Create inference.py

inference.py

@@ -0,0 +1,45 @@
+import torch
+import torch.nn as nn
+import torchvision.utils as vutils
+import matplotlib.pyplot as plt
+
+class Generator(nn.Module):
+    def __init__(self, z_dim, channels, features_g):
+        super(Generator, self).__init__()
+        self.net = nn.Sequential(
+            nn.ConvTranspose2d(z_dim, features_g * 16, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(features_g * 16),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(features_g * 16, features_g * 8, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(features_g * 8),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(features_g * 8, features_g * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(features_g * 4),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(features_g * 4, features_g * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(features_g * 2),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(features_g * 2, features_g, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(features_g),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(features_g, channels, 4, 2, 1, bias=False),
+            nn.Tanh()
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+Z_DIM = 128
+FEATURES_G = 256
+model = Generator(Z_DIM, 3, FEATURES_G).to(device)
+
+model.load_state_dict(torch.load("catgen_v2_generator_only.pth", map_location=device))
+model.eval()
+
+with torch.no_grad():
+    noise = torch.randn(16, Z_DIM, 1, 1, device=device)
+    fake_images = model(noise).detach().cpu()
+
+vutils.save_image(fake_images, "generated_cats.png", normalize=True, nrow=4)
+print("16 new cats generated in generated_cats.png!")