| 4 | |
| 5 | |
| 6 | class Net(nn.Module): |
| 7 | def __init__(self, upscale_factor): |
| 8 | super(Net, self).__init__() |
| 9 | |
| 10 | self.relu = nn.ReLU() |
| 11 | self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2)) |
| 12 | self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)) |
| 13 | self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1)) |
| 14 | self.conv4 = nn.Conv2d(32, upscale_factor ** 2, (3, 3), (1, 1), (1, 1)) |
| 15 | self.pixel_shuffle = nn.PixelShuffle(upscale_factor) |
| 16 | |
| 17 | self._initialize_weights() |
| 18 | |
| 19 | def forward(self, x): |
| 20 | x = self.relu(self.conv1(x)) |
| 21 | x = self.relu(self.conv2(x)) |
| 22 | x = self.relu(self.conv3(x)) |
| 23 | x = self.pixel_shuffle(self.conv4(x)) |
| 24 | return x |
| 25 | |
| 26 | def _initialize_weights(self): |
| 27 | init.orthogonal_(self.conv1.weight, init.calculate_gain('relu')) |
| 28 | init.orthogonal_(self.conv2.weight, init.calculate_gain('relu')) |
| 29 | init.orthogonal_(self.conv3.weight, init.calculate_gain('relu')) |
| 30 | init.orthogonal_(self.conv4.weight) |