Create model.py

model.py

@@ -0,0 +1,52 @@
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+
+
+class SimpleCNN(nn.Module):
+    def __init__(self, num_classes=6):
+        super(SimpleCNN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(0.5)
+        self._initialize_fc(num_classes)
+
+    def _initialize_fc(self, num_classes):
+        dummy_input = torch.zeros(1, 3, 448, 448)
+        x = self.pool(self.relu(self.conv1(dummy_input)))
+        x = self.pool(self.relu(self.conv2(x)))
+        x = self.pool(self.relu(self.conv3(x)))
+        x = x.view(x.size(0), -1)
+        flattened_size = x.shape[1]
+        self.fc1 = nn.Linear(flattened_size, 512)
+        self.fc2 = nn.Linear(512, num_classes)
+
+    def forward(self, x):
+        x = self.pool(self.relu(self.conv1(x)))
+        x = self.pool(self.relu(self.conv2(x)))
+        x = self.pool(self.relu(self.conv3(x)))
+        x = x.view(x.size(0), -1)
+        x = self.dropout(self.relu(self.fc1(x)))
+        x = self.fc2(x)
+        return x
+
+def load_model(device: str = 'cpu'):
+    """
+    Downloads and loads the pretrained SimpleCNN model for the 'c' version.
+    """
+    torch_device = torch.device(device)
+
+    weights_path = hf_hub_download(
+        repo_id="Neurazum/Vbai-DPA-2.3",
+        filename="Vbai-DPA 2.3c.pt",
+        repo_type="model"
+    )
+
+    model = SimpleCNN(num_classes=6).to(torch_device)
+    state = torch.load(weights_path, map_location=torch_device)
+    model.load_state_dict(state)
+    model.eval()
+    return model