initial work for elpv and subter datasets

elpv as example dataset/implementation
subter with final dataset

Deep-SAD-PyTorch/src/onnx_export.py

@@ -0,0 +1,35 @@
+import torch
+import torch.onnx
+from networks.mnist_LeNet import MNIST_LeNet_Autoencoder
+
+
+def export_model_to_onnx(model, filepath, input_shape=(1, 1, 28, 28)):
+    model.eval()  # Set the model to evaluation mode
+    dummy_input = torch.randn(input_shape)  # Create a dummy input tensor
+    torch.onnx.export(
+        model,  # model being run
+        dummy_input,  # model input (or a tuple for multiple inputs)
+        filepath,  # where to save the model (can be a file or file-like object)
+        export_params=True,  # store the trained parameter weights inside the model file
+        opset_version=11,  # the ONNX version to export the model to
+        do_constant_folding=True,  # whether to execute constant folding for optimization
+        input_names=["input"],  # the model's input names
+        output_names=["output"],  # the model's output names
+        dynamic_axes={
+            "input": {0: "batch_size"},  # variable length axes
+            "output": {0: "batch_size"},
+        },
+    )
+
+
+if __name__ == "__main__":
+    # Initialize the autoencoder model
+    autoencoder = MNIST_LeNet_Autoencoder(rep_dim=32)
+
+    # Define the file path where the ONNX model will be saved
+    onnx_file_path = "mnist_lenet_autoencoder.onnx"
+
+    # Export the model
+    export_model_to_onnx(autoencoder, onnx_file_path)
+
+    print(f"Model has been exported to {onnx_file_path}")