == 0 else True |
| 105 | ) |
| 106 | ) |
| 107 | in_channels = feature |
| 108 | self.blocks = torch.nn.Sequential(*blocks) |
| 109 | self.classifier = torch.nn.Sequential( |
| 110 | torch.nn.AdaptiveAvgPool2d(output_size=(6,6)), |
| 111 | torch.nn.Flatten(), |
| 112 | torch.nn.Linear(in_features=512 * 6 * 6,out_features=1024), |
| 113 | torch.nn.LeakyReLU(negative_slope=0.2,inplace=True), |
| 114 | torch.nn.Linear(in_features=1024,out_features=1) |
| 115 | ) |
| 116 | def forward(self,x): |
| 117 | out = self.blocks(x) |
| 118 | return self.classifier(out) |
| 119 | |
| 120 | if __name__ == '__main__': |
| 121 | #96 x 96 => 24 x 24 |