Create generator.py

generator.py

@@ -0,0 +1,54 @@
+LATENT_DIM = 500
+EPSILON = 1e-8
+
+class Generator(nn.Module):
+  @nn.compact
+  def __call__(self, latent, training=True):
+    x = nn.Dense(features=64)(latent)
+    # x = nn.BatchNorm(not training)(x)
+    x = nn.relu(x)
+    x = nn.Dense(features=2*2*1024)(x)
+    x = nn.BatchNorm(not training)(x)
+    x = nn.relu(x)
+    x = nn.Dropout(0.25, deterministic=not training)(x)
+    x = x.reshape((x.shape[0], 2, 2, -1))
+    x4 = nn.ConvTranspose(features=512, kernel_size=(3, 3), strides=(2, 2))(x)
+    x4 = LocalResponseNorm()(x4)
+    x4 = nn.relu(x4)
+    x4o = nn.ConvTranspose(features=3, kernel_size=(3, 3))(x4)
+    x4 = nn.ConvTranspose(features=512, kernel_size=(3, 3))(x4)
+    x4 = LocalResponseNorm()(x4)
+    x4 = nn.relu(x4)
+    x8 = nn.ConvTranspose(features=256, kernel_size=(3, 3), strides=(2, 2))(x4)
+    x8 = LocalResponseNorm()(x8)
+    x8 = nn.relu(x8)
+    x8o = nn.ConvTranspose(features=3, kernel_size=(3, 3))(x8)
+    x8 = nn.ConvTranspose(features=256, kernel_size=(3, 3))(x8)
+    x8 = LocalResponseNorm()(x8)
+    x8 = nn.relu(x8)
+    x16 = nn.ConvTranspose(features=128, kernel_size=(3, 3), strides=(2, 2))(x8)
+    x16 = LocalResponseNorm()(x16)
+    x16 = nn.relu(x16)
+    x16o = nn.ConvTranspose(features=3, kernel_size=(3, 3))(x16)
+    x16 = nn.ConvTranspose(features=128, kernel_size=(3, 3))(x16)
+    x16 = LocalResponseNorm()(x16)
+    x16 = nn.relu(x16)
+    x32 = nn.ConvTranspose(features=64, kernel_size=(3, 3), strides=(2, 2))(x16)
+    x32 = LocalResponseNorm()(x32)
+    x32 = nn.relu(x32)
+    x32o = nn.ConvTranspose(features=3, kernel_size=(3, 3))(x32)
+    x32 = nn.ConvTranspose(features=64, kernel_size=(3, 3))(x32)
+    x32 = LocalResponseNorm()(x32)
+    x32 = nn.relu(x32)
+    x64 = nn.ConvTranspose(features=32, kernel_size=(3, 3), strides=(2, 2))(x32)
+    x64 = LocalResponseNorm()(x64)
+    x64 = nn.relu(x64)
+    x64o = nn.ConvTranspose(features=3, kernel_size=(3, 3))(x64)
+    x64 = nn.ConvTranspose(features=32, kernel_size=(3, 3))(x64)
+    x64 = LocalResponseNorm()(x64)
+    x64 = nn.relu(x64)
+    x128 = nn.ConvTranspose(features=64, kernel_size=(3, 3), strides=(2, 2))(x64)
+    x128 = LocalResponseNorm()(x128)
+    x128 = nn.relu(x128)
+    x128o = nn.ConvTranspose(features=3, kernel_size=(3, 3))(x128)
+    return (nn.tanh(x128o), nn.tanh(x64o), nn.tanh(x32o), nn.tanh(x16o), nn.tanh(x8o), nn.tanh(x4o))