code cleanup

models/__init__.py

@@ -0,0 +1 @@
+from .cnn import CNNPredictor

models/cnn/__init__.py

@@ -0,0 +1 @@
+from .cnn import CNNPredictor

models/cnn/cnn.py

@@ -0,0 +1,52 @@
+import torch
+import torch.nn as nn
+
+class CNNPredictor(nn.Module):
+    def __init__(
+            self, 
+            vocab_size=256, 
+            embed_dim=64, 
+            hidden_dim=128,
+        ):
+        super().__init__()
+
+        # 1. Embedding: maps bytes (0–255) → vectors
+        self.embed = nn.Embedding(vocab_size, embed_dim)
+
+        # 2. Convolutional feature extractor
+        self.conv_layers = nn.Sequential(
+            nn.Conv1d(embed_dim, hidden_dim, kernel_size=5, padding=2),
+            nn.ReLU(),
+            nn.Conv1d(hidden_dim, hidden_dim, kernel_size=5, padding=2),
+            nn.ReLU(),
+            nn.Conv1d(hidden_dim, hidden_dim, kernel_size=5, padding=2),
+            nn.ReLU(),
+        )
+
+        # 3. Global pooling to collapse sequence length
+        self.pool = nn.AdaptiveAvgPool1d(1)  # → (B, hidden_channels, 1)
+
+        # 4. Final classifier
+        self.fc = nn.Linear(hidden_dim, vocab_size)  # → (B, 256)
+
+    def forward(self, x):
+        """
+        x: LongTensor of shape (B, 128), values 0-255
+        """
+        # embed: (B, 128, embed_dim)
+        x = self.embed(x)
+
+        # conv1d expects (B, C_in, L) → swap dims
+        x = x.transpose(1, 2)  # (B, embed_dim, 128)
+
+        # apply CNN
+        x = self.conv_layers(x)  # (B, hidden_channels, 128)
+
+        # global average pooling over sequence
+        x = self.pool(x).squeeze(-1)  # (B, hidden_channels)
+
+        # final classifier
+        logits = self.fc(x)  # (B, 256)
+        return logits
+
+