Create model.py

model.py

@@ -0,0 +1,66 @@
+import torch.nn as nn
+
+class Generator(nn.Module):
+    def __init__(self, z_dim=100, img_channels=3):
+        super(Generator, self).__init__()
+        self.gen = nn.Sequential(
+            # input is Z, going into a convolution
+            nn.ConvTranspose2d(z_dim, 512, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(512),
+            nn.ReLU(True),
+            # state size. 512 x 4 x 4
+            nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(256),
+            nn.ReLU(True),
+            # state size. 256 x 8 x 8
+            nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(True),
+            # state size. 128 x 16 x 16
+            nn.ConvTranspose2d(128, 64, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(True),
+            # state size. 64 x 32 x 32
+            nn.ConvTranspose2d(64, img_channels, 4, 2, 1, bias=False),
+            nn.Tanh()
+            # state size. img_channels x 64 x 64
+        )
+
+    def forward(self, input):
+        return self.gen(input)
+
+class Discriminator(nn.Module):
+    def __init__(self, img_channels=3):
+        super(Discriminator, self).__init__()
+        self.disc = nn.Sequential(
+            # input is img_channels x 64 x 64
+            nn.Conv2d(img_channels, 64, 4, 2, 1, bias=False),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. 64 x 32 x 32
+            nn.Conv2d(64, 128, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. 128 x 16 x 16
+            nn.Conv2d(128, 256, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(256),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. 256 x 8 x 8
+            nn.Conv2d(256, 512, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(512),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. 512 x 4 x 4
+            nn.Conv2d(512, 1, 4, 1, 0, bias=False),
+            nn.Sigmoid()
+        )
+
+    def forward(self, input):
+        return self.disc(input).view(-1, 1).squeeze(1)
+
+batch_size = 32
+latent_vector_size = 100
+
+generator = Generator()
+discriminator = Discriminator()
+
+generator.load_state_dict(torch.load('netG.pth', map_location=torch.device('cpu') ))
+discriminator.load_state_dict(torch.load('netD.pth', map_location=torch.device('cpu') ))