Create Model_Loader.py

tasks/Model_Loader.py

@@ -0,0 +1,38 @@
+import torch
+
+class M5(torch.nn.Module):
+    def __init__(self, num_classes=10):
+        super(M5, self).__init__()
+        self.conv1 = torch.nn.Conv1d(in_channels=1, out_channels=32, kernel_size=80, stride=4)
+        self.bn1 = torch.nn.BatchNorm1d(32)
+        self.conv2 = torch.nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3)
+        self.bn2 = torch.nn.BatchNorm1d(64)
+        self.conv3 = torch.nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3)
+        self.bn3 = torch.nn.BatchNorm1d(128)
+        self.conv4 = torch.nn.Conv1d(in_channels=128, out_channels=256, kernel_size=3)
+        self.bn4 = torch.nn.BatchNorm1d(256)
+        self.fc1 = torch.nn.Linear(256, num_classes)
+
+    def forward(self, x):
+        x = torch.nn.functional.relu(self.bn1(self.conv1(x)))
+        x = torch.nn.functional.max_pool1d(x, 4)
+        x = torch.nn.functional.relu(self.bn2(self.conv2(x)))
+        x = torch.nn.functional.max_pool1d(x, 4)
+        x = torch.nn.functional.relu(self.bn3(self.conv3(x)))
+        x = torch.nn.functional.max_pool1d(x, 4)
+        x = torch.nn.functional.relu(self.bn4(self.conv4(x)))
+        x = torch.nn.functional.max_pool1d(x, 4)
+        x = torch.mean(x, dim=2)
+        x = self.fc1(x)
+        return x
+
+    def load_model(model_path, num_classes=2):
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model = M5(num_classes=num_classes).to(device)
+        model.load_state_dict(torch.load(model_path, map_location=device))
+        model.eval()  # Set model to evaluation mode
+        return model, device
+
+if __name__ == "__main__":
+    model, device = load_model("m5_audio_classification.pth")
+print("✅ Model successfully loaded!")