Update pipeline.py

pipeline.py

@@ -3,6 +3,33 @@ import torch.nn as nn
 from torchvision import transforms
 from PIL import Image
 
+class Generator(nn.Module):
+    def __init__(self):
+        super(Generator, self).__init__()
+        self.main = nn.Sequential(
+            nn.ConvTranspose2d(128, 64 * 8, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(64 * 8),
+            nn.LeakyReLU(0.2, inplace=True),
+
+            nn.ConvTranspose2d(64 * 8, 64 * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 4),
+            nn.LeakyReLU(0.2, inplace=True),
+
+            nn.ConvTranspose2d(64 * 4, 64 * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 2),
+            nn.LeakyReLU(0.2, inplace=True),
+
+            nn.ConvTranspose2d(64 * 2, 64, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.LeakyReLU(0.2, inplace=True),
+
+            nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
+            nn.Tanh()
+        )
+
+    def forward(self, input):
+        return self.main(input)
+
 class PretrainedPipeline():
     def __init__(self):
         self.device = torch.device("cpu")