Create model.py

model.py

@@ -0,0 +1,62 @@
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+
+class SimpleCNN(nn.Module):
+    def __init__(self, model_type='f', num_classes=4):
+        super(SimpleCNN, self).__init__()
+        self.num_classes = num_classes
+        self.model_type = model_type
+        if model_type == 'f':
+            self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
+            self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
+            self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+            self.fc1 = nn.Linear(64 * 28 * 28, 256)
+            self.dropout = nn.Dropout(0.5)
+        elif model_type == 'c':
+            self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
+            self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+            self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+            self.fc1 = nn.Linear(128 * 28 * 28, 512)
+            self.dropout = nn.Dropout(0.5)
+        elif model_type == 'q':
+            self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
+            self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+            self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
+            self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
+            self.fc1 = nn.Linear(512 * 14 * 14, 1024)
+            self.dropout = nn.Dropout(0.3)
+        else:
+            raise ValueError(f"Unknown model type: {model_type}")
+
+        self.relu = nn.ReLU()
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.fc2 = nn.Linear(self.fc1.out_features, 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)))
+        if self.model_type == 'q':
+            x = self.pool(self.relu(self.conv4(x)))
+        x = x.view(x.size(0), -1)
+        x = self.relu(self.fc1(x))
+        x = self.dropout(x)
+        x = self.fc2(x)
+        return x
+
+
+def load_model(version='c', device='cpu'):
+    model_type = version.lower()
+    filename = f"Vbai-TS 1.0{model_type}.pt"
+
+    weights_path = hf_hub_download(
+        repo_id="Neurazum/Vbai-TS-1.0",
+        filename=filename,
+        repo_type="model"
+    )
+
+    model = SimpleCNN(model_type=model_type, num_classes=4).to(device)
+    state_dict = torch.load(weights_path, map_location=device)
+    model.load_state_dict(state_dict, strict=False)
+    model.eval()