Create generator.py

generator.py

@@ -0,0 +1,44 @@
+import torch
+import torch.nn as nn
+
+# The Generator model
+class Generator(nn.Module):
+    def __init__(self, channels, noise_dim=100, embed_dim=1024, embed_out_dim=128):
+        super(Generator, self).__init__()
+        self.channels = channels
+        self.noise_dim = noise_dim
+        self.embed_dim = embed_dim
+        self.embed_out_dim = embed_out_dim
+
+        # Text embedding layers
+        self.text_embedding = nn.Sequential(
+            nn.Linear(self.embed_dim, self.embed_out_dim),
+            nn.BatchNorm1d(1),
+            nn.LeakyReLU(0.2, inplace=True)
+        )
+
+        # Generator architecture
+        model = []
+        model += self._create_layer(self.noise_dim + self.embed_out_dim, 512, 4, stride=1, padding=0)
+        model += self._create_layer(512, 256, 4, stride=2, padding=1)
+        model += self._create_layer(256, 128, 4, stride=2, padding=1)
+        model += self._create_layer(128, 64, 4, stride=2, padding=1)
+        model += self._create_layer(64, 32, 4, stride=2, padding=1)
+        model += self._create_layer(32, self.channels, 4, stride=2, padding=1, output=True)
+
+        self.model = nn.Sequential(*model)
+
+    def _create_layer(self, size_in, size_out, kernel_size=4, stride=2, padding=1, output=False):
+        layers = [nn.ConvTranspose2d(size_in, size_out, kernel_size, stride=stride, padding=padding, bias=False)]
+        if output:
+            layers.append(nn.Tanh())  # Tanh activation for the output layer
+        else:
+            layers += [nn.BatchNorm2d(size_out), nn.ReLU(True)]  # Batch normalization and ReLU for other layers
+        return layers
+
+    def forward(self, noise, text):
+        # Apply text embedding to the input text
+        text = self.text_embedding(text)
+        text = text.view(text.shape[0], text.shape[2], 1, 1)  # Reshape to match the generator input size
+        z = torch.cat([text, noise], 1)  # Concatenate text embedding with noise
+        return self.model(z)