| 79 | return out |
| 80 | |
| 81 | class Discriminator(torch.nn.Module): |
| 82 | def __init__(self,img_channels = 1,out_channels = 1,d = 128): |
| 83 | super(Discriminator, self).__init__() |
| 84 | self.conv_layer_1 = torch.nn.Sequential( |
| 85 | torch.nn.Conv2d( |
| 86 | in_channels=img_channels, |
| 87 | out_channels=d // 2, |
| 88 | kernel_size=(4,4), |
| 89 | stride=(2,2), |
| 90 | padding=(1,1) |
| 91 | ), |
| 92 | torch.nn.LeakyReLU(negative_slope=0.2,inplace=True) |
| 93 | ) |
| 94 | self.conv_layer_2 = torch.nn.Sequential( |
| 95 | torch.nn.Conv2d( |
| 96 | in_channels=10, |
| 97 | out_channels=d // 2, |
| 98 | kernel_size=(4, 4), |
| 99 | stride=(2, 2), |
| 100 | padding=(1, 1) |
| 101 | ), |
| 102 | torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) |
| 103 | ) |
| 104 | self.conv_layer_3 = torch.nn.Sequential( |
| 105 | torch.nn.Conv2d( |
| 106 | in_channels=d, |
| 107 | out_channels=d * 2, |
| 108 | kernel_size=(4, 4), |
| 109 | stride=(2, 2), |
| 110 | padding=(1, 1) |
| 111 | ), |
| 112 | torch.nn.BatchNorm2d(num_features=d * 2), |
| 113 | torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) |
| 114 | ) |
| 115 | self.conv_layer_4 = torch.nn.Sequential( |
| 116 | torch.nn.Conv2d( |
| 117 | in_channels=d * 2, |
| 118 | out_channels=d * 4, |
| 119 | kernel_size=(4, 4), |
| 120 | stride=(2, 2), |
| 121 | padding=(1, 1) |
| 122 | ), |
| 123 | torch.nn.BatchNorm2d(num_features=d * 4), |
| 124 | torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) |
| 125 | ) |
| 126 | self.final_layer = torch.nn.Sequential( |
| 127 | torch.nn.Conv2d( |
| 128 | in_channels=d * 4, |
| 129 | out_channels=out_channels, |
| 130 | kernel_size=(4, 4), |
| 131 | stride=(1,1), |
| 132 | padding=(0,0) |
| 133 | ), |
| 134 | torch.nn.Sigmoid() |
| 135 | ) |
| 136 | def forward(self,input,label): |
| 137 | x = self.conv_layer_1(input) |
| 138 | y = self.conv_layer_2(label) |